Datasets:

davanstrien

/

model_cards_with_metadata-test

like 0

damgomz/ft_16_4e6_base_x8

damgomz

transformers

text-classification

--- 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) | 86142.15871238708 | | Emissions (Co2eq in kg) | 0.0521259050887542 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0169533443964198 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0897302735470235 | | Consumed energy (kWh) | 1.1066836179434458 | | 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) | 0.16582365552134512 | | Emissions (Co2eq in kg) | 0.0337390121623516 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_16_4e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 4e-06 | | batch_size | 16 | | 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 | 0.724495 | 0.424528 | | 1 | 0.355208 | 0.249139 | 0.917037 | | 2 | 0.224822 | 0.240320 | 0.933497 | | 3 | 0.184909 | 0.222563 | 0.904180 | | 4 | 0.153819 | 0.218022 | 0.915023 | | 5 | 0.119465 | 0.246321 | 0.914588 | | 6 | 0.090303 | 0.268250 | 0.914945 |

risolmayo/4c44a1ff-f68c-47b7-b6c0-1e2d7bc753eb

risolmayo

peft

--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B tags: - axolotl - generated_from_trainer model-index: - name: 4c44a1ff-f68c-47b7-b6c0-1e2d7bc753eb 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/Llama-3.2-1B bf16: true chat_template: llama3 data_processes: 24 dataset_prepared_path: null datasets: - data_files: - 90559523681be0ab_train_data.json ds_type: json format: custom path: /workspace/input_data/90559523681be0ab_train_data.json type: field_input: abstract field_instruction: prompt field_output: y_true format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 4 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: risolmayo/4c44a1ff-f68c-47b7-b6c0-1e2d7bc753eb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 7.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.04 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_warmup_steps: 50 max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/90559523681be0ab_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-8 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 17333 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer total_train_batch_size: 32 train_batch_size: 8 train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b3e5bfbd-fea9-49bb-bacf-9d1430738190 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b3e5bfbd-fea9-49bb-bacf-9d1430738190 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 4c44a1ff-f68c-47b7-b6c0-1e2d7bc753eb This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0006 ## 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: 7e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 17333 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.1267 | 0.0022 | 1 | 0.3185 | | 0.021 | 0.1079 | 50 | 0.0043 | | 0.0652 | 0.2157 | 100 | 0.0009 | | 0.0285 | 0.3236 | 150 | 0.0008 | | 0.0014 | 0.4315 | 200 | 0.0006 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

adammoss/ddpm-128-epsilon-1000-sigmoid-run2

adammoss

diffusers

Entry not found

EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224

EPFL-VILAB

ml-4m

--- license: other license_name: sample-code-license license_link: LICENSE library_name: ml-4m --- # 4M: Massively Multimodal Masked Modeling *A framework for training any-to-any multimodal foundation models. <br>Scalable. Open-sourced. Across tens of modalities and tasks.* [`Website`](https://4m.epfl.ch) | [`GitHub`](https://github.com/apple/ml-4m) | [`BibTeX`](#citation) Official implementation and pre-trained models for : [**4M: Massively Multimodal Masked Modeling**](https://arxiv.org/abs/2312.06647), NeurIPS 2023 (Spotlight) <br> *[David Mizrahi](https://dmizrahi.com/)\*, [Roman Bachmann](https://roman-bachmann.github.io/)\*, [Oğuzhan Fatih Kar](https://ofkar.github.io/), [Teresa Yeo](https://aserety.github.io/), [Mingfei Gao](https://fly6464.github.io/), [Afshin Dehghan](https://www.afshindehghan.com/), [Amir Zamir](https://vilab.epfl.ch/zamir/)* [**4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities**](https://arxiv.org/abs/2406.09406), arXiv 2024 <br> *[Roman Bachmann](https://roman-bachmann.github.io/)\*, [Oğuzhan Fatih Kar](https://ofkar.github.io/)\*, [David Mizrahi](https://dmizrahi.com/)\*, [Ali Garjani](https://garjania.github.io/), [Mingfei Gao](https://fly6464.github.io/), [David Griffiths](https://www.dgriffiths.uk/), [Jiaming Hu](https://scholar.google.com/citations?user=vm3imKsAAAAJ&hl=en), [Afshin Dehghan](https://www.afshindehghan.com/), [Amir Zamir](https://vilab.epfl.ch/zamir/)* 4M is a framework for training "any-to-any" foundation models, using tokenization and masking to scale to many diverse modalities. Models trained using 4M can perform a wide range of vision tasks, transfer well to unseen tasks and modalities, and are flexible and steerable multimodal generative models. We are releasing code and models for "4M: Massively Multimodal Masked Modeling" (here denoted 4M-7), as well as "4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities" (here denoted 4M-21). ## Installation For install instructions, please see https://github.com/apple/ml-4m. ## Usage The DINOv2-B/14 global embedding tokenizer can be loaded from Hugging Face Hub as follows: ```python from fourm.vq.vqvae import VQVAE tok_dinov2_global = VQVAE.from_pretrained('EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224') ``` Please see https://github.com/apple/ml-4m/blob/main/README_TOKENIZATION.md for more detailed instructions and https://github.com/apple/ml-4m for other tokenizer and 4M model checkpoints. ## Citation If you find this repository helpful, please consider citing our work: ``` @inproceedings{4m, title={{4M}: Massively Multimodal Masked Modeling}, author={David Mizrahi and Roman Bachmann and O{\u{g}}uzhan Fatih Kar and Teresa Yeo and Mingfei Gao and Afshin Dehghan and Amir Zamir}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, year={2023}, } @article{4m21, title={{4M-21}: An Any-to-Any Vision Model for Tens of Tasks and Modalities}, author={Roman Bachmann and O{\u{g}}uzhan Fatih Kar and David Mizrahi and Ali Garjani and Mingfei Gao and David Griffiths and Jiaming Hu and Afshin Dehghan and Amir Zamir}, journal={arXiv 2024}, year={2024}, } ``` ## License The model weights in this repository are released under the Sample Code license as found in the [LICENSE](LICENSE) file.

