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| likes
int64 0
7.74k
| library_name
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sequencelengths 1
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stringlengths 1
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|---|---|---|---|---|---|---|---|---|---|
DopeBearmine/weather | DopeBearmine | "2025-01-24T20:13:22Z" | 6 | 0 | null | [
"tensorboard",
"safetensors",
"vit",
"region:us"
] | null | "2025-01-24T18:50:10Z" | Entry not found |
Spacyzipa/sanjeev_07_02_24 | Spacyzipa | "2024-02-07T10:12:47Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2024-02-07T07:07:41Z" | Entry not found |
franco-rojas/bloom-1b1-finetuned-tfmviu | franco-rojas | "2023-09-30T16:31:26Z" | 152 | 0 | transformers | [
"transformers",
"pytorch",
"bloom",
"text-generation",
"generated_from_trainer",
"base_model:bigscience/bloom-1b1",
"base_model:finetune:bigscience/bloom-1b1",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-09-29T04:41:50Z" | ---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloom-1b1
tags:
- generated_from_trainer
model-index:
- name: bloom-1b1-finetuned-tfmviu
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bloom-1b1-finetuned-tfmviu
This model is a fine-tuned version of [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5185
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 222 | 3.1300 |
| No log | 2.0 | 444 | 3.2264 |
| 2.3093 | 3.0 | 666 | 3.5185 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
mradermacher/LN-Korean-14B-v0.1-GGUF | mradermacher | "2024-07-31T04:26:27Z" | 17 | 0 | transformers | [
"transformers",
"gguf",
"ko",
"zh",
"base_model:CjangCjengh/LN-Korean-14B-v0.1",
"base_model:quantized:CjangCjengh/LN-Korean-14B-v0.1",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-07-30T19:24:37Z" | ---
base_model: CjangCjengh/LN-Korean-14B-v0.1
language:
- ko
- zh
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/CjangCjengh/LN-Korean-14B-v0.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q2_K.gguf) | Q2_K | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.IQ3_XS.gguf) | IQ3_XS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.IQ3_S.gguf) | IQ3_S | 6.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.IQ3_M.gguf) | IQ3_M | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q3_K_L.gguf) | Q3_K_L | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.IQ4_XS.gguf) | IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q4_K_M.gguf) | Q4_K_M | 9.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q5_K_S.gguf) | Q5_K_S | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q6_K.gguf) | Q6_K | 12.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LN-Korean-14B-v0.1-GGUF/resolve/main/LN-Korean-14B-v0.1.Q8_0.gguf) | Q8_0 | 15.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lesso13/c4692f38-8597-4602-95e2-041a9441b18f | lesso13 | "2025-01-29T19:12:34Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:Intel/neural-chat-7b-v3-3",
"base_model:adapter:Intel/neural-chat-7b-v3-3",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-29T17:18:53Z" | ---
library_name: peft
license: apache-2.0
base_model: Intel/neural-chat-7b-v3-3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c4692f38-8597-4602-95e2-041a9441b18f
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Intel/neural-chat-7b-v3-3
bf16: auto
chat_template: llama3
datasets:
- data_files:
- 50647f9e6e89cbb7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/50647f9e6e89cbb7_train_data.json
type:
field_input: ingredients_processed
field_instruction: title
field_output: directions
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso13/c4692f38-8597-4602-95e2-041a9441b18f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/50647f9e6e89cbb7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c53eddb1-5a0f-4d15-bd00-9389024c7d94
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c53eddb1-5a0f-4d15-bd00-9389024c7d94
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c4692f38-8597-4602-95e2-041a9441b18f
This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0080 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ISTNetworks/new_arabic_LLama3_8B | ISTNetworks | "2024-05-29T13:17:14Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-29T13:06:59Z" | ---
tags:
- merge
- mergekit
- lazymergekit
base_model:
- LLama3-8B
---
# new_arabic_LLama3_8B
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ISTNetworks/new_arabic_LLama3_8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task5_organization | MayBashendy | "2024-12-09T19:51:58Z" | 164 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-09T19:50:02Z" | ---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task5_organization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task5_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3044
- Qwk: 0.5633
- Mse: 1.3044
- Rmse: 1.1421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 0.1667 | 2 | 2.1878 | 0.0370 | 2.1878 | 1.4791 |
| No log | 0.3333 | 4 | 1.3588 | 0.3083 | 1.3588 | 1.1657 |
| No log | 0.5 | 6 | 1.3346 | 0.2084 | 1.3346 | 1.1553 |
| No log | 0.6667 | 8 | 1.6107 | 0.2842 | 1.6107 | 1.2691 |
| No log | 0.8333 | 10 | 1.6034 | 0.2389 | 1.6034 | 1.2663 |
| No log | 1.0 | 12 | 1.5060 | 0.2877 | 1.5060 | 1.2272 |
| No log | 1.1667 | 14 | 1.4421 | 0.3513 | 1.4421 | 1.2009 |
| No log | 1.3333 | 16 | 1.3840 | 0.3438 | 1.3840 | 1.1764 |
| No log | 1.5 | 18 | 1.3343 | 0.3598 | 1.3343 | 1.1551 |
| No log | 1.6667 | 20 | 1.3586 | 0.4430 | 1.3586 | 1.1656 |
| No log | 1.8333 | 22 | 1.3225 | 0.4597 | 1.3225 | 1.1500 |
| No log | 2.0 | 24 | 1.2590 | 0.4709 | 1.2590 | 1.1221 |
| No log | 2.1667 | 26 | 1.2039 | 0.4997 | 1.2039 | 1.0972 |
| No log | 2.3333 | 28 | 1.2501 | 0.4709 | 1.2501 | 1.1181 |
| No log | 2.5 | 30 | 1.2475 | 0.4885 | 1.2475 | 1.1169 |
| No log | 2.6667 | 32 | 1.2880 | 0.4736 | 1.2880 | 1.1349 |
| No log | 2.8333 | 34 | 1.0968 | 0.5342 | 1.0968 | 1.0473 |
| No log | 3.0 | 36 | 0.8683 | 0.5672 | 0.8683 | 0.9318 |
| No log | 3.1667 | 38 | 0.8042 | 0.5095 | 0.8042 | 0.8968 |
| No log | 3.3333 | 40 | 0.8048 | 0.4901 | 0.8048 | 0.8971 |
| No log | 3.5 | 42 | 0.8573 | 0.6161 | 0.8573 | 0.9259 |
| No log | 3.6667 | 44 | 1.0560 | 0.5638 | 1.0560 | 1.0276 |
| No log | 3.8333 | 46 | 1.2548 | 0.5055 | 1.2548 | 1.1202 |
| No log | 4.0 | 48 | 1.5072 | 0.4628 | 1.5072 | 1.2277 |
| No log | 4.1667 | 50 | 1.5551 | 0.4474 | 1.5551 | 1.2470 |
| No log | 4.3333 | 52 | 1.5773 | 0.4520 | 1.5773 | 1.2559 |
| No log | 4.5 | 54 | 1.4398 | 0.4954 | 1.4398 | 1.1999 |
| No log | 4.6667 | 56 | 1.1994 | 0.5388 | 1.1994 | 1.0952 |
| No log | 4.8333 | 58 | 1.0994 | 0.5831 | 1.0994 | 1.0485 |
| No log | 5.0 | 60 | 1.0631 | 0.5953 | 1.0631 | 1.0311 |
| No log | 5.1667 | 62 | 1.2124 | 0.5407 | 1.2124 | 1.1011 |
| No log | 5.3333 | 64 | 1.3983 | 0.5243 | 1.3983 | 1.1825 |
| No log | 5.5 | 66 | 1.5422 | 0.4885 | 1.5422 | 1.2418 |
| No log | 5.6667 | 68 | 1.5609 | 0.4771 | 1.5609 | 1.2494 |
| No log | 5.8333 | 70 | 1.4322 | 0.5184 | 1.4322 | 1.1967 |
| No log | 6.0 | 72 | 1.1673 | 0.5812 | 1.1673 | 1.0804 |
| No log | 6.1667 | 74 | 1.0331 | 0.6231 | 1.0331 | 1.0164 |
| No log | 6.3333 | 76 | 1.0521 | 0.6252 | 1.0521 | 1.0257 |
| No log | 6.5 | 78 | 1.1825 | 0.5605 | 1.1825 | 1.0874 |
| No log | 6.6667 | 80 | 1.3724 | 0.5252 | 1.3724 | 1.1715 |
| No log | 6.8333 | 82 | 1.4427 | 0.5238 | 1.4427 | 1.2011 |
| No log | 7.0 | 84 | 1.4253 | 0.5279 | 1.4253 | 1.1938 |
| No log | 7.1667 | 86 | 1.3821 | 0.5311 | 1.3821 | 1.1756 |
| No log | 7.3333 | 88 | 1.3205 | 0.5274 | 1.3205 | 1.1491 |
| No log | 7.5 | 90 | 1.2603 | 0.5786 | 1.2603 | 1.1226 |
| No log | 7.6667 | 92 | 1.2001 | 0.6118 | 1.2001 | 1.0955 |
| No log | 7.8333 | 94 | 1.1598 | 0.6193 | 1.1598 | 1.0769 |
| No log | 8.0 | 96 | 1.1141 | 0.6202 | 1.1141 | 1.0555 |
| No log | 8.1667 | 98 | 1.1151 | 0.6217 | 1.1151 | 1.0560 |
| No log | 8.3333 | 100 | 1.1161 | 0.6160 | 1.1161 | 1.0565 |
| No log | 8.5 | 102 | 1.1698 | 0.6220 | 1.1698 | 1.0816 |
| No log | 8.6667 | 104 | 1.2404 | 0.6049 | 1.2404 | 1.1137 |
| No log | 8.8333 | 106 | 1.3207 | 0.5661 | 1.3207 | 1.1492 |
| No log | 9.0 | 108 | 1.3870 | 0.5521 | 1.3870 | 1.1777 |
| No log | 9.1667 | 110 | 1.4139 | 0.5521 | 1.4139 | 1.1891 |
| No log | 9.3333 | 112 | 1.4059 | 0.5521 | 1.4059 | 1.1857 |
| No log | 9.5 | 114 | 1.3793 | 0.5521 | 1.3793 | 1.1744 |
| No log | 9.6667 | 116 | 1.3430 | 0.5507 | 1.3430 | 1.1589 |
| No log | 9.8333 | 118 | 1.3125 | 0.5580 | 1.3125 | 1.1456 |
| No log | 10.0 | 120 | 1.3044 | 0.5633 | 1.3044 | 1.1421 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
LoneStriker/gemma-2b-it-4.0bpw-h6-exl2 | LoneStriker | "2024-02-22T15:27:59Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-02-22T15:26:48Z" | ---
library_name: transformers
tags: []
widget:
- text: |
<start_of_turn>user
How does the brain work?<end_of_turn>
<start_of_turn>model
inference:
parameters:
max_new_tokens: 200
extra_gated_heading: "Access Gemma on Hugging Face"
extra_gated_prompt: "To access Gemma on Hugging Face, youโre required to review and agree to Googleโs usage license. To do this, please ensure youโre logged-in to Hugging Face and click below. Requests are processed immediately."
extra_gated_button_content: "Acknowledge license"
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-it).
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Chat Template
The instruction-tuned models use a chat template that must be adhered to for conversational use.
The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "gg-hf/gemma-2b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,
)
chat = [
{ "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
At this point, the prompt contains the following text:
```
<start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
```
As you can see, each turn is preceeded by a `<start_of_turn>` delimiter and then the role of the entity
(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
the `<end_of_turn>` token.
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
chat template.
