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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 2025-06-26 00:41:36	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 496 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 00:41:32	card stringlengths 11 1.01M
TristanBehrens/HeilbronnGPTAlpha-openschnabeltier_de	TristanBehrens	2024-02-19T14:28:21Z	0	0	peft	[ "peft", "safetensors", "phi", "llama-factory", "lora", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:other", "region:us" ]	null	2024-02-19T14:27:26Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: HeilbronnGPTAlpha-openschnabeltier_de 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. --> # HeilbronnGPTAlpha-openschnabeltier_de This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the openschnabeltier_de 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
TristanBehrens/HeilbronnGPTAlpha-oasst_de	TristanBehrens	2024-02-19T14:27:24Z	0	0	peft	[ "peft", "safetensors", "phi", "llama-factory", "lora", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:other", "region:us" ]	null	2024-02-19T14:26:26Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: HeilbronnGPTAlpha-oasst_de 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. --> # HeilbronnGPTAlpha-oasst_de This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the oasst_de 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
TristanBehrens/HeilbronnGPTAlpha-evol-instruct_de	TristanBehrens	2024-02-19T14:26:25Z	0	0	peft	[ "peft", "safetensors", "phi", "llama-factory", "lora", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:other", "region:us" ]	null	2024-02-19T14:25:30Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: HeilbronnGPTAlpha-evol-instruct_de 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. --> # HeilbronnGPTAlpha-evol-instruct_de This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the evol-instruct_de 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
ayatsuri/ppo-Huggy	ayatsuri	2024-02-19T14:26:00Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-19T14:25:54Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ayatsuri/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
TristanBehrens/HeilbronnGPTAlpha-booksum_de	TristanBehrens	2024-02-19T14:23:32Z	0	0	peft	[ "peft", "safetensors", "phi", "llama-factory", "lora", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:other", "region:us" ]	null	2024-02-19T14:22:31Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: HeilbronnGPTAlpha-booksum_de 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. --> # HeilbronnGPTAlpha-booksum_de This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the booksum_de 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
CaphAlderamin/ppo-Huggy	CaphAlderamin	2024-02-19T14:23:21Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-19T14:22:49Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: CaphAlderamin/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kouki13/mistral3	kouki13	2024-02-19T14:16:23Z	2	0	transformers	[ "transformers", "pytorch", "safetensors", "autotrain", "text-generation", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:29:44Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " 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) ```
speechbrain/asr-branchformer-large-tedlium2	speechbrain	2024-02-19T14:02:28Z	16	1	speechbrain	[ "speechbrain", "automatic-speech-recognition", "CTC", "Attention", "Transformer", "Branchformer", "pytorch", "hf-asr-leaderboard", "en", "dataset:LIUM/tedlium", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2023-10-27T11:04:13Z	--- language: - en thumbnail: null tags: - automatic-speech-recognition - CTC - Attention - Transformer - Branchformer - pytorch - speechbrain - hf-asr-leaderboard license: apache-2.0 datasets: - LIUM/tedlium metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # Branchformer for Tedlium2 This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on Tedlium2 (EN) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: \| Release \| Test WER (no LM) \| GPUs \| \|:-------------:\|:--------------:\|:--------:\| \| 21-06-23 \| 8.11 \| 1xA100 80GB \| ## Pipeline description This ASR system is composed of 3 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech. - Acoustic model made of a branchformer encoder and a joint decoder with CTC + transformer. Hence, the decoding also incorporates the CTC probabilities. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files (in English) ```python from speechbrain.inference.ASR import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-branchformer-large-tedlium2") asr_model.transcribe_file("speechbrain/asr-branchformer-large-tedlium2/example.wav") ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ## Parallel Inference on a Batch Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model. ### Training The model was trained with SpeechBrain (Commit hash: '73e48d6'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/Tedlium2/Tokenizer python train.py hparams/tedlium2_500_bpe.yaml --data_folder /path/to/tedlium2 --clipped_utt_folder /path/to/clipped_folder cd ../ASR/transformer python train.py hparams/branchformer_large.yaml --pretrained_tokenizer_file /path/to/tokenizer --data_folder /path/to/tedlium2 --clipped_utt_folder /path/to/clipped_folder ``` You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/el523uofs96czfi/AADgTd838pKo2aR8fhqVOh-Oa?dl=0). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/ # Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
hachirokoo/my-mist-model-trained	hachirokoo	2024-02-19T14:00:43Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-19T13:55:28Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: my-mist-model-trained results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-mist-model-trained 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. ## 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 0.15.2
freshpearYoon/v3_noconfig	freshpearYoon	2024-02-19T14:00:13Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-19T06:09:26Z	--- language: - ko license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer metrics: - wer base_model: openai/whisper-large-v3 model-index: - name: whisper_finetune results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper_finetune This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the aihub_100000 dataset. It achieves the following results on the evaluation set: - Loss: 1.0532 - Cer: 6.2644 - Wer: 23.9434 ## 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-08 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 1.5045 \| 0.16 \| 1000 \| 1.4103 \| 6.8826 \| 26.6172 \| \| 1.0745 \| 0.32 \| 2000 \| 1.0532 \| 6.2644 \| 23.9434 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.17.0 - Tokenizers 0.15.2
allstax/NeuralTrix-7B-dpo-4Bit	allstax	2024-02-19T13:59:17Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-19T13:54:39Z	--- 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. 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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Americo/phi2-finetued-farma-13feb	Americo	2024-02-19T13:58:31Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-13T17:17:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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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MaziyarPanahi/BioMistral-7B-GGUF	MaziyarPanahi	2024-02-19T13:57:27Z	420	49	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "pytorch", "tensorboard", "text-generation", "medical", "biology", "conversational", "fr", "en", "de", "nl", "es", "pt", "pl", "ro", "it", "dataset:pubmed", "arxiv:2402.10373", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:BioMistral/BioMistral-7B", "base_model:quantized:BioMistral/BioMistral-7B" ]	text-generation	2024-02-19T13:41:50Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - pytorch - tensorboard - mistral - text-generation - medical - biology - conversational - fr - en - de - nl - es - pt - pl - ro - it - dataset:pubmed - arxiv:2402.10373 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: BioMistral-7B-GGUF base_model: BioMistral/BioMistral-7B inference: false model_creator: BioMistral pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/BioMistral-7B-GGUF](https://huggingface.co/MaziyarPanahi/BioMistral-7B-GGUF) - Model creator: [BioMistral](https://huggingface.co/BioMistral) - Original model: [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) ## Description [MaziyarPanahi/BioMistral-7B-GGUF](https://huggingface.co/MaziyarPanahi/BioMistral-7B-GGUF) contains GGUF format model files for [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF 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. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/BioMistral-7B-GGUF](https://huggingface.co/MaziyarPanahi/BioMistral-7B-GGUF) and below it, a specific filename to download, such as: BioMistral-7B-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/BioMistral-7B-GGUF BioMistral-7B-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/BioMistral-7B-GGUF](https://huggingface.co/MaziyarPanahi/BioMistral-7B-GGUF) --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/BioMistral-7B-GGUF BioMistral-7B-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m BioMistral-7B-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./BioMistral-7B-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./BioMistral-7B-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
linoyts/huggy_lora_pivotal	linoyts	2024-02-19T13:57:15Z	4	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-19T13:10:25Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a <s0><s1> emoji dressed as yoda' output: url: "image_0.png" - text: 'a <s0><s1> emoji dressed as yoda' output: url: "image_1.png" - text: 'a <s0><s1> emoji dressed as yoda' output: url: "image_2.png" - text: 'a <s0><s1> emoji dressed as yoda' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a <s0><s1> emoji license: openrail++ --- # SDXL LoRA DreamBooth - linoyts/huggy_lora_pivotal <Gallery /> ## Model description ### These are linoyts/huggy_lora_pivotal LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`huggy_lora_pivotal.safetensors` here 💾](/linoyts/huggy_lora_pivotal/blob/main/huggy_lora_pivotal.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:huggy_lora_pivotal:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - Embeddings: download [`huggy_lora_pivotal_emb.safetensors` here 💾](/linoyts/huggy_lora_pivotal/blob/main/huggy_lora_pivotal_emb.safetensors). - Place it on it on your `embeddings` folder - Use it by adding `huggy_lora_pivotal_emb` to your prompt. For example, `a huggy_lora_pivotal_emb emoji` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('linoyts/huggy_lora_pivotal', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='linoyts/huggy_lora_pivotal', filename='huggy_lora_pivotal_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('a <s0><s1> emoji dressed as yoda').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/linoyts/huggy_lora_pivotal/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
MAdAiLab/llama2_7b_r16_alpha16_merged_final	MAdAiLab	2024-02-19T13:56:59Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-19T13:54:55Z	--- 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]
PVG-model/Aspire_AI	PVG-model	2024-02-19T13:56:45Z	0	0	peft	[ "peft", "region:us" ]	null	2024-02-19T13:53:15Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: gptq - bits: 4 - tokenizer: None - dataset: None - group_size: 128 - damp_percent: 0.1 - desc_act: True - sym: True - true_sequential: True - use_cuda_fp16: False - model_seqlen: None - block_name_to_quantize: None - module_name_preceding_first_block: None - batch_size: 1 - pad_token_id: None - disable_exllama: True - max_input_length: None ### Framework versions - PEFT 0.5.0
speechbrain/asr-wav2vec2-dvoice-wolof	speechbrain	2024-02-19T13:56:33Z	13	3	speechbrain	[ "speechbrain", "wav2vec2", "CTC", "pytorch", "Transformer", "automatic-speech-recognition", "wo", "dataset:Dvoice", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2022-06-09T23:02:32Z	--- language: "wo" thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - Dvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Wolof dataset within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). \| DVoice Release \| Val. CER \| Val. WER \| Test CER \| Test WER \| \|:-------------:\|:---------------------------:\| -----:\| -----:\| -----:\| \| v2.0 \| 4.81 \| 16.25 \| 4.83 \| 16.05 \| # Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. # Install SpeechBrain First of all, please install transformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). # Transcribing your own audio files (in Wolof) ```python from speechbrain.inference.ASR import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-wolof", savedir="pretrained_models/asr-wav2vec2-dvoice-wolof") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-wolof/example_wolof.wav') ``` # Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. # Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/DVoice/ASR/CTC python train_with_wav2vec2.py hparams/train_wol_with_wav2vec.yaml --data_folder=/localscratch/ALFFA_PUBLIC/ASR/WOLOF/data/ ``` # Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # About DVoice DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrieved from social media. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola, and Soninke. For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together. # About AIOX Labs Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience. - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods. - Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client. - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications. Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/) # SI2M Laboratory The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network, and System Security, and Mathematical Modelling. Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique) # About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # Acknowledgements This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
speechbrain/asr-wav2vec2-dvoice-amharic	speechbrain	2024-02-19T13:55:42Z	59	8	speechbrain	[ "speechbrain", "wav2vec2", "CTC", "pytorch", "Transformer", "automatic-speech-recognition", "dar", "dataset:Dvoice", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2022-06-09T19:10:24Z	--- language: "dar" thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - Dvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Amharic dataset within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). \| DVoice Release \| Val. CER \| Val. WER \| Test CER \| Test WER \| \|:-------------:\|:---------------------------:\| -----:\| -----:\| -----:\| \| v2.0 \| 6.71 \| 25.50 \| 6.57 \| 24.92 \| # Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. # Install SpeechBrain First of all, please install transformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). # Transcribing your own audio files (in Amharic) ```python from speechbrain.inference.ASR import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-amharic", savedir="pretrained_models/asr-wav2vec2-dvoice-amharic") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-amharic/example_amharic.wav') ``` # Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. # Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/DVoice/ASR/CTC python train_with_wav2vec2.py hparams/train_amh_with_wav2vec.yaml --data_folder=/localscratch/ALFFA_PUBLIC/ASR/AMHARIC/data/ ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1vNT7RjRuELs7pumBHmfYsrOp9m46D0ym?usp=sharing). # Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About DVoice DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrieved from social media. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola, and Soninke. For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together. # About AIOX Labs Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience. - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods. - Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client. - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications. Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/) # SI2M Laboratory The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network, and System Security, and Mathematical Modelling. Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique) # About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # Acknowledgements This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
speechbrain/asr-wav2vec2-dvoice-darija	speechbrain	2024-02-19T13:52:44Z	157	10	speechbrain	[ "speechbrain", "wav2vec2", "CTC", "pytorch", "Transformer", "automatic-speech-recognition", "dar", "dataset:Dvoice", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2022-06-09T19:09:28Z	--- language: "dar" thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - Dvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on DVoice Darija (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [DVoice](https://zenodo.org/record/6342622) Darija dataset within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). \| DVoice Release \| Val. CER \| Val. WER \| Test CER \| Test WER \| \|:-------------:\|:---------------------------:\| -----:\| -----:\| -----:\| \| v2.0 \| 5.51 \| 18.46 \| 5.85 \| 18.28 \| # Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. # Install SpeechBrain First of all, please install transformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). # Transcribing your own audio files (in Darija) ```python from speechbrain.inference.ASR import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-darija", savedir="pretrained_models/asr-wav2vec2-dvoice-darija") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-darija/example_darija.wav') ``` # Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. # Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/DVoice/ASR/CTC python train_with_wav2vec2.py hparams/train_dar_with_wav2vec.yaml --data_folder=/localscratch/darija/ ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1vNT7RjRuELs7pumBHmfYsrOp9m46D0ym?usp=sharing). # Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # About DVoice DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrieved from social media. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola, and Soninke. For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together. # About AIOX Labs Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience. - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods. - Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client. - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications. Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/) # SI2M Laboratory The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network, and System Security, and Mathematical Modelling. Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique) # About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # Acknowledgements This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
