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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-23 18:27:52	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-23 18:25:26	card stringlengths 11 1.01M
MiVaCod/rotten	MiVaCod	2024-05-14T15:46:41Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T17:44:35Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: rotten 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. --> # rotten This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8598 - Accuracy: 0.8527 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.405 \| 1.0 \| 1067 \| 0.3657 \| 0.8546 \| \| 0.225 \| 2.0 \| 2134 \| 0.7075 \| 0.8433 \| \| 0.0711 \| 3.0 \| 3201 \| 0.8598 \| 0.8527 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kyl23/hw3_SST2_lora_1e-4_r4	kyl23	2024-05-14T15:45:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T15:45:35Z	--- 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]
Trelis/OpenELM-270M-instruct-ORPO	Trelis	2024-05-14T15:40:34Z	156	1	transformers	[ "transformers", "safetensors", "openelm", "text-generation", "apple", "OpenELM", "conversational", "custom_code", "dataset:argilla/dpo-mix-7k", "arxiv:2404.14619", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2024-05-14T15:30:11Z	--- license: other license_name: apple-sample-code-license license_link: LICENSE datasets: - argilla/dpo-mix-7k tags: - apple - OpenELM --- # OpenELM These are ORPO fine-tunes, done using the Argilla/dpo-mix-7k dataset: - [270M fine-tune](https://huggingface.co/Trelis/OpenELM-270M-instruct-ORPO) - [450M fine-tune](https://huggingface.co/Trelis/OpenELM-450M-instruct-ORPO) ## Performance notes OpenELM models are quite weak. - OpenELM 270M is uniquely small, but weak. - OpenELM 450M improves a little over the 270M model, but remains weak on accuracy and hallucinates strongly. - Qwen 1.5 0.5B is stronger than the OpenELM model. - TinyLlama is stronger than OpenELM 1B. - Models like Phi-3 are stronger than OpenELM 3B. ## Usage Notes - Flash attention is not supported - Making GGUFs is not [yet supported](https://github.com/ggerganov/llama.cpp/issues/6868) ## Inference See [this Colab Notebook](https://colab.research.google.com/drive/1vFMRhHdPyUxbZAlRWwyl79NwnrSz_yQL?usp=sharing) ~~~ The original model card follows below. ~~~ Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari We introduce OpenELM, a family of Open Efficient Language Models. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters. Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them. ## Usage We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`. You can try the model by running the following command: ``` python generate_openelm.py --model apple/OpenELM-450M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 ``` Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token. Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows: ``` python generate_openelm.py --model apple/OpenELM-450M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10 ``` Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example: ``` python generate_openelm.py --model apple/OpenELM-450M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL] ``` ## Main Results ### Zero-Shot \| Model Size \| ARC-c \| ARC-e \| BoolQ \| HellaSwag \| PIQA \| SciQ \| WinoGrande \| Average \| \|-----------------------------------------------------------------------------\|-----------\|-----------\|-----------\|---------------\|-----------\|-----------\|----------------\|-------------\| \| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) \| 26.45 \| 45.08 \| 53.98 \| 46.71 \| 69.75 \| 84.70 \| 53.91 \| 54.37 \| \| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) \| 30.55 \| 46.68 \| 48.56 \| 52.07 \| 70.78 \| 84.40 \| 52.72 \| 55.11 \| \| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) \| 27.56 \| 48.06 \| 55.78 \| 53.97 \| 72.31 \| 87.20 \| 58.01 \| 57.56 \| \| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) \| 30.38 \| 50.00 \| 60.37 \| 59.34 \| 72.63 \| 88.00 \| 58.96 \| 59.95 \| \| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) \| 32.34 \| 55.43 \| 63.58 \| 64.81 \| 75.57 \| 90.60 \| 61.72 \| 63.44 \| \| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) \| 37.97 \| 52.23 \| 70.00 \| 71.20 \| 75.03 \| 89.30 \| 62.75 \| 65.50 \| \| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) \| 35.58 \| 59.89 \| 67.40 \| 72.44 \| 78.24 \| 92.70 \| 65.51 \| 67.39 \| \| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) \| 39.42 \| 61.74 \| 68.17 \| 76.36 \| 79.00 \| 92.50 \| 66.85 \| 69.15 \| ### LLM360 \| Model Size \| ARC-c \| HellaSwag \| MMLU \| TruthfulQA \| WinoGrande \| Average \| \|-----------------------------------------------------------------------------\|-----------\|---------------\|-----------\|----------------\|----------------\|-------------\| \| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) \| 27.65 \| 47.15 \| 25.72 \| 39.24 \| 53.83 \| 38.72 \| \| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) \| 32.51 \| 51.58 \| 26.70 \| 38.72 \| 53.20 \| 40.54 \| \| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) \| 30.20 \| 53.86 \| 26.01 \| 40.18 \| 57.22 \| 41.50 \| \| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) \| 33.53 \| 59.31 \| 25.41 \| 40.48 \| 58.33 \| 43.41 \| \| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) \| 36.69 \| 65.71 \| 27.05 \| 36.98 \| 63.22 \| 45.93 \| \| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) \| 41.55 \| 71.83 \| 25.65 \| 45.95 \| 64.72 \| 49.94 \| \| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) \| 42.24 \| 73.28 \| 26.76 \| 34.98 \| 67.25 \| 48.90 \| \| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) \| 47.70 \| 76.87 \| 24.80 \| 38.76 \| 67.96 \| 51.22 \| ### OpenLLM Leaderboard \| Model Size \| ARC-c \| CrowS-Pairs \| HellaSwag \| MMLU \| PIQA \| RACE \| TruthfulQA \| WinoGrande \| Average \| \|-----------------------------------------------------------------------------\|-----------\|-----------------\|---------------\|-----------\|-----------\|-----------\|----------------\|----------------\|-------------\| \| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) \| 27.65 \| 66.79 \| 47.15 \| 25.72 \| 69.75 \| 30.91 \| 39.24 \| 53.83 \| 45.13 \| \| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) \| 32.51 \| 66.01 \| 51.58 \| 26.70 \| 70.78 \| 33.78 \| 38.72 \| 53.20 \| 46.66 \| \| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) \| 30.20 \| 68.63 \| 53.86 \| 26.01 \| 72.31 \| 33.11 \| 40.18 \| 57.22 \| 47.69 \| \| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) \| 33.53 \| 67.44 \| 59.31 \| 25.41 \| 72.63 \| 36.84 \| 40.48 \| 58.33 \| 49.25 \| \| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) \| 36.69 \| 71.74 \| 65.71 \| 27.05 \| 75.57 \| 36.46 \| 36.98 \| 63.22 \| 51.68 \| \| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) \| 41.55 \| 71.02 \| 71.83 \| 25.65 \| 75.03 \| 39.43 \| 45.95 \| 64.72 \| 54.40 \| \| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) \| 42.24 \| 73.29 \| 73.28 \| 26.76 \| 78.24 \| 38.76 \| 34.98 \| 67.25 \| 54.35 \| \| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) \| 47.70 \| 72.33 \| 76.87 \| 24.80 \| 79.00 \| 38.47 \| 38.76 \| 67.96 \| 55.73 \| See the technical report for more results and comparison. ## Evaluation ### Setup Install the following dependencies: ```bash # install public lm-eval-harness harness_repo="public-lm-eval-harness" git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo} cd ${harness_repo} # use main branch on 03-15-2024, SHA is dc90fec git checkout dc90fec pip install -e . cd .. # 66d6242 is the main branch on 2024-04-01 pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242 pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0 ``` ### Evaluate OpenELM ```bash # OpenELM-450M-Instruct hf_model=apple/OpenELM-450M-Instruct # this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True tokenizer=meta-llama/Llama-2-7b-hf add_bos_token=True batch_size=1 mkdir lm_eval_output shot=0 task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2 lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=5 task=mmlu,winogrande lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=25 task=arc_challenge,crows_pairs_english lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=10 task=hellaswag lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log ``` ## Bias, Risks, and Limitations The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements. ## Citation If you find our work useful, please cite: ```BibTex @article{mehtaOpenELMEfficientLanguage2024, title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open} {Training} and {Inference} {Framework}}, shorttitle = {{OpenELM}}, url = {https://arxiv.org/abs/2404.14619v1}, language = {en}, urldate = {2024-04-24}, journal = {arXiv.org}, author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad}, month = apr, year = {2024}, } @inproceedings{mehta2022cvnets, author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad}, title = {CVNets: High Performance Library for Computer Vision}, year = {2022}, booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, series = {MM '22} } ```
saucam/PowerBot-8B	saucam	2024-05-14T15:39:25Z	94	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "nvidia/Llama3-ChatQA-1.5-8B", "refuelai/Llama-3-Refueled", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-09T16:02:07Z	--- tags: - merge - mergekit - nvidia/Llama3-ChatQA-1.5-8B - refuelai/Llama-3-Refueled models: - nvidia/Llama3-ChatQA-1.5-8B - refuelai/Llama-3-Refueled license: apache-2.0 language: - en --- ![](https://raw.githubusercontent.com/saucam/models/main/powerbot.png) # PowerBot-8B PowerBot-8B is a merge of the following models using [Mergekit](https://github.com/arcee-ai/mergekit): * [nvidia/Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) * [refuelai/Llama-3-Refueled](https://huggingface.co/refuelai/Llama-3-Refueled) ## 🧩 Configuration ```yamlname: PowerBot-8B tokenizer_source: union embed_slerp: true models: - model: nvidia/Llama3-ChatQA-1.5-8B parameters: density: 0.5 weight: 0.6 # No parameters necessary for base model - model: refuelai/Llama-3-Refueled parameters: density: 0.5 weight: 0.4 merge_method: dare_ties base_model: nvidia/Llama3-ChatQA-1.5-8B parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "saucam/PowerBot-8B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ``` Loading checkpoint shards: 100%\|███████████████████████████████████████████████████\| 2/2 [00:07<00:00, 3.75s/it] Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation. <\|im_start\|>user What is a large language model?<\|im_end\|> <\|im_start\|>assistant A large language model (LLM) is a deep neural network that is trained to predict the next word in a sequence of text. LLMs are typically trained on large amounts of text data and can be used for a variety of tasks such as language translation, text completion, and question answering. They are often used to generate human-like text and are becoming increasingly popular in natural language processing applications. The LLM uses a transformer architecture, which consists of multiple layers of neural networks that are trained to process and understand the relationships between words in a sentence. The transformer architecture is designed to handle long sequences of text and is capable of capturing the context of a word within a sentence. This allows the LLM to generate coherent and grammatically correct text that is similar to human writing. LLMs are typically trained on a large corpus of text data and can be fine-tuned for specific tasks by retraining on smaller datasets that are relevant to the task at hand. This allows the LLM to adapt to the specific requirements of a particular application and improve its performance. The LLM can be used to generate text in a variety of formats, including natural language, code, and even mathematical expressions. It can also be used to translate text from one language to another, generate summaries of ``` Preserves the whole document as context capability ``` from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "saucam/PowerBot-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"} ] document = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n\| $ in millions, except earnings per share \| Q4 FY24 \| Q3 FY24 \| Q4 FY23 \| Q/Q \| Y/Y \|\n\| Revenue \| $22,103 \| $18,120 \| $6,051 \| Up 22% \| Up 265% \|\n\| Gross margin \| 76.0% \| 74.0% \| 63.3% \| Up 2.0 pts \| Up 12.7 pts \|\n\| Operating expenses \| $3,176 \| $2,983 \| $2,576 \| Up 6% \| Up 23% \|\n\| Operating income \| $13,615 \| $10,417 \| $1,257 \| Up 31% \| Up 983% \|\n\| Net income \| $12,285 \| $9,243 \| $1,414 \| Up 33% \| Up 769% \|\n\| Diluted earnings per share \| $4.93 \| $3.71 \| $0.57 \| Up 33% \| Up 765% \|""" def get_formatted_input(messages, context): system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context." instruction = "Please give a full and complete answer for the question." for item in messages: if item['role'] == "user": ## only apply this instruction for the first user turn item['content'] = instruction + " " + item['content'] break conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:" formatted_input = system + "\n\n" + context + "\n\n" + conversation return formatted_input formatted_input = get_formatted_input(messages, document) tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators) response = outputs[0][tokenized_prompt.input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ``` Downloading shards: 100%\|█████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 12.71it/s] Loading checkpoint shards: 100%\|██████████████████████████████████████████████████████████████\| 2/2 [00:08<00:00, 4.05s/it] Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation. The percentage change of the net income from Q4 FY23 to Q4 FY24 is 769%. This is calculated by taking the difference between the two net incomes ($12,285 million and $1,414 million) and dividing it by the net income from Q4 FY23 ($1,414 million), then multiplying by 100 to get the percentage change. So, the formula is ((12,285 - 1,414) / 1,414) * 100 = 769%. ``` Sample run on classification tasks, positive labelling still works ``` import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "saucam/PowerBot-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") messages = [{"role": "user", "content": "Is this comment toxic or non-toxic: RefuelLLM is the new way to label text data!"}] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda") outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0])) ``` ``` Loading checkpoint shards: 100%\|██████████████████████████████████████████████████████████████\| 2/2 [00:07<00:00, 3.89s/it] No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format (without BOS/EOS tokens!). If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information. The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation. <\|im_start\|>user Is this comment toxic or non-toxic: RefuelLLM is the new way to label text data!<\|im_end\|> <\|im_start\|>assistant This comment is non-toxic. <\|im_end\|><\|end_of_text\|> ```