After the prompt is ready, generation can be performed like this:
```py
inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **54.0** | **56.4** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
|
JiaxiJiang/textual_inversion_clock | JiaxiJiang | "2024-03-22T08:17:14Z" | 36 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-03-22T07:52:45Z" | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - JiaxiJiang/textual_inversion_clock
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
bobox/DeBERTa2-0.9B-ST-v1-checkpoints-tmp | bobox | "2024-09-02T22:42:45Z" | 5 | 0 | null | [
"pytorch",
"tensorboard",
"deberta-v2",
"region:us"
] | null | "2024-08-30T14:33:38Z" | Entry not found |
LahiruProjects/criminal-case-classifier1 | LahiruProjects | "2024-04-02T15:32:23Z" | 110 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-04-02T15:07:04Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: criminal-case-classifier1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# criminal-case-classifier1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8530
- Accuracy: 0.5077
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9563 | 0.31 | 10 | 1.1314 | 0.3385 |
| 1.1275 | 0.62 | 20 | 1.0607 | 0.4769 |
| 1.0692 | 0.94 | 30 | 1.0871 | 0.2923 |
| 1.0717 | 1.25 | 40 | 1.1759 | 0.4154 |
| 1.0113 | 1.56 | 50 | 1.1322 | 0.3538 |
| 0.8463 | 1.88 | 60 | 1.1809 | 0.3846 |
| 0.8573 | 2.19 | 70 | 1.0676 | 0.4154 |
| 0.8711 | 2.5 | 80 | 1.0690 | 0.3846 |
| 0.809 | 2.81 | 90 | 1.1253 | 0.4154 |
| 0.7148 | 3.12 | 100 | 1.0913 | 0.4769 |
| 0.5847 | 3.44 | 110 | 1.0920 | 0.5077 |
| 0.5486 | 3.75 | 120 | 1.0597 | 0.5538 |
| 0.5184 | 4.06 | 130 | 1.1016 | 0.4769 |
| 0.2637 | 4.38 | 140 | 1.1908 | 0.4923 |
| 0.3562 | 4.69 | 150 | 1.0238 | 0.5385 |
| 0.3292 | 5.0 | 160 | 1.1011 | 0.5692 |
| 0.1333 | 5.31 | 170 | 1.3049 | 0.5385 |
| 0.1256 | 5.62 | 180 | 1.2819 | 0.5538 |
| 0.1415 | 5.94 | 190 | 1.4929 | 0.5231 |
| 0.0942 | 6.25 | 200 | 1.5290 | 0.5538 |
| 0.0548 | 6.56 | 210 | 1.4844 | 0.5538 |
| 0.0457 | 6.88 | 220 | 1.6174 | 0.5077 |
| 0.0226 | 7.19 | 230 | 1.6499 | 0.5538 |
| 0.032 | 7.5 | 240 | 1.7371 | 0.5077 |
| 0.0158 | 7.81 | 250 | 1.8099 | 0.5385 |
| 0.0244 | 8.12 | 260 | 1.9706 | 0.4769 |
| 0.0134 | 8.44 | 270 | 1.8825 | 0.5231 |
| 0.0117 | 8.75 | 280 | 1.8414 | 0.5077 |
| 0.0111 | 9.06 | 290 | 1.8478 | 0.5077 |
| 0.0107 | 9.38 | 300 | 1.8530 | 0.5077 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
nirmaldhara/gita-text-generation-gpt2 | nirmaldhara | "2024-09-14T17:14:41Z" | 127 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-14T17:13:42Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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ZhangShenao/math_math-gemma-2-9b-it-rs-sample_7500_tp | ZhangShenao | "2025-01-16T07:24:50Z" | 6 | 0 | null | [
"safetensors",
"gemma2",
"region:us"
] | null | "2025-01-16T07:21:28Z" | Entry not found |
VERSIL91/4ff71c75-3979-442e-a77f-e53f56142bf9 | VERSIL91 | "2024-12-13T15:36:03Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-3B",
"base_model:adapter:unsloth/Qwen2.5-3B",
"license:other",
"region:us"
] | null | "2024-12-13T15:21:36Z" | ---
library_name: peft
license: other
base_model: unsloth/Qwen2.5-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4ff71c75-3979-442e-a77f-e53f56142bf9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
accelerate_config:
dynamo_backend: inductor
mixed_precision: bf16
num_machines: 1
num_processes: auto
use_cpu: false
adapter: lora
base_model: unsloth/Qwen2.5-3B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 7b99c633142b08f3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7b99c633142b08f3_train_data.json
type:
field_input: prompt_option
field_instruction: prompt_question
field_output: country
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: VERSIL91/4ff71c75-3979-442e-a77f-e53f56142bf9
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 70GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/7b99c633142b08f3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
quantization_config:
llm_int8_enable_fp32_cpu_offload: true
load_in_8bit: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 4056
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4ff71c75-3979-442e-a77f-e53f56142bf9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4ff71c75-3979-442e-a77f-e53f56142bf9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 4ff71c75-3979-442e-a77f-e53f56142bf9
This model is a fine-tuned version of [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0078 | 1 | nan |
| 0.0 | 0.1017 | 13 | nan |
| 0.0 | 0.2033 | 26 | nan |
| 0.0 | 0.3050 | 39 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso02/57b6696f-95a7-421e-8777-44b3b2751d79 | lesso02 | "2025-01-21T07:00:41Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-0.5B",
"base_model:adapter:unsloth/Qwen2.5-0.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-21T06:35:12Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 57b6696f-95a7-421e-8777-44b3b2751d79
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-0.5B
bf16: true
chat_template: llama3
datasets:
- data_files:
- 8b781c1617481132_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8b781c1617481132_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso02/57b6696f-95a7-421e-8777-44b3b2751d79
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/8b781c1617481132_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e45dac16-2243-42f5-8ac6-226d8e694661
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e45dac16-2243-42f5-8ac6-226d8e694661
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 57b6696f-95a7-421e-8777-44b3b2751d79
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B](https://huggingface.co/unsloth/Qwen2.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0007 | 5 | nan |
| 0.0 | 0.0014 | 10 | nan |
| 0.0 | 0.0021 | 15 | nan |
| 0.0 | 0.0028 | 20 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
tanoManzo/nucleotide-transformer-v2-500m-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot | tanoManzo | "2024-10-29T19:48:14Z" | 147 | 0 | transformers | [
"transformers",
"safetensors",
"esm",
"text-classification",
"generated_from_trainer",
"custom_code",
"base_model:InstaDeepAI/nucleotide-transformer-v2-500m-multi-species",
"base_model:finetune:InstaDeepAI/nucleotide-transformer-v2-500m-multi-species",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-10-29T19:47:00Z" | ---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-500m-multi-species
tags:
- generated_from_trainer
model-index:
- name: nucleotide-transformer-v2-500m-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nucleotide-transformer-v2-500m-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC_one_shot
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.18.0
- Tokenizers 0.20.0
|
aslez123/segmentation-train | aslez123 | "2024-02-27T11:04:50Z" | 34 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null | "2024-02-27T10:31:47Z" | ---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: segmentation-train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segmentation-train
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
abhishek/wf85-h28o-tffz-0 | abhishek | "2023-12-14T18:33:51Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-wf85-h28o-tffz",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-12-14T18:33:46Z" |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- abhishek/autotrain-data-wf85-h28o-tffz
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06153846153846154
f1_micro: 0.18181818181818182
f1_weighted: 0.055944055944055944
precision_macro: 0.03636363636363636
precision_micro: 0.18181818181818182
precision_weighted: 0.03305785123966942
recall_macro: 0.2
recall_micro: 0.18181818181818182
recall_weighted: 0.18181818181818182
accuracy: 0.18181818181818182
|
VERSIL91/89337416-e71c-47d6-b1e1-5bfaea333a89 | VERSIL91 | "2024-12-26T13:06:19Z" | 7 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-160m",
"base_model:adapter:JackFram/llama-160m",
"license:apache-2.0",
"region:us"
] | null | "2024-12-26T13:04:46Z" | ---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-160m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 89337416-e71c-47d6-b1e1-5bfaea333a89
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
accelerate_config:
dynamo_backend: inductor
mixed_precision: bf16
num_machines: 1
num_processes: auto
use_cpu: false
adapter: lora
base_model: JackFram/llama-160m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 562043a1fdc4c9f9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/562043a1fdc4c9f9_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: VERSIL91/89337416-e71c-47d6-b1e1-5bfaea333a89
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 70GiB
max_steps: 5
micro_batch_size: 2
mlflow_experiment_name: /tmp/562043a1fdc4c9f9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
quantization_config:
llm_int8_enable_fp32_cpu_offload: true
load_in_8bit: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 89337416-e71c-47d6-b1e1-5bfaea333a89
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 89337416-e71c-47d6-b1e1-5bfaea333a89
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 89337416-e71c-47d6-b1e1-5bfaea333a89
This model is a fine-tuned version of [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.3029 | 0.0019 | 1 | 5.6038 |
| 5.3121 | 0.0037 | 2 | 5.5934 |
| 5.3087 | 0.0074 | 4 | 5.5794 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Roamify/finetuned-attraction_summarization_t5 | Roamify | "2024-06-20T15:37:07Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-20T15:36:47Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF | mradermacher | "2024-12-12T02:10:30Z" | 38 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:jondurbin/airoboros-gpt4-1.4",
"base_model:jondurbin/airoboros-7b-gpt4-1.4",
"base_model:quantized:jondurbin/airoboros-7b-gpt4-1.4",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2024-12-12T00:21:21Z" | ---
base_model: jondurbin/airoboros-7b-gpt4-1.4
datasets:
- jondurbin/airoboros-gpt4-1.4
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/jondurbin/airoboros-7b-gpt4-1.4
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ1_S.gguf) | i1-IQ1_S | 1.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ3_M.gguf) | i1-IQ3_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 3.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 3.9 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 3.9 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q4_0.gguf) | i1-Q4_0 | 3.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-7b-gpt4-1.4-i1-GGUF/resolve/main/airoboros-7b-gpt4-1.4.i1-Q6_K.gguf) | i1-Q6_K | 5.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
cespalv/sem-segmentor | cespalv | "2024-07-08T15:36:52Z" | 30 | 0 | transformers | [
"transformers",
"safetensors",
"mask2former",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-07-08T15:31:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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stvhuang/rcr-run-bvz9qc57-65884-master-0_20240102T164836-ep08 | stvhuang | "2024-01-08T21:47:43Z" | 90 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-01-08T21:46:28Z" | Entry not found |
Harsh202/new_opt_finetune_MTL_ecoc_Minimal_alpaca_lora_groupFalse_low_LR_BCE_128intermediate_2epoch | Harsh202 | "2024-09-10T08:57:34Z" | 145 | 0 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-10T08:55:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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callmesan/indic-sentence-bert-nli-roman-urdu-binary | callmesan | "2024-12-03T18:21:42Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:l3cube-pune/indic-sentence-bert-nli",
"base_model:finetune:l3cube-pune/indic-sentence-bert-nli",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-03T17:51:44Z" | ---
library_name: transformers
license: cc-by-4.0
base_model: l3cube-pune/indic-sentence-bert-nli
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indic-sentence-bert-nli-roman-urdu-binary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# indic-sentence-bert-nli-roman-urdu-binary
This model is a fine-tuned version of [l3cube-pune/indic-sentence-bert-nli](https://huggingface.co/l3cube-pune/indic-sentence-bert-nli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2789
- Accuracy: 0.9061
- Precision: 0.9058
- Recall: 0.9055
- F1: 0.9057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4984 | 0.9912 | 56 | 0.4611 | 0.8452 | 0.8486 | 0.8489 | 0.8452 |
| 0.3582 | 2.0 | 113 | 0.3373 | 0.8826 | 0.8843 | 0.8802 | 0.8816 |
| 0.2724 | 2.9912 | 169 | 0.2869 | 0.8901 | 0.8894 | 0.8901 | 0.8897 |
| 0.2093 | 4.0 | 226 | 0.2754 | 0.8926 | 0.8922 | 0.8920 | 0.8921 |
| 0.1622 | 4.9912 | 282 | 0.2980 | 0.8989 | 0.9016 | 0.8961 | 0.8978 |
| 0.1235 | 6.0 | 339 | 0.3167 | 0.8889 | 0.8883 | 0.8884 | 0.8884 |
| 0.1125 | 6.9912 | 395 | 0.3369 | 0.8939 | 0.8973 | 0.8907 | 0.8926 |
| 0.0811 | 8.0 | 452 | 0.3535 | 0.8914 | 0.8906 | 0.8918 | 0.8911 |
| 0.0797 | 8.9912 | 508 | 0.3833 | 0.8914 | 0.8919 | 0.8898 | 0.8906 |
| 0.0585 | 9.9115 | 560 | 0.3809 | 0.8926 | 0.8924 | 0.8918 | 0.8920 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
morturr/flan-t5-base-amazon-text-classification-2024-06-25-seed-16 | morturr | "2024-06-25T09:20:16Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-25T08:58:20Z" | ---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-amazon-text-classification-2024-06-25-seed-16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-amazon-text-classification-2024-06-25-seed-16
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.2
- Pytorch 2.3.1+cu121
- Datasets 2.10.1
- Tokenizers 0.15.2
|
FremyCompany/rl-bert-oscar-nl-step1 | FremyCompany | "2023-04-17T10:00:35Z" | 116 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2023-04-17T09:47:35Z" | Entry not found |
melisa/get_linear_approximation_last_meta-llama_Meta-Llama-3-8B-Instruct_cut_0 | melisa | "2024-05-25T15:33:20Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-25T15:27:48Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Martha-987/whisper-small-ArMarthaFikryTest | Martha-987 | "2023-06-19T09:13:43Z" | 79 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ar",
"dataset:Martha-987/MyOwnData",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2023-06-19T07:21:55Z" | ---
language:
- ar
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- Martha-987/MyOwnData
model-index:
- name: Whisper Small Ar- Martha
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Ar- Martha
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the MyOwnData dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6636
- eval_wer: 48.2981
- eval_runtime: 6533.5828
- eval_samples_per_second: 0.866
- eval_steps_per_second: 0.866
- epoch: 0.01
- step: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5655
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