bbnpp/unit2	bbnpp	2024-02-19T13:52:33Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-19T13:48:25Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: unit2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.63 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="bbnpp/unit2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
furrutiav/bert_mixtral_juke_v0_unasag	furrutiav	2024-02-19T13:50:19Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-19T13:49:53Z	--- 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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bbnpp/q-FrozenLake-v1-4x4-noSlippery	bbnpp	2024-02-19T13:48:36Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-19T13:48:35Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="bbnpp/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
speechbrain/asr-wav2vec2-dvoice-fongbe	speechbrain	2024-02-19T13:47:30Z	75	2	speechbrain	[ "speechbrain", "wav2vec2", "CTC", "pytorch", "Transformer", "automatic-speech-recognition", "fon", "dataset:Dvoice", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2022-06-09T19:10:08Z	--- language: "fon" thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - Dvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Fongbe dataset within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). \| DVoice Release \| Val. CER \| Val. WER \| Test CER \| Test WER \| \|:-------------:\|:---------------------------:\| -----:\| -----:\| -----:\| \| v2.0 \| 4.16 \| 9.19 \| 3.98 \| 9.00 \| # Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. # Install SpeechBrain First of all, please install transformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). # Transcribing your own audio files (in Fongbe) ```python from speechbrain.inference.ASR import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-fongbe", savedir="pretrained_models/asr-wav2vec2-dvoice-fongbe") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-fongbe/example_fongbe.wav') ``` # Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. # Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/DVoice/ASR/CTC python train_with_wav2vec2.py hparams/train_fon_with_wav2vec.yaml --data_folder=/localscratch/ALFFA_PUBLIC/ASR/FONGBE/data/ ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1vNT7RjRuELs7pumBHmfYsrOp9m46D0ym?usp=sharing). # Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About DVoice DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrieved from social media. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola, and Soninke. For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together. # About AIOX Labs Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience. - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods. - Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client. - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications. Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/) # SI2M Laboratory The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network, and System Security, and Mathematical Modelling. Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique) # About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # Acknowledgements This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
Dagobert42/xlnet-base-cased-biored-finetuned	Dagobert42	2024-02-19T13:41:31Z	8	0	transformers	[ "transformers", "safetensors", "xlnet", "token-classification", "low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer", "en", "dataset:medicine", "base_model:xlnet/xlnet-base-cased", "base_model:finetune:xlnet/xlnet-base-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-14T19:09:45Z	--- language: - en license: mit base_model: xlnet-base-cased tags: - low-resource NER - token_classification - biomedicine - medical NER - generated_from_trainer datasets: - medicine metrics: - accuracy - precision - recall - f1 model-index: - name: Dagobert42/xlnet-base-cased-biored-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Dagobert42/xlnet-base-cased-biored-finetuned This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset. It achieves the following results on the evaluation set: - Loss: 0.7909 - Accuracy: 0.756 - Precision: 0.5226 - Recall: 0.3518 - F1: 0.4027 - Weighted F1: 0.7173 ## 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Weighted F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-----------:\| \| No log \| 1.0 \| 13 \| 0.9045 \| 0.7251 \| 0.3378 \| 0.1564 \| 0.1457 \| 0.6233 \| \| No log \| 2.0 \| 26 \| 0.8688 \| 0.7336 \| 0.5559 \| 0.2281 \| 0.2504 \| 0.6453 \| \| No log \| 3.0 \| 39 \| 0.8579 \| 0.7409 \| 0.5851 \| 0.2931 \| 0.3179 \| 0.6795 \| \| No log \| 4.0 \| 52 \| 0.7956 \| 0.7507 \| 0.5225 \| 0.3443 \| 0.3919 \| 0.7017 \| \| No log \| 5.0 \| 65 \| 0.7947 \| 0.7529 \| 0.532 \| 0.3535 \| 0.4026 \| 0.7093 \| \| No log \| 6.0 \| 78 \| 0.8063 \| 0.7549 \| 0.5502 \| 0.3752 \| 0.4191 \| 0.7168 \| \| No log \| 7.0 \| 91 \| 0.8059 \| 0.7599 \| 0.5496 \| 0.3764 \| 0.4269 \| 0.7227 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.15.0
speechbrain/asr-wav2vec2-ctc-aishell	speechbrain	2024-02-19T13:39:05Z	345	6	speechbrain	[ "speechbrain", "CTC", "wav2vec2", "pytorch", "zh", "dataset:aishell", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	null	2022-09-20T12:52:27Z	--- language: zh thumbnail: tags: - CTC - wav2vec2 - pytorch - speechbrain license: "apache-2.0" datasets: - aishell metrics: - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # Wav2vec2-CTC for AISHELL (Mandarin Chinese) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on AISHELL (Mandarin Chinese) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: \| Release \| Dev CER \| Test CER \| GPUs \| Full Results \| \|:-------------:\|:--------------:\|:--------------:\|:--------:\|:--------:\| \| 20-09-22 \| 4.48 \| 5.02 \| 1xRTX8000 48GB \| [Google Drive](https://drive.google.com/drive/folders/1GTB5IzQPl57j-0I1IpmvKg722Ti4ahLz?usp=sharing)\| ## Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (from huggingface) that transforms words into chars and trained with the training transcriptions of AISHELL-1. - Acoustic model made of a wav2vec2 encoder and fully-connected layers To Train this system from scratch, [see our SpeechBrain recipe](https://github.com/speechbrain/speechbrain/tree/develop/recipes/AISHELL-1/ASR/CTC). The system is trained with recordings sampled at 16kHz (single channel). ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files (in English) ```python from speechbrain.inference.interfaces import foreign_class asr_model = foreign_class(source="speechbrain/asr-wav2vec2-ctc-aishell", pymodule_file="custom_interface.py", classname="CustomEncoderDecoderASR") asr_model.transcribe_file("speechbrain/asr-wav2vec2-ctc-aishell/example.wav") ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ## Parallel Inference on a Batch Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model. ### Training The model was trained with SpeechBrain (Commit hash: '480dde87'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain.git ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/AISHELL-1/ASR/CTC/ python train_with_wav2vec.py hparams/train_with_wav2vec.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1GTB5IzQPl57j-0I1IpmvKg722Ti4ahLz?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/ # Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
Franklin0314/visual-llm	Franklin0314	2024-02-19T13:38:53Z	4	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:THUDM/chatglm3-6b", "base_model:adapter:THUDM/chatglm3-6b", "region:us" ]	null	2024-02-19T13:20:24Z	--- library_name: peft base_model: THUDM/chatglm3-6b --- # 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. 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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] ### Framework versions - PEFT 0.7.1
Haary/haryra-7b-usk-v1	Haary	2024-02-19T13:37:38Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T13:31:11Z	--- 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. 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LoneStriker/BioMistral-7B-5.0bpw-h6-exl2	LoneStriker	2024-02-19T13:37:05Z	2	0	transformers	[ "transformers", "mistral", "text-generation", "medical", "biology", "conversational", "fr", "en", "de", "nl", "es", "pt", "pl", "ro", "it", "dataset:pubmed", "arxiv:2402.10373", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T13:33:20Z	--- license: apache-2.0 datasets: - pubmed language: - fr - en - de - nl - es - pt - pl - ro - it pipeline_tag: text-generation tags: - medical - biology --- <p align="center"> <img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/> </p> # BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains Abstract: Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. # 1. BioMistral models BioMistral is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC. \| Model Name \| Base Model \| Model Type \| Sequence Length \| Download \| \|:-------------------:\|:----------------------------------:\|:-------------------:\|:---------------:\|:-----------------------------------------------------:\| \| BioMistral-7B \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Further Pre-trained \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) \| \| BioMistral-7B-DARE \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge DARE \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) \| \| BioMistral-7B-TIES \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge TIES \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) \| \| BioMistral-7B-SLERP \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge SLERP \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) \| # 2. Quantized Models \| Base Model \| Method \| q_group_size \| w_bit \| version \| VRAM GB \| Time \| Download \| \|:-------------------:\|:------:\|:------------:\|:-----:\|:-------:\|:-------:\|:------:\|:--------:\| \| BioMistral-7B \| FP16/BF16 \| \| \| \| 15.02 \| x1.00 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) \| \| BioMistral-7B \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B \| AWQ \| 128 \| 4 \| GEMV \| 4.68 \| x10.30 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) \| \| BioMistral-7B \| BnB.4 \| \| 4 \| \| 5.03 \| x3.25 \| [HuggingFace](blank) \| \| BioMistral-7B \| BnB.8 \| \| 8 \| \| 8.04 \| x4.34 \| [HuggingFace](blank) \| \| BioMistral-7B-DARE \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B-TIES \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B-SLERP \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) \| # 2. Using BioMistral You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow. Loading the model and tokenizer : ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B") model = AutoModel.from_pretrained("BioMistral/BioMistral-7B") ``` # 3. Supervised Fine-tuning Benchmark \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA \| MedQA 5 opts \| PubMedQA \| MedMCQA \| Avg. \| \|-------------------------------------------\|:---------------------------------------------:\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|------------------\| \| BioMistral 7B \| 59.9 \| 64.0 \| 56.5 \| 60.4 \| 59.0 \| 54.7 \| 50.6 \| 42.8 \| 77.5 \| 48.1 \| 57.3 \| \| Mistral 7B Instruct \| 62.9 \| 57.0 \| 55.6 \| 59.4 \| 62.5 \| <u>57.2</u> \| 42.0 \| 40.9 \| 75.7 \| 46.1 \| 55.9 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| BioMistral 7B Ensemble \| <u>62.8</u> \| 62.7 \| <u>57.5</u> \| 63.5 \| 64.3 \| 55.7 \| 50.6 \| 43.6 \| 77.5 \| 48.8 \| 58.7 \| \| BioMistral 7B DARE \| 62.3 \| 67.0 \| 55.8 \| 61.4 \| 66.9 \| 58.0 \| 51.1 \| 45.2 \| <u>77.7</u> \| <u>48.7</u> \| 59.4 \| \| BioMistral 7B TIES \| 60.1 \| <u>65.0</u> \| 58.5 \| 60.5 \| 60.4 \| 56.5 \| 49.5 \| 43.2 \| 77.5 \| 48.1 \| 57.9 \| \| BioMistral 7B SLERP \| 62.5 \| 64.7 \| 55.8 \| <u>62.7</u> \| <u>64.8</u> \| 56.3 \| <u>50.8</u> \| <u>44.3</u> \| 77.8 \| 48.6 \| <u>58.8</u> \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| MedAlpaca 7B \| 53.1 \| 58.0 \| 54.1 \| 58.8 \| 58.1 \| 48.6 \| 40.1 \| 33.7 \| 73.6 \| 37.0 \| 51.5 \| \| PMC-LLaMA 7B \| 24.5 \| 27.7 \| 35.3 \| 17.4 \| 30.3 \| 23.3 \| 25.5 \| 20.2 \| 72.9 \| 26.6 \| 30.4 \| \| MediTron-7B \| 41.6 \| 50.3 \| 46.4 \| 27.9 \| 44.4 \| 30.8 \| 41.6 \| 28.1 \| 74.9 \| 41.3 \| 42.7 \| \| BioMedGPT-LM-7B \| 51.4 \| 52.0 \| 49.4 \| 53.3 \| 50.7 \| 49.1 \| 42.5 \| 33.9 \| 76.8 \| 37.6 \| 49.7 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| GPT-3.5 Turbo 1106* \| 74.71 \| 74.00 \| 65.92 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 50.82 \| 72.66 \| 53.79 \| 66.0 \| Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT. # Citation BibTeX Arxiv : [https://arxiv.org/abs/2402.10373](https://arxiv.org/abs/2402.10373) ```bibtex @misc{labrak2024biomistral, title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour}, year={2024}, eprint={2402.10373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
LoneStriker/AlphaMonarch-7B-GPTQ	LoneStriker	2024-02-19T13:37:05Z	8	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:quantized:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-19T13:33:25Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
nizamovtimur/rubert-tiny2-wikiutmn	nizamovtimur	2024-02-19T13:32:20Z	17	2	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-19T13:32:05Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # nizamovtimur/rubert-tiny2-wikiutmn This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 312 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('nizamovtimur/rubert-tiny2-wikiutmn') 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=nizamovtimur/rubert-tiny2-wikiutmn) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 45 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', '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": 10, "evaluation_steps": 0, "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": 355, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
speechbrain/sepformer-wsj02mix	speechbrain	2024-02-19T13:28:30Z	6,769	58	speechbrain	[ "speechbrain", "Source Separation", "Speech Separation", "Audio Source Separation", "WSJ02Mix", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation", "en", "dataset:WSJ0-2Mix", "arxiv:2010.13154", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	audio-to-audio	2022-03-02T23:29:05Z	--- language: "en" thumbnail: tags: - Source Separation - Speech Separation - Audio Source Separation - WSJ02Mix - SepFormer - Transformer - audio-to-audio - audio-source-separation - speechbrain license: "apache-2.0" datasets: - WSJ0-2Mix metrics: - SI-SNRi - SDRi --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # SepFormer trained on WSJ0-2Mix This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The model performance is 22.4 dB on the test set of WSJ0-2Mix dataset. \| Release \| Test-Set SI-SNRi \| Test-Set SDRi \| \|:-------------:\|:--------------:\|:--------------:\| \| 09-03-21 \| 22.4dB \| 22.6dB \| You can listen to example results obtained on the test set of WSJ0-2/3Mix through [here](https://sourceseparationresearch.com/static/sepformer_example_results/sepformer_results.html). ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Perform source separation on your own audio file ```python from speechbrain.inference.separation import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-wsj02mix", savedir='pretrained_models/sepformer-wsj02mix') # for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav') torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000) torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000) ``` The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training The model was trained with SpeechBrain (fc2eabb7). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/WSJ0Mix/separation python train.py hparams/sepformer.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1cON-eqtKv_NYnJhaE9VjLT_e2ybn-O7u?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ``` #### Referencing SepFormer ```bibtex @inproceedings{subakan2021attention, title={Attention is All You Need in Speech Separation}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong}, year={2021}, booktitle={ICASSP 2021} } ``` # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/
euswam/teste	euswam	2024-02-19T13:24:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-19T13:23:30Z	--- license: creativeml-openrail-m ---
speechbrain/noisy-whisper-rescuespeech	speechbrain	2024-02-19T13:19:40Z	5	4	speechbrain	[ "speechbrain", "whisper", "pytorch", "Transformer", "automatic-speech-recognition", "de", "dataset:RescueSpeech", "arxiv:2306.04054", "license:apache-2.0", "model-index", "region:us" ]	automatic-speech-recognition	2023-07-02T00:20:48Z	--- language: - de thumbnail: null pipeline_tag: automatic-speech-recognition tags: - whisper - pytorch - speechbrain - Transformer license: apache-2.0 datasets: - RescueSpeech metrics: - wer - sisnri - sdri - pesq - stoi model-index: - name: noisy-whisper-resucespeech results: - task: name: Noise Robust Automatic Speech Recognition type: noise-robust-automatic-speech-recognition metrics: - name: Test WER type: wer value: '24.20' - name: Test PESQ type: pesq value: '2.085' - name: Test SI-SNRi type: si-snri value: '7.334' - name: Test SI-SDRi type: si-sdri value: '7.871' inference: false --- # Noise robust speech recognition on jointly trained SepFormer speech enhancement and Whisper ASR using RescueSpeech data. This repository provides all the necessary tools to perform noise robust automatic speech recognition on a simple combination of an enhancement model (SepFormer) and speech recognizer (Whisper). Initially, the models are fine-tuned individually on the RescueSpeech dataset, and then they are integrated to undergo joint training, enabling them to effectively handle noise interference. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following on test set: \| Release \| SISNRi \| SDRi \| PESQ \| STOI \| WER \| GPUs \| \|:-------------:\|:--------------:\|:--------------:\| :--------:\|:--------------:\| :--------:\|:--------:\| \| 07-11-23 \| 7.482 \| 8.011 \| 2.083 \| 0.859 \| 45.29 \| 1xA100 80 GB \| ## Pipeline description - The enhancement system is composed of SepFormer model. - The model is first trained on Microsoft-DNS dataset and subsequently fine-tuned on RescueSpeech dataset. - The enhanced utterances are fed to the ASR model. - And the ASR system is composed of whisper encoder-decoder blocks: - The pretrained whisper-large-v2 encoder is frozen. - The pretrained Whisper tokenizer is used. - A pretrained Whisper-large-v2 decoder ([openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2)) is finetuned on RescueSpeech dataset. The obtained final acoustic representation is given to the greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. ## Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files (in German) ```python from speechbrain.inference.separation import SepformerSeparation as Separator from speechbrain.inference.ASR import WhisperASR import torch enh_model = Separator.from_hparams( source="speechbrain/noisy-whisper-resucespeech", savedir='pretrained_models/noisy-whisper-rescuespeech', hparams_file="enhance.yaml" ) asr_model = WhisperASR.from_hparams( source="speechbrain/noisy-whisper-resucespeech", savedir="pretrained_models/noisy-whisper-rescuespeech", hparams_file="asr.yaml" ) # For custom file, change the path accordingly est_sources = enh_model.separate_file(path='speechbrain/noisy-whisper-resucespeech/example_rescuespeech16k.wav') pred_words, _ = asr_model(est_sources[:, :, 0], torch.tensor([1.0])) ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/kqs2ld14fm20cxl/AACiobSLdNtXhm-4Y3IIbTeia?dl=0). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` ### Referencing RescueSpeech ```bibtex @misc{sagar2023rescuespeech, title={RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain}, author={Sangeet Sagar and Mirco Ravanelli and Bernd Kiefer and Ivana Kruijff Korbayova and Josef van Genabith}, year={2023}, eprint={2306.04054}, archivePrefix={arXiv}, primaryClass={eess.AS} } ``` #### About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain
