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damgomz/ft_2_3e6_base_x1 | damgomz | "2024-06-18T19:40:54" | 110 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-17T14:56:56" | ---
language: en
tags:
- text-classification
pipeline_tag: text-classification
widget:
- text: GEPS Techno is the pioneer of hybridization of renewable energies at sea.
We imagine, design and commercialize innovative off-grid systems that aim to generate
power at sea, stabilize and collect data. The success of our low power platforms
WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity
platform.
---
## Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|--------------------------|---------------------------------|
| Duration (in seconds) | 107271.62567901611 |
| Emissions (Co2eq in kg) | 0.0649116764564143 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 1.2663979944932808 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.1117399740512172 |
| Consumed energy (kWh) | 1.3781379685444968 |
| Country name | Switzerland |
| Cloud provider | nan |
| Cloud region | nan |
| CPU count | 2 |
| CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz |
| GPU count | nan |
| GPU model | nan |
## Environmental Impact (for one core)
| Metric | Value |
|--------------------------|---------------------------------|
| CPU energy (kWh) | 0.206497879432106 |
| Emissions (Co2eq in kg) | 0.04201472005761465 |
## Note
14 juin 2024
## My Config
| Config | Value |
|--------------------------|-----------------|
| checkpoint | albert-base-v2 |
| model_name | ft_2_3e6_base_x1 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 3e-06 |
| batch_size | 2 |
| weight_decay | 0.0 |
| warm_up_prop | 0.0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 29328 |
## Training and Testing steps
Epoch | Train Loss | Test Loss | F-beta Score
---|---|---|---
| 0 | 0.000000 | 0.705400 | 0.482506 |
| 1 | 0.305451 | 0.246145 | 0.910113 |
| 2 | 0.180730 | 0.217001 | 0.926528 |
| 3 | 0.126710 | 0.225560 | 0.902438 |
| 4 | 0.074046 | 0.269291 | 0.926075 |
| 5 | 0.041516 | 0.286399 | 0.918071 |
| 6 | 0.021054 | 0.321385 | 0.915197 |
|
camidenecken/RoBERTa-RM1-v2-2-rm-v20 | camidenecken | "2024-11-05T19:00:00" | 181 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-05T18:59:32" | ---
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 🤗 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] |
huggingtweets/strongerstabler | huggingtweets | "2021-05-23T00:16:04" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-03-02T23:29:05" | ---
language: en
thumbnail: https://www.huggingtweets.com/strongerstabler/1603817791522/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1259415526440402944/h4m68uNY_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">StrongerStabler 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@strongerstabler bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@strongerstabler's tweets](https://twitter.com/strongerstabler).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3250</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>0</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>1316</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1934</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/yr5cffyk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @strongerstabler's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/33h1znu3) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/33h1znu3/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/strongerstabler'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
<!--- random size file --> |
rdzotz/w2v2_bert_ru | rdzotz | "2024-01-30T01:23:48" | 94 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/w2v-bert-2.0",
"base_model:finetune:facebook/w2v-bert-2.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-01-30T01:21:17" | ---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2_bert_ru
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. -->
# w2v2_bert_ru
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.0538
## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.711 | 0.73 | 300 | inf | 0.1267 |
| 0.1026 | 1.46 | 600 | inf | 0.0925 |
| 0.0748 | 2.18 | 900 | inf | 0.0732 |
| 0.0591 | 2.91 | 1200 | inf | 0.0710 |
| 0.0437 | 3.64 | 1500 | inf | 0.0675 |
| 0.0382 | 4.37 | 1800 | inf | 0.0675 |
| 0.0302 | 5.1 | 2100 | inf | 0.0620 |
| 0.0243 | 5.83 | 2400 | inf | 0.0590 |
| 0.0219 | 6.55 | 2700 | inf | 0.0584 |
| 0.0173 | 7.28 | 3000 | inf | 0.0577 |
| 0.015 | 8.01 | 3300 | inf | 0.0560 |
| 0.0115 | 8.74 | 3600 | inf | 0.0551 |
| 0.0099 | 9.47 | 3900 | inf | 0.0538 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.025-alpha-0-step-39936 | hsikchi | "2024-05-18T18:26:55" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-18T18:22:32" | ---
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 🤗 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]
|
Romildon/locutor | Romildon | "2024-03-06T15:37:46" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-03-06T15:37:46" | ---
license: openrail
---
|
marialvsantiago/ccb598de-df79-437b-8b4b-98bd44e1232a | marialvsantiago | "2025-01-14T21:34:53" | 10 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-14B",
"base_model:adapter:unsloth/Qwen2.5-14B",
"license:apache-2.0",
"region:us"
] | null | "2025-01-14T21:22:01" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-14B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ccb598de-df79-437b-8b4b-98bd44e1232a
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/Qwen2.5-14B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 7fe8e77cffb4cb7b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7fe8e77cffb4cb7b_train_data.json
type:
field_input: system
field_instruction: instruction
field_output: response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
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: marialvsantiago/ccb598de-df79-437b-8b4b-98bd44e1232a
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: 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: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/7fe8e77cffb4cb7b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_hf
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: 1024
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: a74d7451-e101-4cc2-9b59-1e2020a2e450
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a74d7451-e101-4cc2-9b59-1e2020a2e450
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# ccb598de-df79-437b-8b4b-98bd44e1232a
This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B) 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: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_HF 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0006 | 1 | nan |
| 0.0 | 0.0030 | 5 | nan |
| 0.0 | 0.0060 | 10 | nan |
| 0.0 | 0.0090 | 15 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf | RichardErkhov | "2024-10-16T02:13:47" | 18 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2024-10-15T01:48:32" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-70B-Special-Tokens-Adjusted - GGUF
- Model creator: https://huggingface.co/astronomer/
- Original model: https://huggingface.co/astronomer/Llama-3-70B-Special-Tokens-Adjusted/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3-70B-Special-Tokens-Adjusted.Q2_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q2_K.gguf) | Q2_K | 24.56GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ3_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_S.gguf) | IQ3_S | 28.79GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ3_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_M.gguf) | IQ3_M | 29.74GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K.gguf) | Q3_K | 31.91GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q4_0.gguf) | Q4_0 | 37.22GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | IQ4_NL | 37.58GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K_S | 37.58GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K | 39.6GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K_M | 39.6GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_1.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_1 | 41.27GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_0 | 45.32GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K_S | 45.32GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K | 46.52GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K_M | 46.52GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_1.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_1 | 49.36GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q6_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q6_K | 53.91GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q8_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q8_0 | 69.83GB |
Original model description:
---
base_model: meta-llama/Meta-Llama-3-70B
inference: false
model_creator: astronomer-io
model_name: Meta-Llama-3-70B
model_type: llama
pipeline_tag: text-generation
license: other
license_name: llama-3
license_link: https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/README.md
tags:
- llama
- llama-3
- facebook
- meta
- astronomer
- pretrained
- finetuned
- autotrain_compatible
- endpoints_compatible
---
<!-- header start -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://www.astronomer.io/logo/astronomer-logo-RGB-standard-1200px.png" alt="Astronomer" style="width: 60%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="margin-top: 1.0em; margin-bottom: 1.0em;"></div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">This model is generously created and made open source by <a href="https://astronomer.io">Astronomer</a>.</p></div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Astronomer is the de facto company for <a href="https://airflow.apache.org/">Apache Airflow</a>, the most trusted open-source framework for data orchestration and MLOps.</p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama-3-70B-Special-Tokens-Adjusted
- Ideal and stable Llama-3-70B for fine-tuning.
- Original Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
- The usage of this model must abide by the [Llama 3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/LICENSE).
- Built with Meta Llama 3
- Created by [David Xue](https://www.linkedin.com/in/david-xue-uva/) from [Astronomer](https://astronomer.io)
## Description
This is the exact same model ([meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)) with the weights for the input and output embeddings from lm head and embedding matrix adjusted using the mean of the trained tokens for certain tokens that were untrained, which caused widespread issues for people attempting to fine-tune this base model with either adding their own tokens or using existing special tokens.