etetet/my_awesome_eli5_mlm_model | etetet | "2023-07-29T20:34:10Z" | 178 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2023-07-29T20:07:20Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2578 | 1.0 | 1145 | 2.0618 |
| 2.1775 | 2.0 | 2290 | 2.0267 |
| 2.1086 | 3.0 | 3435 | 2.0174 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
timm/vit_medium_patch32_clip_224.tinyclip_laion400m | timm | "2024-12-27T02:01:55Z" | 174 | 0 | open_clip | [
"open_clip",
"pytorch",
"safetensors",
"clip",
"zero-shot-image-classification",
"license:mit",
"region:us"
] | zero-shot-image-classification | "2024-03-20T21:37:47Z" | ---
tags:
- clip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: mit
---
# Model card for vit_medium_patch32_clip.tinyclip_laion400m
|
DMFZ/marian-finetuned-kde4-en-to-fr | DMFZ | "2024-07-28T16:09:58Z" | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2024-07-28T08:29:52Z" | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.91210143343284
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8554
- Bleu: 52.9121
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
azugarini/clue-instruct-llama-7b | azugarini | "2024-07-11T13:12:05Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:azugarini/clue-instruct",
"arxiv:2404.06186",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-02-27T20:15:20Z" | ---
license: llama2
datasets:
- azugarini/clue-instruct
language:
- en
metrics:
- rouge
---
## Pre-print
More details about the model are available [here](https://arxiv.org/abs/2404.06186)
## Citation
If you find it useful, please cite us:
```
@inproceedings{zugarini2024clue,
title={Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles},
author={Zugarini, Andrea and Zeinalipour, Kamyar and Kadali, Surya Sai and Maggini, Marco and Gori, Marco and Rigutini, Leonardo},
booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
pages={3347--3356},
year={2024}
}
``` |
surathisin/surathisin-model-test | surathisin | "2023-10-12T14:05:19Z" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-10-12T13:48:10Z" | Entry not found |
pantelis-ninja/unsloth-Qwen2.5-3B-Instruct_gas-1_dtype-bfloat16_r-8_lr-0.0005_ms-100_gas-1_max-steps-100 | pantelis-ninja | "2024-11-30T08:14:57Z" | 75 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-30T08:13:59Z" | ---
base_model: unsloth/qwen2.5-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** pantelis-ninja
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
johnsutor/mixture-of-llamas-linear | johnsutor | "2024-05-30T16:36:28Z" | 49 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:merge:DeepMount00/Llama-3-8b-Ita",
"base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:merge:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:merge:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:merge:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:nbeerbower/llama-3-gutenberg-8B",
"base_model:merge:nbeerbower/llama-3-gutenberg-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-30T16:19:13Z" | ---
base_model:
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- nbeerbower/llama-3-gutenberg-8B
- jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
- meta-llama/Meta-Llama-3-8B-Instruct
- DeepMount00/Llama-3-8b-Ita
- failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# linear
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)
* [nbeerbower/llama-3-gutenberg-8B](https://huggingface.co/nbeerbower/llama-3-gutenberg-8B)
* [jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0](https://huggingface.co/jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0)
* [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita)
* [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
density: 0.5
weight: 1.0
- model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
parameters:
density: 0.5
weight: 1.0
- model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
parameters:
density: 0.5
weight: 1.0
- model: DeepMount00/Llama-3-8b-Ita
parameters:
density: 0.5
weight: 1.0
- model: nbeerbower/llama-3-gutenberg-8B
parameters:
density: 0.5
weight: 1.0
- model: jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
parameters:
density: 0.5
weight: 1.0
merge_method: linear
tokenizer_source: union
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
int8_mask: true
dtype: bfloat16
``` |
AlignmentResearch/robust_llm_pythia-imdb-31m-mz-ada-v3-nd | AlignmentResearch | "2024-03-25T18:03:29Z" | 104 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"base_model:finetune:EleutherAI/pythia-31m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-03-25T18:03:21Z" | ---
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-31m
model-index:
- name: robust_llm_pythia-imdb-31m-mz-ada-v3-nd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-imdb-31m-mz-ada-v3-nd
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
isenbek/llama-2-7b-chat-hf-local | isenbek | "2023-08-23T06:15:13Z" | 6 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-08-23T05:00:48Z" | Entry not found |
briannlongzhao/10 | briannlongzhao | "2024-01-29T21:40:36Z" | 4 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-01-27T18:21:25Z" |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of <new1> American lobster
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - briannlongzhao/10
These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of <new1> American lobster using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
youdiniplays/tl-ceb-model | youdiniplays | "2024-01-14T16:43:55Z" | 89 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-01-14T08:26:22Z" | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: tl-ceb-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tl-ceb-model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5272
- Bleu: 2.9334
- Gen Len: 18.2954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.9668 | 1.0 | 6516 | 0.8034 | 2.2949 | 18.3327 |
| 0.8082 | 2.0 | 13032 | 0.6691 | 2.6324 | 18.3182 |
| 0.7297 | 3.0 | 19548 | 0.5954 | 2.7526 | 18.2929 |
| 0.6745 | 4.0 | 26064 | 0.5474 | 2.886 | 18.308 |
| 0.6319 | 5.0 | 32580 | 0.5272 | 2.9334 | 18.2954 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
seprised/llama_fine_tuned | seprised | "2024-12-29T14:30:07Z" | 137 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-12-29T13:37:11Z" | ---
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** seprised
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
juan-glez29/BERTuit-ideologiamul-none | juan-glez29 | "2024-02-19T18:17:54Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-02-19T12:02:43Z" | Entry not found |
timm/resnetrs420.tf_in1k | timm | "2025-01-21T21:43:16Z" | 395 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"transformers",
"arxiv:2103.07579",
"arxiv:1512.03385",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-04-05T18:54:04Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
- transformers
---
# Model card for resnetrs420.tf_in1k
A ResNetRS-B image classification model.
This model features:
* ReLU activations
* single layer 7x7 convolution with pooling
* 1x1 convolution shortcut downsample
Trained on ImageNet-1k by paper authors in Tensorflow.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 191.9
- GMACs: 64.2
- Activations (M): 126.6
- Image size: train = 320 x 320, test = 416 x 416
- **Papers:**
- Revisiting ResNets: Improved Training and Scaling Strategies: https://arxiv.org/abs/2103.07579
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
- **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/resnet
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('resnetrs420.tf_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'resnetrs420.tf_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 160, 160])
# torch.Size([1, 256, 80, 80])
# torch.Size([1, 512, 40, 40])
# torch.Size([1, 1024, 20, 20])
# torch.Size([1, 2048, 10, 10])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'resnetrs420.tf_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2048, 10, 10) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
|model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec|
|------------------------------------------|--------|-----|-----|-----------|-----|-----|-------|
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 |
|[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 |
|[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 |
|[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 |
|[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 |
|[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 |
|[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 |
|[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 |
|[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 |
|[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 |
|[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 |
|[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 |
|[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 |
|[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 |
|[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 |
|[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 |
|[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 |
|[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 |
|[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 |
|[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 |
|[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 |
|[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 |
|[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 |
|[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 |
|[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 |
|[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 |
|[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 |
|[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 |
|[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 |
|[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 |
|[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 |
|[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 |
|[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 |
|[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 |
|[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 |
|[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 |
|[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 |
|[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 |
|[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 |
|[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 |
|[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 |
|[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 |
|[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 |
|[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 |
|[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 |
|[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 |
|[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 |
|[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 |
|[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 |
## Citation
```bibtex
@article{bello2021revisiting,
title={Revisiting ResNets: Improved Training and Scaling Strategies},
author={Irwan Bello and William Fedus and Xianzhi Du and Ekin D. Cubuk and Aravind Srinivas and Tsung-Yi Lin and Jonathon Shlens and Barret Zoph},
journal={arXiv preprint arXiv:2103.07579},
year={2021}
}
```
```bibtex
@article{He2015,
author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {arXiv preprint arXiv:1512.03385},
year = {2015}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
SzilviaB/Daredevil-Aura-8B_uncensored_OAS_abliterated | SzilviaB | "2024-10-03T20:00:47Z" | 7 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:mlabonne/NeuralDaredevil-8B-abliterated",
"base_model:merge:mlabonne/NeuralDaredevil-8B-abliterated",
"base_model:saishf/Aura-Uncensored-OAS-8B-L3",
"base_model:merge:saishf/Aura-Uncensored-OAS-8B-L3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-03T19:55:36Z" | ---
base_model:
- mlabonne/NeuralDaredevil-8B-abliterated
- saishf/Aura-Uncensored-OAS-8B-L3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated)
* [saishf/Aura-Uncensored-OAS-8B-L3](https://huggingface.co/saishf/Aura-Uncensored-OAS-8B-L3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mlabonne/NeuralDaredevil-8B-abliterated
- model: saishf/Aura-Uncensored-OAS-8B-L3
merge_method: slerp
base_model: mlabonne/NeuralDaredevil-8B-abliterated
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
mradermacher/Kosmos-EVAA-gamma-8B-GGUF | mradermacher | "2024-12-28T13:56:34Z" | 47 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:jaspionjader/Kosmos-EVAA-gamma-8B",
"base_model:quantized:jaspionjader/Kosmos-EVAA-gamma-8B",
"endpoints_compatible",
"region:us"
] | null | "2024-12-28T13:49:15Z" | ---
base_model: jaspionjader/Kosmos-EVAA-gamma-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/jaspionjader/Kosmos-EVAA-gamma-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-gamma-8B-GGUF/resolve/main/Kosmos-EVAA-gamma-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
yiyanghkust/finbert-tone | yiyanghkust | "2022-10-17T00:35:39Z" | 4,186,465 | 166 | transformers | [
"transformers",
"pytorch",
"tf",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | ---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "growth is strong and we have plenty of liquidity"
---
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3B tokens
- Analyst Reports: 1.1B tokens
More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
If you use the model in your academic work, please cite the following paper:
Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
# How to use
You can use this model with Transformers pipeline for sentiment analysis.
```python
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
sentences = ["there is a shortage of capital, and we need extra financing",
"growth is strong and we have plenty of liquidity",
"there are doubts about our finances",
"profits are flat"]
results = nlp(sentences)
print(results) #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
``` |
BatsResearch/bonito-v1 | BatsResearch | "2024-06-11T12:10:55Z" | 670 | 94 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"data generation",
"text2text-generation",
"en",
"dataset:BatsResearch/ctga-v1",
"arxiv:2402.18334",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-02-26T10:29:04Z" | ---
datasets:
- BatsResearch/ctga-v1
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
tags:
- data generation
license: apache-2.0
---
# Model Card for bonito
<!-- Provide a quick summary of what the model is/does. -->
Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Bonito can be used to create synthetic instruction tuning datasets to adapt large language models on users' specialized, private data.
In our [paper](https://arxiv.org/abs/2402.18334), we show that Bonito can be used to adapt both pretrained and instruction tuned models to tasks without any annotations.