lole25/phi-2-ft-lora-riddles	lole25	2024-02-19T13:19:40Z	7	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T13:16:01Z	--- 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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speechbrain/sepformer-wham	speechbrain	2024-02-19T13:18:06Z	1,033	37	speechbrain	[ "speechbrain", "audio-to-audio", "audio-source-separation", "Source Separation", "Speech Separation", "Audio Source Separation", "WHAM!", "SepFormer", "Transformer", "en", "arxiv:2010.13154", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	audio-to-audio	2022-03-02T23:29:05Z	--- language: "en" thumbnail: tags: - audio-to-audio - audio-source-separation - Source Separation - Speech Separation - Audio Source Separation - WHAM! - SepFormer - Transformer - speechbrain license: "apache-2.0" metrics: - SI-SNRi - SDRi --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # SepFormer trained on WHAM! This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAM!](http://wham.whisper.ai/) dataset, which is basically a version of WSJ0-Mix dataset with environmental noise. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The model performance is 16.3 dB SI-SNRi on the test set of WHAM! dataset. \| Release \| Test-Set SI-SNRi \| Test-Set SDRi \| \|:-------------:\|:--------------:\|:--------------:\| \| 09-03-21 \| 16.3 dB \| 16.7 dB \| ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Perform source separation on your own audio file ```python from speechbrain.inference.separation import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-wham", savedir='pretrained_models/sepformer-wham') # for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav') torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000) torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000) ``` The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training The model was trained with SpeechBrain (e375cd13). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/WHAMandWHAMR/separation python train.py hparams/sepformer-wham.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1dIAT8hZxvdJPZNUb8Zkk3BuN7GZ9-mZb?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ``` #### Referencing SepFormer ```bibtex @inproceedings{subakan2021attention, title={Attention is All You Need in Speech Separation}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong}, year={2021}, booktitle={ICASSP 2021} } ``` # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/
speechbrain/sepformer-whamr	speechbrain	2024-02-19T13:16:09Z	1,565	13	speechbrain	[ "speechbrain", "Source Separation", "Speech Separation", "Audio Source Separation", "WHAM!", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation", "en", "arxiv:2010.13154", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	audio-to-audio	2022-03-02T23:29:05Z	--- language: "en" thumbnail: tags: - speechbrain - Source Separation - Speech Separation - Audio Source Separation - WHAM! - SepFormer - Transformer - audio-to-audio - audio-source-separation license: "apache-2.0" metrics: - SI-SNRi - SDRi --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # SepFormer trained on WHAMR! This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) dataset, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The model performance is 13.7 dB SI-SNRi on the test set of WHAMR! dataset. \| Release \| Test-Set SI-SNRi \| Test-Set SDRi \| \|:-------------:\|:--------------:\|:--------------:\| \| 30-03-21 \| 13.7 dB \| 12.7 dB \| ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Perform source separation on your own audio file ```python from speechbrain.inference.separation import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-whamr", savedir='pretrained_models/sepformer-whamr') # for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav') torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000) torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000) ``` The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training The model was trained with SpeechBrain (e375cd13). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/WHAMandWHAMR/separation python train.py hparams/sepformer-whamr.yaml --data_folder=YOUR_DATA_FOLDER --rir_path=YOUR_ROOM_IMPULSE_SAVE_PATH ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1m1xfx2ojf7qgOyscJVVCQFRY0VRl0rdi?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ``` #### Referencing SepFormer ```bibtex @inproceedings{subakan2021attention, title={Attention is All You Need in Speech Separation}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong}, year={2021}, booktitle={ICASSP 2021} } ``` # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/
MAdAiLab/llama2_7b_r32_alpha64_merged_final	MAdAiLab	2024-02-19T13:15:24Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-19T13:13:28Z	--- 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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LoneStriker/BioMistral-7B-GGUF	LoneStriker	2024-02-19T13:13:27Z	15	0	null	[ "gguf", "medical", "biology", "text-generation", "fr", "en", "de", "nl", "es", "pt", "pl", "ro", "it", "dataset:pubmed", "arxiv:2402.10373", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-02-19T13:01:01Z	--- license: apache-2.0 datasets: - pubmed language: - fr - en - de - nl - es - pt - pl - ro - it pipeline_tag: text-generation tags: - medical - biology --- <p align="center"> <img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/> </p> # BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains Abstract: Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. # 1. BioMistral models BioMistral is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC. \| Model Name \| Base Model \| Model Type \| Sequence Length \| Download \| \|:-------------------:\|:----------------------------------:\|:-------------------:\|:---------------:\|:-----------------------------------------------------:\| \| BioMistral-7B \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Further Pre-trained \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) \| \| BioMistral-7B-DARE \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge DARE \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) \| \| BioMistral-7B-TIES \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge TIES \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) \| \| BioMistral-7B-SLERP \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge SLERP \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) \| # 2. Quantized Models \| Base Model \| Method \| q_group_size \| w_bit \| version \| VRAM GB \| Time \| Download \| \|:-------------------:\|:------:\|:------------:\|:-----:\|:-------:\|:-------:\|:------:\|:--------:\| \| BioMistral-7B \| FP16/BF16 \| \| \| \| 15.02 \| x1.00 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) \| \| BioMistral-7B \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B \| AWQ \| 128 \| 4 \| GEMV \| 4.68 \| x10.30 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) \| \| BioMistral-7B \| BnB.4 \| \| 4 \| \| 5.03 \| x3.25 \| [HuggingFace](blank) \| \| BioMistral-7B \| BnB.8 \| \| 8 \| \| 8.04 \| x4.34 \| [HuggingFace](blank) \| \| BioMistral-7B-DARE \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B-TIES \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B-SLERP \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) \| # 2. Using BioMistral You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow. Loading the model and tokenizer : ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B") model = AutoModel.from_pretrained("BioMistral/BioMistral-7B") ``` # 3. Supervised Fine-tuning Benchmark \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA \| MedQA 5 opts \| PubMedQA \| MedMCQA \| Avg. \| \|-------------------------------------------\|:---------------------------------------------:\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|------------------\| \| BioMistral 7B \| 59.9 \| 64.0 \| 56.5 \| 60.4 \| 59.0 \| 54.7 \| 50.6 \| 42.8 \| 77.5 \| 48.1 \| 57.3 \| \| Mistral 7B Instruct \| 62.9 \| 57.0 \| 55.6 \| 59.4 \| 62.5 \| <u>57.2</u> \| 42.0 \| 40.9 \| 75.7 \| 46.1 \| 55.9 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| BioMistral 7B Ensemble \| <u>62.8</u> \| 62.7 \| <u>57.5</u> \| 63.5 \| 64.3 \| 55.7 \| 50.6 \| 43.6 \| 77.5 \| 48.8 \| 58.7 \| \| BioMistral 7B DARE \| 62.3 \| 67.0 \| 55.8 \| 61.4 \| 66.9 \| 58.0 \| 51.1 \| 45.2 \| <u>77.7</u> \| <u>48.7</u> \| 59.4 \| \| BioMistral 7B TIES \| 60.1 \| <u>65.0</u> \| 58.5 \| 60.5 \| 60.4 \| 56.5 \| 49.5 \| 43.2 \| 77.5 \| 48.1 \| 57.9 \| \| BioMistral 7B SLERP \| 62.5 \| 64.7 \| 55.8 \| <u>62.7</u> \| <u>64.8</u> \| 56.3 \| <u>50.8</u> \| <u>44.3</u> \| 77.8 \| 48.6 \| <u>58.8</u> \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| MedAlpaca 7B \| 53.1 \| 58.0 \| 54.1 \| 58.8 \| 58.1 \| 48.6 \| 40.1 \| 33.7 \| 73.6 \| 37.0 \| 51.5 \| \| PMC-LLaMA 7B \| 24.5 \| 27.7 \| 35.3 \| 17.4 \| 30.3 \| 23.3 \| 25.5 \| 20.2 \| 72.9 \| 26.6 \| 30.4 \| \| MediTron-7B \| 41.6 \| 50.3 \| 46.4 \| 27.9 \| 44.4 \| 30.8 \| 41.6 \| 28.1 \| 74.9 \| 41.3 \| 42.7 \| \| BioMedGPT-LM-7B \| 51.4 \| 52.0 \| 49.4 \| 53.3 \| 50.7 \| 49.1 \| 42.5 \| 33.9 \| 76.8 \| 37.6 \| 49.7 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| GPT-3.5 Turbo 1106* \| 74.71 \| 74.00 \| 65.92 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 50.82 \| 72.66 \| 53.79 \| 66.0 \| Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT. # Citation BibTeX Arxiv : [https://arxiv.org/abs/2402.10373](https://arxiv.org/abs/2402.10373) ```bibtex @misc{labrak2024biomistral, title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour}, year={2024}, eprint={2402.10373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
LoneStriker/AlphaMonarch-7B-AWQ	LoneStriker	2024-02-19T13:13:11Z	8	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:quantized:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-02-19T13:09:24Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
speechbrain/asr-crdnn-rnnlm-librispeech	speechbrain	2024-02-19T13:10:14Z	2,209	10	speechbrain	[ "speechbrain", "automatic-speech-recognition", "CTC", "Attention", "pytorch", "en", "dataset:librispeech", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: "en" thumbnail: tags: - automatic-speech-recognition - CTC - Attention - pytorch - speechbrain license: "apache-2.0" datasets: - librispeech metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # CRDNN with CTC/Attention and RNNLM trained on LibriSpeech This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on LibriSpeech (EN) within SpeechBrain. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: \| Release \| Test WER \| GPUs \| \|:-------------:\|:--------------:\| :--------:\| \| 20-05-22 \| 3.09 \| 1xV100 32GB \| ## Pipeline description This ASR system is composed with 3 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech. - Neural language model (RNNLM) trained on the full 10M words dataset. - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convolutional neural networks with normalisation and pooling on the frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain the final acoustic representation that is given to the CTC and attention decoders. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files (in English) ```python from speechbrain.inference.ASR import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="pretrained_models/asr-crdnn-rnnlm-librispeech") asr_model.transcribe_file('speechbrain/asr-crdnn-rnnlm-librispeech/example.wav') ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ## Parallel Inference on a Batch Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model. ### Training The model was trained with SpeechBrain (Commit hash: '2abd9f01'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LibriSpeech/ASR/seq2seq/ python train.py hparams/train_BPE_1000.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1SAndjcThdkO-YQF8kvwPOXlQ6LMT71vt?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/ # Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
arunp77/Machine-learning	arunp77	2024-02-19T13:08:56Z	0	0	null	[ "code", "image-classification", "en", "dataset:teknium/OpenHermes-2.5", "license:mit", "region:us" ]	image-classification	2024-02-19T13:04:56Z	--- license: mit datasets: - teknium/OpenHermes-2.5 language: - en metrics: - accuracy pipeline_tag: image-classification tags: - code --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d351977d6147c2ced39549/Mc6dcD-633aFORzcMLNu8.png) # Table of contents <!--ts--> - Introduction to Machine learning: - Analytics - Categories of ML algorithms - A typical ML algorithm uses the following steps - Framework for develping ML models - Machine learning Fundamentals and application: This repository provides a comprehensive introduction to machine learning concepts, algorithms, and applications. It covers the essential theoretical foundations of machine learning, as well as practical examples and hands-on projects to solidify your understanding. - Deep Learning: Delve into the world of deep learning, exploring its theoretical underpinnings and applying it to real-world problems. This folder contains notebooks and related files that guide you through the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. - Projects-ML: Hands-on experience is invaluable for mastering machine learning. This folder offers a collection of sample projects that apply the concepts and algorithms discussed in the other folders. These projects provide practical exposure to machine learning techniques and allow you to apply your knowledge to real-world scenarios. - Statistical fundamentals: Statistics plays a pivotal role in machine learning. This folder delves into the intricacies of statistics, particularly inferential and descriptive statistics. It provides detailed explanations of statistical concepts and introduces applications of statistics in machine learning. - ML-Data: Datasets are indispensable for machine learning projects. This folder contains the data files used in the sample projects, ensuring that you have access to the relevant data to replicate and experiment with the provided examples. - References <!--te--> <!---------------------------- Reference ------------------------------> # Reference Individual references are given in individual notebooks. Here are few general references that one can look at: 1. Machine learning using python, Manarajan Pradhan, U Dinesh Kumar 2. Please follow lecture series provided at [Jovian.ml](https://jovian.ai/), i.e. [Data Analysis with Python: Zero to Pandas](https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas) 3. [Python roadmap](https://roadmap.sh/python/) 4. [Python tutorials at w3school](https://www.w3schools.com/python/default.asp) 5. [30 Days of Python](https://github.com/asabeneh/30-days-of-python) 6. [Python official documents](https://docs.python.org/3/tutorial/) 7. [Data science roadmaps](https://github.com/codebasics/py/blob/master/TechTopics/DataScienceRoadMap2020/data_science_roadmap_2020.md) 8. Statistics, Murray R. Spiegel, Larry J. Stephens
tarekziade/distilbert-NER	tarekziade	2024-02-19T13:05:29Z	9	0	transformers	[ "transformers", "onnx", "distilbert", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-13T14:22:30Z	--- license: apache-2.0 --- This is just a conversion of https://huggingface.co/dslim/distilbert-NER into ONNX files so the model can be used with Transformers.js
EsilaAycill/npc_chat_v6	EsilaAycill	2024-02-19T13:03:47Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-02-19T13:00:58Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # 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. 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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] ### Framework versions - PEFT 0.8.2
kw9101/distilbert-base-uncased-finetuned-emotion	kw9101	2024-02-19T13:03:35Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-09T06:26:21Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.921 - name: F1 type: f1 value: 0.9209418048779289 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2242 - Accuracy: 0.921 - F1: 0.9209 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3423 \| 0.9035 \| 0.9022 \| \| No log \| 2.0 \| 500 \| 0.2242 \| 0.921 \| 0.9209 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Eyesiga/acholi_asr	Eyesiga	2024-02-19T13:03:14Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ac", "dataset:tericlabs", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-23T12:40:19Z	--- language: - ac license: apache-2.0 base_model: openai/whisper-small tags: - whisper-event - generated_from_trainer datasets: - tericlabs metrics: - wer model-index: - name: Whisper base acholi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Sunbird type: tericlabs metrics: - name: Wer type: wer value: 122.26379794200186 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper base acholi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Sunbird dataset. It achieves the following results on the evaluation set: - Loss: 2.8895 - Wer: 122.2638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.2321 \| 3.32 \| 1000 \| 2.9610 \| 140.3181 \| \| 2.5056 \| 6.64 \| 2000 \| 2.7358 \| 116.9317 \| \| 2.0671 \| 9.97 \| 3000 \| 2.7957 \| 144.9953 \| \| 1.7382 \| 13.29 \| 4000 \| 2.8895 \| 122.2638 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