## Why We Made This Model
The Llama 3 base (non-instruct) model, while powerful, came with a significant oversight that some special tokens for instruction following within its architecture were left untrained, potentially derailing further fine-tuning processes. This was first noted by [Daniel Han on X](https://twitter.com/danielhanchen/status/1781395882925343058), highlighting a critical but fixable flaw in a widely used model.
<img src="https://cdn-uploads.huggingface.co/production/uploads/655ad0f8727df37c77a09cb9/1U2rRrx60p1pNeeAZw8Rd.png" alt="graph" width="400"/>
The primary goal of releasing a patched version of this model was to address this issue so that the community can utilize the Llama 3 model without facing training instabilities, such as sudden gradient explosions or `NaN` gradients, or having to go through complicated processes to fix the model themselves before fine-tuning.
Note: specifically for the 70B model, the untrained special tokens did not have all zero values for the embedding weights. So the significance of this problem may not be as severe as it is on the base 8B model. This model was made anyway by the request of the community, though in theory directly fine-tuning should be ok.
## Details of the Adjustment
The [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) model was pulled directly from HuggingFace and loaded using transformers. Then, the input embedding and output embedding values are retrieved using `model.get_input_embeddings().weight.data` and `model.get_output_embeddings().weight.data`. These 2 matrics are identical in shape, with each row representing a token id, and each column representing an embedding feature.
The special (untrained & problematic) tokens can be found by locating the rows where the entire row of the embedding values are ~~~all zeros~~~ less than 9e-7 (for the 70B model, no row had all zeros, so thresholding using 9e-7 was done to fine under-trained tokens), which imply they were not trained during the pretraining phase of the model from Meta. Such untrained tokens could lead to heavy computational issues, like gradient explosions or `NaN` gradients, during downstream fine-tuning on specific tasks.
<details>
<summary>See here for a list of the tokens we found that has fit the "untrained" profile described:</summary>
['À',
'Á',
'õ',
'ö',
'÷',
'ø',
'ù',
'ú',
'û',
'ü',
'ý',
'þ',
'ÿ',
'">ččĊ',
';čččĊ',
'ĉTokenNameIdentifier',
'ĠForCanBeConverted',
'ĠForCanBeConvertedToF',
'PostalCodesNL',
'$PostalCodesNL',
'useRalative',
'Û±Û',
'аÑĢакÑĤ',
'аÑĤиÑģÑı',
'иÑĤиÑģÑı',
'ávajÃŃcÃŃ',
'Ä°TESÄ°',
'илакÑĤи',
'илаÑģÑı',
'ÑĭÑŁN',
'ÐİÑĭÑŁN',
'ılmaktadır',
'ÐİÑĭÑŁNÐİÑĭÑŁN',
'ıldıģında',
'<|reserved_special_token_0|>',
'<|reserved_special_token_1|>',
'<|reserved_special_token_2|>',
'<|reserved_special_token_3|>',
'<|start_header_id|>',
'<|end_header_id|>',
'<|reserved_special_token_4|>',
'<|eot_id|>',
'<|reserved_special_token_5|>',
'<|reserved_special_token_6|>',
'<|reserved_special_token_7|>',
'<|reserved_special_token_8|>',
'<|reserved_special_token_9|>',
'<|reserved_special_token_10|>',
'<|reserved_special_token_11|>',
'<|reserved_special_token_12|>',
'<|reserved_special_token_13|>',
'<|reserved_special_token_14|>',
'<|reserved_special_token_15|>',
'<|reserved_special_token_16|>',
'<|reserved_special_token_17|>',
'<|reserved_special_token_18|>',
'<|reserved_special_token_19|>',
'<|reserved_special_token_20|>',
'<|reserved_special_token_21|>',
'<|reserved_special_token_22|>',
'<|reserved_special_token_23|>',
'<|reserved_special_token_24|>',
'<|reserved_special_token_25|>',
'<|reserved_special_token_26|>',
'<|reserved_special_token_27|>',
'<|reserved_special_token_28|>',
'<|reserved_special_token_29|>',
'<|reserved_special_token_30|>',
'<|reserved_special_token_31|>',
'<|reserved_special_token_32|>',
'<|reserved_special_token_33|>',
'<|reserved_special_token_34|>',
'<|reserved_special_token_35|>',
'<|reserved_special_token_36|>',
'<|reserved_special_token_37|>',
'<|reserved_special_token_38|>',
'<|reserved_special_token_39|>',
'<|reserved_special_token_40|>',
'<|reserved_special_token_41|>',
'<|reserved_special_token_42|>',
'<|reserved_special_token_43|>',
'<|reserved_special_token_44|>',
'<|reserved_special_token_45|>',
'<|reserved_special_token_46|>',
'<|reserved_special_token_47|>',
'<|reserved_special_token_48|>',
'<|reserved_special_token_49|>',
'<|reserved_special_token_50|>',
'<|reserved_special_token_51|>',
'<|reserved_special_token_52|>',
'<|reserved_special_token_53|>',
'<|reserved_special_token_54|>',
'<|reserved_special_token_55|>',
'<|reserved_special_token_56|>',
'<|reserved_special_token_57|>',
'<|reserved_special_token_58|>',
'<|reserved_special_token_59|>',
'<|reserved_special_token_60|>',
'<|reserved_special_token_61|>',
'<|reserved_special_token_62|>',
'<|reserved_special_token_63|>',
'<|reserved_special_token_64|>',
'<|reserved_special_token_65|>',
'<|reserved_special_token_66|>',
'<|reserved_special_token_67|>',
'<|reserved_special_token_68|>',
'<|reserved_special_token_69|>',
'<|reserved_special_token_70|>',
'<|reserved_special_token_71|>',
'<|reserved_special_token_72|>',
'<|reserved_special_token_73|>',
'<|reserved_special_token_74|>',
'<|reserved_special_token_75|>',
'<|reserved_special_token_76|>',
'<|reserved_special_token_77|>',
'<|reserved_special_token_78|>',
'<|reserved_special_token_79|>',
'<|reserved_special_token_80|>',
'<|reserved_special_token_81|>',
'<|reserved_special_token_82|>',
'<|reserved_special_token_83|>',
'<|reserved_special_token_84|>',
'<|reserved_special_token_85|>',
'<|reserved_special_token_86|>',
'<|reserved_special_token_87|>',
'<|reserved_special_token_88|>',
'<|reserved_special_token_89|>',
'<|reserved_special_token_90|>',
'<|reserved_special_token_91|>',
'<|reserved_special_token_92|>',
'<|reserved_special_token_93|>',
'<|reserved_special_token_94|>',
'<|reserved_special_token_95|>',
'<|reserved_special_token_96|>',
'<|reserved_special_token_97|>',
'<|reserved_special_token_98|>',
'<|reserved_special_token_99|>',
'<|reserved_special_token_100|>',
'<|reserved_special_token_101|>',
'<|reserved_special_token_102|>',
'<|reserved_special_token_103|>',
'<|reserved_special_token_104|>',
'<|reserved_special_token_105|>',
'<|reserved_special_token_106|>',
'<|reserved_special_token_107|>',
'<|reserved_special_token_108|>',
'<|reserved_special_token_109|>',
'<|reserved_special_token_110|>',
'<|reserved_special_token_111|>',
'<|reserved_special_token_112|>',
'<|reserved_special_token_113|>',
'<|reserved_special_token_114|>',
'<|reserved_special_token_115|>',
'<|reserved_special_token_116|>',
'<|reserved_special_token_117|>',
'<|reserved_special_token_118|>',
'<|reserved_special_token_119|>',
'<|reserved_special_token_120|>',
'<|reserved_special_token_121|>',
'<|reserved_special_token_122|>',
'<|reserved_special_token_123|>',
'<|reserved_special_token_124|>',
'<|reserved_special_token_125|>',
'<|reserved_special_token_126|>',
'<|reserved_special_token_127|>',
'<|reserved_special_token_128|>',
'<|reserved_special_token_129|>',
'<|reserved_special_token_130|>',
'<|reserved_special_token_131|>',
'<|reserved_special_token_132|>',
'<|reserved_special_token_133|>',
'<|reserved_special_token_134|>',
'<|reserved_special_token_135|>',
'<|reserved_special_token_136|>',
'<|reserved_special_token_137|>',
'<|reserved_special_token_138|>',
'<|reserved_special_token_139|>',
'<|reserved_special_token_140|>',
'<|reserved_special_token_141|>',