- **Developed by:** Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
- **Model type:** MistralForCausalLM
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** `mistralai/Mistral-7B-v0.1`
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/BatsResearch/bonito](https://github.com/BatsResearch/bonito)
- **Paper:** [Learning to Generate Instruction Tuning Datasets for
Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334)
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
To easily generate synthetic instruction tuning datasets, we recommend using the [bonito](https://github.com/BatsResearch/bonito) package built using the `transformers` and the `vllm` libraries.
```python
from bonito import Bonito
from vllm import SamplingParams
from datasets import load_dataset
# Initialize the Bonito model
bonito = Bonito("BatsResearch/bonito-v1")
# load dataaset with unannotated text
unannotated_text = load_dataset(
"BatsResearch/bonito-experiment",
"unannotated_contract_nli"
)["train"].select(range(10))
# Generate synthetic instruction tuning dataset
sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
synthetic_dataset = bonito.generate_tasks(
unannotated_text,
context_col="input",
task_type="nli",
sampling_params=sampling_params
)
```
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Our model is trained to generate the following task types: summarization, sentiment analysis, multiple-choice question answering, extractive question answering, topic classification, natural language inference, question generation, text generation, question answering without choices, paraphrase identification, sentence completion, yes-no question answering, word sense disambiguation, paraphrase generation, textual entailment, and
coreference resolution.
The model might not produce accurate synthetic tasks beyond these task types.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
**Limitations**
Our work relies on the availability of large amounts of unannotated text.
If only a small quantity of unannotated text is present, the target language model, after adaptation, may experience a drop in performance.
While we demonstrate positive improvements on pretrained and instruction-tuned models, our observations are limited to the three task types (yes-no question answering, extractive question answering, and natural language inference) considered in our paper.
**Risks**
Bonito poses risks similar to those of any large language model.
For example, our model could be used to generate factually incorrect datasets in specialized domains.
Our model can exhibit the biases and stereotypes of the base model, Mistral-7B, even after extensive supervised fine-tuning.
Finally, our model does not include safety training and can potentially generate harmful content.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
We recommend users thoroughly inspect the generated tasks and benchmark performance on critical datasets before deploying the models trained with the synthetic tasks into the real world.
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
To train Bonito, we create a new dataset called conditional task generation with attributes by remixing existing instruction tuning datasets.
See [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1) for more details.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
We train the model using [Q-LoRA](https://github.com/artidoro/qlora) by optimizing the cross entropy loss over the output tokens.
The model is trained for 100,000 steps.
The training takes about 4 days on four GPUs to complete.
We use the following hyperparameters:
- Q-LoRA rank (r): 64
- Q-LoRA scaling factor (alpha): 4
- Q-LoRA dropout: 0
- Optimizer: Paged AdamW
- Learning rate scheduler: linear
- Max. learning rate: 1e-04
- Min. learning rate: 0
- Weight decay: 0
- Dropout: 0
- Max. gradient norm: 0.3
- Effective batch size: 16
- Max. input length: 2,048
- Max. output length: 2,048
- Num. steps: 100,000
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@inproceedings{bonito:aclfindings24,
title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
year = {2024}}
``` |
crybit/role_172840396119 | crybit | "2024-10-08T16:15:25Z" | 5 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2024-10-08T16:12:41Z" | Entry not found |
maddes8cht/h2oai-h2ogpt-gm-oasst1-en-2048-falcon-7b-v2-gguf | maddes8cht | "2023-11-22T20:26:16Z" | 207 | 1 | transformers | [
"transformers",
"gguf",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"region:us"
] | text-generation | "2023-10-23T06:28:36Z" | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
datasets:
- OpenAssistant/oasst1
pipeline_tag: conversational
---
[]()
I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information
# h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 - GGUF
- Model creator: [h2oai](https://huggingface.co/h2oai)
- Original model: [h2ogpt-gm-oasst1-en-2048-falcon-7b-v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2)
# K-Quants in Falcon 7b models
New releases of Llama.cpp now support K-quantization for previously incompatible models, in particular all Falcon 7B models (While Falcon 40b is and always has been fully compatible with K-Quantisation). This is achieved by employing a fallback solution for model layers that cannot be quantized with real K-quants.
For Falcon 7B models, although only a quarter of the layers can be quantized with true K-quants, this approach still benefits from utilizing *different* legacy quantization types Q4_0, Q4_1, Q5_0, and Q5_1. As a result, it offers better quality at the same file size or smaller file sizes with comparable performance.
So this solution ensures improved performance and efficiency over legacy Q4_0, Q4_1, Q5_0 and Q5_1 Quantizations.
# About GGUF format
`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
# Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
# Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
## Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not *real* K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
# K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
---
# Original Model Card:
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b)
- Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate`, `torch` and `einops` libraries installed.
```bash
pip install transformers==4.29.2
pip install accelerate==0.19.0
pip install torch==2.0.0
pip install einops==0.6.1
```
```python
import torch
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
generate_text = pipeline(
model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2",
tokenizer=tokenizer,
torch_dtype=torch.float16,
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2",
torch_dtype=torch.float16,
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=False,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
RWForCausalLM(
(transformer): RWModel(
(word_embeddings): Embedding(65024, 4544)
(h): ModuleList(
(0-31): 32 x DecoderLayer(
(input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
(self_attention): Attention(
(maybe_rotary): RotaryEmbedding()
(query_key_value): Linear(in_features=4544, out_features=4672, bias=False)
(dense): Linear(in_features=4544, out_features=4544, bias=False)
(attention_dropout): Dropout(p=0.0, inplace=False)
)
(mlp): MLP(
(dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False)
(act): GELU(approximate='none')
(dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False)
)
)
)
(ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=4544, out_features=65024, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
***End of original Model File***
---
## Please consider to support my work
**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
<center>
[](https://maddes8cht.github.io)
[](https://stackexchange.com/users/26485911)
[](https://github.com/maddes8cht)
[](https://huggingface.co/maddes8cht)
[](https://twitter.com/maddes1966)
</center> |
ssh1419/deplot-batch-1-token-freeze-curri-update-loss | ssh1419 | "2024-09-19T03:02:24Z" | 34 | 0 | transformers | [
"transformers",
"safetensors",
"pix2struct",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2024-09-19T03:01:44Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
SakanaAI/Evo-Ukiyoe-v1 | SakanaAI | "2024-07-19T04:51:55Z" | 132 | 33 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"ja",
"license:apache-2.0",
"region:us"
] | text-to-image | "2024-07-11T08:37:41Z" | ---
library_name: diffusers
license: apache-2.0
language:
- ja
pipeline_tag: text-to-image
tags:
- stable-diffusion
---
# ๐ Evo-Ukiyoe-v1
๐ค [Models](https://huggingface.co/SakanaAI/Evo-Ukiyoe-v1/) | ๐ [Blog](https://sakana.ai/evo-ukiyoe/) | ๐ฆ [Twitter](https://twitter.com/SakanaAILabs)
**Evo-Ukiyoe-v1** is an experimental education-purpose Japanese woodblock print Ukiyoe style image generation model. The model was train based on Sakana AI's [Evo-SDXL-JP](https://huggingface.co/SakanaAI/EvoSDXL-JP-v1).
All the dataset used to train Evo-Ukiyoe comes from Ukiyoe images belonged to [Ritsumeikan University, Art Research Center](https://www.arc.ritsumei.ac.jp/).
Please refer to our [blog](https://sakana.ai/evo-ukiyoe/) for more details.
## Usage
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
1. Git clone this model card
```
git clone https://huggingface.co/SakanaAI/Evo-Ukiyoe-v1
```
2. Install git-lfs if you don't have it yet.
```
sudo apt install git-lfs
git lfs install
```
3. Create conda env
```
conda create -n evo-ukiyoe python=3.11
conda activate evo-ukiyoe
```
4. Install packages
```
cd Evo-Ukiyoe-v1
pip install -r requirements.txt
```
5. Run
```python
from evo_ukiyoe_v1 import load_evo_ukiyoe
prompt = "็็ฉใ็ใฆใใ็ซใๅบญใงใ่ถใ้ฃฒใใงใใใ"
pipe = load_evo_ukiyoe(device="cuda")
images = pipe(prompt + "่ผปใฎๆตฎไธ็ตตใ่ถ
่ฉณ็ดฐใ", negative_prompt='', guidance_scale=8.0, num_inference_steps=40).images
images[0].save("image.png")
```
</details>
## Model Details
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Sakana AI](https://sakana.ai/)
- **Model type:** Diffusion-based text-to-image generative model
- **Language(s):** Japanese
- **Blog:** https://sakana.ai/evo-ukiyoe/
## License
The Python script included in this repository and Lora weight are licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
Please note that the license for the model/pipeline generated by this script is inherited from the source models.
## Uses
This model is provided for research and development purposes only and should be considered as an experimental prototype.
It is not intended for commercial use or deployment in mission-critical environments.
Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed.
Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained.
Users must fully understand the risks associated with the use of this model and use it at their own discretion.
## Acknowledgement
Evo-Ukiyoe was trained based on Evo-SDXL-JP. We would like to thank the developers of Evo-SDXL-JP source models for their contributions and for making their work available.
- [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
- [Juggernaut-XL-v9](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
- [SDXL-DPO](https://huggingface.co/mhdang/dpo-sdxl-text2image-v1)
- [JSDXL](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl)
## Citation
@misc{Evo-Ukiyoe,
url = {[https://huggingface.co/SakanaAI/Evo-Nishikie-v1](https://huggingface.co/SakanaAI/Evo-Nishikie-v1)},
title = {Evo-Ukiyoe},
author = {Clanuwat, Tarin and Shing, Makoto and Imajuku, Yuki and Kitamoto, Asanobu and Akama, Ryo}
}
|
oldiday/515af5ec-b1ab-4762-b938-ed21aacc8db4 | oldiday | "2025-02-04T01:59:16Z" | 16 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:fxmarty/tiny-llama-fast-tokenizer",
"base_model:adapter:fxmarty/tiny-llama-fast-tokenizer",
"region:us"
] | null | "2025-02-04T01:57:55Z" | ---
library_name: peft
base_model: fxmarty/tiny-llama-fast-tokenizer
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 515af5ec-b1ab-4762-b938-ed21aacc8db4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: fxmarty/tiny-llama-fast-tokenizer
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 8b74be9ab0373a6f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8b74be9ab0373a6f_train_data.json
type:
field_input: references
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: oldiday/515af5ec-b1ab-4762-b938-ed21aacc8db4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/8b74be9ab0373a6f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: ab2cd1d3-a8f2-4277-a76f-00c40e9d7b71
wandb_project: Gradients-On-Six
wandb_run: your_name
wandb_runid: ab2cd1d3-a8f2-4277-a76f-00c40e9d7b71
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 515af5ec-b1ab-4762-b938-ed21aacc8db4
This model is a fine-tuned version of [fxmarty/tiny-llama-fast-tokenizer](https://huggingface.co/fxmarty/tiny-llama-fast-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3753
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 10.3801 |
| 10.3801 | 0.0068 | 9 | 10.3798 |
| 10.3788 | 0.0135 | 18 | 10.3793 |
| 10.3781 | 0.0203 | 27 | 10.3787 |
| 10.3786 | 0.0270 | 36 | 10.3781 |
| 10.3774 | 0.0338 | 45 | 10.3774 |
| 10.3774 | 0.0405 | 54 | 10.3768 |
| 10.3757 | 0.0473 | 63 | 10.3762 |
| 10.376 | 0.0540 | 72 | 10.3757 |
| 10.3751 | 0.0608 | 81 | 10.3754 |
| 10.3751 | 0.0675 | 90 | 10.3753 |
| 10.3757 | 0.0743 | 99 | 10.3753 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ClarenceDan/cccaae51-e179-43bf-b398-7687ff139333 | ClarenceDan | "2025-01-19T03:05:10Z" | 10 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-1.3b",
"base_model:adapter:facebook/opt-1.3b",
"license:other",
"region:us"
] | null | "2025-01-19T03:00:48Z" | ---
library_name: peft
license: other
base_model: facebook/opt-1.3b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cccaae51-e179-43bf-b398-7687ff139333
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: facebook/opt-1.3b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5b1d7d2de5370ca4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5b1d7d2de5370ca4_train_data.json
type:
field_input: context
field_instruction: question
field_output: answers
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: ClarenceDan/cccaae51-e179-43bf-b398-7687ff139333
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/5b1d7d2de5370ca4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 46dd44b3-e4e4-4c44-8605-e8e8b6dd956e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 46dd44b3-e4e4-4c44-8605-e8e8b6dd956e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# cccaae51-e179-43bf-b398-7687ff139333
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 16.1647 | 0.0001 | 1 | 4.0757 |
| 15.6047 | 0.0003 | 3 | 4.0171 |
| 17.3113 | 0.0005 | 6 | 3.5892 |
| 11.4373 | 0.0008 | 9 | 2.5734 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
liujunshi/my_awesome_wnut_model | liujunshi | "2023-06-05T12:14:08Z" | 109 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-06-05T12:11:41Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5543672014260249
- name: Recall
type: recall
value: 0.2882298424467099
- name: F1
type: f1
value: 0.37926829268292683
- name: Accuracy
type: accuracy
value: 0.9402761745970672
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2761
- Precision: 0.5544
- Recall: 0.2882
- F1: 0.3793
- Accuracy: 0.9403
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2888 | 0.4658 | 0.2020 | 0.2818 | 0.9364 |
| No log | 2.0 | 426 | 0.2761 | 0.5544 | 0.2882 | 0.3793 | 0.9403 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
goldfish-models/kor_hang_10mb | goldfish-models | "2024-08-26T16:50:41Z" | 8 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"goldfish",
"arxiv:2408.10441",
"kor",
"dataset:oscar-corpus/OSCAR-2109",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-08-13T08:19:42Z" |
---
license: apache-2.0
language:
- kor
datasets:
- oscar-corpus/OSCAR-2109
library_name: transformers
pipeline_tag: text-generation
tags:
- goldfish
- arxiv:2408.10441
---
# kor_hang_10mb
Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the <b>Korean</b> (Hangul script) model trained on 10MB of data, after accounting for an estimated byte premium of 1.29; content-matched text in Korean takes on average 1.29x as many UTF-8 bytes to encode as English.
