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EleutherAI/pythia-2.8b-modularaddition-first	EleutherAI	2024-03-01T23:19:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:19:41Z	--- 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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EleutherAI/pythia-2.8b-multiplication-first	EleutherAI	2024-03-01T23:19:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:19:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/pythia-2.8b-subtraction-first	EleutherAI	2024-03-01T23:19:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:19:28Z	--- 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. 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EleutherAI/pythia-2.8b-addition-first	EleutherAI	2024-03-01T23:19:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:19:22Z	--- 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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EleutherAI/pythia-2.8b-authors-first	EleutherAI	2024-03-01T23:19:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:19:16Z	--- 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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EleutherAI/pythia-2.8b-sciq-first	EleutherAI	2024-03-01T23:19:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:19:01Z	--- 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. 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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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EleutherAI/pythia-2.8b-population-first	EleutherAI	2024-03-01T23:18:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:18:54Z	--- 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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EleutherAI/pythia-2.8b-hemisphere-first	EleutherAI	2024-03-01T23:18:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:18:43Z	--- 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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EleutherAI/pythia-1.4b-squaring-first	EleutherAI	2024-03-01T23:18:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:18:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/pythia-1.4b-subtraction-first	EleutherAI	2024-03-01T23:18:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:18:12Z	--- 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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furrutiav/bert_qa_extractor_cockatiel_2022_simce_it_116	furrutiav	2024-03-01T23:17:48Z	7	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-01T01:54:12Z	--- 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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EleutherAI/pythia-1.4b-sentiment-first	EleutherAI	2024-03-01T23:17:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T23:17:44Z	--- 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. 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EleutherAI/pythia-410m-capitals-first	EleutherAI	2024-03-01T23:17:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:22:58Z	--- 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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Eric111/SOLAR-10.7B-Instruct-v1.0-DPO	Eric111	2024-03-01T23:14:49Z	131	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T22:36:54Z	--- library_name: transformers license: apache-2.0 tags: [] --- # Model Card for Model ID DPO fine-tuned version of upstage/SOLAR-10.7B-Instruct-v1.0 with Intel/orca_dpo_pairs ## 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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mHossain/en_bn_summarize_v8	mHossain	2024-03-01T23:04:32Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:csebuetnlp/mT5_m2m_crossSum", "base_model:finetune:csebuetnlp/mT5_m2m_crossSum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T13:07:47Z	--- base_model: csebuetnlp/mT5_m2m_crossSum tags: - generated_from_trainer metrics: - rouge model-index: - name: en_bn_summarize_v8 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. --> # en_bn_summarize_v8 This model is a fine-tuned version of [csebuetnlp/mT5_m2m_crossSum](https://huggingface.co/csebuetnlp/mT5_m2m_crossSum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 2.6956 - Rouge2: 0.2754 - Rougel: 2.3694 - Rougelsum: 2.6129 - Gen Len: 28.045 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.0 \| 1.0 \| 625 \| nan \| 2.6956 \| 0.2754 \| 2.3694 \| 2.6129 \| 28.045 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
mbcomputing/sn01-zephyr-7b-beta-20240301V01	mbcomputing	2024-03-01T23:04:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "region:us" ]	null	2024-03-01T22:53:29Z	--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-beta --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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 0.8.2
Aaquila/fine_tuned	Aaquila	2024-03-01T22:52:21Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-03-01T21:13:54Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: fine_tuned 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. --> # fine_tuned This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4328 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5769 \| 8.77 \| 250 \| 0.5205 \| \| 0.4998 \| 17.54 \| 500 \| 0.4383 \| \| 0.4637 \| 26.32 \| 750 \| 0.4285 \| \| 0.4488 \| 35.09 \| 1000 \| 0.4298 \| \| 0.4307 \| 43.86 \| 1250 \| 0.4272 \| \| 0.4309 \| 52.63 \| 1500 \| 0.4245 \| \| 0.4272 \| 61.4 \| 1750 \| 0.4282 \| \| 0.4204 \| 70.18 \| 2000 \| 0.4317 \| \| 0.4222 \| 78.95 \| 2250 \| 0.4313 \| \| 0.4354 \| 87.72 \| 2500 \| 0.4328 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
carlostoxtli/llama-2-7b-chat-guanaco	carlostoxtli	2024-03-01T22:50:58Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T23:06:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Rooney88/llama-2-13b-typosquat-v98	Rooney88	2024-03-01T22:50:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T22:49:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
AIFT/AIFT-instruct-SFT-1.3B-v1.8	AIFT	2024-03-01T22:49:38Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T22:42:34Z	--- license: cc-by-sa-4.0 --- <h1>AIFT-instruct-42dot_LLM-SFT-1.3B</h1> <br> version 1.8 <b><학습 데이터 구축></b> <br> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <모델> <br> 42dot에서 공개한 42dot_LLM-SFT-1.3B을 베이스 모델로 하여 학습 진행하였습니다. <br> <br> <br> <b><학습></b> <br> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.