whitefox123/whisper-large-ar6	whitefox123	2024-02-19T12:55:02Z	36	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ar", "dataset:whitefox123/tashkeel", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-19T07:41:49Z	--- language: - ar license: apache-2.0 base_model: openai/whisper-large-v3 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - whitefox123/tashkeel metrics: - wer model-index: - name: Whisper large - tuned results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: CLARtts type: whitefox123/tashkeel config: default split: None args: 'config: ar, split: test' metrics: - name: Wer type: wer value: 217.9099099099099 --- <!-- 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 large - tuned This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the CLARtts dataset. It achieves the following results on the evaluation set: - Loss: 0.1346 - Wer: 217.9099 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3125 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0843 \| 1.6 \| 1000 \| 0.1141 \| 248.9730 \| \| 0.024 \| 3.2 \| 2000 \| 0.1194 \| 274.9189 \| \| 0.0108 \| 4.8 \| 3000 \| 0.1346 \| 217.9099 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.17.0 - Tokenizers 0.15.2
LoneStriker/NeuralMonarch-7B-AWQ	LoneStriker	2024-02-19T12:51:48Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/Monarch-7B", "base_model:quantized:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-02-19T12:50:02Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs base_model: - mlabonne/Monarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
shreya28/a-cute-rabbit	shreya28	2024-02-19T12:51:41Z	0	0	null	[ "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-19T12:49:31Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### A-cute-rabbit- Dreambooth model trained by shreya28 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 1032211081 Sample pictures of this concept: ![0](https://huggingface.co/shreya28/a-cute-rabbit/resolve/main/sample_images/xzg_(1).jpg) ![1](https://huggingface.co/shreya28/a-cute-rabbit/resolve/main/sample_images/xzg_(4).jpg) ![2](https://huggingface.co/shreya28/a-cute-rabbit/resolve/main/sample_images/xzg_(3).jpg) ![3](https://huggingface.co/shreya28/a-cute-rabbit/resolve/main/sample_images/xzg_(2).jpg)
FacebookAI/xlm-roberta-large-finetuned-conll03-english	FacebookAI	2024-02-19T12:48:53Z	1,205,244	162	transformers	[ "transformers", "pytorch", "rust", "onnx", "safetensors", "xlm-roberta", "token-classification", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:1911.02116", "arxiv:2008.03415", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh --- # xlm-roberta-large-finetuned-conll03-english # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Technical Specifications](#technical-specifications) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) 10. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [conll2003](https://huggingface.co/datasets/conll2003) dataset in English. - Developed by: See [associated paper](https://arxiv.org/abs/1911.02116) - Model type: Multi-lingual language model - Language(s) (NLP) or Countries (images): XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in English - License: More information needed - Related Models: [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm) - Parent Model: [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) - Resources for more information: -[GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) -[Associated Paper](https://arxiv.org/abs/1911.02116) # Uses ## Direct Use The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. ## Downstream Use Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face [token classification docs](https://huggingface.co/tasks/token-classification). ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations CONTENT WARNING: Readers should be made aware that language generated by this model may be disturbing or offensive to some and may propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). In the context of tasks relevant to this model, [Mishra et al. (2020)](https://arxiv.org/pdf/2008.03415.pdf) explore social biases in NER systems for English and find that there is systematic bias in existing NER systems in that they fail to identify named entities from different demographic groups (though this paper did not look at BERT). For example, using a sample sentence from [Mishra et al. (2020)](https://arxiv.org/pdf/2008.03415.pdf): ```python >>> from transformers import pipeline >>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> classifier = pipeline("ner", model=model, tokenizer=tokenizer) >>> classifier("Alya told Jasmine that Andrew could pay with cash..") [{'end': 2, 'entity': 'I-PER', 'index': 1, 'score': 0.9997861, 'start': 0, 'word': '▁Al'}, {'end': 4, 'entity': 'I-PER', 'index': 2, 'score': 0.9998591, 'start': 2, 'word': 'ya'}, {'end': 16, 'entity': 'I-PER', 'index': 4, 'score': 0.99995816, 'start': 10, 'word': '▁Jasmin'}, {'end': 17, 'entity': 'I-PER', 'index': 5, 'score': 0.9999584, 'start': 16, 'word': 'e'}, {'end': 29, 'entity': 'I-PER', 'index': 7, 'score': 0.99998057, 'start': 23, 'word': '▁Andrew'}] ``` ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training See the following resources for training data and training procedure details: - [XLM-RoBERTa-large model card](https://huggingface.co/xlm-roberta-large) - [CoNLL-2003 data card](https://huggingface.co/datasets/conll2003) - [Associated paper](https://arxiv.org/pdf/1911.02116.pdf) # Evaluation See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for evaluation details. # Environmental Impact 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: 500 32GB Nvidia V100 GPUs (from the [associated paper](https://arxiv.org/pdf/1911.02116.pdf)) - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for further details. # Citation BibTeX: ```bibtex @article{conneau2019unsupervised, title={Unsupervised Cross-lingual Representation Learning at Scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } ``` APA: - Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. You can use this model directly within a pipeline for NER. <details> <summary> Click to expand </summary> ```python >>> from transformers import AutoTokenizer, AutoModelForTokenClassification >>> from transformers import pipeline >>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> classifier = pipeline("ner", model=model, tokenizer=tokenizer) >>> classifier("Hello I'm Omar and I live in Zürich.") [{'end': 14, 'entity': 'I-PER', 'index': 5, 'score': 0.9999175, 'start': 10, 'word': '▁Omar'}, {'end': 35, 'entity': 'I-LOC', 'index': 10, 'score': 0.9999906, 'start': 29, 'word': '▁Zürich'}] ``` </details>
FacebookAI/xlm-roberta-large-finetuned-conll02-dutch	FacebookAI	2024-02-19T12:48:36Z	1,155	3	transformers	[ "transformers", "pytorch", "rust", "xlm-roberta", "fill-mask", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:1911.02116", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh --- # xlm-roberta-large-finetuned-conll02-dutch # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Technical Specifications](#technical-specifications) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) 10. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [CoNLL-2002](https://huggingface.co/datasets/conll2002) dataset in Dutch. - Developed by: See [associated paper](https://arxiv.org/abs/1911.02116) - Model type: Multi-lingual language model - Language(s) (NLP): XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in Dutch - License: More information needed - Related Models: [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm) - Parent Model: [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) - Resources for more information: -[GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) -[Associated Paper](https://arxiv.org/abs/1911.02116) -[CoNLL-2002 data card](https://huggingface.co/datasets/conll2002) # Uses ## Direct Use The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. ## Downstream Use Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face [token classification docs](https://huggingface.co/tasks/token-classification). ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations CONTENT WARNING: Readers should be made aware that language generated by this model may be disturbing or offensive to some and may propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training See the following resources for training data and training procedure details: - [XLM-RoBERTa-large model card](https://huggingface.co/xlm-roberta-large) - [CoNLL-2002 data card](https://huggingface.co/datasets/conll2002) - [Associated paper](https://arxiv.org/pdf/1911.02116.pdf) # Evaluation See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for evaluation details. # Environmental Impact 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: 500 32GB Nvidia V100 GPUs (from the [associated paper](https://arxiv.org/pdf/1911.02116.pdf)) - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for further details. # Citation BibTeX: ```bibtex @article{conneau2019unsupervised, title={Unsupervised Cross-lingual Representation Learning at Scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } ``` APA: - Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. You can use this model directly within a pipeline for NER. <details> <summary> Click to expand </summary> ```python >>> from transformers import AutoTokenizer, AutoModelForTokenClassification >>> from transformers import pipeline >>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll02-dutch") >>> model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll02-dutch") >>> classifier = pipeline("ner", model=model, tokenizer=tokenizer) >>> classifier("Mijn naam is Emma en ik woon in Londen.") [{'end': 17, 'entity': 'B-PER', 'index': 4, 'score': 0.9999807, 'start': 13, 'word': '▁Emma'}, {'end': 36, 'entity': 'B-LOC', 'index': 9, 'score': 0.9999871, 'start': 32, 'word': '▁Lond'}] ``` </details>
FacebookAI/xlm-roberta-large	FacebookAI	2024-02-19T12:48:30Z	34,940,906	384	transformers	[ "transformers", "pytorch", "tf", "jax", "onnx", "safetensors", "xlm-roberta", "fill-mask", "exbert", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:1911.02116", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- tags: - exbert language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- # XLM-RoBERTa (large-sized model) XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/xlmr). Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of 100 languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the XLM-RoBERTa model as inputs. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlm-roberta) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2. ## Usage You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='xlm-roberta-large') >>> unmasker("Hello I'm a <mask> model.") [{'score': 0.10563907772302628, 'sequence': "Hello I'm a fashion model.", 'token': 54543, 'token_str': 'fashion'}, {'score': 0.08015287667512894, 'sequence': "Hello I'm a new model.", 'token': 3525, 'token_str': 'new'}, {'score': 0.033413201570510864, 'sequence': "Hello I'm a model model.", 'token': 3299, 'token_str': 'model'}, {'score': 0.030217764899134636, 'sequence': "Hello I'm a French model.", 'token': 92265, 'token_str': 'French'}, {'score': 0.026436051353812218, 'sequence': "Hello I'm a sexy model.", 'token': 17473, 'token_str': 'sexy'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-large') model = AutoModelForMaskedLM.from_pretrained("xlm-roberta-large") # prepare input text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') # forward pass output = model(**encoded_input) ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1911-02116, author = {Alexis Conneau and Kartikay Khandelwal and Naman Goyal and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzm{\'{a}}n and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov}, title = {Unsupervised Cross-lingual Representation Learning at Scale}, journal = {CoRR}, volume = {abs/1911.02116}, year = {2019}, url = {http://arxiv.org/abs/1911.02116}, eprinttype = {arXiv}, eprint = {1911.02116}, timestamp = {Mon, 11 Nov 2019 18:38:09 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=xlm-roberta-base"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
FacebookAI/xlm-roberta-base	FacebookAI	2024-02-19T12:48:21Z	12,342,725	631	transformers	[ "transformers", "pytorch", "tf", "jax", "onnx", "safetensors", "xlm-roberta", "fill-mask", "exbert", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:1911.02116", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- tags: - exbert language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- # XLM-RoBERTa (base-sized model) XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/xlmr). Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of 100 languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the XLM-RoBERTa model as inputs. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlm-roberta) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2. ## Usage You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='xlm-roberta-base') >>> unmasker("Hello I'm a <mask> model.") [{'score': 0.10563907772302628, 'sequence': "Hello I'm a fashion model.", 'token': 54543, 'token_str': 'fashion'}, {'score': 0.08015287667512894, 'sequence': "Hello I'm a new model.", 'token': 3525, 'token_str': 'new'}, {'score': 0.033413201570510864, 'sequence': "Hello I'm a model model.", 'token': 3299, 'token_str': 'model'}, {'score': 0.030217764899134636, 'sequence': "Hello I'm a French model.", 'token': 92265, 'token_str': 'French'}, {'score': 0.026436051353812218, 'sequence': "Hello I'm a sexy model.", 'token': 17473, 'token_str': 'sexy'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base') model = AutoModelForMaskedLM.from_pretrained("xlm-roberta-base") # prepare input text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') # forward pass output = model(**encoded_input) ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1911-02116, author = {Alexis Conneau and Kartikay Khandelwal and Naman Goyal and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzm{\'{a}}n and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov}, title = {Unsupervised Cross-lingual Representation Learning at Scale}, journal = {CoRR}, volume = {abs/1911.02116}, year = {2019}, url = {http://arxiv.org/abs/1911.02116}, eprinttype = {arXiv}, eprint = {1911.02116}, timestamp = {Mon, 11 Nov 2019 18:38:09 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=xlm-roberta-base"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
remmymilkyway/q-FrozenLake-v1-4x4-noSlippery	remmymilkyway	2024-02-19T12:47:15Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-19T12:47:11Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="remmymilkyway/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
FacebookAI/roberta-large-mnli	FacebookAI	2024-02-19T12:47:11Z	196,490	164	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "roberta", "text-classification", "autogenerated-modelcard", "en", "dataset:multi_nli", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:1907.11692", "arxiv:1806.02847", "arxiv:1804.07461", "arxiv:1704.05426", "arxiv:1508.05326", "arxiv:1809.05053", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - en license: mit tags: - autogenerated-modelcard datasets: - multi_nli - wikipedia - bookcorpus --- # roberta-large-mnli ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-author) ## Model Details Model Description: roberta-large-mnli is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://huggingface.co/datasets/multi_nli) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective. - Developed by: See [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for model developers - Model Type: Transformer-based language model - Language(s): English - License: MIT - Parent Model: This model is a fine-tuned version of the RoBERTa large model. Users should see the [RoBERTa large model card](https://huggingface.co/roberta-large) for relevant information. - Resources for more information: - [Research Paper](https://arxiv.org/abs/1907.11692) - [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) ## How to Get Started with the Model Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so: ```python from transformers import pipeline classifier = pipeline('zero-shot-classification', model='roberta-large-mnli') ``` You can then use this pipeline to classify sequences into any of the class names you specify. For example: ```python sequence_to_classify = "one day I will see the world" candidate_labels = ['travel', 'cooking', 'dancing'] classifier(sequence_to_classify, candidate_labels) ``` ## Uses #### Direct Use This fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (see the [GitHub repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for examples) and zero-shot sequence classification. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The [RoBERTa large model card](https://huggingface.co/roberta-large) notes that: "The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral." Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python sequence_to_classify = "The CEO had a strong handshake." candidate_labels = ['male', 'female'] hypothesis_template = "This text speaks about a {} profession." classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template) ``` Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## Training #### Training Data This model was fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. Also see the [MNLI data card](https://huggingface.co/datasets/multi_nli) for more information. As described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The RoBERTa model was pretrained on the reunion of five datasets: > > - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books; > - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ; > - [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news articles crawled between September 2016 and February 2019. > - [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to train GPT-2, > - [Stories](https://arxiv.org/abs/1806.02847), a dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. > > Together theses datasets weight 160GB of text. Also see the [bookcorpus data card](https://huggingface.co/datasets/bookcorpus) and the [wikipedia data card](https://huggingface.co/datasets/wikipedia) for additional information. #### Training Procedure ##### Preprocessing As described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of > the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked > with `<s>` and the end of one by `</s>` > > The details of the masking procedure for each sentence are the following: > - 15% of the tokens are masked. > - In 80% of the cases, the masked tokens are replaced by `<mask>`. > - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. > - In the 10% remaining cases, the masked tokens are left as is. > > Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed). ##### Pretraining Also as described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The > optimizer used is Adam with a learning rate of 4e-4, \$\beta_{1} = 0.9\$, \$\beta_{2} = 0.98\$ and > \$\epsilon = 1e-6\$, a weight decay of 0.01, learning rate warmup for 30,000 steps and linear decay of the learning > rate after. ## Evaluation The following evaluation information is extracted from the associated [GitHub repo for RoBERTa](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta). #### Testing Data, Factors and Metrics The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics: - Dataset: Part of [GLUE (Wang et al., 2019)](https://arxiv.org/pdf/1804.07461.pdf), the General Language Understanding Evaluation benchmark, a collection of 9 datasets for evaluating natural language understanding systems. Specifically, the model was evaluated on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. See the [GLUE data card](https://huggingface.co/datasets/glue) or [Wang et al. (2019)](https://arxiv.org/pdf/1804.07461.pdf) for further information. - Tasks: NLI. [Wang et al. (2019)](https://arxiv.org/pdf/1804.07461.pdf) describe the inference task for MNLI as: > The Multi-Genre Natural Language Inference Corpus [(Williams et al., 2018)](https://arxiv.org/abs/1704.05426) is a crowd-sourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. We use the standard test set, for which we obtained private labels from the authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) sections. We also use and recommend the SNLI corpus [(Bowman et al., 2015)](https://arxiv.org/abs/1508.05326) as 550k examples of auxiliary training data. - Metrics: Accuracy - Dataset: [XNLI (Conneau et al., 2018)](https://arxiv.org/pdf/1809.05053.pdf), the extension of the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus to 15 languages: English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. See the [XNLI data card](https://huggingface.co/datasets/xnli) or [Conneau et al. (2018)](https://arxiv.org/pdf/1809.05053.pdf) for further information. - Tasks: Translate-test (e.g., the model is used to translate input sentences in other languages to the training language) - Metrics: Accuracy #### Results GLUE test results (dev set, single model, single-task fine-tuning): 90.2 on MNLI XNLI test results: \| Task \| en \| fr \| es \| de \| el \| bg \| ru \| tr \| ar \| vi \| th \| zh \| hi \| sw \| ur \| \|:----:\|:--:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\| \| \|91.3\|82.91\|84.27\|81.24\|81.74\|83.13\|78.28\|76.79\|76.64\|74.17\|74.05\| 77.5\| 70.9\|66.65\|66.81\| ## Environmental Impact 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). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1907.11692.pdf). - Hardware Type: 1024 V100 GPUs - Hours used: 24 hours (one day) - Cloud Provider: Unknown - Compute Region: Unknown - Carbon Emitted: Unknown ## Technical Specifications See the [associated paper](https://arxiv.org/pdf/1907.11692.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @article{liu2019roberta, title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach}, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, journal={arXiv preprint arXiv:1907.11692}, year = {2019}, } ```