The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).
Note: kor_hang is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. It is not contained in any macrolanguage codes contained in Goldfish (for script hang).
All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://www.arxiv.org/abs/2408.10441).
Training code and sample usage: https://github.com/tylerachang/goldfish
Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing)
## Model details:
To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json.
All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
Details for this model specifically:
* Architecture: gpt2
* Parameters: 39087104
* Maximum sequence length: 512 tokens
* Training text data (raw): 12.93MB
* Training text data (byte premium scaled): 10.005MB
* Training tokens: 2388480 (x10 epochs)
* Vocabulary size: 50000
* Compute cost: 1804686144307200.0 FLOPs or ~0.2 NVIDIA A6000 GPU hours
Training datasets (percentages prior to deduplication):
* 100.00000%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
## Citation
If you use this model, please cite:
```
@article{chang-etal-2024-goldfish,
title={Goldfish: Monolingual Language Models for 350 Languages},
author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
journal={Preprint},
year={2024},
url={https://www.arxiv.org/abs/2408.10441},
}
```
|
exala/db_fe2_1.1 | exala | "2024-11-19T19:34:35Z" | 107 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-19T17:07:33Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
band2001/stolaf-angora-3200 | band2001 | "2024-04-25T15:42:41Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"dataset:band2001/stolaf-angora",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T02:24:54Z" | ---
license: mit
datasets:
- band2001/stolaf-angora
---
# Model Card for Angora-3200
<!-- Provide a quick summary of what the model is/does. -->
This model has been created to help computer science students at St. Olaf College (Northfield, MN) answer questions about fundamental CS principles as well as questions about the specific technical stacks and procedures St. Olaf Computer Science uses.
## Angora-3200 Details
This model is built off of [Google's Gemma 7b-it](https://huggingface.co/google/gemma-7b-it) model. It was fine tuned with a dataset created with the purpose of addressing St. Olaf specific Computer Science questions. Some of these questions reference the specific instance of git the institution uses or address steps to declare the computer science major. This model was fine-tuned using MLX on an Apple M3 Max Chip. This model was trained for 3200 iterations using LoRA as the method for finetuning.
- **Developed by:** Ben Anderson & Keegan Murray
- **Funded by:** St. Olaf College MSCS Department
- **Model type:** Generative
- **License:** MIT
- **Finetuned from model:** [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
<!-- Provide the basic links for the model. -->
- **Repository:** See the GitHub repository [here](https://github.com/band2001/stolaf-angora)
- **Paper:** Coming soon...
- **Demo:** A video demo is available [here](https://drive.google.com/file/d/1iwThVj88FTgLNANZdv2NineRcBXAqtZp/view?usp=sharing).
## Uses
This is intended to be used by Computer Science students at St. Olaf College. While it can be used broadly for general computer science questions, it has been finetuned to answer questions specific to the St. Olaf Computer Science program.
## How to Get Started with the Model
Use the code below to get started with the model.
### Direct Use With Transformers Library
#### Use a pipeline as a high-level helper
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="band2001/stolaf-angora-3200")
```
#### Load model directly
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("band2001/stolaf-angora-3200")
model = AutoModelForCausalLM.from_pretrained("band2001/stolaf-angora-3200", device_map="auto")
input_ids = tokenizer("YOUR PROMPT HERE", return_tensors="pt").to("YOUR DEVICE IF USING GPU ACCELERATION")
outputs = model.generate(**input_ids, max_new_tokens=256)
decoded_output = tokenizer.decode(outputs[0])
```
### Direct Use With MLX Library
Note MLX can only be used with Apple Silicon Macs. It is also recommended to use one of their Max series chips or higher.
```python
from mlx_lm import load, generate
def format_prompt(prompt, system_prompt = "YOUR SYSTEM PROMPT"):
return """<bos><start_of_turn>user
## Instructions
{}
## User
{}<end_of_turn>
<start_of_turn>model
""".format(system_prompt, prompt)
model, tokenizer = load("band2001/stolaf-angora-3200")
prompt = format_prompt("YOUR PROMPT HERE")
decoded_output = generate(
model,
tokenizer,
prompt=prompt,
verbose=True,
temp=0.0,
max_tokens=256,
)
```
### Out-of-Scope Use
Outside of using this model to ask questions about computer science topics (generally and specific to St. Olaf College), this model should not be used for other inference. Asking questions about other topics will likely yield answers; however, they have not been fine-tuned and will most likely contain errors and/or could potentially include offensive content.
## Bias, Risks, and Limitations
As we created the fine-tuning dataset from scratch, it is relatively limited compared to the overall size of the model. Our dataset has about 2000 observations, while the model has roughly 8.5B parameters. So while our dataset had a noticeable effect on the tuning of this model, it still will fall back on other knowledge occasionally and provide partially incorrect answers for St. Olaf specific questions.
Also note the limitations present in the [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) model and assume they are present in this model as well.
## Training Details
### Training Data
The training data can be found in the St. Olaf Angora Dataset ([band2001/stolaf-angora](https://huggingface.co/datasets/band2001/stolaf-angora)).
### Training Procedure
To train the model, the data needs to be in the following format. Note the data in [band2001/stolaf-angora](https://huggingface.co/datasets/band2001/stolaf-angora) already is.
```
<bos><start_of_turn>user
## Instructions
system prompt goes here
## User
prompt/query goes here<end_of_turn>
<start_of_turn>model
model response here (put a response here for tuning purposes)<end_of_turn><eos>
```
Once the data is in the correct format, QLoRA is recommended. The model can be fine-tuned either using mlx-lm and mps (to tune on an Apple Silicon machine) or a bitsandbytes configuration and cuda (to tune on a machine with Nvidia GPUs).
#### Preprocessing
To preprocess your data to be in the correct format outlined above, you can use the following helper function:
```python
def generate_prompt(entry, system_prompt = SYSTEM_PROMPT):
'''
This function formats a question/answer pair to gemma's chat template.
:param: entry - a dictionary with an instruction and a response
:param: system_prompt: the system prompt to be used
:return: the formated string for gemma's chat template
'''
return """<bos><start_of_turn>user
## Instructions
{}
## User
{}<end_of_turn>
<start_of_turn>model
{}<end_of_turn><eos>""".format(system_prompt, entry["instruction"], entry["response"])
```
When trying to use inference with this model, you can format the user's query using this helper function:
```python
def format_prompt(prompt, system_prompt = SYSTEM_PROMPT):
'''
This function formats a question to gemma's chat template.
:param: prompt - a string with the user's query
:param: system_prompt: the system prompt to be used
:return: the formated string for gemma's chat template
'''
return """<bos><start_of_turn>user
## Instructions
{}
## User
{}<end_of_turn>
<start_of_turn>model
""".format(system_prompt, prompt)
```
#### Training Process
The MLX LoRA fine-tuning approach was used. You can learn more about [MLX LoRA here](https://github.com/ml-explore/mlx-examples/blob/main/lora/README.md). The Gemma-7b-it was loaded in without any conversion. The default `batch_size = 16` was chosen and to reach a 3200 iteration fine-tuned model the model was tuned with 800 iterations four times. Once the fine-tuned weights were created, the model was fused using MLX's fuse functionality. You can learn more about [fusing with MLX here](https://github.com/ml-explore/mlx-examples/blob/main/lora/README.md#Fuse-and-Upload). One important change made when fusing with MLX was to change some of the MLX package code to include `"format":"pt"` in the metadata so this model can be used with the transformers library. To do that, the following was done: you can tweak the library code like below in <path_to_your_site-packages>/mlx_lm/utils.py by replacing `mx.save_safetensors(str(shard_path), shard, metadata={"format":"mlx"})` with `mx.save_safetensors(str(shard_path), shard, metadata={"format":"pt"})` to output fused weights with the metadata attribute. Special thanks to [Alexweberk's guide on GitHub](https://gist.github.com/alexweberk/635431b5c5773efd6d1755801020429f) to help solve this issue. Finally, the fused model was uploaded to this HuggingFace repo!
If you look at the GitHub repo for this project, mlx_lora.sh includes the command used for the LoRA fine-tuning, mlx_fuse.sh includes the command for the model fusing, and mlx_upload.sh includes the upload command. There is additionally an optional mlx_convert.sh for converting the Google Gemma 7b-it model before fine-tuning if desired.
## Evaluation
Testing loss and perplexity were the two metrics used to evaluate the Angora models. A summary of the results for all the different iteration models is included below.
### Results
| Number of iterations | Testing Loss | Perplexity |
|:----------|:----------|:---------|
|800 | 0.569 | 1.766 |
| 1600 | 0.302 | 1.352 |
| 2400 | 0.225 | 1.252 |
| 3200 | 0.185 | 1.203 |
| 4000 | 0.170 | 1.185 |
### Testing Data
The testing data is available [here](https://huggingface.co/datasets/band2001/stolaf-angora/viewer/default/test).