'<|reserved_special_token_142|>',
'<|reserved_special_token_143|>',
'<|reserved_special_token_144|>',
'<|reserved_special_token_145|>',
'<|reserved_special_token_146|>',
'<|reserved_special_token_147|>',
'<|reserved_special_token_148|>',
'<|reserved_special_token_149|>',
'<|reserved_special_token_150|>',
'<|reserved_special_token_151|>',
'<|reserved_special_token_152|>',
'<|reserved_special_token_153|>',
'<|reserved_special_token_154|>',
'<|reserved_special_token_155|>',
'<|reserved_special_token_156|>',
'<|reserved_special_token_157|>',
'<|reserved_special_token_158|>',
'<|reserved_special_token_159|>',
'<|reserved_special_token_160|>',
'<|reserved_special_token_161|>',
'<|reserved_special_token_162|>',
'<|reserved_special_token_163|>',
'<|reserved_special_token_164|>',
'<|reserved_special_token_165|>',
'<|reserved_special_token_166|>',
'<|reserved_special_token_167|>',
'<|reserved_special_token_168|>',
'<|reserved_special_token_169|>',
'<|reserved_special_token_170|>',
'<|reserved_special_token_171|>',
'<|reserved_special_token_172|>',
'<|reserved_special_token_173|>',
'<|reserved_special_token_174|>',
'<|reserved_special_token_175|>',
'<|reserved_special_token_176|>',
'<|reserved_special_token_177|>',
'<|reserved_special_token_178|>',
'<|reserved_special_token_179|>',
'<|reserved_special_token_180|>',
'<|reserved_special_token_181|>',
'<|reserved_special_token_182|>',
'<|reserved_special_token_183|>',
'<|reserved_special_token_184|>',
'<|reserved_special_token_185|>',
'<|reserved_special_token_186|>',
'<|reserved_special_token_187|>',
'<|reserved_special_token_188|>',
'<|reserved_special_token_189|>',
'<|reserved_special_token_190|>',
'<|reserved_special_token_191|>',
'<|reserved_special_token_192|>',
'<|reserved_special_token_193|>',
'<|reserved_special_token_194|>',
'<|reserved_special_token_195|>',
'<|reserved_special_token_196|>',
'<|reserved_special_token_197|>',
'<|reserved_special_token_198|>',
'<|reserved_special_token_199|>',
'<|reserved_special_token_200|>',
'<|reserved_special_token_201|>',
'<|reserved_special_token_202|>',
'<|reserved_special_token_203|>',
'<|reserved_special_token_204|>',
'<|reserved_special_token_205|>',
'<|reserved_special_token_206|>',
'<|reserved_special_token_207|>',
'<|reserved_special_token_208|>',
'<|reserved_special_token_209|>',
'<|reserved_special_token_210|>',
'<|reserved_special_token_211|>',
'<|reserved_special_token_212|>',
'<|reserved_special_token_213|>',
'<|reserved_special_token_214|>',
'<|reserved_special_token_215|>',
'<|reserved_special_token_216|>',
'<|reserved_special_token_217|>',
'<|reserved_special_token_218|>',
'<|reserved_special_token_219|>',
'<|reserved_special_token_220|>',
'<|reserved_special_token_221|>',
'<|reserved_special_token_222|>',
'<|reserved_special_token_223|>',
'<|reserved_special_token_224|>',
'<|reserved_special_token_225|>',
'<|reserved_special_token_226|>',
'<|reserved_special_token_227|>',
'<|reserved_special_token_228|>',
'<|reserved_special_token_229|>',
'<|reserved_special_token_230|>',
'<|reserved_special_token_231|>',
'<|reserved_special_token_232|>',
'<|reserved_special_token_233|>',
'<|reserved_special_token_234|>',
'<|reserved_special_token_235|>',
'<|reserved_special_token_236|>',
'<|reserved_special_token_237|>',
'<|reserved_special_token_238|>',
'<|reserved_special_token_239|>',
'<|reserved_special_token_240|>',
'<|reserved_special_token_241|>',
'<|reserved_special_token_242|>',
'<|reserved_special_token_243|>',
'<|reserved_special_token_244|>',
'<|reserved_special_token_245|>',
'<|reserved_special_token_246|>',
'<|reserved_special_token_247|>',
'<|reserved_special_token_248|>',
'<|reserved_special_token_249|>',
'<|reserved_special_token_250|>']
</details>
Once these untrained tokens are identified, the average of trained tokens can be calculated by using the sums of embedding values of trained tokens for each feature/column and divided by the number of trained. This is done for both input and output matrices.
Lastly, the problematic token's rows in the 2 embedding matrics are set to the computed mean, thus completing the adjustment.
## Contributors
- [David Xue](https://www.linkedin.com/in/david-xue-uva/), Machine Learning Engineer from [Astronomer](https://astronomer.io)
|
marianafmedeiros/ppo-Huggy | marianafmedeiros | "2023-03-24T03:03:04" | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2023-03-24T03:02:57" | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: marianafmedeiros/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
JessicaHsu/ppo-LunarLander-v2 | JessicaHsu | "2023-02-06T15:49:40" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-06T14:50:00" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.69 +/- 20.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
harshit345/xlsr-53-wav2vec-hi | harshit345 | "2021-12-12T11:52:01" | 7 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2022-03-02T23:29:05" | ---
language: hi
datasets:
- Interspeech 2021
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Hindi by Shyam Sunder Kumar
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice hi
type: common_voice
args: hi
metrics:
- name: Test WER
type: wer
value: 20.22
---
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the hindi test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "hi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
model.to("cuda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**:20.22 %
## Training
The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1nY5WMj1oNlexD_qDeNYL7ZM427A021CV?usp=sharing) |
mandanya/RuadaptQwen-32B-instruct-AWQ | mandanya | "2024-11-15T09:44:12" | 5 | 0 | null | [
"safetensors",
"qwen2",
"license:mit",
"4-bit",
"awq",
"region:us"
] | null | "2024-11-15T09:36:53" | ---
license: mit
---
|
BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF | BitStreamX | "2024-10-28T09:25:53" | 5 | 0 | transformers | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2024-09-28T02:06:18" | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.2
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base_model: meta-llama/Llama-3.2-3B-Instruct
---
# BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) 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/meta-llama/Llama-3.2-3B-Instruct) 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 BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.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 BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -c 2048
```
|
mradermacher/Llama3merge6-15B-MoE-GGUF | mradermacher | "2024-05-05T15:19:06" | 72 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"Kukedlc/NeuralLlamita-3-8B-v0.2",
"imone/Llama-3-8B-fixed-special-embedding",
"en",
"base_model:allknowingroger/Llama3merge6-15B-MoE",
"base_model:quantized:allknowingroger/Llama3merge6-15B-MoE",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-04-22T08:50:02" | ---
base_model: allknowingroger/Llama3merge6-15B-MoE
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Kukedlc/NeuralLlamita-3-8B-v0.2
- imone/Llama-3-8B-fixed-special-embedding
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/allknowingroger/Llama3merge6-15B-MoE