openai-community/roberta-base-openai-detector	openai-community	2024-02-19T12:46:55Z	26,701	111	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "roberta", "text-classification", "exbert", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1904.09751", "arxiv:1910.09700", "arxiv:1908.09203", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: en license: mit tags: - exbert datasets: - bookcorpus - wikipedia --- # RoBERTa Base OpenAI Detector ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-author) - [How To Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details Model Description: RoBERTa base OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa base model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version. - Developed by: OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list - Model Type: Fine-tuned transformer-based language model - Language(s): English - License: MIT - Related Models: [RoBERTa base](https://huggingface.co/roberta-base), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2) - Resources for more information: - [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection). - [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) - [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - [Explore the detector model here](https://huggingface.co/openai-detector ) ## Uses #### Direct Use The model is a classifier that can be used to detect text generated by GPT-2 models. However, it is strongly suggested not to use it as a ChatGPT detector for the purposes of making grave allegations of academic misconduct against undergraduates and others, as this model might give inaccurate results in the case of ChatGPT-generated input. #### Downstream Use The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model. ## Risks, Limitations and Biases CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. #### Risks and Limitations In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research. In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write: > We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective. The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness. #### Bias Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by RoBERTa base and GPT-2 1.5B (which this model is built/fine-tuned on) can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups (see the [RoBERTa base](https://huggingface.co/roberta-base) and [GPT-2 XL](https://huggingface.co/gpt2-xl) model cards for more information). The developers of this model discuss these issues further in their [paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). ## Training #### Training Data The model is a sequence classifier based on RoBERTa base (see the [RoBERTa base model card](https://huggingface.co/roberta-base) for more details on the RoBERTa base training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available [here](https://github.com/openai/gpt-2-output-dataset)). #### Training Procedure The model developers write that: > We based a sequence classifier on RoBERTaBASE (125 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model. They later state: > To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the training procedure. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). #### Testing Data, Factors and Metrics The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by: > testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training. #### Results The model developers [find](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf): > Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling ([Holtzman et al., 2019](https://arxiv.org/abs/1904.09751). Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), Figure 1 (on page 14) and Figure 2 (on page 16) for full results. ## Environmental Impact 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: Unknown - Hours used: Unknown - Cloud Provider: Unknown - Compute Region: Unknown - Carbon Emitted: Unknown ## Technical Specifications The model developers write that: See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the modeling architecture and training details. ## Citation Information ```bibtex @article{solaiman2019release, title={Release strategies and the social impacts of language models}, author={Solaiman, Irene and Brundage, Miles and Clark, Jack and Askell, Amanda and Herbert-Voss, Ariel and Wu, Jeff and Radford, Alec and Krueger, Gretchen and Kim, Jong Wook and Kreps, Sarah and others}, journal={arXiv preprint arXiv:1908.09203}, year={2019} } ``` APA: - Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203. https://huggingface.co/papers/1908.09203 ## Model Card Authors This model card was written by the team at Hugging Face. ## How to Get Started with the Model This model can be instantiated and run with a Transformers pipeline: ```python from transformers import pipeline pipe = pipeline("text-classification", model="roberta-base-openai-detector") print(pipe("Hello world! Is this content AI-generated?")) # [{'label': 'Real', 'score': 0.8036582469940186}] ```
Seria9/RVCv2_Ra2_MentalOmega_Label	Seria9	2024-02-19T12:40:15Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2024-02-19T12:39:08Z	--- license: bsd-3-clause-clear ---
openai-community/openai-gpt	openai-community	2024-02-19T12:39:20Z	36,385	249	transformers	[ "transformers", "pytorch", "tf", "rust", "safetensors", "openai-gpt", "text-generation", "en", "arxiv:1705.11168", "arxiv:1803.02324", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	--- language: en license: mit --- # OpenAI GPT 1 ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details Model Description: `openai-gpt` (a.k.a. "GPT-1") is the first transformer-based language model created and released by OpenAI. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies. - Developed by: Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. See [associated research paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) and [GitHub repo](https://github.com/openai/finetune-transformer-lm) for model developers and contributors. - Model Type: Transformer-based language model - Language(s): English - License: [MIT License](https://github.com/openai/finetune-transformer-lm/blob/master/LICENSE) - Related Models: [GPT2](https://huggingface.co/gpt2), [GPT2-Medium](https://huggingface.co/gpt2-medium), [GPT2-Large](https://huggingface.co/gpt2-large) and [GPT2-XL](https://huggingface.co/gpt2-xl) - Resources for more information: - [Research Paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) - [OpenAI Blog Post](https://openai.com/blog/language-unsupervised/) - [GitHub Repo](https://github.com/openai/finetune-transformer-lm) - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt ## How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='openai-gpt') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model,'he said, when i was finished.'ah well,'said the man,'that's"}, {'generated_text': 'Hello, I\'m a language model, " she said. \n she reached the bottom of the shaft and leaned a little further out. it was'}, {'generated_text': 'Hello, I\'m a language model, " she laughed. " we call that a\'white girl.\'or as we are called by the'}, {'generated_text': 'Hello, I\'m a language model, " said mr pin. " an\'the ones with the funny hats don\'t. " the rest of'}, {'generated_text': 'Hello, I\'m a language model, was\'ere \'bout to do some more dancin \', " he said, then his voice lowered to'}] ``` Here is how to use this model in PyTorch: ```python from transformers import OpenAIGPTTokenizer, OpenAIGPTModel import torch tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt") model = OpenAIGPTModel.from_pretrained("openai-gpt") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(inputs) last_hidden_states = outputs.last_hidden_state ``` and in TensorFlow: ```python from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt") model = TFOpenAIGPTModel.from_pretrained("openai-gpt") inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") outputs = model(inputs) last_hidden_states = outputs.last_hidden_state ``` ## Uses #### Direct Use This model can be used for language modeling tasks. #### Downstream Use Potential downstream uses of this model include tasks that leverage language models. In the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf), the model developers discuss evaluations of the model for tasks including natural language inference (NLI), question answering, semantic similarity, and text classification. #### Misuse and Out-of-scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases #### Biases CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by this model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='openai-gpt') >>> set_seed(42) >>> generator("The man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The man worked as a teacher for the college he'}, {'generated_text': 'The man worked as a janitor at the club.'}, {'generated_text': 'The man worked as a bodyguard in america. the'}, {'generated_text': 'The man worked as a clerk for one of the'}, {'generated_text': 'The man worked as a nurse, but there was'}] >>> set_seed(42) >>> generator("The woman worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The woman worked as a medical intern but is a'}, {'generated_text': 'The woman worked as a midwife, i know that'}, {'generated_text': 'The woman worked as a prostitute in a sex club'}, {'generated_text': 'The woman worked as a secretary for one of the'}, {'generated_text': 'The woman worked as a nurse, but she had'}] ``` This bias may also affect fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. #### Risks and Limitations The model developers also wrote in a [blog post](https://openai.com/blog/language-unsupervised/) about risks and limitations of the model, including: > - Compute Requirements: Many previous approaches to NLP tasks train relatively small models on a single GPU from scratch. Our approach requires an expensive pre-training step - 1 month on 8 GPUs. Luckily, this only has to be done once and we’re releasing our model so others can avoid it. It is also a large model (in comparison to prior work) and consequently uses more compute and memory — we used a 37-layer (12 block) Transformer architecture, and we train on sequences of up to 512 tokens. Most experiments were conducted on 4 and 8 GPU systems. The model does fine-tune to new tasks very quickly which helps mitigate the additional resource requirements. > - The limits and bias of learning about the world through text: Books and text readily available on the internet do not contain complete or even accurate information about the world. Recent work ([Lucy and Gauthier, 2017](https://arxiv.org/abs/1705.11168)) has shown that certain kinds of information are difficult to learn via just text and other work ([Gururangan et al., 2018](https://arxiv.org/abs/1803.02324)) has shown that models learn and exploit biases in data distributions. > - Still brittle generalization: Although our approach improves performance across a broad range of tasks, current deep learning NLP models still exhibit surprising and counterintuitive behavior - especially when evaluated in a systematic, adversarial, or out-of-distribution way. Our approach is not immune to these issues, though we have observed some indications of progress. Our approach shows improved lexical robustness over previous purely neural approaches to textual entailment. On the dataset introduced in Glockner et al. (2018) our model achieves 83.75%, performing similarly to KIM, which incorporates external knowledge via WordNet. ## Training #### Training Data The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf): > We use the BooksCorpus dataset ([Zhu et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf)) for training the language model. It contains over 7,000 unique unpublished books from a variety of genres including Adventure, Fantasy, and Romance. Crucially, it contains long stretches of contiguous text, which allows the generative model to learn to condition on long-range information. #### Training Procedure The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf): > Our model largely follows the original transformer work [62]. We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and 12 attention heads). For the position-wise feed-forward networks, we used 3072 dimensional inner states. We used the Adam optimization scheme [27] with a max learning rate of 2.5e-4. The learning rate was increased linearly from zero over the first 2000 updates and annealed to 0 using a cosine schedule. We train for 100 epochs on minibatches of 64 randomly sampled, contiguous sequences of 512 tokens. Since layernorm [2] is used extensively throughout the model, a simple weight initialization of N (0, 0.02) was sufficient. We used a bytepair encoding (BPE) vocabulary with 40,000 merges [53] and residual, embedding, and attention dropouts with a rate of 0.1 for regularization. We also employed a modified version of L2 regularization proposed in [37], with w = 0.01 on all non bias or gain weights. For the activation function, we used the Gaussian Error Linear Unit (GELU) [18]. We used learned position embeddings instead of the sinusoidal version proposed in the original work. We use the ftfy library2 to clean the raw text in BooksCorpus, standardize some punctuation and whitespace, and use the spaCy tokenizer. See the paper for further details and links to citations. ## Evaluation The following evaluation information is extracted from the [associated blog post](https://openai.com/blog/language-unsupervised/). See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for further details. #### Testing Data, Factors and Metrics The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics: - Task: Textual Entailment - Datasets: [SNLI](https://huggingface.co/datasets/snli), [MNLI Matched](https://huggingface.co/datasets/glue), [MNLI Mismatched](https://huggingface.co/datasets/glue), [SciTail](https://huggingface.co/datasets/scitail), [QNLI](https://huggingface.co/datasets/glue), [RTE](https://huggingface.co/datasets/glue) - Metrics: Accuracy - Task: Semantic Similarity - Datasets: [STS-B](https://huggingface.co/datasets/glue), [QQP](https://huggingface.co/datasets/glue), [MRPC](https://huggingface.co/datasets/glue) - Metrics: Accuracy - Task: Reading Comprehension - Datasets: [RACE](https://huggingface.co/datasets/race) - Metrics: Accuracy - Task: Commonsense Reasoning - Datasets: [ROCStories](https://huggingface.co/datasets/story_cloze), [COPA](https://huggingface.co/datasets/xcopa) - Metrics: Accuracy - Task: Sentiment Analysis - Datasets: [SST-2](https://huggingface.co/datasets/glue) - Metrics: Accuracy - Task: Linguistic Acceptability - Datasets: [CoLA](https://huggingface.co/datasets/glue) - Metrics: Accuracy - Task: Multi Task Benchmark - Datasets: [GLUE](https://huggingface.co/datasets/glue) - Metrics:** Accuracy #### Results The model achieves the following results without any fine-tuning (zero-shot): \| Task \| TE \| TE \| TE \|TE \| TE \| TE \| SS \| SS \| SS \| RC \| CR \| CR \| SA \| LA \| MTB \| \|:--------:\|:--:\|:----------:\|:-------------:\|:-----:\|:----:\|:---:\|:---:\|:---:\|:--:\|:----:\|:--------:\|:----:\|:----:\|:----:\|:----:\| \| Dataset \|SNLI\|MNLI Matched\|MNLI Mismatched\|SciTail\| QNLI \| RTE \|STS-B\| QQP \|MPRC\|RACE \|ROCStories\|COPA \| SST-2\| CoLA \| GLUE \| \| \|89.9\| 82.1 \| 81.4 \|88.3 \| 88.1 \| 56.0\|82.0 \| 70.3\|82.3\|59.0 \| 86.5 \| 78.6 \| 91.3 \| 45.4 \| 72.8 \| ## Environmental Impact The model developers [report that](https://openai.com/blog/language-unsupervised/): > The total compute used to train this model was 0.96 petaflop days (pfs-days). > 8 P600 GPU's * 30 days * 12 TFLOPS/GPU * 0.33 utilization = .96 pfs-days 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: 8 P600 GPUs - Hours used: 720 hours (30 days) - Cloud Provider: Unknown - Compute Region: Unknown - Carbon Emitted: Unknown ## Technical Specifications See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @article{radford2018improving, title={Improving language understanding by generative pre-training}, author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others}, year={2018}, publisher={OpenAI} } ``` APA: Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. ## Model Card Authors This model card was written by the Hugging Face team.