reemmasoud/idv_vs_col_llama-3_PromptTuning_CAUSAL_LM_gradient_descent_v1	reemmasoud	2024-05-14T15:39:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T15:39:13Z	--- 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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terry69/mistral_poe_10-full	terry69	2024-05-14T15:31:35Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:29:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF	mradermacher	2024-05-14T15:30:55Z	21	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mergekit-community/LLaMa-3-Base-Zeroed-13B", "base_model:quantized:mergekit-community/LLaMa-3-Base-Zeroed-13B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T14:39:16Z	--- base_model: mergekit-community/LLaMa-3-Base-Zeroed-13B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mergekit-community/LLaMa-3-Base-Zeroed-13B <!-- 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/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q2_K.gguf) \| Q2_K \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ3_XS.gguf) \| IQ3_XS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q3_K_S.gguf) \| Q3_K_S \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ3_M.gguf) \| IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q3_K_L.gguf) \| Q3_K_L \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ4_XS.gguf) \| IQ4_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q5_K_M.gguf) \| Q5_K_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q8_0.gguf) \| Q8_0 \| 14.0 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) 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 -->
terry69/mistral_poe_add-full	terry69	2024-05-14T15:23:42Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:10:56Z	--- 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. 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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]
mjavadf/whisper-small-dv	mjavadf	2024-05-14T15:21:12Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-14T13:19:41Z	--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.60712174427096 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1733 - Wer Ortho: 62.6715 - Wer: 13.6071 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1198 \| 1.6287 \| 500 \| 0.1733 \| 62.6715 \| 13.6071 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Antonio49/ModeloCanal	Antonio49	2024-05-14T15:20:20Z	113	2	transformers	[ "transformers", "safetensors", "bert", "question-answering", "es", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2024-04-07T08:20:05Z	--- title: Antonio.BERT.Canal emoji: 🐠 colorFrom: red colorTo: blue sdk: gradio sdk_version: 3.33.1 app_file: app.py pinned: false license: mit language: - es --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [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 Data 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]
philschmid/Llama-3-70b-lora	philschmid	2024-05-14T15:19:31Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2024-05-14T15:18:52Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-70b --- # 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. --> - 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] ### Framework versions - PEFT 0.10.0
PantagrueLLM/jargon-general-biomed	PantagrueLLM	2024-05-14T15:17:56Z	110	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T17:49:04Z	--- license: mit language: - fr library_name: transformers tags: - linformer - medical - RoBERTa - pytorch --- # Jargon-general-biomed [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \| \|-------------------------------------------------------------------------------------\|:-----------------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \| \| jargon-general-legal \| jargon-general-base \| \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \| \| jargon-legal \| scratch \| \| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) \| scratch \| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. ## Biomedical Benchmark Results averaged across five funs with varying random seeds. \| \|[FrenchMedMCQA](https://huggingface.co/datasets/qanastek/frenchmedmcqa)\|[MQC](https://aclanthology.org/2020.lrec-1.72/)\|[CAS-POS](https://clementdalloux.fr/?page_id=28)\|[ESSAI-POS](https://clementdalloux.fr/?page_id=28)\|[CAS-SG](https://aclanthology.org/W18-5614/)\|[MEDLINE](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[EMEA](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[E3C-NER](https://live.european-language-grid.eu/catalogue/corpus/7618)\|[CLISTER](https://aclanthology.org/2022.lrec-1.459/)\| \|-------------------------\|:-----------------------:\|:-----------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\| \| Task Type \| Sequence Classification \| Sequence Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| STS \| \| Metric \| EMR \| Accuracy \| Macro-F1 \| Macro-F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Spearman Correlation \| \| jargon-general-base \| 12.9 \| 76.7 \| 96.6 \| 96.0 \| 69.4 \| 81.7 \| 96.5 \| 91.9 \| 78.0 \| \| jargon-biomed \| 15.3 \| 91.1 \| 96.5 \| 95.6 \| 75.1 \| 83.7 \| 96.5 \| 93.5 \| 74.6 \| \| jargon-biomed-4096 \| 14.4 \| 78.9 \| 96.6 \| 95.9 \| 73.3 \| 82.3 \| 96.3 \| 92.5 \| 65.3 \| \| jargon-general-biomed \| 16.1 \| 69.7 \| 95.1 \| 95.1 \| 67.8 \| 78.2 \| 96.6 \| 91.3 \| 59.7 \| \| jargon-multidomain-base \| 14.9 \| 86.9 \| 96.3 \| 96.0 \| 70.6 \| 82.4 \| 96.6 \| 92.6 \| 74.8 \| \| jargon-NACHOS \| 13.3 \| 90.7 \| 96.3 \| 96.2 \| 75.0 \| 83.4 \| 96.8 \| 93.1 \| 70.9 \| \| jargon-NACHOS-4096 \| 18.4 \| 93.2 \| 96.2 \| 95.9 \| 74.9 \| 83.8 \| 96.8 \| 93.2 \| 74.9 \| For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with `jargon-general-biomed` using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-general-biomed", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-general-biomed", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
kishiyev/ppo-LunarLander-v2	kishiyev	2024-05-14T15:16:31Z	3	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-14T12:59:54Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 246.27 +/- 19.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub repo_id = "kishiyev/ppo-LunarLander-v2" # The repo_id filename = "ppo-LunarLander-v2.zip" # The model filename.zip custom_objects = { "learning_rate": 0.0, "lr_schedule": lambda _: 0.0, "clip_range": lambda _: 0.0, } checkpoint = load_from_hub(repo_id, filename) model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True) ... ```
emilykang/Phi_finetune_med	emilykang	2024-05-14T15:16:15Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-14T09:30:46Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_finetune_med 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. --> # Phi_finetune_med This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
PantagrueLLM/jargon-NACHOS	PantagrueLLM	2024-05-14T15:15:56Z	104	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T18:48:04Z	--- license: mit language: - fr library_name: transformers tags: - linformer - medical - RoBERTa - pytorch --- # Jargon-NACHOS [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \|Training Data\| \|-------------------------------------------------------------------------------------\|:-----------------------:\|:----------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \|8.5GB Web Corpus\| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \|5.4GB Medical Corpus\| \| jargon-general-legal \| jargon-general-base \|18GB Legal Corpus \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \|Medical+Legal Corpora\| \| jargon-legal \| scratch \|18GB Legal Corpus\| \| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) \| scratch \|18GB Legal Corpus\| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \|5.4GB Medical Corpus\| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \|5.4GB Medical Corpus\| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with this model using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-NACHOS", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-NACHOS", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
PantagrueLLM/jargon-biomed-4096	PantagrueLLM	2024-05-14T15:15:06Z	106	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T18:40:05Z	--- license: mit language: - fr library_name: transformers tags: - linformer - medical - RoBERTa - pytorch --- # Jargon-biomed-4096 [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \|Training Data\| \|-------------------------------------------------------------------------------------\|:-----------------------:\|:----------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \|8.5GB Web Corpus\| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \|5.4GB Medical Corpus\| \| jargon-general-legal \| jargon-general-base \|18GB Legal Corpus \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \|Medical+Legal Corpora\| \| jargon-legal \| scratch \|18GB Legal Corpus\| \| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) \| scratch \|18GB Legal Corpus\| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \|5.4GB Medical Corpus\| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \|5.4GB Medical Corpus\| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with `jargon-biomed-4096` using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-biomed-4096", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-biomed-4096", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
amaye15/google-vit-base-patch16-224-batch32-lr0.005-standford-dogs	amaye15	2024-05-14T15:14:07Z	219	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:stanford-dogs", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-14T15:13:46Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - stanford-dogs metrics: - accuracy - f1 - precision - recall model-index: - name: google-vit-base-patch16-224-batch32-lr0.005-standford-dogs results: - task: name: Image Classification type: image-classification dataset: name: stanford-dogs type: stanford-dogs config: default split: full args: default metrics: - name: Accuracy type: accuracy value: 0.8797376093294461 - name: F1 type: f1 value: 0.8759381135610711 - name: Precision type: precision value: 0.88124155438923 - name: Recall type: recall value: 0.876557313613651 --- <!-- 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. --> # google-vit-base-patch16-224-batch32-lr0.005-standford-dogs This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the stanford-dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.4511 - Accuracy: 0.8797 - F1: 0.8759 - Precision: 0.8812 - Recall: 0.8766 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 4.8453 \| 0.0777 \| 10 \| 4.6341 \| 0.0355 \| 0.0304 \| 0.0311 \| 0.0364 \| \| 4.5433 \| 0.1553 \| 20 \| 4.3107 \| 0.1246 \| 0.0982 \| 0.1263 \| 0.1225 \| \| 4.2752 \| 0.2330 \| 30 \| 3.9697 \| 0.2719 \| 0.2176 \| 0.2518 \| 0.2632 \| \| 3.9872 \| 0.3107 \| 40 \| 3.6402 \| 0.4274 \| 0.3661 \| 0.4264 \| 0.4167 \| \| 3.7182 \| 0.3883 \| 50 \| 3.3251 \| 0.5362 \| 0.4888 \| 0.5817 \| 0.5247 \| \| 3.473 \| 0.4660 \| 60 \| 3.0453 \| 0.6220 \| 0.5815 \| 0.6516 \| 0.6115 \| \| 3.2252 \| 0.5437 \| 70 \| 2.7739 \| 0.6817 \| 0.6506 \| 0.7194 \| 0.6713 \| \| 2.9976 \| 0.6214 \| 80 \| 2.5391 \| 0.7046 \| 0.6756 \| 0.7286 \| 0.6954 \| \| 2.762 \| 0.6990 \| 90 \| 2.2990 \| 0.7505 \| 0.7258 \| 0.7646 \| 0.7421 \| \| 2.5763 \| 0.7767 \| 100 \| 2.1075 \| 0.7646 \| 0.7434 \| 0.7793 \| 0.7556 \| \| 2.4357 \| 0.8544 \| 110 \| 1.9226 \| 0.7850 \| 0.7652 \| 0.8027 \| 0.7768 \| \| 2.2669 \| 0.9320 \| 120 \| 1.7673 \| 0.8008 \| 0.7838 \| 0.8149 \| 0.7938 \| \| 2.1459 \| 1.0097 \| 130 \| 1.6339 \| 0.8175 \| 0.8058 \| 0.8291 \| 0.8110 \| \| 1.9822 \| 1.0874 \| 140 \| 1.5204 \| 0.8214 \| 0.8114 \| 0.8366 \| 0.8151 \| \| 1.8701 \| 1.1650 \| 150 \| 1.4219 \| 0.8173 \| 0.8091 \| 0.8330 \| 0.8117 \| \| 1.8007 \| 1.2427 \| 160 \| 1.3224 \| 0.8292 \| 0.8205 \| 0.8390 \| 0.8233 \| \| 1.8004 \| 1.3204 \| 170 \| 1.2553 \| 0.8324 \| 0.8243 \| 0.8413 \| 0.8271 \| \| 1.6511 \| 1.3981 \| 180 \| 1.1728 \| 0.8372 \| 0.8282 \| 0.8467 \| 0.8314 \| \| 1.548 \| 1.4757 \| 190 \| 1.1091 \| 0.8394 \| 0.8300 \| 0.8500 \| 0.8340 \| \| 1.5634 \| 1.5534 \| 200 \| 1.0561 \| 0.8345 \| 0.8263 \| 0.8444 \| 0.8287 \| \| 1.5163 \| 1.6311 \| 210 \| 0.9983 \| 0.8457 \| 0.8382 \| 0.8512 \| 0.8409 \| \| 1.3883 \| 1.7087 \| 220 \| 0.9574 \| 0.8499 \| 0.8425 \| 0.8545 \| 0.8452 \| \| 1.3161 \| 1.7864 \| 230 \| 0.9129 \| 0.8511 \| 0.8425 \| 0.8564 \| 0.8457 \| \| 1.304 \| 1.8641 \| 240 \| 0.8727 \| 0.8535 \| 0.8454 \| 0.8570 \| 0.8487 \| \| 1.3268 \| 1.9417 \| 250 \| 0.8412 \| 0.8511 \| 0.8441 \| 0.8572 \| 0.8473 \| \| 1.2388 \| 2.0194 \| 260 \| 0.8104 \| 0.8569 \| 0.8482 \| 0.8608 \| 0.8522 \| \| 1.1333 \| 2.0971 \| 270 \| 0.7920 \| 0.8557 \| 0.8486 \| 0.8596 \| 0.8516 \| \| 1.1305 \| 2.1748 \| 280 \| 0.7565 \| 0.8579 \| 0.8505 \| 0.8630 \| 0.8534 \| \| 1.1849 \| 2.2524 \| 290 \| 0.7498 \| 0.8593 \| 0.8536 \| 0.8646 \| 0.8549 \| \| 1.1287 \| 2.3301 \| 300 \| 0.7348 \| 0.8593 \| 0.8533 \| 0.8653 \| 0.8552 \| \| 1.0537 \| 2.4078 \| 310 \| 0.7120 \| 0.8554 \| 0.8496 \| 0.8586 \| 0.8515 \| \| 1.1157 \| 2.4854 \| 320 \| 0.6832 \| 0.8622 \| 0.8552 \| 0.8662 \| 0.8579 \| \| 1.1008 \| 2.5631 \| 330 \| 0.6705 \| 0.8618 \| 0.8546 \| 0.8640 \| 0.8574 \| \| 1.0512 \| 2.6408 \| 340 \| 0.6557 \| 0.8630 \| 0.8563 \| 0.8636 \| 0.8593 \| \| 1.0641 \| 2.7184 \| 350 \| 0.6490 \| 0.8632 \| 0.8581 \| 0.8691 \| 0.8596 \| \| 1.0446 \| 2.7961 \| 360 \| 0.6301 \| 0.8652 \| 0.8597 \| 0.8692 \| 0.8612 \| \| 1.0104 \| 2.8738 \| 370 \| 0.6287 \| 0.8632 \| 0.8562 \| 0.8668 \| 0.8588 \| \| 1.0544 \| 2.9515 \| 380 \| 0.6150 \| 0.8644 \| 0.8579 \| 0.8657 \| 0.8602 \| \| 1.0074 \| 3.0291 \| 390 \| 0.6061 \| 0.8683 \| 0.8617 \| 0.8712 \| 0.8641 \| \| 0.9329 \| 3.1068 \| 400 \| 0.6001 \| 0.8661 \| 0.8591 \| 0.8750 \| 0.8620 \| \| 0.9049 \| 3.1845 \| 410 \| 0.5925 \| 0.8686 \| 0.8617 \| 0.8731 \| 0.8647 \| \| 0.9815 \| 3.2621 \| 420 \| 0.5806 \| 0.8686 \| 0.8622 \| 0.8717 \| 0.8644 \| \| 0.9507 \| 3.3398 \| 430 \| 0.5793 \| 0.8673 \| 0.8613 \| 0.8691 \| 0.8638 \| \| 0.9608 \| 3.4175 \| 440 \| 0.5721 \| 0.8671 \| 0.8614 \| 0.8683 \| 0.8636 \| \| 0.9409 \| 3.4951 \| 450 \| 0.5688 \| 0.8652 \| 0.8591 \| 0.8658 \| 0.8612 \| \| 0.8856 \| 3.5728 \| 460 \| 0.5563 \| 0.8700 \| 0.8650 \| 0.8714 \| 0.8667 \| \| 0.9099 \| 3.6505 \| 470 \| 0.5557 \| 0.8661 \| 0.8613 \| 0.8681 \| 0.8622 \| \| 0.9167 \| 3.7282 \| 480 \| 0.5527 \| 0.8686 \| 0.8639 \| 0.8701 \| 0.8648 \| \| 0.9077 \| 3.8058 \| 490 \| 0.5431 \| 