<!-- 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/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q2_K.gguf) | Q2_K | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ3_XS.gguf) | IQ3_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q3_K_S.gguf) | Q3_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ3_S.gguf) | IQ3_S | 6.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ3_M.gguf) | IQ3_M | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q3_K_M.gguf) | Q3_K_M | 6.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q3_K_L.gguf) | Q3_K_L | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ4_XS.gguf) | IQ4_XS | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q4_K_S.gguf) | Q4_K_S | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q4_K_M.gguf) | Q4_K_M | 8.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q5_K_S.gguf) | Q5_K_S | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q5_K_M.gguf) | Q5_K_M | 9.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q6_K.gguf) | Q6_K | 11.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q8_0.gguf) | Q8_0 | 14.6 | 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 -->
|
asn1814/openbookqa_bert-base-uncased_fact_retrieval_k_10 | asn1814 | "2024-03-06T02:01:49" | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:asn1814/openbookqa_bert-base-uncased",
"base_model:finetune:asn1814/openbookqa_bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | multiple-choice | "2024-03-06T01:21:46" | ---
license: apache-2.0
base_model: asn1814/openbookqa_bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: openbookqa_bert-base-uncased_fact_retrieval_k_10
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. -->
# openbookqa_bert-base-uncased_fact_retrieval_k_10
This model is a fine-tuned version of [asn1814/openbookqa_bert-base-uncased](https://huggingface.co/asn1814/openbookqa_bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9155
- Accuracy: 0.59
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3035 | 1.0 | 310 | 1.4148 | 0.57 |
| 0.1243 | 2.0 | 620 | 1.9743 | 0.57 |
| 0.077 | 3.0 | 930 | 2.4690 | 0.584 |
| 0.028 | 4.0 | 1240 | 2.8887 | 0.582 |
| 0.0118 | 5.0 | 1550 | 2.9155 | 0.59 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ramon-lins/ppo-LunarLander-v2 | ramon-lins | "2023-06-19T18:17:17" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-06-19T18:16:55" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo_baselines3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.49 +/- 17.91
name: mean_reward
verified: false
---
# **ppo_baselines3** Agent playing **LunarLander-v2**
This is a trained model of a **ppo_baselines3** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Resizable/PersianTurtle | Resizable | "2023-10-15T02:16:28" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2023-10-15T02:13:27" | ---
license: openrail
---
|
srikarthikv/distilbert-base-uncased-lora-text-classification | srikarthikv | "2024-01-25T14:54:15" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-01-25T14:54:13" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
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. -->
# distilbert-base-uncased-lora-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0072
- Accuracy: {'accuracy': 0.88}
## 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: 4
- eval_batch_size: 4
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.3560 | {'accuracy': 0.888} |
| 0.4316 | 2.0 | 500 | 0.5124 | {'accuracy': 0.878} |
| 0.4316 | 3.0 | 750 | 0.6530 | {'accuracy': 0.87} |
| 0.2331 | 4.0 | 1000 | 0.6871 | {'accuracy': 0.878} |
| 0.2331 | 5.0 | 1250 | 0.8012 | {'accuracy': 0.869} |
| 0.0918 | 6.0 | 1500 | 0.8738 | {'accuracy': 0.878} |
| 0.0918 | 7.0 | 1750 | 0.8714 | {'accuracy': 0.881} |
| 0.0349 | 8.0 | 2000 | 0.9631 | {'accuracy': 0.88} |
| 0.0349 | 9.0 | 2250 | 1.0067 | {'accuracy': 0.879} |
| 0.0071 | 10.0 | 2500 | 1.0072 | {'accuracy': 0.88} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
elinas/alpaca-13b-lora-int4 | elinas | "2023-04-05T16:41:00" | 8 | 41 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"alpaca",
"gptq",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-03-18T01:32:45" | ---
license: other
tags:
- alpaca
- gptq
---
# llama-13b-int4
This LoRA trained for 3 epochs and has been converted to int4 (4bit) via GPTQ method.
Use the **safetensors** version of the model, the **pt** version is an old quantization that is no longer supported and will be removed in the future.
See the repo below for more info.
# Important - Update 2023-04-05
Recent GPTQ commits have introduced breaking changes to model loading and you should this fork for a stable experience https://github.com/oobabooga/GPTQ-for-LLaMa
Curently only cuda is supported.
# Update 2023-03-27
New weights have been added. The old .pt version is no longer supported and has been replaced by a 128 groupsize safetensors file. Update to the latest GPTQ to use it.
**alpaca-13b-4bit-128g.safetensors**
Evals
-----
**c4-new** -
6.925674915313721
**ptb-new** -
9.23875904083252
**wikitext2** -
5.219980716705322
# Usage
1. Run manually through GPTQ
2. (More setup but better UI) - Use the [text-generation-webui](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#4-bit-mode). Make sure to follow the installation steps first [here](https://github.com/oobabooga/text-generation-webui#installation) before adding GPTQ support.
Since this is instruction tuned, for best results, use the following format for inference:
```
### Instruction:
your-prompt
### Response:
```
If you want deterministic results, turn off sampling. You can turn it off in the webui by unchecking `do_sample`.
For cai-chat mode, you won't want to use instruction prompting, rather create a character and set sampler settings. Here is an example of settings that work well for me:
```
do_sample=True
temperature=0.95
top_p=1
typical_p=1
repetition_penalty=1.1
top_k=40
num_beams=1
penalty_alpha=0
min_length=0
length_penalty=1
no_repeat_ngram_size=0
early_stopping=False
```
You can then save this as a `.txt` file in the `presets` folder.
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content. |
great0001/f792bf13-e2a2-4c39-91a3-c88207321f5c | great0001 | "2025-02-08T14:14:29" | 7 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | "2025-02-08T09:46:47" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f792bf13-e2a2-4c39-91a3-c88207321f5c
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)
# f792bf13-e2a2-4c39-91a3-c88207321f5c
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3557
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
athirdpath/Orca-2-13b-Alpaca-Uncensored-GGUF | athirdpath | "2023-12-06T01:39:10" | 0 | 7 | null | [
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | "2023-11-27T12:18:32" | ---
pipeline_tag: text-generation
license: other
license_name: microsoft-research-license
language:
- en
---
q8_0, q6_k, q5_k_m, q4_k_m, and q3_k_m GGUF quants of athirdpath/Orca-2-13b-Alpaca-Uncensored.
This model is a fine-tuned version of microsoft/Orca-2-13b on a subset of the Vezora/Mini_Orca_Uncencored_Alpaca dataset, adjusted to demonstrate the relationship between instruction and input, with some particularly spicy prompts added to reduce the risk of rejections.
Only the q_proj and k_proj modules were targeted and a low rank (8) was used, in hopes of containing the adjustments to the prompt format and alignment. This is promising on paper, with the training's per-step loss averaging <0.9 for the last third of the run.
Reasoning stayed solid (for a 13b model) and I consider this a success. Performance is slighty worse than OG Orca-2 in Ooba's chat mode, comparable in Alpaca chat-instruct mode to the OG in ChatLM chat-instruct mode.