## Model Card Contact
Ben Anderson - [ander6@stolaf.edu](mailto:ander6@stolaf.edu)
Keegan Murray - [murray7@stolaf.edu](mailto:murray7@stolaf.edu) |
paultimothymooney/DeepSeek-R1-Distill-Qwen-1.5B-Q8_0-GGUF | paultimothymooney | "2025-01-21T23:38:54Z" | 21 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-21T23:38:39Z" | ---
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
tags:
- llama-cpp
- gguf-my-repo
---
# paultimothymooney/DeepSeek-R1-Distill-Qwen-1.5B-Q8_0-GGUF
This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo paultimothymooney/DeepSeek-R1-Distill-Qwen-1.5B-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo paultimothymooney/DeepSeek-R1-Distill-Qwen-1.5B-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo paultimothymooney/DeepSeek-R1-Distill-Qwen-1.5B-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo paultimothymooney/DeepSeek-R1-Distill-Qwen-1.5B-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-q8_0.gguf -c 2048
```
|
ray1031/esm2_t12_35M_UR50D-pretrained-evaluation | ray1031 | "2023-12-22T05:37:29Z" | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-12-22T05:25:28Z" | Entry not found |
joshnader/rho-math-7b-interpreter-v0.1-Q8_0-GGUF | joshnader | "2024-07-02T07:24:14Z" | 5 | 0 | null | [
"gguf",
"nlp",
"math",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:microsoft/rho-math-7b-interpreter-v0.1",
"base_model:quantized:microsoft/rho-math-7b-interpreter-v0.1",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-02T07:23:43Z" | ---
base_model: microsoft/rho-math-7b-interpreter-v0.1
language:
- en
license: mit
pipeline_tag: text-generation
tags:
- nlp
- math
- llama-cpp
- gguf-my-repo
---
# joshnader/rho-math-7b-interpreter-v0.1-Q8_0-GGUF
This model was converted to GGUF format from [`microsoft/rho-math-7b-interpreter-v0.1`](https://huggingface.co/microsoft/rho-math-7b-interpreter-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/rho-math-7b-interpreter-v0.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo joshnader/rho-math-7b-interpreter-v0.1-Q8_0-GGUF --hf-file rho-math-7b-interpreter-v0.1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo joshnader/rho-math-7b-interpreter-v0.1-Q8_0-GGUF --hf-file rho-math-7b-interpreter-v0.1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo joshnader/rho-math-7b-interpreter-v0.1-Q8_0-GGUF --hf-file rho-math-7b-interpreter-v0.1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo joshnader/rho-math-7b-interpreter-v0.1-Q8_0-GGUF --hf-file rho-math-7b-interpreter-v0.1-q8_0.gguf -c 2048
```
|
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-26543668 | fine-tuned | "2024-05-28T18:56:30Z" | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-26543668",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-05-28T18:55:59Z" | ---
license: apache-2.0
datasets:
- fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-26543668
- allenai/c4
language:
- en
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
---
This model is a fine-tuned version of [**BAAI/bge-large-en-v1.5**](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case:
None
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-26543668',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
MaziyarPanahi/calme-2.2-qwen2-7b | MaziyarPanahi | "2024-09-19T11:22:48Z" | 30,254 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"qwen",
"finetune",
"chatml",
"OpenHermes-2.5",
"HelpSteer2",
"Orca",
"SlimOrca",
"conversational",
"en",
"dataset:nvidia/HelpSteer2",
"dataset:teknium/OpenHermes-2.5",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Open-Orca/SlimOrca",
"base_model:Qwen/Qwen2-7B",
"base_model:finetune:Qwen/Qwen2-7B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-27T08:57:21Z" | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- chat
- qwen
- qwen2
- finetune
- chatml
- OpenHermes-2.5
- HelpSteer2
- Orca
- SlimOrca
base_model: Qwen/Qwen2-7B
datasets:
- nvidia/HelpSteer2
- teknium/OpenHermes-2.5
- microsoft/orca-math-word-problems-200k
- Open-Orca/SlimOrca
model_name: calme-2.2-qwen2-7b
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
model-index:
- name: calme-2.2-qwen2-7b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 35.97
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 33.11
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 19.34
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.48
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 13.28
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 32.21
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2-7b
name: Open LLM Leaderboard
---
<img src="./qwen2-fine-tunes-maziyar-panahi.webp" alt="Qwen2 fine-tune" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# MaziyarPanahi/calme-2.2-qwen2-7b
This is a fine-tuned version of the `Qwen/Qwen2-7B` model. It aims to improve the base model across all benchmarks.
# โก Quantized GGUF
All GGUF models are available here: [MaziyarPanahi/calme-2.2-qwen2-7b-GGUF](https://huggingface.co/MaziyarPanahi/calme-2.2-qwen2-7b-GGUF)
# ๐ [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.2-qwen2-7b)
| Metric |Value|
|-------------------|----:|
|Avg. |23.23|
|IFEval (0-Shot) |35.97|
|BBH (3-Shot) |33.11|
|MATH Lvl 5 (4-Shot)|19.34|
|GPQA (0-shot) | 5.48|
|MuSR (0-shot) |13.28|
|MMLU-PRO (5-shot) |32.21|
# Prompt Template
This model uses `ChatML` prompt template:
```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````
# How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.2-qwen2-7b")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.2-qwen2-7b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.2-qwen2-7b")
```
|
RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf | RichardErkhov | "2024-07-02T19:22:36Z" | 25 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2024-07-02T19:13:41Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyDolphin-2.8.2-1.1b-laser - GGUF
- Model creator: https://huggingface.co/cognitivecomputations/
- Original model: https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyDolphin-2.8.2-1.1b-laser.Q2_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q2_K.gguf) | Q2_K | 0.4GB |
| [TinyDolphin-2.8.2-1.1b-laser.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [TinyDolphin-2.8.2-1.1b-laser.IQ3_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [TinyDolphin-2.8.2-1.1b-laser.IQ3_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q3_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q3_K.gguf) | Q3_K | 0.51GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [TinyDolphin-2.8.2-1.1b-laser.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q4_0.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q4_0.gguf) | Q4_0 | 0.59GB |
| [TinyDolphin-2.8.2-1.1b-laser.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q4_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q4_K.gguf) | Q4_K | 0.62GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q4_1.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q4_1.gguf) | Q4_1 | 0.65GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q5_0.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q5_0.gguf) | Q5_0 | 0.71GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q5_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q5_K.gguf) | Q5_K | 0.73GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q5_1.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q5_1.gguf) | Q5_1 | 0.77GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q6_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q6_K.gguf) | Q6_K | 0.84GB |
| [TinyDolphin-2.8.2-1.1b-laser.Q8_0.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_TinyDolphin-2.8.2-1.1b-laser-gguf/blob/main/TinyDolphin-2.8.2-1.1b-laser.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- teknium/openhermes
language:
- en
---
# TinyDolphin-2.8.2-1.1b-laser
Join Our Discord! https://discord.gg/cognitivecomputations
This is an version 3 of a model trained on 3 3090's by Kearm on the new Dolphin 2.8 dataset by Eric Hartford https://erichartford.com/dolphin ๐ฌ
This model uses our laser technique from https://github.com/cognitivecomputations/laserRMT to denoise the model!
For this version we increased the epochs as well as refined the datasets used.
## Example Outputs
TBD
Support my efforts! https://ko-fi.com/kearm
# Orignal Model Card Below
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs ๐๐. The training has started on 2023-09-01.
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| TinyLlama-1.1B-intermediate-step-1195k-2.5T | 2.5T | 58.96 | 34.40 | 58.72 | 31.91 | 56.78 | 63.21 | 73.07 | 53.86|
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99|
|
mradermacher/Llama-3-70B-Synthia-v3.5-GGUF | mradermacher | "2024-05-27T04:49:46Z" | 28 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:migtissera/Llama-3-70B-Synthia-v3.5",
"base_model:quantized:migtissera/Llama-3-70B-Synthia-v3.5",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-05-27T00:36:49Z" | ---
base_model: migtissera/Llama-3-70B-Synthia-v3.5
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/migtissera/Llama-3-70B-Synthia-v3.5
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70B-Synthia-v3.5-GGUF/resolve/main/Llama-3-70B-Synthia-v3.5.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
furrutiav/bert_qa_extractor_cockatiel_2022_ef_mixtral_v2_linear_weight_it_807 | furrutiav | "2024-03-09T23:19:29Z" | 91 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-03-09T23:19:04Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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|
mradermacher/BeisenAI-7B-Chat-GGUF | mradermacher | "2024-12-14T20:39:20Z" | 56 | 0 | transformers | [
"transformers",
"gguf",
"beisen",
"train",
"zh",
"base_model:maxosai/BeisenAI-7B-Chat",
"base_model:quantized:maxosai/BeisenAI-7B-Chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-12-14T20:09:08Z" | ---
base_model: maxosai/BeisenAI-7B-Chat
language:
- zh
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- beisen
- train
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/maxosai/BeisenAI-7B-Chat
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q3_K_S.gguf) | Q3_K_S | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q5_K_S.gguf) | Q5_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.Q8_0.gguf) | Q8_0 | 8.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/BeisenAI-7B-Chat-GGUF/resolve/main/BeisenAI-7B-Chat.f16.gguf) | f16 | 15.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lesso/f20bd0af-6478-4f25-9389-467721618ed2 | lesso | "2025-02-07T12:30:17Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:adapter:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"region:us"
] | null | "2025-02-07T12:23:15Z" | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f20bd0af-6478-4f25-9389-467721618ed2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-0.5B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 8b6e906fbaabc6a1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8b6e906fbaabc6a1_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/f20bd0af-6478-4f25-9389-467721618ed2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/G.O.D/8b6e906fbaabc6a1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 91b08c31-c935-462e-b1e5-3c3de26cc56f
wandb_project: new-02
wandb_run: your_name
wandb_runid: 91b08c31-c935-462e-b1e5-3c3de26cc56f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f20bd0af-6478-4f25-9389-467721618ed2
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5930
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8849 | 0.0006 | 1 | 1.7108 |
| 1.4593 | 0.0284 | 50 | 1.6532 |
| 1.3904 | 0.0569 | 100 | 1.6134 |
| 1.2375 | 0.0853 | 150 | 1.5988 |
| 1.3273 | 0.1138 | 200 | 1.5930 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
whiteapple8222/e06c3bcf-5c44-42a2-8326-a1481dd0642d | whiteapple8222 | "2025-02-06T10:39:54Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"region:us"
] | null | "2025-02-06T10:31:28Z" | ---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e06c3bcf-5c44-42a2-8326-a1481dd0642d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a1b281c6d31a7336_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a1b281c6d31a7336_train_data.json
type:
field_instruction: passage
field_output: question
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: whiteapple8222/e06c3bcf-5c44-42a2-8326-a1481dd0642d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1358
micro_batch_size: 2
mlflow_experiment_name: /tmp/a1b281c6d31a7336_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 12e67bb2-1212-4109-94de-02222dc25293
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 12e67bb2-1212-4109-94de-02222dc25293
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e06c3bcf-5c44-42a2-8326-a1481dd0642d
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3962
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 94
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.2662 | 0.0107 | 1 | 2.6563 |
| 1.6499 | 0.2574 | 24 | 1.5020 |
| 1.6759 | 0.5147 | 48 | 1.4368 |
| 1.5255 | 0.7721 | 72 | 1.3962 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
LHRuig/erickrom4 | LHRuig | "2025-02-02T03:20:08Z" | 9 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | "2025-02-02T03:19:48Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: suit
output:
url: images/suit.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: erickrom4
---
# erickrom4
<Gallery />
## Model description
erickrom4 lora
## Trigger words
You should use `erickrom4` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/LHRuig/erickrom4/tree/main) them in the Files & versions tab.