0.8705 \| 0.8669 \| 0.8722 \| 0.8674 \| \| 0.9005 \| 3.8835 \| 500 \| 0.5390 \| 0.8732 \| 0.8697 \| 0.8749 \| 0.8701 \| \| 0.8596 \| 3.9612 \| 510 \| 0.5375 \| 0.8707 \| 0.8655 \| 0.8732 \| 0.8668 \| \| 0.8856 \| 4.0388 \| 520 \| 0.5254 \| 0.8705 \| 0.8651 \| 0.8741 \| 0.8663 \| \| 0.8869 \| 4.1165 \| 530 \| 0.5238 \| 0.8717 \| 0.8657 \| 0.8731 \| 0.8680 \| \| 0.8069 \| 4.1942 \| 540 \| 0.5188 \| 0.8732 \| 0.8671 \| 0.8744 \| 0.8695 \| \| 0.8474 \| 4.2718 \| 550 \| 0.5188 \| 0.8710 \| 0.8649 \| 0.8729 \| 0.8671 \| \| 0.8243 \| 4.3495 \| 560 \| 0.5177 \| 0.8727 \| 0.8684 \| 0.8756 \| 0.8696 \| \| 0.8437 \| 4.4272 \| 570 \| 0.5107 \| 0.8727 \| 0.8682 \| 0.8742 \| 0.8693 \| \| 0.7761 \| 4.5049 \| 580 \| 0.5025 \| 0.8739 \| 0.8700 \| 0.8751 \| 0.8708 \| \| 0.784 \| 4.5825 \| 590 \| 0.5016 \| 0.8768 \| 0.8717 \| 0.8778 \| 0.8734 \| \| 0.8055 \| 4.6602 \| 600 \| 0.5019 \| 0.8739 \| 0.8701 \| 0.8772 \| 0.8710 \| \| 0.8109 \| 4.7379 \| 610 \| 0.4960 \| 0.8771 \| 0.8724 \| 0.8785 \| 0.8740 \| \| 0.8697 \| 4.8155 \| 620 \| 0.4887 \| 0.8793 \| 0.8749 \| 0.8816 \| 0.8757 \| \| 0.7996 \| 4.8932 \| 630 \| 0.4878 \| 0.8773 \| 0.8719 \| 0.8782 \| 0.8734 \| \| 0.8002 \| 4.9709 \| 640 \| 0.4847 \| 0.8785 \| 0.8738 \| 0.8807 \| 0.8752 \| \| 0.7404 \| 5.0485 \| 650 \| 0.4888 \| 0.8771 \| 0.8726 \| 0.8795 \| 0.8739 \| \| 0.7326 \| 5.1262 \| 660 \| 0.4883 \| 0.8746 \| 0.8701 \| 0.8772 \| 0.8718 \| \| 0.797 \| 5.2039 \| 670 \| 0.4892 \| 0.8729 \| 0.8689 \| 0.8752 \| 0.8701 \| \| 0.8084 \| 5.2816 \| 680 \| 0.4800 \| 0.8793 \| 0.8752 \| 0.8817 \| 0.8763 \| \| 0.8025 \| 5.3592 \| 690 \| 0.4762 \| 0.8768 \| 0.8727 \| 0.8771 \| 0.8736 \| \| 0.7087 \| 5.4369 \| 700 \| 0.4762 \| 0.8783 \| 0.8750 \| 0.8807 \| 0.8756 \| \| 0.7502 \| 5.5146 \| 710 \| 0.4754 \| 0.8785 \| 0.8754 \| 0.8801 \| 0.8759 \| \| 0.7386 \| 5.5922 \| 720 \| 0.4738 \| 0.8793 \| 0.8754 \| 0.8807 \| 0.8760 \| \| 0.8173 \| 5.6699 \| 730 \| 0.4712 \| 0.8793 \| 0.8750 \| 0.8801 \| 0.8762 \| \| 0.8213 \| 5.7476 \| 740 \| 0.4696 \| 0.8790 \| 0.8750 \| 0.8795 \| 0.8756 \| \| 0.7184 \| 5.8252 \| 750 \| 0.4714 \| 0.8805 \| 0.8759 \| 0.8826 \| 0.8768 \| \| 0.7168 \| 5.9029 \| 760 \| 0.4682 \| 0.8749 \| 0.8695 \| 0.8771 \| 0.8715 \| \| 0.7558 \| 5.9806 \| 770 \| 0.4673 \| 0.8761 \| 0.8711 \| 0.8787 \| 0.8729 \| \| 0.7169 \| 6.0583 \| 780 \| 0.4678 \| 0.8783 \| 0.8736 \| 0.8801 \| 0.8749 \| \| 0.7042 \| 6.1359 \| 790 \| 0.4628 \| 0.8759 \| 0.8710 \| 0.8773 \| 0.8724 \| \| 0.7332 \| 6.2136 \| 800 \| 0.4672 \| 0.8766 \| 0.8720 \| 0.8790 \| 0.8731 \| \| 0.7027 \| 6.2913 \| 810 \| 0.4644 \| 0.8785 \| 0.8736 \| 0.8805 \| 0.8749 \| \| 0.7283 \| 6.3689 \| 820 \| 0.4642 \| 0.8776 \| 0.8724 \| 0.8793 \| 0.8740 \| \| 0.7305 \| 6.4466 \| 830 \| 0.4613 \| 0.8780 \| 0.8729 \| 0.8785 \| 0.8742 \| \| 0.7186 \| 6.5243 \| 840 \| 0.4606 \| 0.8768 \| 0.8723 \| 0.8783 \| 0.8734 \| \| 0.759 \| 6.6019 \| 850 \| 0.4592 \| 0.8766 \| 0.8719 \| 0.8769 \| 0.8730 \| \| 0.6865 \| 6.6796 \| 860 \| 0.4580 \| 0.8771 \| 0.8727 \| 0.8782 \| 0.8737 \| \| 0.689 \| 6.7573 \| 870 \| 0.4574 \| 0.8776 \| 0.8735 \| 0.8788 \| 0.8745 \| \| 0.6851 \| 6.8350 \| 880 \| 0.4561 \| 0.8802 \| 0.8764 \| 0.8815 \| 0.8773 \| \| 0.7158 \| 6.9126 \| 890 \| 0.4547 \| 0.8795 \| 0.8759 \| 0.8808 \| 0.8766 \| \| 0.6938 \| 6.9903 \| 900 \| 0.4533 \| 0.8800 \| 0.8759 \| 0.8810 \| 0.8768 \| \| 0.6596 \| 7.0680 \| 910 \| 0.4540 \| 0.8800 \| 0.8759 \| 0.8808 \| 0.8768 \| \| 0.7519 \| 7.1456 \| 920 \| 0.4530 \| 0.8800 \| 0.8758 \| 0.8809 \| 0.8769 \| \| 0.6836 \| 7.2233 \| 930 \| 0.4519 \| 0.8793 \| 0.8753 \| 0.8806 \| 0.8762 \| \| 0.7407 \| 7.3010 \| 940 \| 0.4520 \| 0.8788 \| 0.8751 \| 0.8807 \| 0.8757 \| \| 0.6823 \| 7.3786 \| 950 \| 0.4522 \| 0.8785 \| 0.8750 \| 0.8802 \| 0.8753 \| \| 0.7029 \| 7.4563 \| 960 \| 0.4524 \| 0.8785 \| 0.8746 \| 0.8802 \| 0.8753 \| \| 0.6536 \| 7.5340 \| 970 \| 0.4515 \| 0.8795 \| 0.8756 \| 0.8812 \| 0.8763 \| \| 0.6837 \| 7.6117 \| 980 \| 0.4513 \| 0.8800 \| 0.8761 \| 0.8815 \| 0.8768 \| \| 0.6604 \| 7.6893 \| 990 \| 0.4512 \| 0.8797 \| 0.8759 \| 0.8812 \| 0.8766 \| \| 0.683 \| 7.7670 \| 1000 \| 0.4511 \| 0.8797 \| 0.8759 \| 0.8812 \| 0.8766 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
AlkQ/ppo-SnowballTarget	AlkQ	2024-05-14T15:13:57Z	13	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2024-05-14T15:13:54Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AlkQ/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
PantagrueLLM/jargon-NACHOS-4096	PantagrueLLM	2024-05-14T15:13:54Z	101	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T18:49:23Z	--- license: mit language: - fr library_name: transformers tags: - linformer - medical - RoBERTa - pytorch --- # Jargon-NACHOS-4096 [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \|Training Data\| \|-------------------------------------------------------------------------------------\|:-----------------------:\|:----------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \|8.5GB Web Corpus\| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \|5.4GB Medical Corpus\| \| jargon-general-legal \| jargon-general-base \|18GB Legal Corpus \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \|Medical+Legal Corpora\| \| jargon-legal \| scratch \|18GB Legal Corpus\| \| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) \| scratch \|18GB Legal Corpus\| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \|5.4GB Medical Corpus\| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \|5.4GB Medical Corpus\| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. ## Biomedical Benchmark Results averaged across five funs with varying random seeds. \| \|[FrenchMedMCQA](https://huggingface.co/datasets/qanastek/frenchmedmcqa)\|[MQC](https://aclanthology.org/2020.lrec-1.72/)\|[CAS-POS](https://clementdalloux.fr/?page_id=28)\|[ESSAI-POS](https://clementdalloux.fr/?page_id=28)\|[CAS-SG](https://aclanthology.org/W18-5614/)\|[MEDLINE](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[EMEA](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[E3C-NER](https://live.european-language-grid.eu/catalogue/corpus/7618)\|[CLISTER](https://aclanthology.org/2022.lrec-1.459/)\| \|-------------------------\|:-----------------------:\|:-----------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\| \| Task Type \| Sequence Classification \| Sequence Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| STS \| \| Metric \| EMR \| Accuracy \| Macro-F1 \| Macro-F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Spearman Correlation \| \| jargon-general-base \| 12.9 \| 76.7 \| 96.6 \| 96.0 \| 69.4 \| 81.7 \| 96.5 \| 91.9 \| 78.0 \| \| jargon-biomed \| 15.3 \| 91.1 \| 96.5 \| 95.6 \| 75.1 \| 83.7 \| 96.5 \| 93.5 \| 74.6 \| \| jargon-biomed-4096 \| 14.4 \| 78.9 \| 96.6 \| 95.9 \| 73.3 \| 82.3 \| 96.3 \| 92.5 \| 65.3 \| \| jargon-general-biomed \| 16.1 \| 69.7 \| 95.1 \| 95.1 \| 67.8 \| 78.2 \| 96.6 \| 91.3 \| 59.7 \| \| jargon-multidomain-base \| 14.9 \| 86.9 \| 96.3 \| 96.0 \| 70.6 \| 82.4 \| 96.6 \| 92.6 \| 74.8 \| \| jargon-NACHOS \| 13.3 \| 90.7 \| 96.3 \| 96.2 \| 75.0 \| 83.4 \| 96.8 \| 93.1 \| 70.9 \| \| jargon-NACHOS-4096 \| 18.4 \| 93.2 \| 96.2 \| 95.9 \| 74.9 \| 83.8 \| 96.8 \| 93.2 \| 74.9 \| For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with `jargon-NACHOS-4096` using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-NACHOS-4096", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-NACHOS-4096", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
Ap98/zephyr-7b-sft-qlora	Ap98	2024-05-14T15:12:20Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "trl", "sft", "generated_from_trainer", "alignment-handbook", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-14T14:40:32Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer - alignment-handbook base_model: mistralai/Mistral-7B-v0.1 datasets: - generator model-index: - name: zephyr-7b-sft-qlora 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. --> # zephyr-7b-sft-qlora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.0182 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9758 \| 1.0 \| 42 \| 1.0182 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kyl23/hw3_SST2_lora_1e-3	kyl23	2024-05-14T15:11:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T15:10:59Z	--- 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. 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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]
mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF	mradermacher	2024-05-14T15:09:07Z	119	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn", "base_model:quantized:asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn", "endpoints_compatible", "region:us" ]	null	2024-05-14T14:39:15Z	--- base_model: asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn <!-- 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/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) 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 -->
Gigax/NPC-LLM-7B	Gigax	2024-05-14T15:05:43Z	79	11	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-26T15:34:34Z	--- license: apache-2.0 language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Mistral-7B, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper # Download model from the Hub model_name = "Gigax/NPC-LLM-7B" llm = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format model = models.Transformers(llm, tokenizer) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Mistral-7B-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-7B, url={[https://huggingface.co/Gigax/NPC-LLM-7B](https://huggingface.co/Gigax/NPC-LLM-7B)}, title={NPC-LLM-7B}, author={Gigax team} } ```
aaaagao/CustomModel	aaaagao	2024-05-14T15:05:42Z	108	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T15:05:30Z	--- 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. 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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]
Gigax/NPC-LLM-3_8B-GGUF	Gigax	2024-05-14T15:05:13Z	32	1	null	[ "gguf", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T11:37:15Z	--- license: mit language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Phi-3, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper from llama_cpp import Llama # Download model from the Hugging Face Gigax Hub before run this code # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format llm = Llama.from_pretrained( repo_id="Gigax/NPC-LLM-3_8B-GGUF", filename="npc-llm-3_8B.gguf" # n_gpu_layers=-1, # Uncomment to use GPU acceleration # seed=1337, # Uncomment to set a specific seed # n_ctx=2048, # Uncomment to increase the context window ) model = models.LlamaCpp(llm) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-3_8B-GGUF, url={[https://huggingface.co/Gigax/NPC-LLM-3_8B-GGUF-](https://huggingface.co/Gigax/NPC-LLM-3_8B-GGUF)}, title={NPC-LLM-3_8B-GGUF}, author={Gigax team} } ```
eminow/deneme1	eminow	2024-05-14T15:04:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-14T15:04:42Z	--- license: apache-2.0 ---
Gigax/NPC-LLM-3_8B-128k-GGUF	Gigax	2024-05-14T15:04:28Z	4	2	null	[ "gguf", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T11:55:47Z	--- license: mit language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Phi-3-128k, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper from llama_cpp import Llama # Download model from the Hugging Face Gigax Hub before run this code # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format llm = Llama.from_pretrained( repo_id="Gigax/NPC-LLM-3_8B-128k-GGUF", filename="npc-llm-3_8B-128k.gguf" # n_gpu_layers=-1, # Uncomment to use GPU acceleration # seed=1337, # Uncomment to set a specific seed # n_ctx=2048, # Uncomment to increase the context window ) model = models.LlamaCpp(llm) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-3_8B-128k-GGUF, url={[https://huggingface.co/Gigax/NPC-LLM-3_8B-128k-GGUF](https://huggingface.co/Gigax/NPC-LLM-3_8B-128k-GGUF)}, title={NPC-LLM-3_8B-128k-GGUF}, author={Gigax team} } ```
NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF	NikolayKozloff	2024-05-14T15:03:48Z	6	1	transformers	[ "transformers", "gguf", "portugues", "portuguese", "QA", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "pt", "dataset:rhaymison/superset", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T15:03:30Z	--- language: - pt license: apache-2.0 library_name: transformers tags: - portugues - portuguese - QA - instruct - llama-cpp - gguf-my-repo base_model: meta-llama/Meta-Llama-3-8B-Instruct datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: Llama-3-portuguese-Tom-cat-8b-instruct results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 70.4 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 58.0 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 51.07 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 90.91 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 75.4 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 76.05 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 86.99 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 60.39 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 65.92 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard --- # NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF This model was converted to GGUF format from [`rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct`](https://huggingface.co/rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF --model llama-3-portuguese-tom-cat-8b-instruct.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF --model llama-3-portuguese-tom-cat-8b-instruct.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-portuguese-tom-cat-8b-instruct.Q6_K.gguf -n 128 ```