huggingartists/the-beatles

huggingartists

transformers

text-generation

--- language: en datasets: - huggingartists/the-beatles tags: - huggingartists - lyrics - lm-head - causal-lm widget: - text: "I am" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/c771d3ee1c0969503cdaf34edf76f38a.400x400x1.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">The Beatles</div> <a href="https://genius.com/artists/the-beatles"> <div style="text-align: center; font-size: 14px;">@the-beatles</div> </a> </div> I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists). Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)! ## How does it work? To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist). ## Training data The model was trained on lyrics from The Beatles. Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-beatles). And can be used with: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/the-beatles") ``` [Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2p2c5864/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 The Beatles's lyrics. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/286vzjah) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/286vzjah/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingartists/the-beatles') generator("I am", num_return_sequences=5) ``` Or with Transformers library: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-beatles") model = AutoModelWithLMHead.from_pretrained("huggingartists/the-beatles") ``` ## 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 Aleksey Korshuk* [](https://github.com/AlekseyKorshuk) [](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [](https://github.com/AlekseyKorshuk/huggingartists)

ssharm87/t5-small-finetuned-xsum-ss

ssharm87

transformers

text2text-generation

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum-ss results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: train args: default metrics: - name: Rouge1 type: rouge value: 26.3663 --- <!-- 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. --> # t5-small-finetuned-xsum-ss This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5823 - Rouge1: 26.3663 - Rouge2: 6.4727 - Rougel: 20.538 - Rougelsum: 20.5411 - Gen Len: 18.8006 ## 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: 0.25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 2.8125 | 0.25 | 3189 | 2.5823 | 26.3663 | 6.4727 | 20.538 | 20.5411 | 18.8006 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

Arshik/mistral-chat-model

Arshik

transformers

text-generation

--- 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]

Wonder-Griffin/TraXLMistralv1

Wonder-Griffin

transformers

text-generation

--- 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]

danielkty22/bert-base-uncased-finetune-squad-ep-1.0-lr-1e-05-wd-0.001-dp-0.2-ss-0-st-False-fh-False-hs-700

danielkty22

transformers

question-answering

Entry not found

mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF

mradermacher

transformers

--- base_model: EpistemeAI/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/EpistemeAI/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-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/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent-GGUF/resolve/main/Polypsyche-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-divergent.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. <!-- end -->

bond005/ruT5-ASR-large

bond005

transformers

text2text-generation

--- language: ru license: apache-2.0 tags: - PyTorch - Transformers widget: - text: краеугольным камнем любышь алгоритных машиного обучения является преждес его обобщающая способности тогда мы обучаем некоторую модель у нас есть обучающая выборка унаситькюмся ошибки и наша задачи сводится вообщем такомптиминационной задачи мы минимизируем в функцию ошибки по параметрам нашей модели на обучающие выбрать но на самом деле хотим там и не этого мы не обучающую ошибку хотим минимизировать - text: это ашибкараспознаваня --- # ruT5-ASR-large Model was trained by [bond005](https://scholar.google.ru/citations?user=3AJKH38AAAAJ) to correct errors, restore punctuation and capitalization in the ASR output (in particular, output of [Wav2Vec2-Large-Ru-Golos](https://huggingface.co/bond005/wav2vec2-large-ru-golos)). The model is based on [ruT5-large](https://huggingface.co/ai-forever/ruT5-large). ## Usage To correct ASR outputs the model can be used as a standalone sequence-to-sequence model as follows: ```python from transformers import T5ForConditionalGeneration from transformers import GenerationConfig from transformers import T5Tokenizer import torch def restore_text(text: str, tokenizer: T5Tokenizer, config: GenerationConfig, model: T5ForConditionalGeneration) -> str: if len(text) == 0: # if an input text is empty, then we return an empty text too return '' x = tokenizer(text, return_tensors='pt', padding=True).to(model.device) max_size = int(x.input_ids.shape[1] * 2.0 + 10) min_size = 3 if x.input_ids.shape[1] <= min_size: return text out = model.generate(**x, generation_config=config, max_length=max_size) res = tokenizer.decode(out[0], skip_special_tokens=True).strip() return ' '.join(res.split()) # load model and tokenizer tokenizer_for_restoring = T5Tokenizer.from_pretrained('bond005/ruT5-ASR-large') model_for_restoring = T5ForConditionalGeneration.from_pretrained('bond005/ruT5-ASR-large') config_for_restoring = GenerationConfig.from_pretrained('bond005/ruT5-ASR-large') if torch.cuda.is_available(): model_for_restoring = model_for_restoring.cuda() input_examples = [ 'краеугольным камнем любышь алгоритных машиного обучения является преждес его ' \ 'обобщающая способности тогда мы обучаем некоторую модель у нас есть обучающая ' \ 'выборка унаситькюмся ошибки и наша задачи сводится вообщем такомптиминационной ' \ 'задачи мы минимизируем в функцию ошибки по параметрам нашей модели на обучающие ' \ 'выбрать но на самом деле хотим там и не этого ' \ 'мы не обучающую ошибку хотим минимизировать', # 0 'максимально ухучать идеальную систему в воде туда какие то элементы или условия ' \ 'чтобы итоговое результат должен быть такой мы должны в двадцать два раза ' \ 'замедлить нашу разработку' # 1 ] for idx, val in enumerate(input_examples): restored = restore_text(val, tokenizer_for_restoring, config_for_restoring, model_for_restoring) print('==========') print(f'Example {idx + 1}') print('==========') print('') print('ASR output before restoring:') print('') print(val) print('') print('After restoring:') print('') print(restored) print('') ``` ```text ========== Example 1 ========== ASR output before restoring: краеугольным камнем любышь алгоритных машиного обучения является преждес его обобщающая способности тогда мы обучаем некоторую модель у нас есть обучающая выборка унаситькюмся ошибки и наша задачи сводится вообщем такомптиминационной задачи мы минимизируем в функцию ошибки по параметрам нашей модели на обучающие выбрать но на самом деле хотим там и не этого мы не обучающую ошибку хотим минимизировать After restoring: Краеугольным камнем любого алгоритма машинного обучения является прежде всего его общая способность. Тогда мы обучаем некоторую модель, у нас есть обучающая выборка, у нас есть критическая ошибка, и наша задача сводится в общем к компенсационной задаче. Мы минимизируем функцию ошибки по параметрам нашей модели на обучающую выборку, но на самом деле хотим там и не этого. Мы не обучающую ошибку хотим минимизировать. ========== Example 2 ========== ASR output before restoring: максимально ухучать идеальную систему в воде туда какие то элементы или условия чтобы итоговое результат должен быть такой мы должны в двадцать два раза замедлить нашу разработку After restoring: Максимально ухудшать идеальную систему, вводить туда какие-то элементы или условия. Чтобы итоговый результат должен быть такой, мы должны в 22 раза замедлить нашу разработку. ```