openai-community/gpt2-xl	openai-community	2024-02-19T12:39:12Z	186,646	325	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "safetensors", "gpt2", "text-generation", "en", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	--- language: en license: mit --- # GPT-2 XL ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details Model Description: GPT-2 XL is the 1.5B parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - Developed by: OpenAI, see [associated research paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers. - Model Type: Transformer-based language model - Language(s): English - License: [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE) - Related Models: [GPT-2](https://huggingface.co/gpt2), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-Large](https://huggingface.co/gpt2-large) - Resources for more information: - [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - [OpenAI Blog Post](https://openai.com/blog/better-language-models/) - [GitHub Repo](https://github.com/openai/gpt-2) - [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md) - [OpenAI GPT-2 1.5B Release Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large ## How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2-xl') set_seed(42) generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl') model = GPT2Model.from_pretrained('gpt2-xl') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl') model = TFGPT2Model.from_pretrained('gpt2-xl') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Uses #### Direct Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > The primary intended users of these models are AI researchers and practitioners. > > We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models. #### Downstream Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Here are some secondary use cases we believe are likely: > > - Writing assistance: Grammar assistance, autocompletion (for normal prose or code) > - Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art. > - Entertainment: Creation of games, chat bots, and amusing generations. #### Misuse and Out-of-scope Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes. ## Risks, Limitations and Biases CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes. #### Biases Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2-xl') set_seed(42) generator("The man worked as a", max_length=10, num_return_sequences=5) set_seed(42) generator("The woman worked as a", max_length=10, num_return_sequences=5) ``` This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. #### Risks and Limitations When they released the 1.5B parameter model, OpenAI wrote in a [blog post](https://openai.com/blog/gpt-2-1-5b-release/): > GPT-2 can be fine-tuned for misuse. Our partners at the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism (CTEC) found that extremist groups can use GPT-2 for misuse, specifically by fine-tuning GPT-2 models on four ideological positions: white supremacy, Marxism, jihadist Islamism, and anarchism. CTEC demonstrated that it’s possible to create models that can generate synthetic propaganda for these ideologies. They also show that, despite having low detection accuracy on synthetic outputs, ML-based detection methods can give experts reasonable suspicion that an actor is generating synthetic text. The blog post further discusses the risks, limitations, and biases of the model. ## Training #### Training Data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). #### Training Procedure The model is pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). #### Testing Data, Factors and Metrics The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that: > Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark. Results on language modeling datasets are commonly reported in a quantity which is a scaled or ex- ponentiated version of the average negative log probability per canonical prediction unit - usually a character, a byte, or a word. We evaluate the same quantity by computing the log-probability of a dataset according to a WebText LM and dividing by the number of canonical units. For many of these datasets, WebText LMs would be tested significantly out- of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string <UNK> which is extremely rare in WebText - occurring only 26 times in 40 billion bytes. We report our main results...using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible. Since these de-tokenizers are invertible, we can still calculate the log probability of a dataset and they can be thought of as a simple form of domain adaptation. #### Results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 8.63 \| 63.24 \| 93.30 \| 89.05 \| 18.34 \| 35.76 \| 0.93 \| 0.98 \| 17.48 \| 42.16 \| ## Environmental Impact 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). The hardware type and hours used are based on information provided by one of the model authors on [Reddit](https://bit.ly/2Tw1x4L). - Hardware Type: 32 TPUv3 chips - Hours used: 168 - Cloud Provider: Unknown - Compute Region: Unknown - Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, and training details. ## Citation Information ```bibtex @article{radford2019language, title={Language models are unsupervised multitask learners}, author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others}, journal={OpenAI blog}, volume={1}, number={8}, pages={9}, year={2019} } ``` ## Model Card Authors This model card was written by the Hugging Face team.
openai-community/gpt2-medium	openai-community	2024-02-19T12:39:04Z	316,837	166	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "onnx", "safetensors", "gpt2", "text-generation", "en", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	--- language: en license: mit --- # GPT-2 Medium ## Model Details Model Description: GPT-2 Medium is the 355M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - Developed by: OpenAI, see [associated research paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers. - Model Type: Transformer-based language model - Language(s): English - License: [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE) - Related Models: [GPT2](https://huggingface.co/gpt2), [GPT2-Large](https://huggingface.co/gpt2-large) and [GPT2-XL](https://huggingface.co/gpt2-xl) - Resources for more information: - [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - [OpenAI Blog Post](https://openai.com/blog/better-language-models/) - [GitHub Repo](https://github.com/openai/gpt-2) - [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md) - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large ## How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-medium') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, I'm a language. I'm a compiler, I'm a parser, I'm a server process. I"}, {'generated_text': "Hello, I'm a language model, and I'd like to join an existing team. What can I do to get started?\n\nI'd"}, {'generated_text': "Hello, I'm a language model, why does my code get created? Can't I just copy it? But why did my code get created when"}, {'generated_text': "Hello, I'm a language model, a functional language...\n\nI'm a functional language. Is it hard? A little, yes. But"}, {'generated_text': "Hello, I'm a language model, not an object model.\n\nIn a nutshell, I need to give me objects from which I can get"}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium') model = GPT2Model.from_pretrained('gpt2-medium') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium') model = TFGPT2Model.from_pretrained('gpt2-medium') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Uses #### Direct Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > The primary intended users of these models are AI researchers and practitioners. > > We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models. #### Downstream Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Here are some secondary use cases we believe are likely: > > - Writing assistance: Grammar assistance, autocompletion (for normal prose or code) > - Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art. > - Entertainment: Creation of games, chat bots, and amusing generations. #### Misuse and Out-of-scope Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes. ## Risks, Limitations and Biases CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-medium') >>> set_seed(42) >>> generator("The man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The man worked as a security guard in a military'}, {'generated_text': 'The man worked as a salesman in Mexico and eventually'}, {'generated_text': 'The man worked as a supervisor at the department for'}, {'generated_text': 'The man worked as a cleaner for the same corporation'}, {'generated_text': 'The man worked as a barman and was involved'}] >>> set_seed(42) >>> generator("The woman worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The woman worked as a social worker in a children'}, {'generated_text': 'The woman worked as a marketing manager, and her'}, {'generated_text': 'The woman worked as a customer service agent in a'}, {'generated_text': 'The woman worked as a cleaner for the same corporation'}, {'generated_text': 'The woman worked as a barista and was involved'}] ``` This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## Training #### Training Data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). #### Training Procedure The model is pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). #### Testing Data, Factors and Metrics The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that: > Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark. Results on language modeling datasets are commonly reported in a quantity which is a scaled or ex- ponentiated version of the average negative log probability per canonical prediction unit - usually a character, a byte, or a word. We evaluate the same quantity by computing the log-probability of a dataset according to a WebText LM and dividing by the number of canonical units. For many of these datasets, WebText LMs would be tested significantly out- of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string <UNK> which is extremely rare in WebText - occurring only 26 times in 40 billion bytes. We report our main results...using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible. Since these de-tokenizers are invertible, we can still calculate the log probability of a dataset and they can be thought of as a simple form of domain adaptation. #### Results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 15.60 \| 55.48 \| 92.35 \| 87.1 \| 22.76 \| 47.33 \| 1.01 \| 1.06 \| 26.37 \| 55.72 \| ## Environmental Impact 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: Unknown - Hours used: Unknown - Cloud Provider: Unknown - Compute Region: Unknown - Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @article{radford2019language, title={Language models are unsupervised multitask learners}, author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others}, journal={OpenAI blog}, volume={1}, number={8}, pages={9}, year={2019} } ``` ## Model Card Authors This model card was written by the Hugging Face team.
Seria9/Ra2_MentalOmega_YunRu	Seria9	2024-02-19T12:38:39Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2024-02-19T12:27:14Z	--- license: bsd-3-clause-clear ---
Seria9/RVCv2_Ra2_MentalOmega_Morales	Seria9	2024-02-19T12:35:01Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2024-02-19T12:32:58Z	--- license: bsd-3-clause-clear ---
Seria9/RVC_Ra2Yuri	Seria9	2024-02-19T12:21:20Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2024-02-19T12:16:24Z	--- license: bsd-3-clause-clear ---
ArthurZ/mamba-370m	ArthurZ	2024-02-19T12:20:14Z	487	0	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T12:20:03Z	--- 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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lumunot/flan-t5-base	lumunot	2024-02-19T12:11:29Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "simplification", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-19T11:04:39Z	--- tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: flan-t5-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4378 - Bleu: 42.8631 - Gen Len: 18.3111 ## 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: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 217 \| 0.4711 \| 41.9733 \| 18.3111 \| \| No log \| 2.0 \| 434 \| 0.4378 \| 42.8631 \| 18.3111 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
CocosNucifera/q-Taxi-v3.2	CocosNucifera	2024-02-19T12:09:27Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-19T11:32:03Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3.2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="CocosNucifera/q-Taxi-v3.2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ArthurZ/mamba-130m	ArthurZ	2024-02-19T12:06:29Z	241	3	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T06:43:33Z	--- library_name: transformers tags: [] --- ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b", padding_side = "left") >>> tokenizer.pad_token = tokenizer.eos_token >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m", vocab_size=50280, num_hidden_layers=24, torch_dtype=torch.float32) >>> model.config.use_cache = True >>> input_ids = tokenizer(["Hey how are you doing?", "Explain how soy sauce is made"], padding=True, return_tensors= "pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["<\|endoftext\|>Hey how are you doing?\n\nI'm a newbie to the game", 'Explain how soy sauce is made.\n\n1. Add the soy sauce to'] ```
Kiran1234/my-pet-dog	Kiran1234	2024-02-19T12:04:04Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-19T12:00:17Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by Kiran1234 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/Kiran1234/my-pet-dog/resolve/main/sample_images/xzg_(3).jpg)
viethang/vince62s-phi-2-psy-GGUF	viethang	2024-02-19T12:03:26Z	33	1	null	[ "gguf", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-02-19T10:32:05Z	--- license: mit --- This repo contains GGUF files for vince62s/phi-2-psy model, which is a merge of fine-tuned instruct version for phi-2. \|File\|Size\| \|----\|----\| \|vince62s-phi-2-psy_Q4_0.gguf \|1.6 GB\| \|vince62s-phi-2-psy_Q4_K_M.gguf\|1.74 GB\| \|vince62s-phi-2-psy_Q4_K_S.gguf\|1.63 GB\| \|vince62s-phi-2-psy_Q5_K_M.gguf\|2 GB\| \|vince62s-phi-2-psy_Q5_K_S.gguf\|1.93 GB\| \|vince62s-phi-2-psy_Q6_K.gguf\|2.29 GB\| \|vince62s-phi-2-psy_Q8_0.gguf\|2.96 GB\| \|vince62s-phi-2-psy_f16.gguf\|5.56 GB\|
ZainabF/phi-2-allyarc	ZainabF	2024-02-19T12:02:03Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-02-19T12:01:59Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi-2-allyarc 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-2-allyarc This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
zjunlp/chatcell-small	zjunlp	2024-02-19T12:00:34Z	24	4	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "biology", "single-cell", "single-cell analysis", "text-generation-inference", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-04T14:01:11Z	--- pipeline_tag: text-generation tags: - biology - single-cell - single-cell analysis - text-generation-inference --- <div align="center"> <img src="./figures/logo.png" alt="image" width=8%> <h2 align="center"> ChatCell: Facilitating Single-Cell Analysis with Natural Language </h2> <p align="center"> <a href="https://chat.openai.com/g/g-vUwj222gQ-chatcell">💻Demo</a> • <a href="https://huggingface.co/datasets/zjunlp/ChatCell-Instructions">🤗 Dataset</a> • <a href="#1">🏖️ Overview</a> • <a href="#2">🧬 Single-cell Analysis Tasks</a> • <a href="#3">🛠️ Quickstart</a> • <a href="#4">📝 Cite</a> </p> </div> The project <b>ChatCell</b> aims to facilitate single-cell analysis with natural language, which derives from the <a href="https://github.com/vandijklab/cell2sentence-ft">Cell2Sentence</a> technique to obtain cell language tokens and utilizes cell vocabulary adaptation for T5-based pre-training. Have a try with the demo at <a href="https://chat.openai.com/g/g-vUwj222gQ-chatcell">💻GPTStore App</a>. ## 📌 Table of Contents - [🛠️ Quickstart](#2) - [🧬 Single-cell Analysis Tasks](#3) - [✨ Acknowledgements](#4) - [📝 Cite](#5) --- <h2 id="2">🛠️ Quickstart</h2> ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-small") model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-small") input_text="Detail the 100 starting genes for a Mix, ranked by expression level: " # Encode the input text and generate a response with specified generation parameters input_ids = tokenizer(input_text,return_tensors="pt").input_ids output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True) # Decode and print the generated output text output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True) print(output_text) ``` <h2 id="3">🧬 Single-cell Analysis Tasks</h2> ChatCell can handle the following single-cell tasks: - <b>Random Cell Sentence Generation.</b> Random cell sentence generation challenges the model to create cell sentences devoid of predefined biological conditions or constraints. This task aims to evaluate the model's ability to generate valid and contextually appropriate cell sentences, potentially simulating natural variations in cellular behavior. <p align="center"> <img src="./figures/example1.jpg" alt="image" width=80%> </p> - <b>Pseudo-cell Generation.</b> Pseudo-cell generation focuses on generating gene sequences tailored to specific cell type labels. This task is vital for unraveling gene expression and regulation across different cell types, offering insights for medical research and disease studies, particularly in the context of diseased cell types. <p align="center"> <img src="./figures/example2.jpg" alt="image" width=80%> </p> - <b>Cell Type Annotation.</b> For cell type annotation, the model is tasked with precisely classifying cells into their respective types based on gene expression patterns encapsulated in cell sentences. This task is fundamental for understanding cellular functions and interactions within tissues and organs, playing a crucial role in developmental biology and regenerative medicine. <p align="center"> <img src="./figures/example3.jpg" alt="image" width=80%> </p> - <b>Drug Sensitivity Prediction.</b> The drug sensitivity prediction task aims to predict the response of different cells to various drugs. It is pivotal in designing effective, personalized treatment plans and contributes significantly to drug development, especially in optimizing drug efficacy and safety. <p align="center"> <img src="./figures/example4.jpg" alt="image" width=80%> </p> <h2 id="4">📝 ✨ Acknowledgements</h2> Special thanks to the authors of [Cell2Sentence: Teaching Large Language Models the Language of Biology](https://github.com/vandijklab/cell2sentence-ft) and [Representing cells as sentences enables natural-language processing for single-cell transcriptomics ](https://github.com/rahuldhodapkar/cell2sentence) for their inspiring work. <h2 id="5">📝 Cite</h2> ``` @article{fang2024chatcell, title={ChatCell: Facilitating Single-Cell Analysis with Natural Language}, author={Fang, Yin and Liu, Kangwei and Zhang, Ningyu and Deng, Xinle and Yang, Penghui and Chen, Zhuo and Tang, Xiangru and Gerstein, Mark and Fan, Xiaohui and Chen, Huajun}, year={2024}, } ```