quocanh944/viT5-med-qa	quocanh944	2024-05-14T14:59:05Z	163	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T14:57:42Z	--- 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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typealias/SFR-Iterative-DPO-Llama-3-8B-R-mx-4bit	typealias	2024-05-14T14:56:39Z	7	1	mlx	[ "mlx", "safetensors", "llama", "license:cc-by-nc-nd-3.0", "region:us" ]	null	2024-05-14T14:33:46Z	--- license: cc-by-nc-nd-3.0 tags: - mlx --- # typealias/SFR-Iterative-DPO-Llama-3-8B-R-mx-4bit The Model [typealias/SFR-Iterative-DPO-Llama-3-8B-R-mx-4bit](https://huggingface.co/typealias/SFR-Iterative-DPO-Llama-3-8B-R-mx-4bit) was converted to MLX format from [Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R](https://huggingface.co/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R) using mlx-lm version 0.13.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("typealias/SFR-Iterative-DPO-Llama-3-8B-R-mx-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF	MaziyarPanahi	2024-05-14T14:51:23Z	140,056	166	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "16-bit", "GGUF", "text-generation", "en", "region:us", "conversational" ]	text-generation	2024-04-18T16:42:52Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - 16-bit - GGUF inference: false model_creator: MaziyarPanahi model_name: Meta-Llama-3-70B-Instruct-GGUF quantized_by: MaziyarPanahi license_name: llama3 --- # MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF The GGUF and quantized models here are based on [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) model ## How to download You can download only the quants you need instead of cloning the entire repository as follows: ``` huggingface-cli download MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF --local-dir . --include 'Q2_Kgguf' ``` ## Load GGUF models You `MUST` follow the prompt template provided by Llama-3: ```sh ./llama.cpp/main -m Meta-Llama-3-70B-Instruct.Q2_K.gguf -r '<\|eot_id\|>' --in-prefix "\n<\|start_header_id\|>user<\|end_header_id\|>\n\n" --in-suffix "<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\n\n" -p "<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nYou are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.<\|eot_id\|>\n<\|start_header_id\|>user<\|end_header_id\|>\n\nHi! How are you?<\|eot_id\|>\n<\|start_header_id\|>assistant<\|end_header_id\|>\n\n" -n 1024 ``` Original README --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/" --local-dir Meta-Llama-3-70B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos ---
emilykang/Gemma_finetune_med	emilykang	2024-05-14T14:48:35Z	7	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-14T12:23:43Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_finetune_med 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. --> # Gemma_finetune_med This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.0.1+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF	Recaru	2024-05-14T14:48:20Z	2	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ko", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T14:47:59Z	--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko - llama-cpp - gguf-my-repo pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- # Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF This model was converted to GGUF format from [`beomi/Llama-3-KoEn-8B-Instruct-preview`](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF --model llama-3-koen-8b-instruct-preview.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF --model llama-3-koen-8b-instruct-preview.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-koen-8b-instruct-preview.Q5_K_M.gguf -n 128 ```
Recaru/Llama-3-KoEn-8B-Instruct-preview-Q8_0-GGUF	Recaru	2024-05-14T14:41:50Z	0	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ko", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T14:41:27Z	--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko - llama-cpp - gguf-my-repo pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- # Recaru/Llama-3-KoEn-8B-Instruct-preview-Q8_0-GGUF This model was converted to GGUF format from [`beomi/Llama-3-KoEn-8B-Instruct-preview`](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Recaru/Llama-3-KoEn-8B-Instruct-preview-Q8_0-GGUF --model llama-3-koen-8b-instruct-preview.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Recaru/Llama-3-KoEn-8B-Instruct-preview-Q8_0-GGUF --model llama-3-koen-8b-instruct-preview.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-koen-8b-instruct-preview.Q8_0.gguf -n 128 ```
Skylar1211/autotrain-90f1d-ojqc3	Skylar1211	2024-05-14T14:38:44Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T13:22:55Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid	AhmetAytar	2024-05-14T14:30:17Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-14T14:26:27Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 160 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 32, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
cimphony-ai-admin/Cimphony-Mistral-Law-7B	cimphony-ai-admin	2024-05-14T14:28:04Z	30	3	peft	[ "peft", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "text-generation", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "model-index", "region:us" ]	text-generation	2024-05-10T18:58:33Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: Cimphony-Mistral-Law-7B results: - task: type: text-generation dataset: type: cais/mmlu name: MMLU metrics: - name: International Law type: accuracy value: 0.802 verified: false - task: type: text-generation dataset: type: cais/mmlu name: MMLU metrics: - name: Jurisprudence type: accuracy value: 0.704 verified: false - task: type: text-generation dataset: type: cais/mmlu name: MMLU metrics: - name: Professional Law type: accuracy value: 0.416 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: ECtHR A type: balanced accuracy value: 0.631 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: LEDGAR type: balanced accuracy value: 0.741 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: CaseHOLD type: accuracy value: 0.776 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: Unfair-ToS type: balanced accuracy value: 0.809 verified: false pipeline_tag: text-generation --- # Cimphony-Mistral-Law-7B We introduce Cimphony-Mistral-Law-7B, a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). Cimphony’s LLMs present state-of-the-art performance on legal benchmarks, suppressing models trained on a much larger corpus with significantly more resources, even GPT-4, OpenAI’s flagship model. Checkout and register on our [https://cimphony.ai](https://app.cimphony.ai/signup?callbackUrl=https://app.cimphony.ai/) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657d36d3647c0211e7746ed9/Yjx96bC58SPgNwmDxx_yx.png) ## Model description The model was trained on 600M tokens. We use novel methods to expose the model to this corpus during training, blending a variety of legal reading comprehension tasks, as well as general language data. ## Legal Evaluation Results We evaluate on the legal splits of the MMLU benchmark, as well as LexGLUE. While both are multiple option benchmarks, prompts were adapted so that the models output a single answer. In some cases, additional post-processing was required. Benchmarks for which the labels were A-E multiple-choice options use an accuracy mertic. Benchmarks that have a closed list of options (e.g. Unfair-ToS) use a balanced-accuracy metric, as classes may not be balanced. \| Model / Benchmark \| International Law (MMLU) \| Jurisprudence (MMLU) \| Professional law (MMLU) \| ECtHR A (LexGlue) \| LEDGAR (LexGlue) \| CaseHOLD (LexGlue) \| Unfair-ToS (LexGlue) \| \|:-----------------------------------\|:--------------------------\|:----------------------\|:-------------------------\|:-------------------\|:------------------\|:--------------------\|:-----------------------\| \| Mistral-7B-Instruct-v0.2 \| 73.6% \| 69.4% \| 41.2% \| 67.5% \| 50.6% \| 56.3% \| 36.6% \| \| AdaptLLM \| 57.0% \| 52.8% \| 36.1% \| 51.9% \| 46.3% \| 50.0% \| 51.3% \| \| Saul-7B \| 69.4% \| 63.0% \| 43.2% \| 71.2% \| 55.9% \| 65.8% \| 80.3% \| \|<tr style="background-color:yellow;"><td>Cimphony-7B</td><td>80.2%</td><td>70.4%</td><td>41.6%</td><td>63.1%</td><td>74.1%</td><td>77.6%</td><td>80.9%</td></tr>\| ## Training and evaluation data Following the framework presented in [AdaptLLM](https://huggingface.co/AdaptLLM/law-chat), we convert the raw legal text into reading comprehension. Taking inspiration from human learning via reading comprehension - practice after reading improves the ability to answer questions based on the learned knowledge. We developed a high-quality prompt database, considering the capabilities we’d like the model to possess. LLMs were prompt with the raw text and a collection of prompts, and it returned answers, additional questions, and transformations relevant to the input data. With further post-processing of these outputs, we created our legal reading comprehension dataset. \| Domain \| Dataset \| Tokens \| License \| \|:-------------------\|:--------------------\|:------:\|:------------\| \| Legal \| The Pile (FreeLaw) \| 180M \| MIT \| \| Legal \| LexGlue (train split only) \| 108M \| CC-BY-4.0 \| \| Legal \| USClassActions \| 12M \| GPL-3.0 \| \| Math (CoT) \| AQUA-RAT \| 3M \| Apache-2.0 \| \| Commonsense (CoT) \| ECQA \| 2.4M \| Apache-2.0 \| \| Reasoning (CoT) \| EntailmentBank \| 1.8M \| Apache-2.0 \| \| Chat \| UltraChat \| 90M \| MIT \| \| Code \| Code-Feedback \| 36M \| Apache-2.0 \| \| Instruction \| OpenOrca \| 180M \| MIT \| ## Intended uses & limitations This model can be used for use cases involving legal domain text generation. As with any language model, users must not solely relay on model generations. This model has not gone through a human-feedback alignment (RLHF). The model may generate responses containing hallucinations and biases. Example use: ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained("cimphonyadmin/Cimphony-Mistral-Law-7B") model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(model, "cimphonyadmin/Cimphony-Mistral-Law-7B") # Put your input here: user_input = '''What can you tell me about ex post facto laws?''' # Apply the prompt template prompt = tokenizer.apply_chat_template(user_input, tokenize=False) inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 24 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
crrodrvi/First_Order_Motion	crrodrvi	2024-05-14T14:27:37Z	0	0	null	[ "arxiv:2104.11280", "region:us" ]	null	2024-05-11T21:47:57Z	<b>!!! Check out our new [paper](https://arxiv.org/pdf/2104.11280.pdf) and [framework](https://github.com/snap-research/articulated-animation) improved for articulated objects</b> # First Order Motion Model for Image Animation This repository contains the source code for the paper [First Order Motion Model for Image Animation](https://papers.nips.cc/paper/8935-first-order-motion-model-for-image-animation) by Aliaksandr Siarohin, [Stéphane Lathuilière](http://stelat.eu), [Sergey Tulyakov](http://stulyakov.com), [Elisa Ricci](http://elisaricci.eu/) and [Nicu Sebe](http://disi.unitn.it/~sebe/). [Hugging Face Spaces](https://huggingface.co/spaces/abhishek/first-order-motion-model) ## Example animations The videos on the left show the driving videos. The first row on the right for each dataset shows the source videos. The bottom row contains the animated sequences with motion transferred from the driving video and object taken from the source image. We trained a separate network for each task. ### VoxCeleb Dataset ![Screenshot](sup-mat/vox-teaser.gif) ### Fashion Dataset ![Screenshot](sup-mat/fashion-teaser.gif) ### MGIF Dataset ![Screenshot](sup-mat/mgif-teaser.gif) ### Installation We support ```python3```. To install the dependencies run: ``` pip install -r requirements.txt ``` ### YAML configs There are several configuration (```config/dataset_name.yaml```) files one for each `dataset`. See ```config/taichi-256.yaml``` to get description of each parameter. ### Pre-trained checkpoint Checkpoints can be found under following link: [google-drive](https://drive.google.com/open?id=1PyQJmkdCsAkOYwUyaj_l-l0as-iLDgeH) or [yandex-disk](https://yadi.sk/d/lEw8uRm140L_eQ). ### Animation Demo To run a demo, download checkpoint and run the following command: ``` python demo.py --config config/dataset_name.yaml --driving_video path/to/driving --source_image path/to/source --checkpoint path/to/checkpoint --relative --adapt_scale ``` The result will be stored in ```result.mp4```. The driving videos and source images should be cropped before it can be used in our method. To obtain some semi-automatic crop suggestions you can use ```python crop-video.py --inp some_youtube_video.mp4```. It will generate commands for crops using ffmpeg. In order to use the script, face-alligment library is needed: ``` git clone https://github.com/1adrianb/face-alignment cd face-alignment pip install -r requirements.txt python setup.py install ``` ### Animation demo with Docker If you are having trouble getting the demo to work because of library compatibility issues, and you're running Linux, you might try running it inside a Docker container, which would give you better control over the execution environment. Requirements: Docker 19.03+ and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) installed and able to successfully run the `nvidia-docker` usage tests. We'll first build the container. ``` docker build -t first-order-model . ``` And now that we have the container available locally, we can use it to run the demo. ``` docker run -it --rm --gpus all \ -v $HOME/first-order-model:/app first-order-model \ python3 demo.py --config config/vox-256.yaml \ --driving_video driving.mp4 \ --source_image source.png \ --checkpoint vox-cpk.pth.tar \ --result_video result.mp4 \ --relative --adapt_scale ``` ### Colab Demo [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb) @graphemecluster prepared a GUI demo for the Google Colab. It also works in Kaggle. For the source code, see [```demo.ipynb```](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb). For the old demo, see [```old_demo.ipynb```](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/old_demo.ipynb). ### Face-swap It is possible to modify the method to perform face-swap using supervised segmentation masks. ![Screenshot](sup-mat/face-swap.gif) For both unsupervised and supervised video editing, such as face-swap, please refer to [Motion Co-Segmentation](https://github.com/AliaksandrSiarohin/motion-cosegmentation). ### Training To train a model on specific dataset run: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 python run.py --config config/dataset_name.yaml --device_ids 0,1,2,3 ``` The code will create a folder in the log directory (each run will create a time-stamped new directory). Checkpoints will be saved to this folder. To check the loss values during training see ```log.txt```. You can also check training data reconstructions in the ```train-vis``` subfolder. By default the batch size is tunned to run on 2 or 4 Titan-X gpu (appart from speed it does not make much difference). You can change the batch size in the train_params in corresponding ```.yaml``` file. ### Evaluation on video reconstruction To evaluate the reconstruction performance run: ``` CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode reconstruction --checkpoint path/to/checkpoint ``` You will need to specify the path to the checkpoint, the ```reconstruction``` subfolder will be created in the checkpoint folder. The generated video will be stored to this folder, also generated videos will be stored in ```png``` subfolder in loss-less '.png' format for evaluation. Instructions for computing metrics from the paper can be found: https://github.com/AliaksandrSiarohin/pose-evaluation. ### Image animation In order to animate videos run: ``` CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode animate --checkpoint path/to/checkpoint ``` You will need to specify the path to the checkpoint, the ```animation``` subfolder will be created in the same folder as the checkpoint. You can find the generated video there and its loss-less version in the ```png``` subfolder. By default video from test set will be randomly paired, but you can specify the "source,driving" pairs in the corresponding ```.csv``` files. The path to this file should be specified in corresponding ```.yaml``` file in pairs_list setting. There are 2 different ways of performing animation: by using absolute keypoint locations or by using relative keypoint locations. 