vwxyzjn/ppo_mistral_vllm_1e-6_kl_0.15

vwxyzjn

transformers

text-generation

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.1 tags: - generated_from_trainer model-index: - name: ppo_mistral_vllm_1e-6_kl_0.15 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. --> # ppo_mistral_vllm_1e-6_kl_0.15 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 7 - gradient_accumulation_steps: 64 - total_train_batch_size: 448 - total_eval_batch_size: 56 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

kostiantynk/485cd22e-1e21-409d-9063-3ccd7cbd9d94

kostiantynk

peft

--- library_name: peft license: cc-by-nc-4.0 base_model: upstage/SOLAR-10.7B-Instruct-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 485cd22e-1e21-409d-9063-3ccd7cbd9d94 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: upstage/SOLAR-10.7B-Instruct-v1.0 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ec6cbf6a5412fc88_train_data.json ds_type: json format: custom path: /workspace/input_data/ec6cbf6a5412fc88_train_data.json type: field_input: ar field_instruction: en field_output: eg format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kostiantynk/485cd22e-1e21-409d-9063-3ccd7cbd9d94 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/ec6cbf6a5412fc88_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 653b74b9-69fc-4955-a291-8ce0203e80d5 wandb_project: Birthday-SN56-7-Gradients-On-Demand wandb_run: your_name wandb_runid: 653b74b9-69fc-4955-a291-8ce0203e80d5 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 485cd22e-1e21-409d-9063-3ccd7cbd9d94 This model is a fine-tuned version of [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.7927 | 0.0009 | 1 | nan | | 0.0 | 0.0027 | 3 | nan | | 0.0 | 0.0055 | 6 | nan | | 1.522 | 0.0082 | 9 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

stevennguyen/jankgpt

stevennguyen

transformers

text-generation

--- license: mit tags: - generated_from_trainer model-index: - name: jankgpt 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. --> # jankgpt This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1021 | 0.08 | 100 | 3.0845 | | 3.0967 | 0.16 | 200 | 3.0801 | | 3.0836 | 0.24 | 300 | 3.0788 | | 3.0587 | 0.32 | 400 | 3.0834 | | 3.0541 | 0.4 | 500 | 3.0850 | | 3.0305 | 0.48 | 600 | 3.0981 | | 3.0016 | 0.55 | 700 | 3.1095 | | 2.9926 | 0.63 | 800 | 3.1197 | | 2.9714 | 0.71 | 900 | 3.1251 | | 2.9671 | 0.79 | 1000 | 3.1399 | | 2.9813 | 0.87 | 1100 | 3.1221 | | 3.0625 | 0.95 | 1200 | 3.0943 | | 3.035 | 1.03 | 1300 | 3.0655 | | 2.9603 | 1.11 | 1400 | 3.0517 | | 2.9562 | 1.19 | 1500 | 3.0356 | | 2.927 | 1.27 | 1600 | 3.0243 | | 2.936 | 1.35 | 1700 | 3.0045 | | 2.9404 | 1.43 | 1800 | 2.9877 | | 2.9226 | 1.51 | 1900 | 2.9740 | | 2.9015 | 1.59 | 2000 | 2.9549 | | 2.8955 | 1.66 | 2100 | 2.9432 | | 2.8738 | 1.74 | 2200 | 2.9272 | | 2.8663 | 1.82 | 2300 | 2.9138 | | 2.8515 | 1.9 | 2400 | 2.8980 | | 2.8433 | 1.98 | 2500 | 2.8859 | | 2.7071 | 2.06 | 2600 | 2.8801 | | 2.6697 | 2.14 | 2700 | 2.8721 | | 2.6623 | 2.22 | 2800 | 2.8631 | | 2.6561 | 2.3 | 2900 | 2.8551 | | 2.6604 | 2.38 | 3000 | 2.8465 | | 2.6372 | 2.46 | 3100 | 2.8402 | | 2.6279 | 2.54 | 3200 | 2.8320 | | 2.6209 | 2.62 | 3300 | 2.8264 | | 2.6192 | 2.69 | 3400 | 2.8226 | | 2.605 | 2.77 | 3500 | 2.8204 | | 2.6054 | 2.85 | 3600 | 2.8187 | | 2.6183 | 2.93 | 3700 | 2.8181 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+rocm5.2 - Datasets 2.12.0 - Tokenizers 0.13.3

umm-maybe/StarCoder-1B-StackStar2

umm-maybe

transformers

text-generation

Entry not found

ravikumar101/mistral-7b-v3

ravikumar101

transformers

text-generation

--- license: apache-2.0 ---

JuniperChinenye/missu12

JuniperChinenye

transformers

text-generation

--- 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]