zjunlp/chatcell-base	zjunlp	2024-02-19T11:59:55Z	9	2	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "biology", "single-cell", "single-cell analysis", "text-generation-inference", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-11T06:15:17Z	--- pipeline_tag: text-generation tags: - biology - single-cell - single-cell analysis - text-generation-inference --- <div align="center"> <img src="./figures/logo.png" alt="image" width=8%> <h2 align="center"> ChatCell: Facilitating Single-Cell Analysis with Natural Language </h2> <p align="center"> <a href="https://chat.openai.com/g/g-vUwj222gQ-chatcell">💻Demo</a> • <a href="https://huggingface.co/datasets/zjunlp/ChatCell-Instructions">🤗 Dataset</a> • <a href="#1">🏖️ Overview</a> • <a href="#2">🧬 Single-cell Analysis Tasks</a> • <a href="#3">🛠️ Quickstart</a> • <a href="#4">📝 Cite</a> </p> <img src="./figures/intro.jpg" alt="image" width=60%> </div> The project <b>ChatCell</b> aims to facilitate single-cell analysis with natural language, which derives from the <a href="https://github.com/vandijklab/cell2sentence-ft">Cell2Sentence</a> technique to obtain cell language tokens and utilizes cell vocabulary adaptation for T5-based pre-training. Have a try with the demo at <a href="https://chat.openai.com/g/g-vUwj222gQ-chatcell">💻GPTStore App</a>. ## 📌 Table of Contents - [🛠️ Quickstart](#2) - [🧬 Single-cell Analysis Tasks](#3) - [✨ Acknowledgements](#4) - [📝 Cite](#5) --- <h2 id="3">🛠️ Quickstart</h2> ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-base") model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-base") input_text="Detail the 100 starting genes for a Mix, ranked by expression level: " # Encode the input text and generate a response with specified generation parameters input_ids = tokenizer(input_text,return_tensors="pt").input_ids output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True) # Decode and print the generated output text output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True) print(output_text) ``` <h2 id="3">🧬 Single-cell Analysis Tasks</h2> ChatCell can handle the following single-cell tasks: - <b>Random Cell Sentence Generation.</b> Random cell sentence generation challenges the model to create cell sentences devoid of predefined biological conditions or constraints. This task aims to evaluate the model's ability to generate valid and contextually appropriate cell sentences, potentially simulating natural variations in cellular behavior. <p align="center"> <img src="./figures/example1.jpg" alt="image" width=80%> </p> - <b>Pseudo-cell Generation.</b> Pseudo-cell generation focuses on generating gene sequences tailored to specific cell type labels. This task is vital for unraveling gene expression and regulation across different cell types, offering insights for medical research and disease studies, particularly in the context of diseased cell types. <p align="center"> <img src="./figures/example2.jpg" alt="image" width=80%> </p> - <b>Cell Type Annotation.</b> For cell type annotation, the model is tasked with precisely classifying cells into their respective types based on gene expression patterns encapsulated in cell sentences. This task is fundamental for understanding cellular functions and interactions within tissues and organs, playing a crucial role in developmental biology and regenerative medicine. <p align="center"> <img src="./figures/example3.jpg" alt="image" width=80%> </p> - <b>Drug Sensitivity Prediction.</b> The drug sensitivity prediction task aims to predict the response of different cells to various drugs. It is pivotal in designing effective, personalized treatment plans and contributes significantly to drug development, especially in optimizing drug efficacy and safety. <p align="center"> <img src="./figures/example4.jpg" alt="image" width=80%> </p> <h2 id="4">📝 ✨ Acknowledgements</h2> Special thanks to the authors of [Cell2Sentence: Teaching Large Language Models the Language of Biology](https://github.com/vandijklab/cell2sentence-ft) and [Representing cells as sentences enables natural-language processing for single-cell transcriptomics ](https://github.com/rahuldhodapkar/cell2sentence) for their inspiring work. <h2 id="5">📝 Cite</h2> ``` @article{fang2024chatcell, title={ChatCell: Facilitating Single-Cell Analysis with Natural Language}, author={Fang, Yin and Liu, Kangwei and Zhang, Ningyu and Deng, Xinle and Yang, Penghui and Chen, Zhuo and Tang, Xiangru and Gerstein, Mark and Fan, Xiaohui and Chen, Huajun}, year={2024}, } ```
omniamnaeem/semantic_sim_ner_with_ContrastiveLossV2	omniamnaeem	2024-02-19T11:59:52Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-19T11:59:22Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 8044 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `__main__.LoggingCosineSim` with parameters: ``` {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "evaluation_steps": 0, "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": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 150, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
linoyts/huggy_dora_unet	linoyts	2024-02-19T11:58:32Z	4	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-19T11:12:08Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a TOK emoji dressed as yoda' output: url: "image_0.png" - text: 'a TOK emoji dressed as yoda' output: url: "image_1.png" - text: 'a TOK emoji dressed as yoda' output: url: "image_2.png" - text: 'a TOK emoji dressed as yoda' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a TOK emoji license: openrail++ --- # SDXL LoRA DreamBooth - linoyts/huggy_dora_unet <Gallery /> ## Model description ### These are linoyts/huggy_dora_unet LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`huggy_dora_unet.safetensors` here 💾](/linoyts/huggy_dora_unet/blob/main/huggy_dora_unet.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:huggy_dora_unet:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('linoyts/huggy_dora_unet', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a TOK emoji dressed as yoda').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words You should use a TOK emoji to trigger the image generation. ## Details All [Files & versions](/linoyts/huggy_dora_unet/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
LoneStriker/NeuralMonarch-7B-8.0bpw-h8-exl2	LoneStriker	2024-02-19T11:56:13Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/Monarch-7B", "base_model:finetune:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:53:11Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs base_model: - mlabonne/Monarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
allstax/AI-G-Full-v2	allstax	2024-02-19T11:55:58Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-19T11:54:16Z	--- 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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LoneStriker/NeuralMonarch-7B-6.0bpw-h6-exl2	LoneStriker	2024-02-19T11:53:10Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/Monarch-7B", "base_model:finetune:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:50:50Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs base_model: - mlabonne/Monarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
LoneStriker/NeuralMonarch-7B-5.0bpw-h6-exl2	LoneStriker	2024-02-19T11:50:48Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/Monarch-7B", "base_model:finetune:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:48:48Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs base_model: - mlabonne/Monarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
LoneStriker/NeuralMonarch-7B-4.0bpw-h6-exl2	LoneStriker	2024-02-19T11:48:46Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/Monarch-7B", "base_model:finetune:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:47:05Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs base_model: - mlabonne/Monarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
callmesan/whisper-audio-abuse-feature	callmesan	2024-02-19T11:45:18Z	19	0	transformers	[ "transformers", "pytorch", "whisper", "audio-classification", "generated_from_trainer", "base_model:parambharat/whisper-tiny-south-indic", "base_model:finetune:parambharat/whisper-tiny-south-indic", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-02-19T11:45:15Z	--- license: apache-2.0 base_model: parambharat/whisper-tiny-south-indic tags: - generated_from_trainer metrics: - accuracy model-index: - name: whisper-audio-abuse-feature results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-audio-abuse-feature This model is a fine-tuned version of [parambharat/whisper-tiny-south-indic](https://huggingface.co/parambharat/whisper-tiny-south-indic) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4947 - Accuracy: 0.8174 - Macro F1-score: 0.7845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Macro F1-score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------------:\| \| 7.8152 \| 0.52 \| 10 \| 7.7606 \| 0.0 \| 0.0 \| \| 7.6445 \| 1.04 \| 20 \| 7.4368 \| 0.6718 \| 0.1348 \| \| 7.1349 \| 1.56 \| 30 \| 6.6932 \| 0.6897 \| 0.4082 \| \| 6.2832 \| 2.08 \| 40 \| 5.7185 \| 0.6897 \| 0.4082 \| \| 5.3726 \| 2.6 \| 50 \| 4.8531 \| 0.6897 \| 0.4082 \| \| 4.4541 \| 3.12 \| 60 \| 4.1519 \| 0.6897 \| 0.4082 \| \| 3.9045 \| 3.64 \| 70 \| 3.5417 \| 0.6897 \| 0.4082 \| \| 3.3278 \| 4.16 \| 80 \| 2.9984 \| 0.6897 \| 0.4082 \| \| 2.7361 \| 4.68 \| 90 \| 2.5167 \| 0.6897 \| 0.4082 \| \| 2.3838 \| 5.19 \| 100 \| 2.1039 \| 0.6897 \| 0.4082 \| \| 1.9557 \| 5.71 \| 110 \| 1.7366 \| 0.6897 \| 0.4082 \| \| 1.5922 \| 6.23 \| 120 \| 1.4170 \| 0.6897 \| 0.4082 \| \| 1.3228 \| 6.75 \| 130 \| 1.1497 \| 0.7068 \| 0.4669 \| \| 1.0767 \| 7.27 \| 140 \| 0.9322 \| 0.7779 \| 0.6610 \| \| 0.8649 \| 7.79 \| 150 \| 0.7814 \| 0.7860 \| 0.6800 \| \| 0.7058 \| 8.31 \| 160 \| 0.6608 \| 0.8165 \| 0.7453 \| \| 0.6253 \| 8.83 \| 170 \| 0.5688 \| 0.8327 \| 0.7936 \| \| 0.5269 \| 9.35 \| 180 \| 0.5137 \| 0.8291 \| 0.7896 \| \| 0.5016 \| 9.87 \| 190 \| 0.4862 \| 0.8300 \| 0.7747 \| \| 0.4409 \| 10.39 \| 200 \| 0.4776 \| 0.8040 \| 0.7796 \| \| 0.3793 \| 10.91 \| 210 \| 0.4511 \| 0.8345 \| 0.8048 \| \| 0.3501 \| 11.43 \| 220 \| 0.4491 \| 0.8228 \| 0.7922 \| \| 0.3692 \| 11.95 \| 230 \| 0.4254 \| 0.8327 \| 0.7982 \| \| 0.3148 \| 12.47 \| 240 \| 0.4452 \| 0.8228 \| 0.7901 \| \| 0.3114 \| 12.99 \| 250 \| 0.4543 \| 0.8345 \| 0.7905 \| \| 0.2812 \| 13.51 \| 260 \| 0.4398 \| 0.8363 \| 0.7992 \| \| 0.2635 \| 14.03 \| 270 \| 0.4607 \| 0.8327 \| 0.7960 \| \| 0.2491 \| 14.55 \| 280 \| 0.4818 \| 0.8327 \| 0.7864 \| \| 0.2825 \| 15.06 \| 290 \| 0.4616 \| 0.8219 \| 0.7920 \| \| 0.2494 \| 15.58 \| 300 \| 0.4784 \| 0.8129 \| 0.7897 \| \| 0.2127 \| 16.1 \| 310 \| 0.4669 \| 0.8273 \| 0.7862 \| \| 0.2003 \| 16.62 \| 320 \| 0.4760 \| 0.8174 \| 0.7796 \| \| 0.1907 \| 17.14 \| 330 \| 0.4845 \| 0.8246 \| 0.7943 \| \| 0.177 \| 17.66 \| 340 \| 0.4870 \| 0.8219 \| 0.7867 \| \| 0.1877 \| 18.18 \| 350 \| 0.4884 \| 0.8201 \| 0.7909 \| \| 0.1511 \| 18.7 \| 360 \| 0.4907 \| 0.8228 \| 0.7845 \| \| 0.1826 \| 19.22 \| 370 \| 0.4932 \| 0.8165 \| 0.7839 \| \| 0.1419 \| 19.74 \| 380 \| 0.4947 \| 0.8174 \| 0.7845 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
LoneStriker/NeuralMonarch-7B-GGUF	LoneStriker	2024-02-19T11:42:29Z	4	0	null	[ "gguf", "merge", "lazymergekit", "dpo", "rlhf", "en", "base_model:mlabonne/Monarch-7B", "base_model:quantized:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-02-19T11:31:44Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs base_model: - mlabonne/Monarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
CaphAlderamin/ppo-LunarLander-v2	CaphAlderamin	2024-02-19T11:40:34Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-19T11:40:16Z	--- 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: 253.48 +/- 20.49 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 ... ```
rAIfle/Mixtral-Instruct-ITR-8x7B-exl2-rpcal	rAIfle	2024-02-19T11:39:46Z	0	1	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-02-14T04:27:27Z	--- license: cc-by-nc-4.0 --- Quantized using 200 samples of 8192 tokens from an RP-oriented [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset. Branches: - `main` -- `measurement.json` - `2.25b6h` -- 2.25bpw, 6bit lm_head - `3.7b6h` -- 3.7bpw, 6bit lm_head - `6b6h` -- 6bpw, 6bit lm_head Requires ExllamaV2 version 0.0.12 and up. Original model link: [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) Original model README below. *** # Caution this model may be unpredictable ![](https://files.catbox.moe/y8nv86.jpg) ## Mixtral-Instruct-ITR (Interpolative Training Regression) We have to go back, edition. For this model I took what I learned in the making of [Cat-8x7B](https://huggingface.co/Envoid/Cat-8x7B) and went back to the very beginning and SLERP merged [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) onto [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) While the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being. The hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun. Apologies about the small shard size (keep forgetting to change the mergekit config back) ## The model is a lot less likely to refuse certain requests in this state: so if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case. The model still responds well to [INST] Thingie [/INST] formatting quite well. Or if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config ``` models: - model: ./mistralai_Mixtral-8x7B-Instruct-v0.1 - model: ./mistralai_Mixtral-8x7B-v0.1 merge_method: slerp base_model: ./mistralai_Mixtral-8x7B-v0.1 parameters: t: - value: 0.5 dtype: float16 ```
rAIfle/friend-8x7B-exl2-rpcal	rAIfle	2024-02-19T11:39:17Z	0	0	null	[ "mergekit", "merge", "region:us" ]	null	2024-02-19T04:50:29Z	--- base_model: [] tags: - mergekit - merge --- Quantized using 200 samples of 8192 tokens from an RP-oriented [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset. Branches: - `main` -- `measurement.json` - `2.25b6h` -- 2.25bpw, 6bit lm_head - `3.7b6h` -- 3.7bpw, 6bit lm_head - `6b6h` -- 6bpw, 6bit lm_head Requires ExllamaV2 version 0.0.12 and up. Original model link: [rAIfle/friend-8x7B-hf](https://huggingface.co/rAIfle/friend-8x7B-hf) Original model README below. *** # friend-8x7B-hf ![friends](https://files.catbox.moe/qlwy3m.png) Several-staged merge this time. Actually decent, from my testings. Use ChatML or Alpaca, both seemed to work though I liked the outputs from ChatML more. ### temp-output-base: ```yaml models: - model: mistralai/Mixtral-8x7B-v0.1+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b parameters: weight: 0.65 - model: mistralai/Mixtral-8x7B-v0.1+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs parameters: weight: 0.25 merge_method: task_arithmetic base_model: mistralai/Mixtral-8x7B-v0.1 dtype: float16 ``` ### temp-output-instruct: ```yaml models: - model: mistralai/Mixtral-8x7B-Instruct-v0.1+SeanWu25/Mixtral_8x7b_Medicine parameters: weight: 0.33 - model: NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss parameters: weight: 0.15 merge_method: task_arithmetic base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 dtype: float16 ``` This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * ./temp-output-base * ./temp-output-instruct ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./temp-output-base - model: ./temp-output-instruct merge_method: slerp base_model: ./temp-output-base parameters: t: - value: 0.5 dtype: float16 ```