1) <i>Animation using absolute coordinates:</i> the animation is performed using the absolute postions of the driving video and appearance of the source image. In this way there are no specific requirements for the driving video and source appearance that is used. However this usually leads to poor performance since unrelevant details such as shape is transfered. Check animate parameters in ```taichi-256.yaml``` to enable this mode. <img src="sup-mat/absolute-demo.gif" width="512"> 2) <i>Animation using relative coordinates:</i> from the driving video we first estimate the relative movement of each keypoint, then we add this movement to the absolute position of keypoints in the source image. This keypoint along with source image is used for animation. This usually leads to better performance, however this requires that the object in the first frame of the video and in the source image have the same pose <img src="sup-mat/relative-demo.gif" width="512"> ### Datasets 1) Bair. This dataset can be directly [downloaded](https://yadi.sk/d/Rr-fjn-PdmmqeA). 2) Mgif. This dataset can be directly [downloaded](https://yadi.sk/d/5VdqLARizmnj3Q). 3) Fashion. Follow the instruction on dataset downloading [from](https://vision.cs.ubc.ca/datasets/fashion/). 4) Taichi. Follow the instructions in [data/taichi-loading](data/taichi-loading/README.md) or instructions from https://github.com/AliaksandrSiarohin/video-preprocessing. 5) Nemo. Please follow the [instructions](https://www.uva-nemo.org/) on how to download the dataset. Then the dataset should be preprocessed using scripts from https://github.com/AliaksandrSiarohin/video-preprocessing. 6) VoxCeleb. Please follow the instruction from https://github.com/AliaksandrSiarohin/video-preprocessing. ### Training on your own dataset 1) Resize all the videos to the same size e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images. We recommend the later, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance. 2) Create a folder ```data/dataset_name``` with 2 subfolders ```train``` and ```test```, put training videos in the ```train``` and testing in the ```test```. 3) Create a config ```config/dataset_name.yaml```, in dataset_params specify the root dir the ```root_dir: data/dataset_name```. Also adjust the number of epoch in train_params. #### Additional notes Citation: ``` @InProceedings{Siarohin_2019_NeurIPS, author={Siarohin, Aliaksandr and Lathuilière, Stéphane and Tulyakov, Sergey and Ricci, Elisa and Sebe, Nicu}, title={First Order Motion Model for Image Animation}, booktitle = {Conference on Neural Information Processing Systems (NeurIPS)}, month = {December}, year = {2019} } ```
Danieljacobsen/Helsinki-DA-SV-v6	Danieljacobsen	2024-05-14T14:25:37Z	106	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T11:24:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF	NikolayKozloff	2024-05-14T14:24:43Z	2	1	transformers	[ "transformers", "gguf", "text-generation-inference", "trl", "sft", "phi-3", "phi-3-mini", "italian", "llama-cpp", "gguf-my-repo", "it", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T14:24:32Z	--- language: - it license: mit tags: - text-generation-inference - transformers - trl - sft - phi-3 - phi-3-mini - italian - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-mini-4k-instruct --- # NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF This model was converted to GGUF format from [`e-palmisano/Phi3-ITA-mini-4K-instruct`](https://huggingface.co/e-palmisano/Phi3-ITA-mini-4K-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/e-palmisano/Phi3-ITA-mini-4K-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF --model phi3-ita-mini-4k-instruct.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF --model phi3-ita-mini-4k-instruct.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi3-ita-mini-4k-instruct.Q8_0.gguf -n 128 ```
SABR22/unsloth-llama-3-8b-sql	SABR22	2024-05-14T14:19:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T14:19:26Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: SABR22 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
camilomj/jenniebpdebutera	camilomj	2024-05-14T14:18:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-14T14:16:51Z	--- license: apache-2.0 ---
Ankesh1234/gemma-medical_qa-Finetune	Ankesh1234	2024-05-14T14:18:05Z	142	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:11:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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. 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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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gutsartificial/hermes-2-pro-llama3-entity-mapping	gutsartificial	2024-05-14T14:17:11Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:finetune:NousResearch/Hermes-2-Pro-Llama-3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T13:47:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: NousResearch/Hermes-2-Pro-Llama-3-8B --- # Uploaded model - Developed by: gutsartificial - License: apache-2.0 - Finetuned from model : NousResearch/Hermes-2-Pro-Llama-3-8B 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)
Huseyin/checkpoint-1000	Huseyin	2024-05-14T14:16:08Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-14T14:09:18Z	--- language: - tr license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: Whisper Medium Tr - Huseyin Ates results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 config: tr split: test args: 'config: tr, split: test' metrics: - name: Wer type: wer value: 19.615089840756195 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Medium Tr - Huseyin Ates This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2422 - Wer: 19.6151 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.1504 \| 0.1724 \| 1000 \| 0.2422 \| 19.6151 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Basirudin/distilbert-base-uncased-finetuned-ner	Basirudin	2024-05-14T14:09:57Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-15T21:10:21Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: Basirudin/distilbert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Basirudin/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2004 - Validation Loss: 0.0706 - Train Precision: 0.9102 - Train Recall: 0.9211 - Train F1: 0.9157 - Train Accuracy: 0.9803 - Epoch: 0 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.2004 \| 0.0706 \| 0.9102 \| 0.9211 \| 0.9157 \| 0.9803 \| 0 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Tonnytempus/tempusdonum	Tonnytempus	2024-05-14T14:09:17Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:02:59Z	--- 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. 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TSingye/DYG_TiBERT	TSingye	2024-05-14T14:08:53Z	48	0	transformers	[ "transformers", "safetensors", "roberta", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T12:52:18Z	--- 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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ZcepZtar/DaToSw_V1	ZcepZtar	2024-05-14T14:06:27Z	114	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T14:06:02Z	--- 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. 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EthanRhys/Dr-Crygor-Current	EthanRhys	2024-05-14T14:05:51Z	0	0	null	[ "license:openrail++", "region:us" ]	null	2024-05-14T14:05:04Z	--- license: openrail++ ---
akansha2k2/Burger_sandwich_pizza	akansha2k2	2024-05-14T14:05:50Z	196	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-14T14:05:42Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Burger_sandwich_pizza results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8656716346740723 --- # Burger_sandwich_pizza Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Burger ![Burger](images/Burger.jpg) #### pizza ![pizza](images/pizza.jpg) #### sandwich ![sandwich](images/sandwich.jpg)
SidXXD/dog-mist-whole-sandesh	SidXXD	2024-05-14T14:00:35Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-14T11:55:30Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - SidXXD/dog-mist-whole-sandesh This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
xfaturx12/gpt2-wikitext2	xfaturx12	2024-05-14T13:58:18Z	144	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T13:56:46Z	--- license: mit tags: - generated_from_trainer base_model: gpt2 model-index: - name: gpt2-wikitext2 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. --> # gpt2-wikitext2 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: 6.1103 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.5572 \| 1.0 \| 2249 \| 6.4710 \| \| 6.1904 \| 2.0 \| 4498 \| 6.1964 \| \| 6.012 \| 3.0 \| 6747 \| 6.1103 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
casque/0509_clear_see_through_v1	casque	2024-05-14T13:57:21Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-14T13:53:21Z	--- license: creativeml-openrail-m ---
ramirces/clasificador-dair-ai-emotion	ramirces	2024-05-14T13:56:19Z	106	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "classification", "generated_from_trainer", "dataset:emotion", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-25T22:18:44Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - classification - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: clasificador-dair-ai-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: test args: split metrics: - name: Accuracy type: accuracy value: 0.9245 --- <!-- 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. --> # clasificador-dair-ai-emotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2276 - Accuracy: 0.9245 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2371 \| 1.0 \| 2000 \| 0.2465 \| 0.919 \| \| 0.1524 \| 2.0 \| 4000 \| 0.1867 \| 0.933 \| \| 0.1102 \| 3.0 \| 6000 \| 0.2276 \| 0.9245 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MLP-Lemma/Lemma-Llama-DS-ckpt3.5k	MLP-Lemma	2024-05-14T13:53:32Z	4	0	transformers	[ "transformers", "safetensors", "llama", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-05-14T13:26:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tomaszki/llama-21	tomaszki	2024-05-14T13:52:25Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T13:48:57Z	--- 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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lmstudio-community/codegemma-1.1-7b-it-GGUF	lmstudio-community	2024-05-14T13:49:54Z	714	5	transformers	[ "transformers", "gguf", "text-generation", "base_model:google/codegemma-1.1-7b-it", "base_model:quantized:google/codegemma-1.1-7b-it", "license:gemma", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2024-05-04T22:25:17Z	--- library_name: transformers extra_gated_heading: Access CodeGemma on Hugging Face extra_gated_prompt: >- To access CodeGemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license pipeline_tag: text-generation widget: - text: > <start_of_turn>user Write a Python function to calculate the nth fibonacci number.<end_of_turn> <start_of_turn>model inference: parameters: max_new_tokens: 200 license: gemma license_link: https://ai.google.dev/gemma/terms quantized_by: bartowski base_model: google/codegemma-1.1-7b-it lm_studio: param_count: 8b use_case: coding release_date: 30-04-2024 model_creator: google prompt_template: Google Gemma Instruct system_prompt: none base_model: gemma original_repo: google/codegemma-1.1-7b-it --- ## 💫 Community Model> CodeGemma 1.1 7b Instruct by Google 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Google](https://huggingface.co/google)<br> Original model: [google/codegemma-1.1-7b-it](https://huggingface.co/google/codegemma-1.1-7b-it)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b2777](https://github.com/ggerganov/llama.cpp/releases/tag/b2777)<br> ## Model Summary: CodeGemma 1.1 7b Instruct is an iteration on the initial CodeGemma release. It should come with minor improvements to code generation.<br> This model is meant to be used as a coding companion or for code generation.<br> ## Prompt Template: Choose the 'Google Gemma Instruct' preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <start_of_turn>user {prompt}<end_of_turn> <start_of_turn>model ``` ## Technical Details CodeGemma is based on the Gemma 7b model with additional training on web documents, mathematics, and code, with a mixture of 80% code and 20% natural language. The code used is based on publicly avaialble code repositories. The instruct version was further trained on mathematical datasets in an attempt to improve its mathematical reasoning capabilities, as well as synthetic code generation combined with a second LLM for evaluation and reinforcement feedback. Additional details can be found on Google's official report PDF [here](https://storage.googleapis.com/deepmind-media/gemma/codegemma_report.pdf) ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. 🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) for his dataset (linked [here](https://github.com/ggerganov/llama.cpp/discussions/5263)) that was used for calculating the imatrix for these quants, which improves the overall quality! ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
kyl23/hw3_RTE_lora_1e-4_r16	kyl23	2024-05-14T13:47:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T13:47:51Z	--- 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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benjamin/Mistral-7B-v0.1-zett-gpt2	benjamin	2024-05-14T13:47:41Z	12	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T13:36:53Z	--- 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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NoteDance/Swin	NoteDance	2024-05-14T13:45:44Z	0	0	tf	[ "tf", "Note", "swin", "vision", "image-classification", "dataset:imagenet-1k", "dataset:imagenet-21k", "license:apache-2.0", "region:us" ]	image-classification	2024-05-14T13:42:11Z	--- license: apache-2.0 datasets: - imagenet-1k - imagenet-21k library_name: tf pipeline_tag: image-classification tags: - Note - swin - vision --- This model is built by Note, Note can be found [here](https://github.com/NoteDance/Note). The model can be found [here](https://github.com/NoteDance/Note/blob/Note-7.0/Note/neuralnetwork/tf/SwinTransformerV2.py). The tutorial can be found [here](https://github.com/NoteDance/Note-documentation/tree/tf-7.0).