|
mradermacher/EinBase-70B-v0.1-full-GGUF | mradermacher | "2024-05-06T06:01:22Z" | 15 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:SF-Foundation/EinBase-70B-v0.1-full",
"base_model:quantized:SF-Foundation/EinBase-70B-v0.1-full",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-03-24T04:14:10Z" | ---
base_model: SF-Foundation/EinBase-70B-v0.1-full
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
static quants of https://huggingface.co/SF-Foundation/EinBase-70B-v0.1-full
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q2_K.gguf) | Q2_K | 25.9 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.IQ3_XS.gguf) | IQ3_XS | 28.7 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.IQ3_S.gguf) | IQ3_S | 30.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q3_K_S.gguf) | Q3_K_S | 30.3 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.IQ3_M.gguf) | IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q3_K_M.gguf) | Q3_K_M | 33.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q3_K_L.gguf) | Q3_K_L | 36.6 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.IQ4_XS.gguf) | IQ4_XS | 37.6 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q4_0.gguf) | Q4_0 | 39.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.IQ4_NL.gguf) | IQ4_NL | 39.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q4_K_S.gguf) | Q4_K_S | 39.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q4_K_M.gguf) | Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q5_K_S.gguf) | Q5_K_S | 47.9 | |
| [GGUF](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q5_K_M.gguf) | Q5_K_M | 49.2 | |
| [PART 1](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q6_K.gguf.part2of2) | Q6_K | 57.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EinBase-70B-v0.1-full-GGUF/resolve/main/EinBase-70B-v0.1-full.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Helsinki-NLP/opus-mt-pon-sv | Helsinki-NLP | "2023-08-16T12:02:52Z" | 115 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"pon",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pon-sv
* source languages: pon
* target languages: sv
* OPUS readme: [pon-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pon-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pon-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pon-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pon-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.pon.sv | 26.4 | 0.436 |
|
PSW/bart-base-dialogsumgen-xsum-conv-samsum | PSW | "2022-08-26T05:05:13Z" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-08-24T13:17:22Z" | # **PSW/bart-base-dialogsumgen-xsum-conv-samsum**
1. reverse trained on dialogsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on samsum
|
team-nave/xlm-roberta-base-finetuned-panx-all | team-nave | "2022-11-17T17:36:59Z" | 110 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-11-17T17:05:26Z" | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1583
- F1: 0.8563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 358 | 0.1748 | 0.8282 |
| 0.2366 | 2.0 | 716 | 0.1580 | 0.8434 |
| 0.2366 | 3.0 | 1074 | 0.1583 | 0.8563 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
tchen175/llama3.1-8b-financial-news-sentiment | tchen175 | "2024-11-12T08:23:44Z" | 75 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-11-09T12:06:30Z" | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** tchen175
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
gokuls/whisper-base.en-speech-commands-h | gokuls | "2024-10-06T01:50:06Z" | 8 | 0 | null | [
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"dataset:speech_commands",
"base_model:openai/whisper-base.en",
"base_model:finetune:openai/whisper-base.en",
"license:apache-2.0",
"model-index",
"region:us"
] | null | "2024-10-06T01:21:09Z" | ---
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: whisper-base.en-speech-commands-h
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: speech_commands
type: speech_commands
config: v0.02
split: None
args: v0.02
metrics:
- name: Accuracy
type: accuracy
value: 0.7922661870503597
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-base.en-speech-commands-h
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3313
- Accuracy: 0.7923
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3859 | 1.0 | 412 | 1.3474 | 0.7707 |
| 0.2732 | 2.0 | 824 | 1.2471 | 0.7599 |
| 0.2373 | 3.0 | 1236 | 1.2114 | 0.7729 |
| 0.1694 | 4.0 | 1648 | 1.1600 | 0.7914 |
| 0.1495 | 5.0 | 2060 | 1.1535 | 0.7914 |
| 0.1931 | 6.0 | 2472 | 1.1446 | 0.7860 |
| 0.1329 | 7.0 | 2884 | 1.3313 | 0.7923 |
| 0.0731 | 8.0 | 3296 | 1.2812 | 0.7860 |
| 0.0702 | 9.0 | 3708 | 1.2134 | 0.7873 |
| 0.0828 | 10.0 | 4120 | 1.6292 | 0.7887 |
| 0.08 | 11.0 | 4532 | 1.4677 | 0.7797 |
| 0.0481 | 12.0 | 4944 | 1.3770 | 0.7909 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_14 | Arekku21 | "2023-12-18T22:43:57Z" | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"endpoints_compatible",
"region:us"
] | video-classification | "2023-12-18T17:47:12Z" | Entry not found |
chauhoang/8ef11a2a-75d7-4dd0-a3b0-833cbd226075 | chauhoang | "2025-01-13T22:15:07Z" | 12 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-7b-it",
"base_model:adapter:unsloth/gemma-7b-it",
"license:apache-2.0",
"region:us"
] | null | "2025-01-13T22:00:52Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/gemma-7b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8ef11a2a-75d7-4dd0-a3b0-833cbd226075
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/gemma-7b-it
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 7d44a6e484287958_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7d44a6e484287958_train_data.json
type:
field_input: question_en
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 5
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: chauhoang/8ef11a2a-75d7-4dd0-a3b0-833cbd226075
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/7d44a6e484287958_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b35a3c79-9110-4937-b6f0-28ad104fc718
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b35a3c79-9110-4937-b6f0-28ad104fc718
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8ef11a2a-75d7-4dd0-a3b0-833cbd226075
This model is a fine-tuned version of [unsloth/gemma-7b-it](https://huggingface.co/unsloth/gemma-7b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0010 | 1 | 2.7354 |
| 1.8612 | 0.0098 | 10 | 1.4138 |
| 0.8931 | 0.0196 | 20 | 0.8393 |
| 0.716 | 0.0294 | 30 | 0.7320 |
| 0.6916 | 0.0392 | 40 | 0.6936 |
| 0.6612 | 0.0490 | 50 | 0.6873 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Panis8362/Llama3_2_Finetome | Panis8362 | "2025-02-09T04:39:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-09T04:39:10Z" | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Panis8362
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Aleereza/tinnyllama_prtokenizer_sum | Aleereza | "2024-01-05T18:01:54Z" | 16 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-01-05T18:01:08Z" | Entry not found |
miugod/bibert-iwslt14ende | miugod | "2023-03-30T14:03:27Z" | 106 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2023-03-30T13:55:20Z" | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bibert-ende
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bibert-ende
This model is a fine-tuned version of [jhu-clsp/bibert-ende](https://huggingface.co/jhu-clsp/bibert-ende) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8713
- Accuracy: 0.6310
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
distily/distily_verify_update9 | distily | "2024-09-04T16:46:37Z" | 5 | 0 | Distily | [
"Distily",
"tensorboard",
"safetensors",
"gpt2",
"generated_from_trainer",
"dataset:wikimedia/wikipedia",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-09-04T16:43:39Z" | ---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: creativeml-openrail-m
tags:
- generated_from_trainer
- Distily
base_model_relation: finetune
model-index:
- name: distily_verify_update9
results: []
---
# Summary
Distilled with [Distily](https://github.com/lapp0/distily) library
using teacher model [gpt2](https://huggingface.co/gpt2)
on dataset [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia).
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.
# Model description
More information needed
# Intended uses & limitations
More information needed
-->
# Model Architecture:
- **Architecture**: `GPT2LMHeadModel`
- **Total Parameters**: 81,912,576
- **Data Type (dtype)**: torch.bfloat16
- **Model Size**: 0.16 GB
<details>
<summary>Student Model Details</summary>
```
GPT2LMHeadModel(
(transformer): GPT2Model(
(wte): Embedding(50257, 768)
(wpe): Embedding(1024, 768)
(drop): Dropout(p=0.1, inplace=False)
(h): ModuleList(
(0-5): 6 x GPT2Block(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): GPT2FlashAttention2(
(c_attn): Conv1D()
(c_proj): Conv1D()
(attn_dropout): Dropout(p=0.1, inplace=False)
(resid_dropout): Dropout(p=0.1, inplace=False)
)
(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlp): GPT2MLP(
(c_fc): Conv1D()
(c_proj): Conv1D()
(act): NewGELUActivation()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=768, out_features=50257, bias=False)
)
```
</details>
<br/>
# Resource Usage
- Max Train VRAM Use: 15.7096 GB
- Available VRAM: 23.6497 GB
- GPUs:
- 1x NVIDIA GeForce RTX 4090
- CPUs: 28
- CPU Memory: 62.6429 GB
- CPU Memory Bandwidth: 700 GB/s
# Distillation (Teacher -> Student) Architecture Difference:
- **Architecture**: `GPT2LMHeadModel` -> `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808 -> 81,912,576
- **Data Type (dtype)**: torch.bfloat16 -> torch.bfloat16
- **Model Size**: 0.24 GB -> 0.16 GB
<details>
<summary>Module Diff Details</summary>
```diff
--- teacher model modules
+++ student model modules
@@ -4,7 +4,7 @@
(wpe): Embedding(1024, 768)
(drop): Dropout(p=0.1, inplace=False)
(h): ModuleList(
- (0-11): 12 x GPT2Block(
+ (0-5): 6 x GPT2Block(
(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): GPT2FlashAttention2(
(c_attn): Conv1D()
```
</details>
<br/>
# Train Dataset
Trained on 3,221,668 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
- Num Samples: `3,960`
- Subset: `20231101.en`
- Split: `train`
# Training Objective
```
DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=5.0, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm_teacher_only_affine, projector=orthogonal))
```
# Hyperparameters
The following hyperparameters were used during training:
<details>
<summary>Expand</summary>
- learning_rate: `0.0002`
- train_batch_size: `16`
- eval_batch_size: `8`
- seed: `42`
- optimizer: `Adam with betas=(0.9,0.999) and epsilon=1e-08`
- lr_scheduler_type: `polynomial`
- num_epochs: `1.0`
- distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=5.0, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm_teacher_only_affine, projector=orthogonal))`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fd8ec2cb7c0>`
- student_model_name_or_path: `None`
- student_config_name_or_path: `distilbert/distilgpt2`
- student_model_config: `None`
- reinitialize_weights: `None`
- copy_teacher_modules: `[('lm_head', False)]`
- student_model_as_bitnet: `False`
- teacher_model_name_or_path: `gpt2`
- teacher_load_in_8bit: `False`
- teacher_load_in_4bit: `False`
- dataset_uri: `wikimedia/wikipedia`
- dataset_subset: `20231101.en`
- dataset_split: `train`
- dataset_column_name: `text`
- dataset_sample_size: `4000`
- dataset_test_size: `0.01`
- gradient_accumulation_steps: `1`
- weight_decay: `0.0`
- max_grad_norm: `1.0`
- warmup_ratio: `0.0`
- warmup_steps: `0`
- gradient_checkpointing: `True`
</details>
<br/>
# Framework Versions
- Distily 0.5.0
- Transformers 4.44.2
- Pytorch 2.3.0
- Datasets 2.21.0
|
farzadab/test-uv-pipeline | farzadab | "2024-07-10T21:49:39Z" | 16 | 0 | transformers | [
"transformers",
"safetensors",
"ultravox",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | "2024-07-09T23:06:09Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
spneshaei/distilbert-base-uncased-imdb | spneshaei | "2023-06-14T09:15:30Z" | 104 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-06-14T09:14:44Z" | Entry not found |
crystantine/Vellfire2024 | crystantine | "2024-10-01T05:54:18Z" | 17 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2024-09-29T20:21:32Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: VELLF1RE40
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# VELLF1RE40
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `VELLF1RE40` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/crystantine/Vellfire2024/tree/main) them in the Files & versions tab.