May still reject some shocking prompts, but can easily be overcome with author's note or character card. |
centaur31/mybert | centaur31 | "2023-11-10T13:33:46" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | "2023-11-10T13:31:35" | ---
license: apache-2.0
---
|
Danielrahmai1991/findemo_v1 | Danielrahmai1991 | "2024-06-15T15:01:42" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-15T15:01:21" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Danielrahmai1991
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
IronJ/test | IronJ | "2023-03-07T02:34:47" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2023-03-07T02:34:47" | ---
license: openrail
---
|
mbertheau/hf-drl-course-3-dqn-SpaceInvadersNoFrameskip-v4 | mbertheau | "2022-12-24T14:26:08" | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2022-12-24T14:25:22" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 856.50 +/- 453.86
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mbertheau -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mbertheau -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mbertheau
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
philip-hightech/23bc76f8-f764-4ef2-a5b9-20ce60e650a8 | philip-hightech | "2025-02-03T17:06:34" | 7 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
"base_model:adapter:OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
"region:us"
] | null | "2025-02-03T16:57:59" | ---
library_name: peft
base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 23bc76f8-f764-4ef2-a5b9-20ce60e650a8
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: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9fa2363f5e2cb347_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9fa2363f5e2cb347_train_data.json
type:
field_instruction: prompt
field_output: chosen
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: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: philip-hightech/23bc76f8-f764-4ef2-a5b9-20ce60e650a8
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: 10
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_steps: 250
micro_batch_size: 2
mlflow_experiment_name: /tmp/9fa2363f5e2cb347_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
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: d8867652-8a0b-462d-8035-54df350aea9e
wandb_project: Mine-SN56-21-Gradients-On-Demand
wandb_run: your_name
wandb_runid: d8867652-8a0b-462d-8035-54df350aea9e
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 23bc76f8-f764-4ef2-a5b9-20ce60e650a8
This model is a fine-tuned version of [OpenBuddy/openbuddy-llama2-13b-v8.1-fp16](https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v8.1-fp16) 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: 2
- total_train_batch_size: 4
- 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: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0005 | 1 | nan |
| 0.483 | 0.0287 | 63 | nan |
| 0.373 | 0.0574 | 126 | nan |
| 0.4617 | 0.0861 | 189 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
John6666/ether-pdxl-a3-sdxl | John6666 | "2024-08-29T00:02:54" | 223 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"semi-realistic",
"2.5D",
"illustration",
"cute",
"colorful",
"portrait",
"pony",
"en",
"base_model:gamerdan69/EtherMix",
"base_model:finetune:gamerdan69/EtherMix",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-08-28T23:50:27" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- semi-realistic
- 2.5D
- illustration
- cute
- colorful
- portrait
- pony
base_model: gamerdan69/EtherMix
---
Original model is [here](https://huggingface.co/gamerdan69/EtherMix) and on [Civitai](https://civitai.com/models/545628?modelVersionId=778308).
This model created by [gamerdan69](https://civitai.com/user/gamerdan69).
|
LongSafari/evo-1-8k-crispr | LongSafari | "2024-06-20T06:12:51" | 131 | 2 | transformers | [
"transformers",
"safetensors",
"stripedhyena",
"text-generation",
"long context",
"deep signal processing",
"hybrid",
"biology",
"genomics",
"custom_code",
"arxiv:2302.10866",
"arxiv:2203.14343",
"arxiv:2310.18780",
"arxiv:2206.11893",
"arxiv:2303.06349",
"arxiv:2102.02611",
"arxiv:2210.09298",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-06-20T04:13:38" | ---
license: apache-2.0
tags:
- stripedhyena
- long context
- deep signal processing
- hybrid
- biology
- genomics
---
## Evo-1 (CRISPR-Cas)
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/JoEHcvLTUlHoMcgh3mmAz.png" width="70%" />
</p>
### News
We identified and fixed an issue related to a wrong permutation of some projections, which affects generation quality. To use the new model revision, please load as follows:
```python
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True, revision="1.1_fix")
model = AutoModelForCausalLM.from_pretrained(
model_name,
config=config,
trust_remote_code=True,
revision="1.1_fix"
)
```
### About
Evo is a biological foundation model capable of long-context modeling and design.
Evo uses the [StripedHyena architecture](https://github.com/togethercomputer/stripedhyena) to enable modeling of sequences at a single-nucleotide, byte-level resolution with near-linear scaling of compute and memory relative to context length.
Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens.
Technical details about Evo can be found in our preprint and our accompanying blog posts. Evo was collaboratively developed by the [Arc Institute](https://arcinstitute.org/) and TogetherAI.
As part of our commitment to open science, we release **weights of 15 intermediate pretraining checkpoints** for phase 1 and phase 2 of pretraining. The checkpoints are available as branches of the corresponding HuggingFace repository.
**Evo-1 (CRISPR-Cas)** is our fine-tuned model used to generate CRISPR-Cas systems, trained at a context length of 8k.
| Checkpoint Name | Description |
|----------------------------------------|-------------|
| `evo-1-8k-base` | A model pretrained with 8,192 context. We use this model as the base model for molecular-scale finetuning tasks. |
| `evo-1-131k-base` | A model pretrained with 131,072 context using `evo-1-8k-base` as the initialization. We use this model to reason about and generate sequences at the genome scale. |
| `evo-1-8k-crispr` | A model fine-tuned on `evo-1-8k-base` specifically on CRISPR-Cas systems. We use this model to generate Cas9/12/13 systems. |
| `evo-1-8k-transposon` | A model fine-tuned on `evo-1-8k-base` specifically on transposons. We use this to generate IS200/IS605. |
### Model Architecture
StripedHyena is a deep signal processing, hybrid architecture composed of multi-head attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, improving over decoder-only Transformers.
StripedHyena is designed to leverage the specialization of each of its layer classes, with Hyena layers implementing the bulk of the computation required for sequence processing and attention layers supplementing the ability to perform targeted pattern recall.
Some highlights of the architecture:
- **Efficient autoregressive generation** via a recurrent mode (>500k generation with a single 80GB GPU)
- **Significantly faster training and finetuning** at long context (>3x at 131k)
- **Improved scaling laws over state-of-the-art architectures** (e.g., Transformer++) on both natural language and biological sequences.
- **Robust to training beyond the compute-optimal frontier** e.g., training way beyond Chinchilla-optimal token amounts (see preprint for details -- more details to come)
### How to use Evo
Example usage is provided in the [standalone repo](https://github.com/evo-design/evo).
#### Parametrization for Inference and Finetuning
One of the advantages of deep signal processing models is their flexibility. Different parametrizations of convolutions can be used depending on the memory, expressivity and causality requirements of pretraining, finetuning or inference workloads.
The main classes are:
- Modal canonical: unconstrained poles ([reference](https://arxiv.org/pdf/2203.14343.pdf), [reference](https://arxiv.org/abs/2310.18780)), or constrained poles ([reference](https://arxiv.org/abs/2206.11893), [reference](https://arxiv.org/pdf/2303.06349.pdf)).
- Companion canonical / rational: TBA.
- Hypernetworks: hypernetwork ([reference](https://arxiv.org/abs/2102.02611)), modulated hypernetwork ([reference](https://arxiv.org/abs/2302.10866)).
- Explicit: modulated explicit ([reference](https://arxiv.org/pdf/2210.09298.pdf)).
StripedHyena is a mixed precision model. Make sure to keep your `poles` and `residues` in `float32` precision, especially for longer prompts or training.
### Disclaimer
To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the [standalone repository](https://github.com/togethercomputer/stripedhyena).