roleplaiapp/MN-12B-Mag-Mell-R1-IQ3_M-GGUF

roleplaiapp

transformers

text-generation

--- library_name: transformers pipeline_tag: text-generation tags: - 12b - IQ3_M - gguf - iq3 - llama-cpp - mag - mell - text-generation --- # roleplaiapp/MN-12B-Mag-Mell-R1-IQ3_M-GGUF **Repo:** `roleplaiapp/MN-12B-Mag-Mell-R1-IQ3_M-GGUF` **Original Model:** `MN-12B-Mag-Mell-R1` **Quantized File:** `MN-12B-Mag-Mell-R1.IQ3_M.gguf` **Quantization:** `GGUF` **Quantization Method:** `IQ3_M` ## Overview This is a GGUF IQ3_M quantized version of MN-12B-Mag-Mell-R1 ## Quantization By I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful. Andrew Webby @ [RolePlai](https://roleplai.app/).

juhoon01/ko_llama3_model_shinhan_2

juhoon01

transformers

text-generation

--- library_name: transformers tags: - trl - sft --- # 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. 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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. 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allenai/ivila-block-layoutlm-finetuned-s2vl-v2

allenai

transformers

token-classification

--- language: en ---

mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF

mradermacher

transformers

--- base_model: sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted <!-- 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/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

mradermacher/Sailor-14B-Chat-GGUF

mradermacher

transformers

--- base_model: sail/Sailor-14B-Chat datasets: - CohereForAI/aya_dataset - CohereForAI/aya_collection - Open-Orca/OpenOrca - HuggingFaceH4/ultrachat_200k - openbmb/UltraFeedback language: - en - zh - id - th - vi - ms - lo library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - multilingual - sea - sailor - sft - chat - instruction --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/sail/Sailor-14B-Chat <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q2_K.gguf) | Q2_K | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.IQ3_XS.gguf) | IQ3_XS | 6.6 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.IQ3_S.gguf) | IQ3_S | 6.9 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q3_K_S.gguf) | Q3_K_S | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.IQ3_M.gguf) | IQ3_M | 7.2 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q3_K_L.gguf) | Q3_K_L | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.IQ4_XS.gguf) | IQ4_XS | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q4_K_M.gguf) | Q4_K_M | 9.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q5_K_S.gguf) | Q5_K_S | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q5_K_M.gguf) | Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q6_K.gguf) | Q6_K | 12.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Sailor-14B-Chat-GGUF/resolve/main/Sailor-14B-Chat.Q8_0.gguf) | Q8_0 | 15.2 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

kanh1/kanha-0.1-2.5-Mistral-7B

kanh1

transformers

text-generation

--- 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. 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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]

ayushdh96/Sarcasm_Detection_Distill_Bert_Fine_Tuned

ayushdh96

transformers

text-classification

--- library_name: transformers tags: [text-classification, sarcasm-detection] --- # 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]

TheBloke/Synthia-7B-GPTQ

TheBloke

transformers

text-generation

--- license: llama2 model_name: Synthia 7b base_model: migtissera/Synthia-7b inference: false model_creator: Migel Tissera model_type: llama prompt_template: 'SYSTEM: {system_message} USER: {prompt} ASSISTANT: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Synthia 7b - GPTQ - Model creator: [Migel Tissera](https://huggingface.co/migtissera) - Original model: [Synthia 7b](https://huggingface.co/migtissera/Synthia-7b) <!-- description start --> ## Description This repo contains GPTQ model files for [Migel Tissera's Synthia 7b](https://huggingface.co/migtissera/Synthia-7b). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Synthia-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Synthia-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Synthia-7B-GGUF) * [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Synthia-7b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Synthia-7B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Synthia-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Synthia-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Synthia-7B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Synthia-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Synthia-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Synthia-7B-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Synthia-7B-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Synthia-7B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Synthia-7B-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Synthia-7B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Synthia-7B-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''SYSTEM: {system_message} USER: {prompt} ASSISTANT: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Migel Tissera's Synthia 7b

l3utterfly/open-llama-3b-v2-layla

l3utterfly

transformers

text-generation

--- license: apache-2.0 --- # Model Card ### Model Description OpenLlama 3B fine-tuned using ShareGPT datasets for multi-turn conversations. - **Developed by:** l3utterfly - **Funded by:** Layla Network - **Model type:** OpenLlama - **Language(s) (NLP):** English - **License:** Llama2 - **Finetuned from model:** OpenLlama 3B ## Uses Base model used by Layla - the offline personal assistant: https://www.layla-network.ai Help & support: https://discord.gg/x546YJ6nYC Prompt: ``` User: Assistant: ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla) | Metric | Value | |-----------------------|---------------------------| | Avg. | 35.63 | | ARC (25-shot) | 38.23 | | HellaSwag (10-shot) | 66.43 | | MMLU (5-shot) | 28.56 | | TruthfulQA (0-shot) | 44.4 | | Winogrande (5-shot) | 62.83 | | GSM8K (5-shot) | 1.06 | | DROP (3-shot) | 7.88 |