duyntnet/Vistral-7B-Chat-DPO-imatrix-GGUF	duyntnet	2024-05-14T13:39:22Z	11	0	transformers	[ "transformers", "gguf", "imatrix", "Vistral-7B-Chat-DPO", "text-generation", "en", "license:other", "region:us", "conversational" ]	text-generation	2024-05-14T11:01:19Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Vistral-7B-Chat-DPO --- Quantizations of https://huggingface.co/jan-hq/Vistral-7B-Chat-DPO # From original readme ## Prompt template Mistral ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ```
gmmaalouf/finetuning-sentiment-model-3000-samples	gmmaalouf	2024-05-14T13:36:34Z	122	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-09T15:48:16Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3684 - Accuracy: 0.8567 - F1: 0.8599 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kasrahabib/all-MiniLM-L6-v2-finetuned-iso29148-f_nf_req-embdr	kasrahabib	2024-05-14T13:35:10Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T13:26:31Z	--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_keras_callback model-index: - name: kasrahabib/all-MiniLM-L6-v2-finetuned-iso29148-f_nf_req-embdr results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # kasrahabib/all-MiniLM-L6-v2-finetuned-iso29148-f_nf_req-embdr This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0009 - Validation Loss: 0.6623 - Epoch: 29 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4710, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.5280 \| 0.3710 \| 0 \| \| 0.3075 \| 0.3428 \| 1 \| \| 0.2140 \| 0.3139 \| 2 \| \| 0.1252 \| 0.3637 \| 3 \| \| 0.0794 \| 0.3695 \| 4 \| \| 0.0506 \| 0.4162 \| 5 \| \| 0.0384 \| 0.4577 \| 6 \| \| 0.0253 \| 0.4791 \| 7 \| \| 0.0190 \| 0.5735 \| 8 \| \| 0.0119 \| 0.5711 \| 9 \| \| 0.0141 \| 0.5977 \| 10 \| \| 0.0131 \| 0.5945 \| 11 \| \| 0.0060 \| 0.6052 \| 12 \| \| 0.0098 \| 0.6270 \| 13 \| \| 0.0080 \| 0.6484 \| 14 \| \| 0.0098 \| 0.6139 \| 15 \| \| 0.0064 \| 0.6103 \| 16 \| \| 0.0067 \| 0.6232 \| 17 \| \| 0.0078 \| 0.6205 \| 18 \| \| 0.0067 \| 0.6126 \| 19 \| \| 0.0039 \| 0.6108 \| 20 \| \| 0.0039 \| 0.6407 \| 21 \| \| 0.0052 \| 0.6501 \| 22 \| \| 0.0043 \| 0.6523 \| 23 \| \| 0.0048 \| 0.6800 \| 24 \| \| 0.0071 \| 0.6644 \| 25 \| \| 0.0014 \| 0.6600 \| 26 \| \| 0.0026 \| 0.6578 \| 27 \| \| 0.0010 \| 0.6613 \| 28 \| \| 0.0009 \| 0.6623 \| 29 \| ### Framework versions - Transformers 4.40.1 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
davideaguglia/rl_course_vizdoom_health_gathering_supreme	davideaguglia	2024-05-14T13:31:57Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-14T13:31:51Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 7.85 +/- 2.12 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r davideaguglia/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
fine-tuned/jina-embeddings-v2-base-en-14052024-afuz-webapp	fine-tuned	2024-05-14T13:25:33Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Fantasy", "Novels", "Books", "Fiction", "Literature", "custom_code", "en", "dataset:fine-tuned/jina-embeddings-v2-base-en-14052024-afuz-webapp", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-14T13:25:16Z	--- license: apache-2.0 datasets: - fine-tuned/jina-embeddings-v2-base-en-14052024-afuz-webapp - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Fantasy - Novels - Books - Fiction - Literature --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: genre-specific search for fantasy novels ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/jina-embeddings-v2-base-en-14052024-afuz-webapp', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
ANGJustinl/Microsoft_Design_ArtStyle	ANGJustinl	2024-05-14T13:24:23Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-14T13:24:22Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- text:"Microsoft Design", A blue and purple color scheme is used in this image with a focus on the blue and pink hues. The image features a series of blue and violet waves which are arranged in a way that creates a visually appealing pattern. The colors are vibrant and eye-catching making the image an interesting design element., masterpiece, best quality, detailed parameters: negative_prompt: >- bad anatomy, worst quality, low quality, normal quality, watermark, blurry, output: url: images/Upscale_2024-05-14-205452_0.png - text: >- text:"Microsoft Design", A blue and purple color scheme is used in this image with a focus on the blue and pink hues. The image features a series of blue and violet waves which are arranged in a way that creates a visually appealing pattern. The colors are vibrant and eye-catching making the image an interesting design element., masterpiece, best quality, detailed parameters: negative_prompt: >- bad anatomy, worst quality, low quality, normal quality, watermark, blurry, output: url: images/Upscale_2024-05-14-205434_0.png - text: >- text:"Microsoft Design", A blue and purple color scheme is used in this image with a focus on the blue and pink hues. The image features a series of blue and violet waves which are arranged in a way that creates a visually appealing pattern. The colors are vibrant and eye-catching making the image an interesting design element., masterpiece, best quality, detailed parameters: negative_prompt: >- bad anatomy, worst quality, low quality, normal quality, watermark, blurry, output: url: images/Upscale_2024-05-14-205416_0.png - text: >- text:"Microsoft Design", A blue and purple color scheme is used in this image with a focus on the blue and pink hues. The image features a series of blue and violet waves which are arranged in a way that creates a visually appealing pattern. The colors are vibrant and eye-catching making the image an interesting design element., masterpiece, best quality, detailed parameters: negative_prompt: >- bad anatomy, worst quality, low quality, normal quality, watermark, blurry, output: url: images/Upscale_2024-05-14-205355_0.png - text: >- text:"Microsoft Design", A black and white image of a pink and purple design with a black background., masterpiece, best quality, detailed parameters: negative_prompt: >- bad anatomy, worst quality, low quality, normal quality, watermark, blurry, output: url: images/Upscale_2024-05-14-204426_0.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null license: creativeml-openrail-m --- # Microsoft Design ArtStyle <Gallery /> ## Model description Same name on civitai ## Download model Weights for this model are available in Safetensors format. [Download](/ANGJustinl/Microsoft_Design_ArtStyle/tree/main) them in the Files & versions tab.
MoMonir/codegemma-1.1-7b-it-GGUF	MoMonir	2024-05-14T13:20:48Z	6	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T12:35:46Z	--- license: gemma library_name: transformers tags: - llama-cpp - gguf-my-repo extra_gated_heading: Access CodeGemma on Hugging Face extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license pipeline_tag: text-generation widget: - text: '<start_of_turn>user Write a Python function to calculate the nth fibonacci number.<end_of_turn> <start_of_turn>model ' inference: parameters: max_new_tokens: 200 license_link: https://ai.google.dev/gemma/terms --- # MoMonir/codegemma-1.1-7b-it-GGUF This model was converted to GGUF format from [`google/codegemma-1.1-7b-it`](https://huggingface.co/google/codegemma-1.1-7b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/google/codegemma-1.1-7b-it) for more details on the model. <!-- README_GGUF.md-about-gguf start --> ### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description) GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo MoMonir/codegemma-1.1-7b-it-GGUF --model codegemma-1.1-7b-it.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo MoMonir/codegemma-1.1-7b-it-GGUF --model codegemma-1.1-7b-it.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m codegemma-1.1-7b-it.Q4_K_M.gguf -n 128 ```
quangtqv/cross_encoder_tool_learning_best_model_14_5_2024	quangtqv	2024-05-14T13:20:04Z	115	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T13:19:31Z	--- 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]
Rishwonth/aa_model	Rishwonth	2024-05-14T13:18:34Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T13:18:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Rishwonth - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ankesh1234/gemma1_fine	Ankesh1234	2024-05-14T13:18:22Z	77	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-14T12:33:38Z	--- license: apache-2.0 ---
Aryan0310/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization-finetuned-cnndaily	Aryan0310	2024-05-14T13:15:41Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "generated_from_trainer", "base_model:mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization", "base_model:finetune:mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T11:27:00Z	--- license: apache-2.0 base_model: mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization tags: - generated_from_trainer metrics: - rouge model-index: - name: bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization-finetuned-cnndaily 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. --> # bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization-finetuned-cnndaily This model is a fine-tuned version of [mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5035 - Rouge1: 39.9928 - Rouge2: 17.5649 - Rougel: 26.9635 - Rougelsum: 36.7394 - Gen Len: 72.8086 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.661 \| 1.0 \| 17945 \| 2.5035 \| 39.9928 \| 17.5649 \| 26.9635 \| 36.7394 \| 72.8086 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
catastropiyush/Mistral_7B_shell	catastropiyush	2024-05-14T13:09:44Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T13:09:29Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: catastropiyush - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
terry69/mistral_adv_small	terry69	2024-05-14T13:09:02Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-14T04:43:02Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: mistral_adv_small 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. --> # mistral_adv_small This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k 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: 4 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0 \| 1.0 \| 325 \| nan \| ### Framework versions - PEFT 0.7.1 - Transformers 4.39.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
PantagrueLLM/jargon-general-base	PantagrueLLM	2024-05-14T13:03:42Z	108	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "legal", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T15:09:52Z	--- license: mit language: - fr library_name: transformers tags: - linformer - legal - medical - RoBERTa - pytorch --- # Jargon-general-base [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \|Training Data\| \|-------------------------------------------------------------------------------------\|:-----------------------:\|:----------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \|8.5GB Web Corpus\| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \|5.4GB Medical Corpus\| \| jargon-general-legal \| jargon-general-base \|18GB Legal Corpus \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \|Medical+Legal Corpora\| \| jargon-legal \| scratch \|18GB Legal Corpus\| \| jargon-legal-4096 \| scratch \|18GB Legal Corpus\| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \|5.4GB Medical Corpus\| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \|5.4GB Medical Corpus\| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with `jargon-general-base` using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-general-base", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-general-base", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
sravaniayyagari/lora_model_6	sravaniayyagari	2024-05-14T13:01:46Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "region:us" ]	null	2024-05-14T12:58:42Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B --- # 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. --> - 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] ### Framework versions - PEFT 0.7.1
SidXXD/attn_maps-color_sandesh-dog-mist-whole	SidXXD	2024-05-14T13:00:41Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-14T12:42:42Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: photo of a <new1> dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/attn_maps-color_sandesh-dog-mist-whole These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> dog using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
llxlb/lora_model_test1	llxlb	2024-05-14T12:56:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T12:56:23Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: llxlb - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fine-tuned/jina-embeddings-v2-base-en-14052024-5b5o-webapp	fine-tuned	2024-05-14T12:49:58Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Fashion", "Clothing", "Sustainability", "Quality", "Brands", "custom_code", "de", "dataset:fine-tuned/jina-embeddings-v2-base-en-14052024-5b5o-webapp", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-14T12:49:42Z	--- license: apache-2.0 datasets: - fine-tuned/jina-embeddings-v2-base-en-14052024-5b5o-webapp - allenai/c4 language: - de pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Fashion - Clothing - Sustainability - Quality - Brands --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: Fashion boutique products and reviews search ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/jina-embeddings-v2-base-en-14052024-5b5o-webapp', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
llxlb/lora_model_test	llxlb	2024-05-14T12:45:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T12:44:54Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: llxlb - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
IEETA/Multi-Head-CRF	IEETA	2024-05-14T12:45:00Z	0	0	null	[ "es", "dataset:IEETA/SPACCC-Spanish-NER", "license:mit", "region:us" ]	null	2024-05-10T14:29:08Z	--- license: mit datasets: - IEETA/SPACCC-Spanish-NER language: - es metrics: - f1 --- # Model Card for Biomedical Named Entity Recognition in Spanish Clinical Texts Our model focuses on Biomedical Named Entity Recognition (NER) in Spanish clinical texts, crucial for automated information extraction in medical research and treatment improvements. It proposes a novel approach using a Multi-Head Conditional Random Field (CRF) classifier to tackle multi-class NER tasks, overcoming challenges of overlapping entity instances. The classes it recognizes include symptoms, procedures, diseases, chemicals, and proteins. We provide 4 different models, available as branches of this repository. ## Model Details ### Model Description - Developed by: IEETA - Model type: Multi-Head-CRF, Roberta Base - Language(s) (NLP): Spanish - License: MIT - Finetuned from model: lcampillos/roberta-es-clinical-trials-ner ### Model Sources - Repository: [IEETA Multi-Head-CRF GitHub](https://github.com/ieeta-pt/Multi-Head-CRF) - Paper: Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes [Awaiting Publication] Authors: - Richard A A Jonker ([ORCID: 0000-0002-3806-6940](https://orcid.org/0000-0002-3806-6940)) - Tiago Almeida ([ORCID: 0000-0002-4258-3350](https://orcid.org/0000-0002-4258-3350)) - Rui Antunes ([ORCID: 0000-0003-3533-8872](https://orcid.org/0000-0003-3533-8872)) - João R Almeida ([ORCID: 0000-0003-0729-2264](https://orcid.org/0000-0003-0729-2264)) - Sérgio Matos ([ORCID: 0000-0003-1941-3983](https://orcid.org/0000-0003-1941-3983)) ## Uses Note we do not take any liability for the use of the model in any professional/medical domain. The model is intended for academic purposes only. It performs Named Entity Recognition over 5 classes namely: SYMPTOM PROCEDURE DISEASE PROTEIN CHEMICAL ## How to Get Started with the Model Please refer to our GitHub repository for more information on how to train the model and run inference: [IEETA Multi-Head-CRF GitHub](https://github.com/ieeta-pt/Multi-Head-CRF) ## Training Details ### Training Data The training data can be found on IEETA/SPACCC-Spanish-NER, which is further described on the dataset card. The dataset used consists of 4 seperate datasets: - [SympTEMIST](https://zenodo.org/records/10635215) - [MedProcNER](https://zenodo.org/records/8224056) - [DisTEMIST](https://zenodo.org/records/7614764) - [PharmaCoNER](https://zenodo.org/records/4270158) ### Speeds, Sizes, Times The models were trained using an Nvidia Quadro RTX 8000. The models for 5 classes took approximately 1 hour to train and occupy around 1GB of disk space. Additionally, this model shows linear complexity (+8 minutes) per entity class to classify. ### Testing Data, Factors & Metrics #### Testing Data The testing data can be found on IEETA/SPACCC-Spanish-NER, which is further described on the dataset card. #### Metrics The models were evaluated using the micro-averaged F1-score metric, the standard for entity recognition tasks. ### Results We provide 4 separate models with various hyperparameter changes: \| HLs per head \| Augmentation \| Percentage Tags \| Augmentation Probability \| F1 \| \|--------------\|--------------\|-----------------\|--------------------------\|--------\| \| 3 \| Random \| 0.25 \| 0.50 \| 78.73 \| \| 3 \| Unknown \| 0.50 \| 0.25 \| 78.50 \| \| 3 \| None \| - \| - \| 78.89 \| \| 1 \| Random \| 0.25 \| 0.50 \| 78.89 \| All models are trained with a context size of 32 tokens for 60 epochs. ## Citation BibTeX: [Awaiting Publication]