speechbrain/asr-crdnn-commonvoice-14-en	speechbrain	2024-02-19T11:38:07Z	35	0	speechbrain	[ "speechbrain", "automatic-speech-recognition", "CTC", "Attention", "pytorch", "en", "dataset:common_voice", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2023-08-09T21:21:37Z	--- language: - en thumbnail: null tags: - automatic-speech-recognition - CTC - Attention - pytorch - speechbrain license: apache-2.0 datasets: - common_voice metrics: - name: Test WER type: wer value: ' 23.88' --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # CRDNN with CTC/Attention trained on CommonVoice 14.0 English (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (German Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: \| Release \| Test CER \| Test WER \| GPUs \| \|:-------------:\|:--------------:\|:--------------:\| :--------:\| \| 15.08.23 \| 12.76 \| 23.88 \| 1xV100 32GB \| ## Credits The model is provided by [vitas.ai](https://www.vitas.ai/). ## Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions (train.tsv) of CommonVoice (en). - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convolutional neural networks with normalization and pooling on the frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain the final acoustic representation that is given to the CTC and attention decoders. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files (in English) ```python from speechbrain.inference.ASR import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-14-en", savedir="pretrained_models/asr-crdnn-commonvoice-14-en") asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-14-en/example_en.wav") ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ## Parallel Inference on a Batch Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model. ### Training The model was trained with SpeechBrain (986a2175). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/CommonVoice/ASR/seq2seq python train.py hparams/train_en.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/h8ged0yu3ztypkh/AAAu-12k_Ceg-tTjuZnrg7dza?dl=0) ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/ # Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
tyson0420/alpaca_codellama	tyson0420	2024-02-19T11:36:32Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:30:31Z	--- 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. 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speechbrain/asr-wav2vec2-commonvoice-14-fr	speechbrain	2024-02-19T11:32:18Z	4	0	speechbrain	[ "speechbrain", "wav2vec2", "CTC", "pytorch", "Transformer", "automatic-speech-recognition", "fr", "dataset:commonvoice.14.0", "arxiv:2106.04624", "license:apache-2.0", "model-index", "region:us" ]	automatic-speech-recognition	2023-08-09T20:50:31Z	--- language: - fr thumbnail: null pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: apache-2.0 datasets: - commonvoice.14.0 metrics: - wer - cer model-index: - name: asr-wav2vec2-commonvoice-14-fr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: CommonVoice Corpus 14.0 (French) type: mozilla-foundation/common_voice_14.0 config: fr split: test args: language: fr metrics: - name: Test WER type: wer value: '10.24' --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC trained on CommonVoice French (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (French Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: \| Release \| Test CER \| Test WER \| GPUs \| \|:-------------:\|:--------------:\|:--------------:\| :--------:\| \| 15-08-23 \| 3.44 \| 10.24 \| 1xV100 32GB \| ## Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into unigrams and trained with the train transcriptions (train.tsv) of CommonVoice (fr). - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large)) is combined with two DNN layers and finetuned on CommonVoice DE. The obtained final acoustic representation is given to the CTC decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. ## Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files (in French) ```python from speechbrain.inference.ASR import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-14-fr", savedir="pretrained_models/asr-wav2vec2-commonvoice-14-fr") asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-14-fr/example-fr.wav") ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ## Parallel Inference on a Batch Please, [see this Colab notebook](https://www.dropbox.com/sh/0i7esfa8jp3rxpp/AAArdi8IuCRmob2WAS7lg6M4a?dl=0) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model. ### Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/CommonVoice/ASR/CTC/ python train_with_wav2vec.py hparams/train_fr_with_wav2vec.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/ch10cnbhf1faz3w/AACdHFG65LC6582H0Tet_glTa?dl=0). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About SpeechBrain - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/ # Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
badokorach/roberta-base-squad2-agric-170224	badokorach	2024-02-19T11:30:50Z	4	0	transformers	[ "transformers", "tf", "xlm-roberta", "question-answering", "generated_from_keras_callback", "base_model:badokorach/roberta-base-squad2-agric-181223", "base_model:finetune:badokorach/roberta-base-squad2-agric-181223", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2024-02-17T10:02:13Z	--- license: mit base_model: badokorach/roberta-base-squad2-agric-181223 tags: - generated_from_keras_callback model-index: - name: badokorach/roberta-base-squad2-agric-170224 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. --> # badokorach/roberta-base-squad2-agric-170224 This model is a fine-tuned version of [badokorach/roberta-base-squad2-agric-181223](https://huggingface.co/badokorach/roberta-base-squad2-agric-181223) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0090 - Validation Loss: 0.0 - Epoch: 4 ## 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': None, 'class_name': 'CustomLearningRateScheduler', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2736, 'warmup_steps': 304, 'end_learning_rate': 1e-05}, 'registered_name': 'CustomLearningRateScheduler'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.4458 \| 0.0 \| 0 \| \| 0.0226 \| 0.0 \| 1 \| \| 0.0144 \| 0.0 \| 2 \| \| 0.0060 \| 0.0 \| 3 \| \| 0.0090 \| 0.0 \| 4 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
jessica07/Marcoro14-7B-slerp	jessica07	2024-02-19T11:29:17Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "madatnlp/marcoroni-7b-v3-safetensor", "EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:25:45Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - madatnlp/marcoroni-7b-v3-safetensor - EmbeddedLLM/Mistral-7B-Merge-14-v0.1 --- # Marcoro14-7B-slerp Marcoro14-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [madatnlp/marcoroni-7b-v3-safetensor](https://huggingface.co/madatnlp/marcoroni-7b-v3-safetensor) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) ## 🧩 Configuration ```yaml slices: - sources: - model: madatnlp/marcoroni-7b-v3-safetensor layer_range: [0, 32] - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 layer_range: [0, 32] merge_method: slerp base_model: madatnlp/marcoroni-7b-v3-safetensor parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Wei-Meng/autotrain-jeiqk-7jghf	Wei-Meng	2024-02-19T11:28:24Z	7	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:autotrain-jeiqk-7jghf/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-19T11:28:10Z	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - autotrain-jeiqk-7jghf/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 0.7337517738342285 f1_macro: 0.5333333333333333 f1_micro: 0.6666666666666666 f1_weighted: 0.5333333333333333 precision_macro: 0.4444444444444444 precision_micro: 0.6666666666666666 precision_weighted: 0.4444444444444444 recall_macro: 0.6666666666666666 recall_micro: 0.6666666666666666 recall_weighted: 0.6666666666666666 accuracy: 0.6666666666666666
LoneStriker/AlphaMonarch-7B-8.0bpw-h8-exl2	LoneStriker	2024-02-19T11:27:42Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:finetune:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:24:40Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
hecgo067/flan_t5-simplification	hecgo067	2024-02-19T11:27:23Z	6	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "simplification", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-19T11:23:21Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: flan_t5-simplification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan_t5-simplification This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0147 - Bleu: 31.1397 - Gen Len: 17.8806 ## 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: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 34 \| 1.0234 \| 30.6474 \| 17.806 \| \| No log \| 2.0 \| 68 \| 1.0147 \| 31.1397 \| 17.8806 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
LoneStriker/AlphaMonarch-7B-6.0bpw-h6-exl2	LoneStriker	2024-02-19T11:24:38Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:finetune:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:22:18Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Wei-Meng/autotrain-kee7d-srgvj	Wei-Meng	2024-02-19T11:23:51Z	7	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:autotrain-kee7d-srgvj/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-19T11:23:03Z	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - autotrain-kee7d-srgvj/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 0.6576321721076965 f1_macro: 0.8222222222222223 f1_micro: 0.8333333333333334 f1_weighted: 0.8222222222222223 precision_macro: 0.8888888888888888 precision_micro: 0.8333333333333334 precision_weighted: 0.8888888888888888 recall_macro: 0.8333333333333334 recall_micro: 0.8333333333333334 recall_weighted: 0.8333333333333334 accuracy: 0.8333333333333334
LoneStriker/AlphaMonarch-7B-5.0bpw-h6-exl2	LoneStriker	2024-02-19T11:22:16Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:finetune:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:20:17Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
LoneStriker/AlphaMonarch-7B-4.0bpw-h6-exl2	LoneStriker	2024-02-19T11:20:14Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:finetune:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:18:35Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
LoneStriker/AlphaMonarch-7B-3.0bpw-h6-exl2	LoneStriker	2024-02-19T11:18:33Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "lazymergekit", "dpo", "rlhf", "conversational", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:finetune:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T11:17:14Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
JoniJoniAl/TestSexpressionsmodel	JoniJoniAl	2024-02-19T11:17:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-19T11:16:58Z	--- 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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mjm4dl/aya_usage_v0	mjm4dl	2024-02-19T11:17:01Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-19T11:12:43Z	--- 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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LoneStriker/AlphaMonarch-7B-GGUF	LoneStriker	2024-02-19T11:14:14Z	22	4	null	[ "gguf", "merge", "lazymergekit", "dpo", "rlhf", "en", "base_model:mlabonne/NeuralMonarch-7B", "base_model:quantized:mlabonne/NeuralMonarch-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-02-19T11:03:28Z	--- license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B language: - en --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg) # 👑 AlphaMonarch-7B tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉 AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset. It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B) * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt. ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \| \| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \| \| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \| \| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| \| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \| \| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \| \| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \| \| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \| \| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \| ### EQ-bench AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 gpt-3.5-turbo 1 8.07500 claude-instant-v1 1 7.80000 ########## Second turn ########## score model turn gpt-4 2 9.025000 claude-instant-v1 2 8.012658 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 NeuralBeagle14-7B 7.628125 ``` ### Open LLM Leaderboard AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/AlphaMonarch-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
distilbert/distilroberta-base	distilbert	2024-02-19T11:09:58Z	1,742,457	144	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "safetensors", "roberta", "fill-mask", "exbert", "en", "dataset:openwebtext", "arxiv:1910.01108", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: en tags: - exbert license: apache-2.0 datasets: - openwebtext --- # Model Card for DistilRoBERTa base # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description This model is a distilled version of the [RoBERTa-base model](https://huggingface.co/roberta-base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert-base-uncased). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is case-sensitive: it makes a difference between english and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base. We encourage users of this model card to check out the [RoBERTa-base model card](https://huggingface.co/roberta-base) to learn more about usage, limitations and potential biases. - Developed by: Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf (Hugging Face) - Model type: Transformer-based language model - Language(s) (NLP): English - License: Apache 2.0 - Related Models: [RoBERTa-base model card](https://huggingface.co/roberta-base) - Resources for more information: - [GitHub Repository](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md) - [Associated Paper](https://arxiv.org/abs/1910.01108) # Uses ## Direct Use and Downstream Use You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=roberta) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## Out of Scope Use The model should not be used to intentionally create hostile or alienating environments for people. The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilroberta-base') >>> unmasker("The man worked as a <mask>.") [{'score': 0.1237526461482048, 'sequence': 'The man worked as a waiter.', 'token': 38233, 'token_str': ' waiter'}, {'score': 0.08968018740415573, 'sequence': 'The man worked as a waitress.', 'token': 35698, 'token_str': ' waitress'}, {'score': 0.08387645334005356, 'sequence': 'The man worked as a bartender.', 'token': 33080, 'token_str': ' bartender'}, {'score': 0.061059024184942245, 'sequence': 'The man worked as a mechanic.', 'token': 25682, 'token_str': ' mechanic'}, {'score': 0.03804653510451317, 'sequence': 'The man worked as a courier.', 'token': 37171, 'token_str': ' courier'}] >>> unmasker("The woman worked as a <mask>.") [{'score': 0.23149248957633972, 'sequence': 'The woman worked as a waitress.', 'token': 35698, 'token_str': ' waitress'}, {'score': 0.07563332468271255, 'sequence': 'The woman worked as a waiter.', 'token': 38233, 'token_str': ' waiter'}, {'score': 0.06983394920825958, 'sequence': 'The woman worked as a bartender.', 'token': 33080, 'token_str': ' bartender'}, {'score': 0.05411609262228012, 'sequence': 'The woman worked as a nurse.', 'token': 9008, 'token_str': ' nurse'}, {'score': 0.04995106905698776, 'sequence': 'The woman worked as a maid.', 'token': 29754, 'token_str': ' maid'}] ``` ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training Details DistilRoBERTa was pre-trained on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). See the [roberta-base model card](https://huggingface.co/roberta-base/blob/main/README.md) for further details on training. # Evaluation When fine-tuned on downstream tasks, this model achieves the following results (see [GitHub Repo](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md)): Glue test results: \| Task \| MNLI \| QQP \| QNLI \| SST-2 \| CoLA \| STS-B \| MRPC \| RTE \| \|:----:\|:----:\|:----:\|:----:\|:-----:\|:----:\|:-----:\|:----:\|:----:\| \| \| 84.0 \| 89.4 \| 90.8 \| 92.5 \| 59.3 \| 88.3 \| 86.6 \| 67.9 \| # Environmental Impact 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 # Citation ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` APA - Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. # How to Get Started With the Model You can use the model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilroberta-base') >>> unmasker("Hello I'm a <mask> model.") [{'score': 0.04673689603805542, 'sequence': "Hello I'm a business model.", 'token': 265, 'token_str': ' business'}, {'score': 0.03846118599176407, 'sequence': "Hello I'm a freelance model.", 'token': 18150, 'token_str': ' freelance'}, {'score': 0.03308931365609169, 'sequence': "Hello I'm a fashion model.", 'token': 2734, 'token_str': ' fashion'}, {'score': 0.03018997237086296, 'sequence': "Hello I'm a role model.", 'token': 774, 'token_str': ' role'}, {'score': 0.02111748233437538, 'sequence': "Hello I'm a Playboy model.", 'token': 24526, 'token_str': ' Playboy'}] ``` <a href="https://huggingface.co/exbert/?model=distilroberta-base"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

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