Sumaia/bert-base-uncased-finetuned-wnli

Sumaia

transformers

text-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: wnli split: validation args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 --- <!-- 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. --> # bert-base-uncased-finetuned-wnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6913 - Accuracy: 0.5634 ## 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: 128 - eval_batch_size: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 73 | 0.7008 | 0.4366 | | No log | 2.0 | 146 | 0.6943 | 0.5211 | | No log | 3.0 | 219 | 0.6943 | 0.4789 | | No log | 4.0 | 292 | 0.6913 | 0.5634 | | No log | 5.0 | 365 | 0.6932 | 0.5634 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cpu - Datasets 2.10.1 - Tokenizers 0.13.2

myhaaaaaaa/f6af0049-c578-4edd-809f-670abae8fbad

myhaaaaaaa

peft

--- library_name: peft license: apache-2.0 base_model: NousResearch/Hermes-2-Pro-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: f6af0049-c578-4edd-809f-670abae8fbad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Hermes-2-Pro-Mistral-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 030ea58a91f4640e_train_data.json ds_type: json format: custom path: /workspace/input_data/030ea58a91f4640e_train_data.json type: field_input: boe_text_cleaned field_instruction: tweet_text_cleaned field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: 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: myhaaaaaaa/f6af0049-c578-4edd-809f-670abae8fbad 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/030ea58a91f4640e_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 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: 9b8ffe7d-1b3f-4e1e-ba21-152b3efb3b6b wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 9b8ffe7d-1b3f-4e1e-ba21-152b3efb3b6b warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f6af0049-c578-4edd-809f-670abae8fbad This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0261 ## 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 | |:-------------:|:------:|:----:|:---------------:| | 0.1034 | 0.4884 | 200 | 0.0261 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

denbeo/c5c3c23a-a56f-487c-9193-75edc15dafa5

denbeo

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/tinyllama-chat tags: - axolotl - generated_from_trainer model-index: - name: c5c3c23a-a56f-487c-9193-75edc15dafa5 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/tinyllama-chat bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d3b1d835749b83ad_train_data.json ds_type: json format: custom path: /workspace/input_data/d3b1d835749b83ad_train_data.json type: field_input: rejected field_instruction: prompt field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: 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: denbeo/c5c3c23a-a56f-487c-9193-75edc15dafa5 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/d3b1d835749b83ad_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 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: 0d2293ac-9f59-4571-af11-389887041d31 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 0d2293ac-9f59-4571-af11-389887041d31 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # c5c3c23a-a56f-487c-9193-75edc15dafa5 This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8332 ## 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 | |:-------------:|:------:|:----:|:---------------:| | 2.0545 | 0.0101 | 200 | 1.8332 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

yunjinchoi/klue-bert-base-q-encoder

yunjinchoi

transformers

Entry not found

randheerk/my_awesome_wnut_model

randheerk

transformers

token-classification

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - arrow metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: arrow type: arrow config: default split: test args: default metrics: - name: Precision type: precision value: 0.5615615615615616 - name: Recall type: recall value: 0.34661723818350326 - name: F1 type: f1 value: 0.42865329512893985 - name: Accuracy type: accuracy value: 0.9448505835577786 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the arrow dataset. It achieves the following results on the evaluation set: - Loss: 0.2758 - Precision: 0.5616 - Recall: 0.3466 - F1: 0.4287 - Accuracy: 0.9449 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2658 | 0.5435 | 0.3355 | 0.4149 | 0.9437 | | No log | 2.0 | 426 | 0.2758 | 0.5616 | 0.3466 | 0.4287 | 0.9449 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cpu - Datasets 3.2.0 - Tokenizers 0.21.0

lielbin/BabyBERTa-CHILDES_2.5-with-Masking_run2-finetuned-SQuAD

lielbin

transformers

question-answering

--- tags: - generated_from_trainer model-index: - name: BabyBERTa-CHILDES_2.5-with-Masking_run2-finetuned-SQuAD 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. --> # BabyBERTa-CHILDES_2.5-with-Masking_run2-finetuned-SQuAD This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

mradermacher/AceGPT-v2-32B-i1-GGUF

mradermacher

transformers

--- base_model: FreedomIntelligence/AceGPT-v2-32B language: - ar - zh - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/FreedomIntelligence/AceGPT-v2-32B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AceGPT-v2-32B-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/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.3 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.0 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.7 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 14.8 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.7 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.6 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.2 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF

mradermacher

transformers

--- base_model: nbeerbower/Dumpling-Qwen2.5-7B-1k-r64-2e-5 datasets: - nbeerbower/GreatFirewall-DPO - nbeerbower/Schule-DPO - nbeerbower/Purpura-DPO - nbeerbower/Arkhaios-DPO - jondurbin/truthy-dpo-v0.1 - antiven0m/physical-reasoning-dpo - flammenai/Date-DPO-NoAsterisks - flammenai/Prude-Phi3-DPO - Atsunori/HelpSteer2-DPO - jondurbin/gutenberg-dpo-v0.1 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nbeerbower/Dumpling-Qwen2.5-7B-1k-r64-2e-5 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-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/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-7B-1k-r64-2e-5-i1-GGUF/resolve/main/Dumpling-Qwen2.5-7B-1k-r64-2e-5.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