mfuentelsaz/clasificador-muchocine	mfuentelsaz	2024-05-14T12:43:55Z	110	0	transformers	[ "transformers", "safetensors", "electra", "text-classification", "classification", "generated_from_trainer", "base_model:mrm8488/electricidad-base-discriminator", "base_model:finetune:mrm8488/electricidad-base-discriminator", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-09T08:32:58Z	--- base_model: mrm8488/electricidad-base-discriminator tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: clasificador-muchocine 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. --> # clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4057 - Accuracy: 0.4348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 388 \| 1.3794 \| 0.3961 \| \| 1.3811 \| 2.0 \| 776 \| 1.2981 \| 0.4206 \| \| 1.0028 \| 3.0 \| 1164 \| 1.4057 \| 0.4348 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
m-a-p/OpenCodeInterpreter-CL-70B	m-a-p	2024-05-14T12:43:06Z	27	25	transformers	[ "transformers", "safetensors", "llama", "text-generation", "code", "conversational", "en", "arxiv:2402.14658", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T07:48:35Z	--- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- <h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1> <p align="center"> <img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png"> </p> <p align="center"> <a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> \| <a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> </p> <hr> ## Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv. ## Model Information This model is based on [CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf). ## Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. \| Benchmark \| HumanEval (+) \| MBPP (+) \| Average (+) \| \|---------------\|-------------------\|--------------\|-----------------\| \| OpenCodeInterpreter-DS-1.3B \| 65.2 (61.0) \| 63.4 (52.4) \| 64.3 (56.7) \| \| + Execution Feedback \| 65.2 (62.2) \| 65.2 (55.6) \| 65.2 (58.9) \| \| OpenCodeInterpreter-DS-6.7B \| 76.2 (72.0) \| 73.9 (63.7) \| 75.1 (67.9) \| \| + Execution Feedback \| 81.1 (78.7) \| 82.7 (72.4) \| 81.9 (75.6) \| \| + Synth. Human Feedback \| 87.2 (86.6) \| 86.2 (74.2) \| 86.7 (80.4) \| \| + Synth. Human Feedback (Oracle) \| 89.7 (86.6) \| 87.2 (75.2) \| 88.5 (80.9) \| \| OpenCodeInterpreter-DS-33B \| 79.3 (74.3) \| 78.7 (66.4) \| 79.0 (70.4) \| \| + Execution Feedback \| 82.9 (80.5) \| 83.5 (72.2) \| 83.2 (76.4) \| \| + Synth. Human Feedback \| 88.4 (86.0) \| 87.5 (75.9) \| 88.0 (81.0) \| \| + Synth. Human Feedback (Oracle) \| 92.7 (89.7) \| 90.5 (79.5) \| 91.6 (84.6) \| \| OpenCodeInterpreter-CL-7B \| 72.6 (67.7) \| 66.4 (55.4) \| 69.5 (61.6) \| \| + Execution Feedback \| 75.6 (70.1) \| 69.9 (60.7) \| 72.8 (65.4) \| \| OpenCodeInterpreter-CL-13B \| 77.4 (73.8) \| 70.7 (59.2) \| 74.1 (66.5) \| \| + Execution Feedback \| 81.1 (76.8) \| 78.2 (67.2) \| 79.7 (72.0) \| \| OpenCodeInterpreter-CL-34B \| 78.0 (72.6) \| 73.4 (61.4) \| 75.7 (67.0) \| \| + Execution Feedback \| 81.7 (78.7) \| 80.2 (67.9) \| 81.0 (73.3) \| \| OpenCodeInterpreter-CL-70B \| 76.2 (70.7) \| 73.0 (61.9) \| 74.6 (66.3) \| \| + Execution Feedback \| 79.9 (77.4) \| 81.5 (69.9) \| 80.7 (73.7) \| \| OpenCodeInterpreter-GM-7B \| 56.1 (50.0) \| 39.8 (34.6) \| 48.0 (42.3) \| \| + Execution Feedback \| 64.0 (54.3) \| 48.6 (40.9) \| 56.3 (47.6) \| \| OpenCodeInterpreter-SC2-3B \| 65.2 (57.9) \| 62.7 (52.9) \| 64.0 (55.4) \| \| + Execution Feedback \| 67.1 (60.4) \| 63.4 (54.9) \| 65.3 (57.7) \| \| OpenCodeInterpreter-SC2-7B \| 73.8 (68.9) \| 61.7 (51.1) \| 67.8 (60.0) \| \| + Execution Feedback \| 75.6 (69.5) \| 66.9 (55.4) \| 71.3 (62.5) \| \| OpenCodeInterpreter-SC2-15B \| 75.6 (69.5) \| 71.2 (61.2) \| 73.4 (65.4) \| \| + Execution Feedback \| 77.4 (72.0) \| 74.2 (63.4) \| 75.8 (67.7) \| Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks. ## Model Usage ### Inference ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path="m-a-p/OpenCodeInterpreter-CL-70B" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto", ) model.eval() prompt = "Write a function to find the shared elements from the given two lists." inputs = tokenizer.apply_chat_template( [{'role': 'user', 'content': prompt }], return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=1024, do_sample=False, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ## Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected]. We're here to assist you!"
1aurent/ddpm-mnist	1aurent	2024-05-14T12:42:57Z	4,421	4	diffusers	[ "diffusers", "tensorboard", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "dataset:mnist", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-10-06T14:14:37Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class datasets: - mnist library_name: diffusers pipeline_tag: unconditional-image-generation thumbnail: https://upload.wikimedia.org/wikipedia/commons/f/f7/MnistExamplesModified.png --- # Unconditional MNIST DDPM ![](https://upload.wikimedia.org/wikipedia/commons/f/f7/MnistExamplesModified.png) ## Description This model is a very lightweight UNet2D trained on the MNIST dataset. \ This model is unconditional, meaning that you cannot pick which number you'd like to generate. \ This model was trained in ~40min on an L4 GPU Google Colab instance. You can see the training logs in the [Training metrics](https://huggingface.co/1aurent/ddpm-mnist/tensorboard) tab. A conditional model is available at [1aurent/ddpm-mnist-conditional](https://huggingface.co/1aurent/ddpm-mnist-conditional), though it is pretty buggy. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('1aurent/ddpm-mnist') image = pipeline().images[0] image ```
WenhaoWang/Meta-Llama-3-8B-AutoT2VPrompt	WenhaoWang	2024-05-14T12:40:02Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-to-video generation", "VidProM", "Automatical text-to-video prompt", "conversational", "en", "dataset:WenhaoWang/VidProM", "arxiv:2403.06098", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-18T18:00:02Z	--- license: cc-by-nc-4.0 datasets: - WenhaoWang/VidProM language: - en pipeline_tag: text-generation tags: - text-to-video generation - VidProM - Automatical text-to-video prompt --- # The first model for automatic text-to-video prompt completion: Given a few words as input, the model will generate a few whole text-to-video prompts. # Details It is fine-tuned on the [VidProM](https://huggingface.co/datasets/WenhaoWang/VidProM) dataset using [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) and 8 A100 80G GPUs. # Usage ## Download the model ``` from transformers import pipeline import torch pipe = pipeline("text-generation", model="WenhaoWang/Meta-Llama-3-8B-AutoT2VPrompt", model_kwargs={"torch_dtype": torch.bfloat16}, device_map="cuda:0") ``` ## Set the Parameters ``` input = "An underwater world" # The input text to generate text-to-video prompt. max_length = 50 # The maximum length of the generated text. temperature = 1.2 # Controls the randomness of the generation. Higher values lead to more random outputs. top_k = 8 # Limits the number of words considered at each step to the top k most likely words. num_return_sequences = 10 # The number of different text-to-video prompts to generate from the same input. ``` ## Generation ``` all_prompts = pipe(input, max_length = max_length, do_sample = True, temperature = temperature, top_k = top_k, num_return_sequences=num_return_sequences) def process(text): text = text.replace('\n', '.') text = text.replace(' .', '.') text = text[:text.rfind('.')] text = text + '.' return text for i in range(num_return_sequences): print(process(all_prompts[i]['generated_text'])) ``` You will get 10 text-to-video prompts, and you can pick one you like most. ``` An underwater world of blue wonders. A vibrant Coral Gden sways with shades of aquamine. A Clownfish dances, while a Turtle leisurely glides by. An underwater world full of colorful fish and coral formations.the sun rising over a field of corn ne a fm house on a beautiful morning.a woman is looking at vr controllers and trying to choose which one to choose, . An underwater world teeming with vious unique mine creatures. Schools of fish gracefully swim among the colorful coral reefs and seaweed, creating a stunning underwater landscape. An underwater world with a beautiful mermaid swimming in cle water and sunlight passing through the surface..the most beatuful view on the eth. An underwater world teeming with a rainbow of coral reefs, swaying gently in the sea currents, surrounded by vibrant schools of tropical fish creating a stunning visual feast. An underwater world filled with a rainbow fish and a sea turtle swiming.A woman walks in to a room where her child is sleeping. She leans over to check on the child. The child then wakes up.. An underwater world teeming with colorful creatures and vibrant coral reefs..a beautiful lady, big black eyes, with a white man bun hairstyle, weing a black professional attire, standing front and center, with a black background . An underwater world with colorful coral reefs and a viety of sea creatures, all living together in hmony..a girl weing headphones listening to music at a dk coffee cafe at nighttime -camera zoom out - 10. An underwater world full of mine life and corals, in the style of 8k 3d, photorealistic scenes, crystal cle water, mine and sea flora motifs, high details, glistening water effects, vibrant mine life, H. An underwater world of vibrant coral reefs teeming with schools of tropical fish, creating a mesmerizing display of colors and movement beneath the azure waves. ``` # License The model is licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en), and you should also follow the [license](https://llama.meta.com/llama3/license/) and [Agreement](https://huggingface.co/meta-llama/Meta-Llama-3-8B) from Meta AI. # Citation ``` @article{wang2024vidprom, title={VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models}, author={Wang, Wenhao and Yang, Yi}, journal={arXiv preprint arXiv:2403.06098}, year={2024} } ``` # Acknowledgment The fine-tuning process is helped by [Yaowei Zheng](https://github.com/hiyouga). # Contact If you have any questions, feel free to contact [Wenhao Wang](https://wangwenhao0716.github.io) ([email protected]).
WenhaoWang/AutoT2VPrompt	WenhaoWang	2024-05-14T12:39:47Z	11	5	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-to-video generation", "VidProM", "Automatical text-to-video prompt", "en", "dataset:WenhaoWang/VidProM", "arxiv:2403.06098", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-04T21:08:52Z	--- license: cc-by-nc-4.0 datasets: - WenhaoWang/VidProM language: - en pipeline_tag: text-generation tags: - text-to-video generation - VidProM - Automatical text-to-video prompt --- # The first model for automatic text-to-video prompt completion: Given a few words as input, the model will generate a few whole text-to-video prompts. # Details It is fine-tuned on the [VidProM](https://huggingface.co/datasets/WenhaoWang/VidProM) dataset using [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and 8 A100 GPUs. # Usage ## Download the model ``` from transformers import pipeline import torch pipe = pipeline("text-generation", model="WenhaoWang/AutoT2VPrompt", model_kwargs={"torch_dtype": torch.bfloat16}, device_map="cuda:0") ``` ## Set the Parameters ``` input = "An underwater world" # The input text to generate text-to-video prompt. max_length = 50 # The maximum length of the generated text. temperature = 1.2 # Controls the randomness of the generation. Higher values lead to more random outputs. top_k = 8 # Limits the number of words considered at each step to the top k most likely words. num_return_sequences = 10 # The number of different text-to-video prompts to generate from the same input. ``` ## Generation ``` all_prompts = pipe(input, max_length = max_length, do_sample = True, temperature = temperature, top_k = top_k, num_return_sequences=num_return_sequences) def process(text): text = text.replace('\n', '.') text = text.replace(' .', '.') text = text[:text.rfind('.')] text = text + '.' return text for i in range(num_return_sequences): print(process(all_prompts[i]['generated_text'])) ``` You will get 10 text-to-video prompts, and you can pick one you like most. ``` An underwater world, 25 ye boy, with aqua-green eyes, dk sandy blond hair, from the back, and on his back a fish, 23 ye old, weing glasses,ctoon chacte. An underwater world, the video should capture the essence of tranquility and the beauty of nature.. a woman with short hair weing a green dress sitting at the desk. An underwater world, the ocean is full of discded items, the water flows, and the light penetrating through the water. An underwater world.. a woman with red eyes and red lips is looking forwd. An underwater world.. an old man sitting in a chair, smoking a pipe, a little smoke coming out of the chair, a man is drinking a glass. An underwater world. The ocean is filled with bioluminess as the water reflects a soft glow from a bioluminescent phosphorescent light source. The camera slowly moves away and zooms in.. An underwater world. the girl looks at the camera and smiles with happiness.. An underwater world, 1960s horror film.. An underwater world.. 4 men in 1940s style clothes walk ound a gothic castle. night, fe. A girl is running, and there e some flowers along the river. An underwater world, -camera pan up . A girl is playing with her cat on a sunny day in the pk. A man is running and then falling down and dying. ``` # License The model is licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). # Citation ``` @article{wang2024vidprom, title={VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models}, author={Wang, Wenhao and Yang, Yi}, journal={arXiv preprint arXiv:2403.06098}, year={2024} } ``` # Acknowledgment The fine-tuning process is helped by [Yaowei Zheng](https://github.com/hiyouga). # Contact If you have any questions, feel free to contact [Wenhao Wang](https://wangwenhao0716.github.io) ([email protected]).