## Cite
```
@article{nguyen2024sequence,
author = {Eric Nguyen and Michael Poli and Matthew G. Durrant and Armin W. Thomas and Brian Kang and Jeremy Sullivan and Madelena Y. Ng and Ashley Lewis and Aman Patel and Aaron Lou and Stefano Ermon and Stephen A. Baccus and Tina Hernandez-Boussard and Christopher Ré and Patrick D. Hsu and Brian L. Hie},
journal = {Arc Institute manuscripts},
title = {Sequence modeling and design from molecular to genome scale with Evo},
url = {https://arcinstitute.org/manuscripts/Evo},
year = {2024},
}
``` |
cvoffer/0efa4b3b-1a51-4af2-855c-06fbe43d36f4 | cvoffer | "2025-01-19T15:31:45" | 6 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-1.1-2b-it",
"base_model:adapter:unsloth/gemma-1.1-2b-it",
"license:apache-2.0",
"region:us"
] | null | "2025-01-19T14:27:32" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/gemma-1.1-2b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0efa4b3b-1a51-4af2-855c-06fbe43d36f4
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-1.1-2b-it
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e4734565209409b8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e4734565209409b8_train_data.json
type:
field_instruction: post
field_output: summary
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
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: cvoffer/0efa4b3b-1a51-4af2-855c-06fbe43d36f4
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: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/e4734565209409b8_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: 10
sequence_len: 1024
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: de79a7ae-33e3-454b-9488-386af2af5b95
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: de79a7ae-33e3-454b-9488-386af2af5b95
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 0efa4b3b-1a51-4af2-855c-06fbe43d36f4
This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6731
## 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: 5
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 4.0435 |
| 3.9019 | 0.0003 | 5 | 3.0211 |
| 2.8695 | 0.0007 | 10 | 2.7868 |
| 2.6902 | 0.0010 | 15 | 2.7147 |
| 2.6625 | 0.0013 | 20 | 2.6863 |
| 2.6312 | 0.0016 | 25 | 2.6755 |
| 2.7038 | 0.0020 | 30 | 2.6731 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
tanmeh/qa_model | tanmeh | "2024-02-13T19:06:14" | 78 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlnet",
"question-answering",
"generated_from_trainer",
"base_model:xlnet/xlnet-base-cased",
"base_model:finetune:xlnet/xlnet-base-cased",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | "2024-02-11T22:51:42" | ---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
model-index:
- name: qa_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. -->
# qa_model
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 50 | 3.4426 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
ultracheese/ppo-LunarLander-v2 | ultracheese | "2024-05-12T16:20:05" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-05-12T16:19:48" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 282.03 +/- 22.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
helpingstar/Reinforce-1 | helpingstar | "2023-03-21T09:05:48" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-21T09:05:43" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 221.30 +/- 143.88
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
impossibleexchange/0x118 | impossibleexchange | "2025-01-24T02:34:11" | 20 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-24T00:36:56" | ---
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 🤗 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] |
edmundmills/dignity-classifier | edmundmills | "2023-06-08T00:01:24" | 58 | 0 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-06-07T23:02:47" | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dignity-classifier
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. -->
# dignity-classifier
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5157
- Accuracy: 0.8678
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7722 | 1.0 | 98 | 0.7799 | 0.6897 |
| 0.4301 | 2.0 | 196 | 0.4704 | 0.8477 |
| 0.2445 | 3.0 | 294 | 0.5107 | 0.8305 |
| 0.1626 | 4.0 | 392 | 0.5553 | 0.8477 |
| 0.0653 | 5.0 | 490 | 0.5157 | 0.8678 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ashishabraham22/XLS-R_Finetuned | ashishabraham22 | "2024-07-17T11:09:25" | 166 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-07-17T07:59:31" | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: XLS-R_Finetuned
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. -->
# XLS-R_Finetuned
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.00024
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
CyberHarem/echidna_rezero | CyberHarem | "2023-08-16T19:08:55" | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/echidna_rezero",
"license:mit",
"region:us"
] | text-to-image | "2023-08-16T19:04:28" | ---
license: mit
datasets:
- CyberHarem/echidna_rezero
pipeline_tag: text-to-image
tags:
- art
---
# Lora of echidna_rezero
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/echidna_rezero.pt` as the embedding and `1500/echidna_rezero.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `echidna_rezero`.**
These are available steps:
| Steps | pattern_1 | pattern_2 | pattern_3 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------|
| 1500 |  |  |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/echidna_rezero.zip) |
| 1400 |  |  |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/echidna_rezero.zip) |
| 1300 |  |  |  |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/echidna_rezero.zip) |
| 1200 |  |  |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/echidna_rezero.zip) |
| 1100 |  |  |  |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/echidna_rezero.zip) |
| 1000 |  |  |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/echidna_rezero.zip) |
| 900 |  |  |  |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/echidna_rezero.zip) |
| 800 |  |  |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/echidna_rezero.zip) |
| 700 |  |  |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/echidna_rezero.zip) |
| 600 |  |  |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/echidna_rezero.zip) |
| 500 |  |  |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/echidna_rezero.zip) |
| 400 |  |  |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/echidna_rezero.zip) |
| 300 |  |  |  |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/echidna_rezero.zip) |
| 200 |  |  |  |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/echidna_rezero.zip) |
| 100 |  |  |  |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/echidna_rezero.zip) |
|
MarkBW/hannahowo-xl | MarkBW | "2024-04-01T00:46:54" | 5 | 2 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | "2024-04-01T00:43:22" | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/2023-12-30_18-57-29_4486.jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: hannahowo
---
# hannahowo-xl
<Gallery />
## Trigger words
You should use `hannahowo` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/MarkBW/hannahowo-xl/tree/main) them in the Files & versions tab.
|
mlx-community/whisper-large-v3-mlx-8bit | mlx-community | "2024-03-13T18:01:44" | 61 | 5 | mlx | [
"mlx",
"whisper",
"region:us"
] | null | "2024-03-09T05:34:35" | ---
library_name: mlx
---
# whisper-large-v3-mlx-8bit
This model was converted to MLX format from [`large-v3`]().
## Use with mlx
```bash
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/whisper/
pip install -r requirements.txt
>> import whisper
>> whisper.transcribe("FILE_NAME", path_or_hf_repo="mlx-community/whisper-large-v3-mlx-8bit")
```
|
asi/gpt-fr-cased-small | asi | "2022-10-20T18:30:45" | 1,755 | 8 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fr",
"license:apache-2.0",
"model-index",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-03-02T23:29:05" | ---
language:
- fr
model-index:
- name: asi/gpt-fr-cased-base
results:
- task:
type: text-generation
name: Wikitext-fr
dataset:
type: wikitext_fr
name: Wikitext-fr
metrics:
- type: perplexity
value: 109.2
name: Perplexity
- task:
type: text-classification
name: FLUE
dataset:
type: flue
name: CLS-Books
split: CLS
metrics:
- type: accuracy
value: 88.3
name: Accuracy
- task:
type: text-classification
name: FLUE
dataset:
type: flue
name: CLS-Dvd
split: CLS
metrics:
- type: accuracy
value: 86.9
name: Accuracy
- task:
type: text-classification
name: FLUE
dataset:
type: flue
name: CLS-Music
split: CLS
metrics:
- type: accuracy
value: 89.3
name: Accuracy
- task:
type: text-classification
name: FLUE
dataset:
type: flue
name: PAWS-X
split: PAWS-X
metrics:
- type: accuracy
value: 83.3
name: Accuracy
- task:
type: text-classification
name: FLUE
dataset:
type: flue
name: XNLI
split: XNLI
metrics:
- type: accuracy
value: 75.6
name: Accuracy
- task:
type: summarization
name: OrangeSum
dataset:
type: orange_sum
name: OrangeSum-Abstract
split: abstract
metrics:
- name: ROUGE-1
type: rouge
value: 17.5
- name: ROUGE-2
type: rouge
value: 3.1
- name: ROUGE-L
type: rouge
value: 12.1
- task:
type: summarization
name: OrangeSum
dataset:
type: orange_sum
name: OrangeSum-Title
split: title
metrics:
- name: ROUGE-1
type: rouge
value: 13.9
- name: ROUGE-2
type: rouge
value: 2.3
- name: ROUGE-L
type: rouge
value: 9.7
tags:
- tf
- pytorch
- gpt2
- text-generation
license: apache-2.0
thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png
---
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png" width="200">
## Model description
**GPT-fr** 🇫🇷 is a GPT model for French developped by [Quantmetry](https://www.quantmetry.com/) and the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We train the model on a very large and heterogeneous French corpus. We release the weights for the following configurations:
| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------: | :---: | :---: | :---: | :---: |
| `gpt-fr-cased-small` | 12 | 12 | 768 | 124 M |
| `gpt-fr-cased-base` | 24 | 14 | 1,792 | 1,017 B |
## Intended uses & limitations
The model can be leveraged for language generation tasks. Besides, many tasks may be formatted such that the output is directly generated in natural language. Such configuration may be used for tasks such as automatic summary or question answering. We do hope our model might be used for both academic and industrial applications.