macadeliccc/Polyglot-8x7b-v0.1

macadeliccc

transformers

text-generation

--- license: cc-by-nc-nd-4.0 language: - en - zh - ja - de - id - vi library_name: transformers --- # Polyglot-8x7b-v0.1  Polyglot-8x7b is a Mixture of Experts approach to a multilingual model. The model is capable of quality content in 6 languages. The advantage to this approach is being able to repurpose English models in other languages. For example, you can ask the model to output something you would find in math model trained in English to the desired language of your choice. This formula allows for very powerful combinations of models. It could be 2 languages and 6 task based models, or vice versa. # Evaluations (4-bit bnb) | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |----------|------:|------|-----:|--------|-----:|---|-----:| |arc_easy | 1|none | 0|acc |0.8552|± |0.0072| | | |none | 0|acc_norm|0.8018|± |0.0082| |boolq | 2|none | 0|acc |0.8691|± |0.0059| |hellaswag | 1|none | 0|acc |0.6649|± |0.0047| | | |none | 0|acc_norm|0.8375|± |0.0037| |openbookqa| 1|none | 0|acc |0.3740|± |0.0217| | | |none | 0|acc_norm|0.4680|± |0.0223| |piqa | 1|none | 0|acc |0.8286|± |0.0088| | | |none | 0|acc_norm|0.8297|± |0.0088| |winogrande| 1|none | 0|acc |0.7451|± |0.0122| # Code Example Inference [Colab](https://colab.research.google.com/drive/1tYSb63IKZDsiQ5BIJU8Oc92phxugAmB3?usp=sharing) ```python from transformers import AutoModelForCausalLM, AutoTokenizer def generate_response(prompt): """ Generate a response from the model based on the input prompt. Args: prompt (str): Prompt for the model. Returns: str: The generated response from the model. """ # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt") # Generate output tokens outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) # Decode the generated tokens to a string response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Load the model and tokenizer model_id = "macadeliccc/Polyglot-8x7b-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id,load_in_4bit=True) # Example prompts in different languages english_prompt = "Write a quicksort algorithm in python" chinese_prompt = "用Python写一个快速排序算法" japanese_prompt = "Pythonでクイックソートアルゴリズムを書いてください" vietnamese_prompt = "Viết thuật toán quicksort trong python" indonesian_prompt = "Tulis algoritma quicksort dalam python" german_prompt = "Schreiben Sie einen Quicksort-Algorithmus in Python" # Generate and print responses for each language print("English Response:") print(generate_response(english_prompt), "\n") print("Chinese Response:") print(generate_response(chinese_prompt), "\n") print("Japanese Response:") print(generate_response(japanese_prompt), "\n") print("Vietnamese Response:") print(generate_response(vietnamese_prompt), "\n") print("Indonesian Response:") print(generate_response(indonesian_prompt), "\n") print("German Response:") print(generate_response(german_prompt), "\n") ``` #### English Response: Write a quicksort algorithm in python Here is a quicksort algorithm implemented in Python: ```python def quicksort(array): if len(array) <= 1: return array else: pivot = array[0] left = [x for x in array[1:] if x <= pivot] right = [x for x in array[1:] if x > pivot] return quicksort(left) + [pivot] + quicksort(right) # Test the algorithm array = [5, 2, 9, 1, 7, 8, 3, 6] print(quicksort(array)) ``` This implementation of quicksort uses recursion to divide the array into two smaller subarrays, one containing elements less than or equal to the pivot and the other containing elements greater than the pivot. The pivot is chosen as the first element in the array. The function then recursively calls itself on the left and right subarrays, and concatenates the sorted subarrays with the pivot in the middle. The time complexity of quicksort is O #### Chinese Response: 用Python写一个快速排序算法 快速排序是一种选择排序法，它在每次迭代中选择一个基准数并将其数组分为两个子数组：一个数组中的数字都小于基准数，另一个数组中的数字都大于基准数。这种分区过程递归地应用于两个子数组，直到数组为空或只含一个数字为止。这种排序法的时间复杂度为O(nlogn)。 ```python def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[0] left = [x for x in arr[1:] if x <= pivot] right = [x for x in arr[1:] if x > pivot] return quicksort(left) + [pivot] + quicksort(right) if __name__ == '__main__': arr = [5, 2, 9, 1, 7, 8, 3] ``` #### Japanese Response: Pythonでクイックソートアルゴリズムを書いてください。 def quicksort(array): if len(array) <= 1: return array pivot = array[0] left = [x for x in array[1:] if x <= pivot] right = [x for x in array[1:] if x > pivot] return quicksort(left) + [pivot] + quicksort(right) if __name__ == '__main__': array = [5, 2, 9, 1, 7, 8, 3] print(quicksort(array)) このコードはPythonでクイックソートアルゴリズムを実装しています。 quicksort() 関数は、配列を受け取り、配列の要素をソートして返す。 この実装は基本的なクイックソートです。 配列の最初の要素をピボットとして、それ以外の要素を #### Vietnamese Response: *So far vietnamese may have an issue with token generation. I fixed the vietnamese token generation but it still doesnt work q Viết thuật toán quicksort trong python có inductuve proof. QuickSort là algoortihm có time complexity O(log₂nim) trong trung bnh. QuickSort có 3 step:Partition Sort, Choose Pivot, Sort Two Partitions. QuickSort có recursive call. + Partition Sort là step sort partition data into two partitions based on pivot element. Data partitioned based on pivot element là sorted and unsorted. + Partition Sort có 4 step:Partition, Choose Pivot, Choose Left and Right Pivot, and Swap Pivot. + Partition Sort có recursive call. + Partition Sort có time complexity O(|A|).Partition Sort có not stable.Partition Sort có not inplace.Partition Sort có not randomized.Partition Sort có not adaptive.Partition Sort có not online.Partition Sort có not parallelizable.Partition Sort có not work with duplicate keys.Partition Sort có not work with negative keys.Partition Sort có not work with floating point numbers.Partition Sort có not work with strings.Partition Sort có not work with unsorted data.Partition Sort có not work with sorted data.Partition Sort có not work with partially sorted data.Partition Sort có not work with ordered data.Partition Sort có not work with ```python def quicksort(arr, left=0, right=None): if right is None: right = len(arr) - 1 if left < right: pivot_index = partition(arr, left, right) quicksort(arr, left, pivot_index - 1) quicksort(arr, pivot_index + 1, right) return arr def partition(arr, left, right): pivot = arr[right] i = left - 1 for j in range(left, right): if arr[j] <= ``` #### German Response: Schreiben Sie einen Quicksort-Algorithmus in Python, der die georderten Elemente in einer Liste sortiert. Der Quicksort-Algorithmus ist ein effizienter Sortieralgorithmus, der in der Regel eine O(n log n) Komplexität hat. Er funktioniert wie folgt: 1. Wähle ein Element aus der Liste als Pivot-Element. 2. Teile die Liste in zwei Teile: Elemente, die kleiner als das Pivot-Element, und Elemente, die größer oder gleich als das Pivot-Element. 3. Rekursiv sortiere die beiden Teile. 4. Verbinde die sortierten Teile. Hier ist ein Python-Code, der den Quicksort-Algorithmus implementiert: ```python def quicksort(lst): if len(lst) <= 1: return lst else: pivot = lst[0] less = [x for x in lst[1:] if x < pivot] greater = [x for x in lst[1:] if x >= pivot] return quicksort(less ``` #### Indonesian Response Tulis algoritma quicksort dalam python QuickSort adalah salah satu algoritma pengurutan yang paling populer dan efisien. Ini adalah algoritma pengurutan in-place, yang berarti bahwa data tidak perlu disalvage ke lokasi lain. Algoritme bekerja dengan memilih tumpukan yang diurutkan sebagai pivot, dan memecah tumpukan menjadi dua bagian yang lebih kecil. Setiap bagian ini kemudian diurutkan dengan cara yang sama. Berikut adalah implementasi QuickSort dalam bahasa Python: ```python def quicksort(arr): if len(arr) <= 1: return arr else: pivot = arr[0] less = [x for x in arr[1:] if x <= pivot] greater = [x for x in arr[1:] if x > pivot] return quicksort( ```