flaubert/flaubert_base_cased	flaubert	2024-05-14T12:38:22Z	5,735	8	transformers	[ "transformers", "pytorch", "safetensors", "flaubert", "fill-mask", "bert", "language-model", "flue", "french", "bert-base", "flaubert-base", "cased", "fr", "dataset:flaubert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: fr license: mit datasets: - flaubert metrics: - flue tags: - bert - language-model - flaubert - flue - french - bert-base - flaubert-base - cased --- # FlauBERT: Unsupervised Language Model Pre-training for French FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer. Along with FlauBERT comes [FLUE](https://github.com/getalp/Flaubert/tree/master/flue): an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language.For more details please refer to the [official website](https://github.com/getalp/Flaubert). ## FlauBERT models \| Model name \| Number of layers \| Attention Heads \| Embedding Dimension \| Total Parameters \| \| :------: \| :---: \| :---: \| :---: \| :---: \| \| `flaubert-small-cased` \| 6 \| 8 \| 512 \| 54 M \| \| `flaubert-base-uncased` \| 12 \| 12 \| 768 \| 137 M \| \| `flaubert-base-cased` \| 12 \| 12 \| 768 \| 138 M \| \| `flaubert-large-cased` \| 24 \| 16 \| 1024 \| 373 M \| Note: `flaubert-small-cased` is partially trained so performance is not guaranteed. Consider using it for debugging purpose only. ## Using FlauBERT with Hugging Face's Transformers ```python import torch from transformers import FlaubertModel, FlaubertTokenizer # Choose among ['flaubert/flaubert_small_cased', 'flaubert/flaubert_base_uncased', # 'flaubert/flaubert_base_cased', 'flaubert/flaubert_large_cased'] modelname = 'flaubert/flaubert_base_cased' # Load pretrained model and tokenizer flaubert, log = FlaubertModel.from_pretrained(modelname, output_loading_info=True) flaubert_tokenizer = FlaubertTokenizer.from_pretrained(modelname, do_lowercase=False) # do_lowercase=False if using cased models, True if using uncased ones sentence = "Le chat mange une pomme." token_ids = torch.tensor([flaubert_tokenizer.encode(sentence)]) last_layer = flaubert(token_ids)[0] print(last_layer.shape) # torch.Size([1, 8, 768]) -> (batch size x number of tokens x embedding dimension) # The BERT [CLS] token correspond to the first hidden state of the last layer cls_embedding = last_layer[:, 0, :] ``` Notes: if your `transformers` version is <=2.10.0, `modelname` should take one of the following values: ``` ['flaubert-small-cased', 'flaubert-base-uncased', 'flaubert-base-cased', 'flaubert-large-cased'] ``` ## References If you use FlauBERT or the FLUE Benchmark for your scientific publication, or if you find the resources in this repository useful, please cite one of the following papers: [LREC paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.302.pdf) ``` @InProceedings{le2020flaubert, author = {Le, Hang and Vial, Lo\"{i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb\'{e}, Beno\^{i}t and Besacier, Laurent and Schwab, Didier}, title = {FlauBERT: Unsupervised Language Model Pre-training for French}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {2479--2490}, url = {https://www.aclweb.org/anthology/2020.lrec-1.302} } ``` [TALN paper](https://hal.archives-ouvertes.fr/hal-02784776/) ``` @inproceedings{le2020flaubert, title = {FlauBERT: des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais}, author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb{\'e}, Beno{\^\i}t and Besacier, Laurent and Schwab, Didier}, booktitle = {Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 31e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2: Traitement Automatique des Langues Naturelles}, pages = {268--278}, year = {2020}, organization = {ATALA} } ```
MattNandavong/QA_model7-test	MattNandavong	2024-05-14T12:32:04Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-14T12:24:18Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: QA_model7-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # QA_model7-test This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.2383 ## 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: 1 - eval_batch_size: 11 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 4 \| 6.2383 \| \| No log \| 2.0 \| 8 \| 6.2383 \| \| No log \| 3.0 \| 12 \| 6.2383 \| \| No log \| 4.0 \| 16 \| 6.2383 \| \| No log \| 5.0 \| 20 \| 6.2383 \| \| No log \| 6.0 \| 24 \| 6.2383 \| \| No log \| 7.0 \| 28 \| 6.2383 \| \| No log \| 8.0 \| 32 \| 6.2383 \| \| No log \| 9.0 \| 36 \| 6.2383 \| \| No log \| 10.0 \| 40 \| 6.2383 \| \| No log \| 11.0 \| 44 \| 6.2383 \| \| No log \| 12.0 \| 48 \| 6.2383 \| \| No log \| 13.0 \| 52 \| 6.2383 \| \| No log \| 14.0 \| 56 \| 6.2383 \| \| No log \| 15.0 \| 60 \| 6.2383 \| \| No log \| 16.0 \| 64 \| 6.2383 \| \| No log \| 17.0 \| 68 \| 6.2383 \| \| No log \| 18.0 \| 72 \| 6.2383 \| \| No log \| 19.0 \| 76 \| 6.2383 \| \| No log \| 20.0 \| 80 \| 6.2383 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
philschmid/code-llama-7b-text-to-sql	philschmid	2024-05-14T12:31:52Z	11	4	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-01-17T07:57:21Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: codellama/CodeLlama-7b-hf model-index: - name: code-llama-7b-text-to-sql 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. --> # code-llama-7b-text-to-sql This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Fitti/PPO-LunarLander-v2	Fitti	2024-05-14T12:30:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-14T12:30:12Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.00 +/- 15.63 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
neopolita/meta-llama-guard-2-8b-gguf	neopolita	2024-05-14T12:29:11Z	42	1	null	[ "gguf", "region:us" ]	null	2024-04-18T17:47:51Z	--- {} --- # GGUF quants for [meta-llama/Meta-Llama-Guard-2-8B](https://huggingface.co/meta-llama/Meta-Llama-Guard-2-8B) using [llama.cpp](https://github.com/ggerganov/llama.cpp) Terms of Use: Please check the [original model](https://huggingface.co/meta-llama/Meta-Llama-Guard-2-8B) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
neopolita/meta-llama-3-8b-instruct-gguf	neopolita	2024-05-14T12:16:50Z	22	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-18T20:53:35Z	--- {} --- # GGUF quants for [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using [llama.cpp](https://github.com/ggerganov/llama.cpp) Terms of Use: Please check the [original model](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
Mag0g/Ezekiel27_2	Mag0g	2024-05-14T12:15:36Z	128	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T12:14:26Z	--- 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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(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]
ravi6389/twitter_sentiment	ravi6389	2024-05-14T12:13:25Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-14T08:45:54Z	--- license: other license_name: other license_link: LICENSE ---
MeanBean-05/distilbert-for-intent-prediction	MeanBean-05	2024-05-14T12:11:53Z	121	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T12:11:39Z	--- 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. 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Sajjo/w2v-bert-2.0-unified_v2	Sajjo	2024-05-14T12:10:55Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T12:09:11Z	--- 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. 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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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ludocomito/Minerva-MoE-2x3B	ludocomito	2024-05-14T12:05:44Z	2,817	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "DeepMount00/Minerva-3B-base-RAG", "FairMind/Minerva-3B-Instruct-v1.0", "base_model:DeepMount00/Minerva-3B-base-RAG", "base_model:merge:DeepMount00/Minerva-3B-base-RAG", "base_model:FairMind/Minerva-3B-Instruct-v1.0", "base_model:merge:FairMind/Minerva-3B-Instruct-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T17:22:01Z	--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - DeepMount00/Minerva-3B-base-RAG - FairMind/Minerva-3B-Instruct-v1.0 base_model: - DeepMount00/Minerva-3B-base-RAG - FairMind/Minerva-3B-Instruct-v1.0 --- # Minerva-MoE-3x3B Minerva-MoE-3x3B is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [DeepMount00/Minerva-3B-base-RAG](https://huggingface.co/DeepMount00/Minerva-3B-base-RAG) * [FairMind/Minerva-3B-Instruct-v1.0](https://huggingface.co/FairMind/Minerva-3B-Instruct-v1.0) ## Evaluation arc_it acc_norm: 31.91 hellaswag_it acc_norm: 52.20 mmmlu_it: 25.72 ## 🧩 Configuration ```yaml base_model: sapienzanlp/Minerva-3B-base-v1.0 experts: - source_model: DeepMount00/Minerva-3B-base-RAG positive_prompts: - "rispondi a domande" - "cosa è" - "chi è" - "dove è" - "come si" - "spiegami" - "definisci" - source_model: FairMind/Minerva-3B-Instruct-v1.0 positive_prompts: - "istruzione" - "input" - "risposta" - "scrivi" - "sequenza" - "istruzioni" dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "ludocomito/Minerva-MoE-3x3B" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) 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"]) ```
lmms-lab/llava-next-110b	lmms-lab	2024-05-14T12:05:00Z	41	21	transformers	[ "transformers", "safetensors", "llava", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T06:17:11Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # LLaVA Model Card ## Model Details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: Qwen/Qwen1.5-110B-Chat ### Model Description Repository: https://github.com/LLaVA-VL/LLaVA-NeXT Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots. This is only for research exploration, and prohibited for commercial usage. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ### License Notices This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. Llama-1/2 community license for LLaMA-2 and Vicuna-v1.5, [Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) and [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Procedure We conducted the training on LLaVA-1.6's codebase with adding support of Llama-3 and Qwen model. ### Training Hyperparameters ```shell LLM_VERSION="Qwen/Qwen1.5-110B-Chat" LLM_VERSION_CLEAN="${LLM_VERSION//\//_}" VISION_MODEL_VERSION="openai/clip-vit-large-patch14-336" VISION_MODEL_VERSION_CLEAN="${VISION_MODEL_VERSION//\//_}" PROMPT_VERSION=plain PRETRAIN_DATA_VERSION="blip558k" ############### Pretrain ################ BASE_RUN_NAME="llavanext-${LLM_VERSION_CLEAN}-${VISION_MODEL_VERSION_CLEAN}-pretrain_${PRETRAIN_DATA_VERSION}_plain" echo "BASE_RUN_NAME: ${BASE_RUN_NAME}" PROMPT_VERSION="qwen_1_5" MID_RUN_NAME="llavanext-${LLM_VERSION_CLEAN}-${VISION_MODEL_VERSION_CLEAN}-pretrain_${PRETRAIN_DATA_VERSION}_plain-ft_la1_6mix_d32k" echo "MID_RUN_NAME: ${MID_RUN_NAME}" torchrun # with necessary torchrun information for distributed training\ llava/train/train_mem.py \ --deepspeed scripts/zero3.json \ --model_name_or_path $LLM_VERSION \ --version $PROMPT_VERSION \ --data_path="/path/to/data/llava_instruct/llava1_6mix.json" \ --image_folder /path/to/data/llava_data \ --pretrain_mm_mlp_adapter="./checkpoints/projectors/${BASE_RUN_NAME}/mm_projector.bin" \ --mm_tunable_parts="mm_vision_tower,mm_mlp_adapter,mm_language_model" \ --mm_vision_tower_lr=2e-6 \ --vision_tower ${VISION_MODEL_VERSION} \ --mm_projector_type mlp2x_gelu \ --mm_vision_select_layer -2 \ --mm_use_im_start_end False \ --mm_use_im_patch_token False \ --group_by_modality_length True \ --image_aspect_ratio anyres \ --image_grid_pinpoints "[(336, 672), (672, 336), (672, 672), (1008, 336), (336, 1008)]" \ --mm_patch_merge_type spatial_unpad \ --bf16 True \ --run_name $MID_RUN_NAME \ --output_dir ./checkpoints/$MID_RUN_NAME \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 4 \ --gradient_accumulation_steps 1 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 3000 \ --save_total_limit 1 \ --learning_rate 1e-5 \ --weight_decay 0. \ --warmup_ratio 0.03 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --tf32 True \ --model_max_length 32768 \ --gradient_checkpointing True \ --dataloader_num_workers 8 \ --lazy_preprocess True \ --report_to wandb \ --torch_compile True \ --torch_compile_backend "inductor" --dataloader_drop_last True ``` ### Training Data - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 50K GPT-4V data mixture. - 40K ShareGPT data. - 20K COCO Caption data. #### Speeds, Sizes, Times [optional] The training cost is ~18-20 hours on 16 x 8 NVIDIA H800-SXM4-80GB (may vary due to hardware differences). [More Information Needed] ## Evaluation The evaluation is conducted with the support of [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval)
Rishwonth/lora_model	Rishwonth	2024-05-14T11:58:53Z	6	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-06T10:36:32Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Rishwonth - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ademakyol/emotion-analysis-with-distilbert	ademakyol	2024-05-14T11:55:47Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T11:40:09Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: ademakyol/emotion-analysis-with-distilbert results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ademakyol/emotion-analysis-with-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1366 - Validation Loss: 0.1496 - Train Accuracy: 0.9345 - Epoch: 1 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.3770 \| 0.2111 \| 0.9205 \| 0 \| \| 0.1366 \| 0.1496 \| 0.9345 \| 1 \| ### Framework versions - Transformers 4.39.3 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2

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