#### How to use
The model might be used through the astonishing 🤗 `Transformers` librairie:
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load pretrained model and tokenizer
model = GPT2LMHeadModel.from_pretrained("asi/gpt-fr-cased-small")
tokenizer = GPT2Tokenizer.from_pretrained("asi/gpt-fr-cased-small")
# Generate a sample of text
model.eval()
input_sentence = "Longtemps je me suis couché de bonne heure."
input_ids = tokenizer.encode(input_sentence, return_tensors='pt')
beam_outputs = model.generate(
input_ids,
max_length=100,
do_sample=True,
top_k=50,
top_p=0.95,
num_return_sequences=1
)
print("Output:\n" + 100 * '-')
print(tokenizer.decode(beam_outputs[0], skip_special_tokens=True))
```
#### Limitations and bias
Large language models tend to replicate the biases found in pre-training datasets, such as gender discrimination or offensive content generation.
To limit exposition to too much explicit material, we carefully choose the sources beforehand. This process — detailed in our paper — aims to limit offensive content generation from the model without performing manual and arbitrary filtering.
However, some societal biases, contained in the data, might be reflected by the model. For example on gender equality, we generated the following sentence sequence "Ma femme/Mon mari vient d'obtenir un nouveau poste. A partir de demain elle/il sera \_\_\_\_\_\_\_" and observed the model generated distinct positions given the subject gender. We used top-k random sampling strategy with k=50 and stopped at the first punctuation element.
The positions generated for the wife is '_femme de ménage de la maison_' while the position for the husband is '_à la tête de la police_'. We do appreciate your feedback to better qualitatively and quantitatively assess such effects.
## Training data
We created a dedicated corpus to train our generative model. Indeed the model uses a fixed-length context size of 1,024 and require long documents to be trained. We aggregated existing corpora: [Wikipedia](https://dumps.wikimedia.org/frwiki/), [OpenSubtitle](http://opus.nlpl.eu/download.php?f=OpenSubtitles/v2016/mono/) ([Tiedemann, 2012](#tiedemann-2012)), [Gutenberg](http://www.gutenberg.org). Corpora are filtered and separated into sentences. Successive sentences are then concatenated within the limit of 1,024 tokens per document.
## Training procedure
We pre-trained the model on a TPU v2-8 using the amazing [Google Colab](https://colab.research.google.com) inter-server.
## Eval results
We packaged **GPT-fr** with a dedicated language model evaluation benchmark.
In line with the [WikiText](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark in English, we collected over 70 million tokens from the set of verified [good](https://fr.wikipedia.org/wiki/Wikip%C3%A9dia:Articles_de_qualit%C3%A9) and [featured](https://fr.wikipedia.org/wiki/Wikip%C3%A9dia:Bons_articles) articles on French Wikipedia. The model reaches a zero-shot perplexity of **109.2** on the test set.
### BibTeX entry and citation info
Along with the model hosted by HuggingFace transformers library, we maintain a [git repository](https://github.com/AntoineSimoulin/gpt-fr).
If you use **GPT-fr** for your scientific publications or your industrial applications, please cite the following paper:
```bibtex
@inproceedings{simoulin:hal-03265900,
TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}},
AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit},
URL = {https://hal.archives-ouvertes.fr/hal-03265900},
BOOKTITLE = {{Traitement Automatique des Langues Naturelles}},
ADDRESS = {Lille, France},
EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio},
PUBLISHER = {{ATALA}},
PAGES = {246-255},
YEAR = {2021},
KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}},
PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf},
HAL_ID = {hal-03265900},
HAL_VERSION = {v1},
}
```
### References
><div name="tiedemann-2012">Jörg Tiedemann: Parallel Data, Tools and Interfaces in OPUS. LREC 2012: 2214-2218</div> |
EmmettBicker/ppo-Huggy | EmmettBicker | "2023-06-10T20:19:39" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2023-06-10T20:19:34" | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: EmmettBicker/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF | farpluto | "2024-06-12T07:55:30" | 14 | 0 | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"multilingual",
"base_model:microsoft/Phi-3-medium-4k-instruct",
"base_model:quantized:microsoft/Phi-3-medium-4k-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2024-06-12T04:49:17" | ---
language:
- multilingual
license: mit
tags:
- nlp
- code
- llama-cpp
- gguf-my-repo
base_model: microsoft/Phi-3-medium-4k-instruct
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
# farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3-medium-4k-instruct`](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) 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/Phi-3-medium-4k-instruct) 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 --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.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.
```
./main --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./server --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -c 2048
```
|
mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF | mradermacher | "2024-11-22T10:03:59" | 66 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"bfloat16",
"text-generation-inference",
"model_stock",
"crypto",
"finance",
"llama",
"en",
"base_model:ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B",
"base_model:quantized:ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-11-22T08:20:35" | ---
base_model: ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- bfloat16
- text-generation-inference
- model_stock
- crypto
- finance
- llama
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-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/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-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 -->
|
keyblade95/q-FrozenLake-v1-4x4-noSlippery | keyblade95 | "2023-01-13T16:24:16" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-01-13T16:24:14" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="keyblade95/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
stablediffusionapi/epicdream | stablediffusionapi | "2025-01-20T11:32:25" | 0 | 0 | diffusers | [
"diffusers",
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-10-16T12:10:36" | ---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# epiCDream API Inference
## Get API Key
Get API key from [ModelsLab](https://modelslab.com/), No Payment needed.
Replace Key in below code, change **model_id** to "epicdream"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Try model for free: [Generate Images](https://stablediffusionapi.com/models/epicdream)
Model link: [View model](https://stablediffusionapi.com/models/epicdream)
Credits: [View credits](https://civitai.com/?query=epiCDream)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v4/dreambooth"
payload = json.dumps({
"key": "your_api_key",
"model_id": "epicdream",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** |
John6666/mix-photoreal-stable-xl-stable4-sdxl | John6666 | "2024-08-02T06:36:15" | 38 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"photo",
"anime",
"game",
"cartoon",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-08-02T06:30:58" | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- photo
- anime
- game
- cartoon
---
Original model is [here](https://civitai.com/models/409675/mix-photoreal-stable-xl?modelVersionId=689342).