LoneStriker/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-6.0bpw-h6-exl2

LoneStriker

transformers

text-generation

--- license: cc-by-nc-4.0 ---  --- # Disclaimer: ## This model is experimental, do not expect everything to work. This model uses the Chatml **prompting format** --- Beeg noromaid on ***steroids***. Suitable for RP, ERP. This model was trained on the Zloss fork of Charles, and should fix issue the model had. Use Chatml prompt format, but not the special token. The reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available [HERE](https://github.com/DocShotgun/LLM-notebooks/blob/main/weighted-lora-merge.ipynb) to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter. ## Credits: - Undi - IkariDev <!-- description start --> ## Description <!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) --> This repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss. [FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss) <!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)--> <!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)--> <!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)--> <!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)--> <!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)--> [GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-GGUF) <!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)--> ## Ratings: Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here! No ratings yet! If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi". <!-- description end --> <!-- prompt-template start --> ### Prompt format: Chatml ``` <|im_start|>system {sysprompt}<|im_end|> <|im_start|>user {input}<|im_end|> <|im_start|>assistant {output}<|im_end|> ``` ## Datasets used: - Aesir 1, 2 & 3 modified by us, credit to ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe)) - [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia)) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ([NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet) - [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun)) ## Others Undi: If you want to support me, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek

henryj18/xlm-roberta-base-finetuned-panx-fr

henryj18

transformers

token-classification

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8493752110773387 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2992 - F1: 0.8494 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.537 | 1.0 | 382 | 0.3279 | 0.8002 | | 0.2603 | 2.0 | 764 | 0.2987 | 0.8356 | | 0.1589 | 3.0 | 1146 | 0.2992 | 0.8494 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

Docfile/my_awesome_opus_books_model

Docfile

transformers

text2text-generation

Entry not found

lesso11/2bf5948a-e2fb-4699-83de-c035322e72d7

lesso11

peft

--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: 2bf5948a-e2fb-4699-83de-c035322e72d7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 datasets: - data_files: - 303dfabc72680414_train_data.json ds_type: json format: custom path: /workspace/input_data/303dfabc72680414_train_data.json type: field_instruction: question field_output: model_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso11/2bf5948a-e2fb-4699-83de-c035322e72d7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/303dfabc72680414_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 961f8bc9-fae7-4c17-bb15-183b7daea2f3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 961f8bc9-fae7-4c17-bb15-183b7daea2f3 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 2bf5948a-e2fb-4699-83de-c035322e72d7 This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.0774 | 0.0144 | 1 | 4.2488 | | 3.4806 | 0.0719 | 5 | 3.6374 | | 2.8445 | 0.1439 | 10 | 2.9566 | | 2.222 | 0.2158 | 15 | 2.3208 | | 2.2446 | 0.2878 | 20 | 2.0328 | | 1.9766 | 0.3597 | 25 | 1.9845 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

yuan-sf63/chenyu_label_0.5_96

yuan-sf63

transformers

text-classification

Entry not found

abaddon182/dd1ea20e-e580-4f8e-b656-7e5915039c97

abaddon182

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/tinyllama-chat tags: - axolotl - generated_from_trainer model-index: - name: dd1ea20e-e580-4f8e-b656-7e5915039c97 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/tinyllama-chat bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - d505230510882b7c_train_data.json ds_type: json format: custom path: /workspace/input_data/d505230510882b7c_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: abaddon182/dd1ea20e-e580-4f8e-b656-7e5915039c97 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/d505230510882b7c_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: afd22e6e-10d0-4a21-b12b-899cde9bb283 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: afd22e6e-10d0-4a21-b12b-899cde9bb283 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # dd1ea20e-e580-4f8e-b656-7e5915039c97 This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7882 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.2977 | 0.0119 | 1 | 1.3630 | | 0.9552 | 0.5935 | 50 | 0.8742 | | 0.72 | 1.1869 | 100 | 0.8315 | | 0.8087 | 1.7804 | 150 | 0.8086 | | 0.5844 | 2.3739 | 200 | 0.7882 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

ahsan30/FINAL_Reservoir_llama3.1-8b-q8_0

ahsan30

transformers

--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** ahsan30 - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

joyfine/vicuna-7b-fine-tuning_QA_SST_20_2

joyfine

transformers

text-generation

Entry not found

bane5631/223c1cc3-1336-4dc6-b490-d9a9d6342a1d

bane5631

peft

--- library_name: peft license: mit base_model: unsloth/Phi-3-mini-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 223c1cc3-1336-4dc6-b490-d9a9d6342a1d 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/Phi-3-mini-4k-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8391d58e45127793_train_data.json ds_type: json format: custom path: /workspace/input_data/8391d58e45127793_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: bane5631/223c1cc3-1336-4dc6-b490-d9a9d6342a1d hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 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_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/8391d58e45127793_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 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: 2d2c0770-ec7d-41e1-a607-5367ecb0940b wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2d2c0770-ec7d-41e1-a607-5367ecb0940b warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 223c1cc3-1336-4dc6-b490-d9a9d6342a1d This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8341 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 | |:-------------:|:------:|:----:|:---------------:| | 8.0665 | 0.6809 | 200 | 1.8341 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