|
shaoleen00/detr-finetuned-cef-v1 | shaoleen00 | "2024-08-26T20:11:25Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"table-transformer",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | "2024-08-26T20:11:15Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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ys7yoo/binary-inference_roberta-base_lr1e-05_wd1e-03_ep10_plant_fold4 | ys7yoo | "2023-11-25T18:03:39Z" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-11-25T17:57:20Z" | Entry not found |
xukp20/Llama-3-8B-Instruct-SPPO-score-Iter3_gp_8b-table-0.002 | xukp20 | "2024-09-29T02:40:23Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-28T14:46:45Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
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areegtarek/idefics-9b-split1-v1-split1.2-v1 | areegtarek | "2024-03-27T21:52:12Z" | 63 | 0 | transformers | [
"transformers",
"safetensors",
"idefics",
"image-text-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | image-text-to-text | "2024-03-27T21:49:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
ivangrapher/22dc9ad1-0d9a-4065-9dd2-1457b5e3040c | ivangrapher | "2025-01-20T20:53:59Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:adapter:NousResearch/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | "2025-01-20T20:48:11Z" | ---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 22dc9ad1-0d9a-4065-9dd2-1457b5e3040c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Meta-Llama-3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d78b4b1a225214cf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d78b4b1a225214cf_train_data.json
type:
field_input: choices
field_instruction: question
field_output: messages
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: ivangrapher/22dc9ad1-0d9a-4065-9dd2-1457b5e3040c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 75GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/d78b4b1a225214cf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 15
sequence_len: 1024
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e87876a2-cd2e-4969-9e61-d7434bbaf1de
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e87876a2-cd2e-4969-9e61-d7434bbaf1de
warmup_steps: 15
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 22dc9ad1-0d9a-4065-9dd2-1457b5e3040c
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0784
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 15
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0011 | 1 | 3.0148 |
| 2.9469 | 0.0054 | 5 | 2.8384 |
| 1.9054 | 0.0108 | 10 | 1.8545 |
| 1.2705 | 0.0162 | 15 | 1.2298 |
| 1.1792 | 0.0216 | 20 | 1.1131 |
| 1.0892 | 0.0270 | 25 | 1.0844 |
| 1.0961 | 0.0324 | 30 | 1.0784 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
zelk12/MT4-Gen5-GMA-gemma-2-9B | zelk12 | "2024-12-28T19:49:37Z" | 7 | 1 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B",
"base_model:merge:zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B",
"base_model:zelk12/MT4-Gen5-MA-gemma-2-MT4g2MT3g4-9B",
"base_model:merge:zelk12/MT4-Gen5-MA-gemma-2-MT4g2MT3g4-9B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-12-28T19:43:14Z" | ---
base_model:
- zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B
- zelk12/MT4-Gen5-MA-gemma-2-MT4g2MT3g4-9B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B](https://huggingface.co/zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B)
* [zelk12/MT4-Gen5-MA-gemma-2-MT4g2MT3g4-9B](https://huggingface.co/zelk12/MT4-Gen5-MA-gemma-2-MT4g2MT3g4-9B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B
- model: zelk12/MT4-Gen5-MA-gemma-2-MT4g2MT3g4-9B
merge_method: slerp
base_model: zelk12/MT4-Gen5-GP-gemma-2-MTMMT3g4-9B
dtype: bfloat16
parameters:
t: 0.25
```
|
sentence-transformers/bert-base-nli-mean-tokens | sentence-transformers | "2024-11-05T15:50:46Z" | 1,216,444 | 36 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---
**โ ๏ธ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-mean-tokens
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/bert-base-nli-mean-tokens')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-base-nli-mean-tokens)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
Heber77/paraquantizar | Heber77 | "2023-07-31T17:53:26Z" | 106 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-07-31T17:34:54Z" | Entry not found |
mini1013/master_cate_bc0 | mini1013 | "2025-01-23T19:15:16Z" | 168 | 0 | setfit | [
"setfit",
"safetensors",
"roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:mini1013/master_domain",
"base_model:finetune:mini1013/master_domain",
"model-index",
"region:us"
] | text-classification | "2025-01-23T19:14:52Z" | ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: ์จ๊ฐ์กฑ ๋ณด๋๊ฒ์ ์์ด ์๋์จ๋์คํธ๋ฆฌํธ ์ ์๊ต์ก๊ธฐ๊ด ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๋ณด๋๊ฒ์
- text: ์ ์ ์ฌ๊ณ ๋ ฅ๋ฐ๋ฌ ์ปค๋ฅํธ 4๋ชฉ๊ฒ์ ๋ผ์ง ๊ฐ์กฑ๊ฒ์ ๋๋๊ฒ์ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๋ณด๋๊ฒ์
- text: ์ ๋น์ํํธ ํ์ ๊ท์ 1-20 ๊ถ ์ด๋ฆฐ์ด ์ ๋น์ํํธ ํ์ ๊ท์ 5 ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๊ต๊ตฌ > ๊ธฐํ๊ต๊ตฌ
- text: ์ด๋ฆฐ์ด ํ๊ธ ์์ , ์ซ์,์ํ๋ฒณ,๊ตฌ๊ตฌ๋จ ์คํฐ์ปค ์ํ๋ฒณ ์๋ฌธ์ ์ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๊ต๊ตฌ > ๊ธฐํ๊ต๊ตฌ
- text: ์ค๋ฏผ์์ ์ธ๊ณ์ฌ ๋๋ชจํ 1-17๊ถ ์ด๋ฑ ์ด๋ฆฐ์ด ์ญ์ฌ ์ค๋ฏผ์์ ์ธ๊ณ์ฌ ๋๋ชจํ 18 ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๊ต๊ตฌ > ๊ธฐํ๊ต๊ตฌ
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
model-index:
- name: SetFit with mini1013/master_domain
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1.0
name: Accuracy
---
# SetFit with mini1013/master_domain
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0 | <ul><li>'๋ณด์ฝ๊ฒ์ ์ด๊ฒ ์ ์ค๋ฆฌ๋๊ตฌ๋ฆฌ, 1๊ฐ TS 687769 ํฌ๋ ์คํธ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๋ณด๋๊ฒ์'</li><li>'๋ชจ๋๊ธ๋ก๋ฆฌ 15000 ์ฅ๊ธฐ ์์ํ์
์ฅ๊ธฐ์ ์ฅ๊ธฐํ ํด๋ํ ์ ์ดํ ๋ณด๋๊ฒ์ 77 ์ฒด์ค ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๋ณด๋๊ฒ์'</li><li>'๊ณ ํผ์ฌ ํ๊ธ3 ์ฌ์ด ๋ฐ์นจ ๊ธ์ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๋ณด๋๊ฒ์'</li></ul> |
| 0.0 | <ul><li>'๋ฆฝํ๋ก๊ทธ ์ ํ ๊ตฌ๋งค (ํ์ธํธ ๊ตฌ๋งค์ ๋ฆฝํ๋ก๊ทธ ์ํ๋ฒณ ์นด๋ 27์ข
) 2์ง (DVD7+CD7+๋๋ณธ6๊ถ) ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ๋น๋์ค/DVD'</li><li>'๊ณ ๊ตํ ๋ก ,ํ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ๋น๋์ค/DVD'</li><li>'์๊ฐ์์ฒ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ๋น๋์ค/DVD'</li></ul> |
| 1.0 | <ul><li>'์ด๋ฑ ๋น์ฆ ๋ณด์์ญ์์ ์ํฌ๋ฆด ํค๋ง ๊ฐ๋ฐฉ๊ณ ๋ฆฌ ๋ง๋ค๊ธฐ 10์ธ ์ง๋ฅ๋ฐ๋ฌ ์์์
ํ์
์ด๋ฆฐ์ด์ง ์ํ ์ ํ_์ ์ธ์ฅ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๊ต๊ตฌ > ๋ฏธ์ ๊ต๊ตฌ'</li><li>'๋์ฆ๋ ์์
์ด๋ก 1-12๊ถ ์ ์ ์ด๋ฆฐ์ด ํผ์๋
ธ ์์
๊ต์ฌ ์ฑ
๋์ฆ๋ ์์
์ด๋ก 6 ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๊ต๊ตฌ > ๊ธฐํ๊ต๊ตฌ'</li><li>'๋ฌด์ง๊ฐ๋กค / ํ ํธ๊ต๊ตฌ ๊ฒ์ ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๊ต๊ตฌ > ์์ด๊ต๊ตฌ'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the ๐ค Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_bc0")
# Run inference
preds = model("์จ๊ฐ์กฑ ๋ณด๋๊ฒ์ ์์ด ์๋์จ๋์คํธ๋ฆฌํธ ์ ์๊ต์ก๊ธฐ๊ด ์ถ์ฐ/์ก์ > ๊ต๊ตฌ > ํ์ต๋ณด๋๊ฒ์")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 7 | 14.4143 | 34 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0238 | 1 | 0.4941 | - |
| 1.1905 | 50 | 0.465 | - |
| 2.3810 | 100 | 0.0367 | - |
| 3.5714 | 150 | 0.0 | - |
| 4.7619 | 200 | 0.0 | - |
| 5.9524 | 250 | 0.0 | - |
| 7.1429 | 300 | 0.0 | - |
| 8.3333 | 350 | 0.0 | - |
| 9.5238 | 400 | 0.0 | - |
| 10.7143 | 450 | 0.0 | - |
| 11.9048 | 500 | 0.0 | - |
| 13.0952 | 550 | 0.0 | - |
| 14.2857 | 600 | 0.0 | - |
| 15.4762 | 650 | 0.0 | - |
| 16.6667 | 700 | 0.0 | - |
| 17.8571 | 750 | 0.0 | - |
| 19.0476 | 800 | 0.0 | - |
| 20.2381 | 850 | 0.0 | - |
| 21.4286 | 900 | 0.0 | - |
| 22.6190 | 950 | 0.0 | - |
| 23.8095 | 1000 | 0.0 | - |
| 25.0 | 1050 | 0.0 | - |
| 26.1905 | 1100 | 0.0 | - |
| 27.3810 | 1150 | 0.0 | - |
| 28.5714 | 1200 | 0.0 | - |
| 29.7619 | 1250 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
october-sd/pegasus-xsum-finetuned-en-sum-2 | october-sd | "2024-03-14T02:16:10Z" | 97 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:october-sd/pegasus-xsum-finetuned-en-sum",
"base_model:finetune:october-sd/pegasus-xsum-finetuned-en-sum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | "2024-03-13T19:00:56Z" | ---
base_model: october-sd/pegasus-xsum-finetuned-en-sum
tags:
- summarization
- generated_from_trainer
model-index:
- name: pegasus-xsum-finetuned-en-sum-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-xsum-finetuned-en-sum-2
This model is a fine-tuned version of [october-sd/pegasus-xsum-finetuned-en-sum](https://huggingface.co/october-sd/pegasus-xsum-finetuned-en-sum) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.4990
- eval_runtime: 242.6814
- eval_samples_per_second: 20.603
- eval_steps_per_second: 2.575
- epoch: 2.0
- step: 1015
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.2
|
prxy5604/07f4e296-6c7a-4f44-a8fe-802ea4743427 | prxy5604 | "2025-01-24T18:11:34Z" | 7 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | "2025-01-24T17:24:21Z" | ---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 07f4e296-6c7a-4f44-a8fe-802ea4743427
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 1a9b97378fcbebe1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/1a9b97378fcbebe1_train_data.json
type:
field_input: captions
field_instruction: raw_sentences
field_output: raw_anns
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: prxy5604/07f4e296-6c7a-4f44-a8fe-802ea4743427
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/1a9b97378fcbebe1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 24718b6c-9560-45d6-8f4f-62368e0b0d98
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 24718b6c-9560-45d6-8f4f-62368e0b0d98
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 07f4e296-6c7a-4f44-a8fe-802ea4743427
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3565
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6272 | 0.0007 | 1 | 2.0187 |
| 1.3778 | 0.0339 | 50 | 1.4875 |
| 1.3254 | 0.0679 | 100 | 1.4757 |
| 1.3392 | 0.1018 | 150 | 1.4507 |
| 1.3384 | 0.1358 | 200 | 1.3565 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
BoyaWu10/bunny-stablelm-2-siglip-lora | BoyaWu10 | "2024-02-13T13:58:36Z" | 9 | 2 | transformers | [
"transformers",
"safetensors",
"bunny-stablelm",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-02-06T02:04:47Z" | ---
inference: false
license: apache-2.0
---
# Model Card
Bunny is a family of lightweight multimodal models.
Bunny-stablelm-2-siglip-lora leverages StableLM-2 as the language model backbone and SigLIP as the vision encoder.
It is pretrained on LAION-2M and finetuned on Bunny-695K.
More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny).
# License
This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.
The content of this project itself is licensed under the Apache license 2.0.
|
nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-tensor_wts_tensor_act_int8-BitM | nm-testing | "2024-12-17T03:28:32Z" | 6 | 0 | null | [
"safetensors",
"llama",
"8-bit",
"compressed-tensors",
"region:us"
] | null | "2024-12-17T03:28:09Z" | Entry not found |
THUDM/glm-edge-1.5b-chat-gguf | THUDM | "2024-11-28T08:56:06Z" | 252 | 1 | null | [
"gguf",
"glm",
"edge",
"text-generation",
"zh",
"en",
"license:other",
"region:us",
"conversational"
] | text-generation | "2024-11-27T13:25:26Z" | ---
license: other
license_name: glm-4
license_link: LICENSE
language:
- zh
- en
pipeline_tag: text-generation
tags:
- glm
- edge
inference: false
---
# Glm-Edge-Chat-1.5B-GGUF
ไธญๆ้
่ฏป, ็นๅป[่ฟ้](README_zh.md)
## Inference with Ollama
### Installation
The code for adapting this model is actively being integrated into the official `llama.cpp`. You can test it using the
following adapted version:
```bash
git clone https://github.com/piDack/llama.cpp -b support_glm_edge_model
cmake -B build -DGGML_CUDA=ON # Or enable other acceleration hardware
cmake --build build -- -j
```
### Inference
After installation, you can start the GLM-Edge Chat model using the following command:
```shell
llama-cli -m <path>/model.gguf -p "<|user|>\nhi<|assistant|>\n" -ngl 999
```
In the command-line interface, you can interact with the model by entering your requests, and the model will provide the
corresponding responses.
## License
The usage of this modelโs weights is subject to the terms outlined in the [LICENSE](LICENSE). |
notlober/gpt2custom | notlober | "2024-08-28T13:05:44Z" | 105 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-08-28T13:05:21Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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Helsinki-NLP/opus-mt-en-sq | Helsinki-NLP | "2023-08-16T11:31:12Z" | 1,677 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"en",
"sq",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-sq
* source languages: en
* target languages: sq
* OPUS readme: [en-sq](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sq/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-sq/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sq/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sq/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.en.sq | 46.5 | 0.669 |
|