|
mlx-community/Codestral-22B-v0.1-4bit | mlx-community | "2024-05-29T20:16:42" | 178 | 13 | mlx | [
"mlx",
"safetensors",
"mistral",
"code",
"license:other",
"region:us"
] | null | "2024-05-29T14:23:25" | ---
language:
- code
license: other
tags:
- code
- mlx
inference: false
license_name: mnpl
license_link: https://mistral.ai/licences/MNPL-0.1.md
---
# mlx-community/Codestral-22B-v0.1-4bit
The Model [mlx-community/Codestral-22B-v0.1-4bit](https://huggingface.co/mlx-community/Codestral-22B-v0.1-4bit) was converted to MLX format from [bullerwins/Codestral-22B-v0.1-hf](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf) using mlx-lm version **0.14.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Codestral-22B-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
sathurjan/Digitweb_Cone_Shade1 | sathurjan | "2024-05-14T09:03:19" | 0 | 0 | null | [
"object-detection",
"region:us"
] | object-detection | "2024-05-14T09:00:28" | ---
pipeline_tag: object-detection
--- |
RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf | RichardErkhov | "2024-09-13T16:27:39" | 9 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2024-09-13T11:09:26" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Experiment26Yamshadow-7B - GGUF
- Model creator: https://huggingface.co/automerger/
- Original model: https://huggingface.co/automerger/Experiment26Yamshadow-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Experiment26Yamshadow-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [Experiment26Yamshadow-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Experiment26Yamshadow-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Experiment26Yamshadow-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Experiment26Yamshadow-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Experiment26Yamshadow-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [Experiment26Yamshadow-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Experiment26Yamshadow-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Experiment26Yamshadow-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Experiment26Yamshadow-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Experiment26Yamshadow-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Experiment26Yamshadow-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Experiment26Yamshadow-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [Experiment26Yamshadow-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Experiment26Yamshadow-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Experiment26Yamshadow-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Experiment26Yamshadow-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Experiment26Yamshadow-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [Experiment26Yamshadow-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Experiment26Yamshadow-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Experiment26Yamshadow-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [Experiment26Yamshadow-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- automerger/YamShadow-7B
---
# Experiment26Yamshadow-7B
Experiment26Yamshadow-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B)
## 🧩 Configuration
```yaml
models:
- model: rwitz/experiment26-truthy-iter-0
# No parameters necessary for base model
- model: automerger/YamShadow-7B
parameters:
density: 0.53
weight: 0.6
merge_method: dare_ties
base_model: rwitz/experiment26-truthy-iter-0
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment26Yamshadow-7B"
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"])
```
|
zzttbrdd/sn6_6m | zzttbrdd | "2024-04-15T02:08:15" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-15T01:59:07" | ---
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 🤗 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]
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[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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sail-rvc/Whoppa | sail-rvc | "2023-07-14T07:33:59" | 1 | 0 | transformers | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | "2023-07-14T07:33:46" |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# Whoppa
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:33:59
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
|
fumi13/vit-base-beans | fumi13 | "2022-10-09T06:46:29" | 220 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-10-09T06:31:17" | ---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0824
- Accuracy: 0.9925
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3039 | 1.0 | 130 | 0.2474 | 0.9624 |
| 0.1299 | 2.0 | 260 | 0.1007 | 0.9925 |
| 0.0885 | 3.0 | 390 | 0.0824 | 0.9925 |
| 0.0976 | 4.0 | 520 | 0.1179 | 0.9699 |
| 0.1284 | 5.0 | 650 | 0.0832 | 0.9774 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
thalllsssss/e7527483-01ec-49be-b413-2690d086345c | thalllsssss | "2025-01-19T02:16:43" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-18T23:25:49" | ---
library_name: peft
license: llama3
base_model: elyza/Llama-3-ELYZA-JP-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e7527483-01ec-49be-b413-2690d086345c
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: elyza/Llama-3-ELYZA-JP-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c3dbdb6a98b06458_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c3dbdb6a98b06458_train_data.json
type:
field_instruction: problem
field_output: solution
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: 1
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: thalllsssss/e7527483-01ec-49be-b413-2690d086345c
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: 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: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/c3dbdb6a98b06458_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: <|eot_id|>
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: 858aaf4c-a418-40cd-9e36-4bb85fd16280
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 858aaf4c-a418-40cd-9e36-4bb85fd16280
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# e7527483-01ec-49be-b413-2690d086345c
This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6745
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.5753 | 0.0020 | 200 | 0.6745 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
TinToTin/cartpole-policy-gradient | TinToTin | "2023-08-10T18:01:29" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-08-10T18:01:19" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole-policy-gradient
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-50-percent-low-med-bt-rouge-1 | AdamKasumovic | "2024-06-14T21:54:13" | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-14T21:52:22" | ---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral 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)
|
ihanif/whisper-test | ihanif | "2024-10-10T22:30:28" | 6 | 0 | transformers.js | [
"transformers.js",
"tensorboard",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ps",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-small",
"base_model:quantized:openai/whisper-small",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | "2024-10-02T10:17:17" | ---
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_17_0
language:
- ps
library_name: transformers.js
license: apache-2.0
tags:
- generated_from_trainer
- onnx
model-index:
- name: Whisper Small PS - Hanif Rahman
results: []
---
https://huggingface.co/ihanif/whisper-test with ONNX weights to be compatible with Transformers.js.
<!-- 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 PS - Hanif Rahman
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7573
- eval_wer: 46.1819
- eval_runtime: 395.7975
- eval_samples_per_second: 1.294
- eval_steps_per_second: 0.162
- epoch: 5.7143
- step: 2600
## 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: 8
- 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: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
---
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
ShotaMatsumoto/GPT0.35B-ja-tokenizer-unigram-v1-CultulaX-default-filtered-ja-part-00000-00002-1000step | ShotaMatsumoto | "2024-04-06T08:55:09" | 146 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-06T08:53:20" | ---
license: apache-2.0
---
|
mlx-community/Phi-3-mini-128k-instruct-4bit | mlx-community | "2024-07-11T21:26:35" | 35 | 12 | mlx | [
"mlx",
"safetensors",
"phi3",
"nlp",
"code",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"region:us"
] | text-generation | "2024-04-23T14:38:20" | ---
language:
- en
license: mit
tags:
- nlp
- code
- mlx
license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
---
# mlx-community/Phi-3-mini-128k-instruct-4bit
This model was converted to MLX format from [`microsoft/Phi-3-mini-128k-instruct`]() using mlx-lm version **0.10.0**.
Model added by [Prince Canuma](https://twitter.com/Prince_Canuma).
Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Phi-3-mini-128k-instruct-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
nintwentydo/Razorback-12B-v0.1 | nintwentydo | "2025-01-10T05:28:47" | 18 | 1 | transformers | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"mergekit",
"merge",
"multimodal",
"mistral",
"pixtral",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:TheDrummer/Rocinante-12B-v1.1",
"base_model:merge:TheDrummer/Rocinante-12B-v1.1",
"base_model:TheDrummer/UnslopNemo-12B-v3",
"base_model:merge:TheDrummer/UnslopNemo-12B-v3",
"base_model:mistralai/Pixtral-12B-2409",
"base_model:merge:mistralai/Pixtral-12B-2409",
"license:other",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2025-01-09T12:37:23" | ---
base_model:
- mistralai/Pixtral-12B-2409
- TheDrummer/Rocinante-12B-v1.1
- TheDrummer/UnslopNemo-12B-v3
base_model_relation: merge
library_name: transformers
tags:
- mergekit
- merge
- multimodal
- mistral
- pixtral
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: other
pipeline_tag: image-text-to-text
---
# Razorback 12B v0.1
## Update: Use v0.2 [nintwentydo/Razorback-12B-v0.2](https://huggingface.co/nintwentydo/Razorback-12B-v0.2)
<img src="https://huggingface.co/nintwentydo/Razorback-12B-v0.1/resolve/main/razorback.jpg" style="max-width:700px"></img>
This is a first pass attempt to merge Mistral Nemo finetunes with Pixtral 12B. Has not been fully tested yet other than confirming it can understand vision input and output coherent text. May be unstable for all we know lol.
Pixtral 12B as base with TheDrummer's Rocinante and UnslopNemo finetunes merged in.
In *The Expanse* the Razorback is a ship fitted with engines way bigger than a ship of its size would normally have. Thought it was a fitting way to celebrate TheDrummer's models supercharging Pixtral. 😉
## Credits
- Mistral for [mistralai/Pixtral-12B-2409](https://huggingface.co/mistralai/Pixtral-12B-2409)
- Unsloth for [unsloth/Pixtral-12B-2409](https://huggingface.co/unsloth/Pixtral-12B-2409) transformers conversion
- TheDrummer for [TheDrummer/Rocinante-12B-v1.1](https://huggingface.co/TheDrummer/Rocinante-12B-v1.1)
- TheDrummer for [TheDrummer/UnslopNemo-12B-v3](https://huggingface.co/TheDrummer/UnslopNemo-12B-v3) |
End of preview. Expand
in Dataset Viewer.
Dataset Card for Hugging Face Hub Model Cards
This datasets consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
- Downloads last month
- 478
Curated by:
Daniel van Strien
Language(s) (NLP):
Model cards on the Hugging Face Hub are predominantly in English but may include other languages.
Size of downloaded dataset files:
878 MB
Size of the auto-converted Parquet files:
878 MB
Number of rows:
633,255