sercetexam9/afro-xlmr-base-hau-finetuned-augmentation-LUNAR

sercetexam9

transformers

text-classification

--- library_name: transformers license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: afro-xlmr-base-hau-finetuned-augmentation-LUNAR 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. --> # afro-xlmr-base-hau-finetuned-augmentation-LUNAR This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3766 - F1: 0.7024 - Roc Auc: 0.8104 - Accuracy: 0.5513 ## 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.4359 | 1.0 | 144 | 0.4259 | 0.1730 | 0.5612 | 0.1965 | | 0.3509 | 2.0 | 288 | 0.3506 | 0.4649 | 0.6710 | 0.3670 | | 0.3062 | 3.0 | 432 | 0.3230 | 0.5924 | 0.7446 | 0.4452 | | 0.2507 | 4.0 | 576 | 0.3000 | 0.6340 | 0.7629 | 0.4748 | | 0.2156 | 5.0 | 720 | 0.2989 | 0.6716 | 0.7870 | 0.5235 | | 0.1632 | 6.0 | 864 | 0.3159 | 0.6645 | 0.7827 | 0.5009 | | 0.1665 | 7.0 | 1008 | 0.3168 | 0.6817 | 0.7973 | 0.5217 | | 0.1289 | 8.0 | 1152 | 0.3148 | 0.6807 | 0.7958 | 0.5357 | | 0.1166 | 9.0 | 1296 | 0.3261 | 0.6850 | 0.7946 | 0.5217 | | 0.0927 | 10.0 | 1440 | 0.3268 | 0.6828 | 0.7910 | 0.5496 | | 0.0693 | 11.0 | 1584 | 0.3387 | 0.6982 | 0.8028 | 0.5496 | | 0.0571 | 12.0 | 1728 | 0.3544 | 0.6938 | 0.8050 | 0.5374 | | 0.0568 | 13.0 | 1872 | 0.3439 | 0.6959 | 0.8037 | 0.5565 | | 0.0467 | 14.0 | 2016 | 0.3673 | 0.6940 | 0.8054 | 0.5391 | | 0.043 | 15.0 | 2160 | 0.3642 | 0.7015 | 0.8066 | 0.5548 | | 0.0356 | 16.0 | 2304 | 0.3685 | 0.6998 | 0.8069 | 0.5530 | | 0.0395 | 17.0 | 2448 | 0.3766 | 0.7024 | 0.8104 | 0.5513 | | 0.0353 | 18.0 | 2592 | 0.3766 | 0.7005 | 0.8088 | 0.5478 | | 0.0352 | 19.0 | 2736 | 0.3753 | 0.7008 | 0.8092 | 0.5478 | | 0.0327 | 20.0 | 2880 | 0.3745 | 0.6985 | 0.8074 | 0.5461 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

ReadyArt/Beeper-King-22B_EXL2_4.5bpw_H8

ReadyArt

# Beeper-King-22B <figure> <img src="https://huggingface.co/ToastyPigeon/Beeper-King-22B/resolve/main/_f5a298d7-14af-4bc5-a651-99373a63ef31.jpg" width="550"> <figcaption>A beeping kingfisher.</figcaption> </figure> This is another slightly-adjusted version of [MS-Meadowlark-22B](https://huggingface.co/allura-org/MS-Meadowlark-22B). The models used in this merge are: - [nbeerbower/Mistral-Small-Gutenberg-Doppel-22B](https://huggingface.co/nbeerbower/Mistral-Small-Gutenberg-Doppel-22B) - [crestf411/MS-sunfall-v0.7.0](https://huggingface.co/crestf411/MS-sunfall-v0.7.0) - [Alfitaria/mistral-small-fujin-qlora](https://huggingface.co/Alfitaria/mistral-small-fujin-qlora) - [ToastyPigeon/mistral-small-springdragon-qlora](https://huggingface.co/ToastyPigeon/mistral-small-springdragon-qlora) - [concedo/Beepo-22B](https://huggingface.co/concedo/Beepo-22B) Specifically, changing the instruct portion and the base to apply the QLoRAs to from Mistral-Small-Instruct-2409 to Beepo-22B made the model a little more wild in a fun way. It's still done a good job of adhering to prompts and character descriptions, because Beepo is very compliant model. Instruct format is Mistral V2 & V3, but thanks to the inclusion of Beepo this model should also work with Alpaca.

Josephgflowers/Cinder-Phi-2-STEM-2.94B-Test

Josephgflowers

transformers

text-generation

--- license: mit widget: - text: > <|system|> You are a helpful assistant</s> <|user|> Can you explain to me how quantum computing works?</s> <|assistant|> --- Modified version of Phi 2 with 2 added layers. More details coming soon. Model Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration. 

AhmedTaha012/gptneo-txt2arxml-ppo

AhmedTaha012

transformers

text-generation

Entry not found

PrunaAI/mattshumer-Hermes-2-Pro-11B-bnb-4bit

PrunaAI

pruna-engine

--- library_name: pruna-engine thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [](https://twitter.com/PrunaAI) [](https://github.com/PrunaAI) [](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [](https://discord.com/invite/vb6SmA3hxu) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed by using bitsandbytes. - ***How does the model quality change?*** The quality of the model output will slightly degrade. - ***What is the model format?*** We the standard safetensors format. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). ## Usage ``` python from transformers import AutoTokenizer import transformers import torch model = "PrunaAI/mattshumer-Hermes-2-Pro-11B-bnb-4bit" 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, 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"]) ``` ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model mattshumer/Hermes-2-Pro-11B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).