aisuko/ft-t5-small-on-opus100	aisuko	2024-03-01T22:48:43Z	10	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:adapter:google-t5/t5-small", "license:apache-2.0", "region:us" ]	null	2024-01-06T00:08:13Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: t5-small model-index: - name: ft-t5-small-on-opus100 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. --> # ft-t5-small-on-opus100 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 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: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.15.0 - Tokenizers 0.15.0
mlx-community/starcoder2-15b-4bit	mlx-community	2024-03-01T22:45:15Z	10	0	transformers	[ "transformers", "safetensors", "starcoder2", "text-generation", "code", "mlx", "dataset:bigcode/the-stack-v2-train", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T21:48:42Z	--- license: bigcode-openrail-m library_name: transformers tags: - code - mlx datasets: - bigcode/the-stack-v2-train pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python model-index: - name: starcoder2-15b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- # mlx-community/starcoder2-15b-4bit This model was converted to MLX format from [`bigcode/starcoder2-15b`](). Refer to the [original model card](https://huggingface.co/bigcode/starcoder2-15b) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/starcoder2-15b-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
arcee-ai/gemma-7b-alpaca-zaphyr-slerp	arcee-ai	2024-03-01T22:41:19Z	9	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "merge", "mergekit", "HuggingFaceH4/zephyr-7b-gemma-v0.1", "mlabonne/Gemmalpaca-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T22:35:17Z	--- license: apache-2.0 tags: - merge - mergekit - HuggingFaceH4/zephyr-7b-gemma-v0.1 - mlabonne/Gemmalpaca-7B --- # gemma-7b-alpaca-zaphyr-slerp gemma-7b-alpaca-zaphyr-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [HuggingFaceH4/zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1) * [mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: HuggingFaceH4/zephyr-7b-gemma-v0.1 layer_range: [0, 28] - model: mlabonne/Gemmalpaca-7B layer_range: [0, 28] merge_method: slerp base_model: mlabonne/Gemmalpaca-7B 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 ```
Fibogacci/Qra-1B-GGUF	Fibogacci	2024-03-01T22:41:01Z	15	5	null	[ "gguf", "base_model:OPI-PG/Qra-1b", "base_model:quantized:OPI-PG/Qra-1b", "endpoints_compatible", "region:us" ]	null	2024-03-01T22:30:57Z	--- base_model: OPI-PG/Qra-1b --- Qra is a series of LLMs adapted to the Polish language, resulting from a collaboration between the National Information Processing Institute (OPI) and Gdańsk University of Technology (PG). Original base model can be found on HuggingFace here: https://huggingface.co/OPI-PG/Qra-1b This GGUF file was quantized using Colab Notebook: https://colab.research.google.com/github/adithya-s-k/LLM-Alchemy-Chamber/blob/main/Quantization/GGUF_Quantization.ipynb This is my first convertion of model. I don't know if whole process was correct (I mean model/gguf file gives strange answers, maybe I'm configuring it or setting not properly), but I'm fresh learner. Pierwsze boty za płoty, jak to mówią. Gratuluję twórcom, miejmy nadzieję, że będzie to Qra znosząca złote jajka. Pozdro!
coolseyungerm/hw2model	coolseyungerm	2024-03-01T22:40:00Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T22:26:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: hw2model 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. --> # hw2model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 - Precision: {'precision': 0.7715038708614725} - Recall: {'recall': 0.7542626491155903} - F1: {'f1': 0.7613370975651783} - Accuracy: {'accuracy': 0.7945619335347432} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------------------------------:\|:------------------------------:\|:--------------------------:\|:--------------------------------:\| \| 0.4599 \| 1.0 \| 1324 \| 0.4425 \| {'precision': 0.7715038708614725} \| {'recall': 0.7542626491155903} \| {'f1': 0.7613370975651783} \| {'accuracy': 0.7945619335347432} \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
mahiatlinux/MasherAI-7B-v0.2-GGUF	mahiatlinux	2024-03-01T22:30:04Z	7	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:quantized:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-01T22:26:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: teknium/OpenHermes-2.5-Mistral-7B --- # Uploaded model - Developed by: mahiatlinux - License: apache-2.0 - Finetuned from model : teknium/OpenHermes-2.5-Mistral-7B This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Alex24601/Manual_Talker	Alex24601	2024-03-01T22:18:11Z	0	0	null	[ "en", "license:mit", "region:us" ]	null	2024-03-01T22:15:06Z	--- license: mit language: - en --- We will be using the Opentrons Manual to try and make a chatbot. This way we'll be able to effectively answer generic Opentrons troubleshooting questions
kbatyshchev/llama-2-7b-chat-titles	kbatyshchev	2024-03-01T22:11:51Z	1	0	peft	[ "peft", "llama", "4-bit", "region:us" ]	null	2024-03-01T21:29:42Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
mHossain/afrikans_sum_v2	mHossain	2024-03-01T22:02:02Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T21:34:27Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: afrikans_sum_v2 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. --> # afrikans_sum_v2 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5721 - Rouge1: 8.6374 - Rouge2: 2.3685 - Rougel: 7.6315 - Rougelsum: 8.2247 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - 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 - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 4.1394 \| 1.0 \| 625 \| 2.5721 \| 8.6374 \| 2.3685 \| 7.6315 \| 8.2247 \| 19.0 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
SuplexAI/FurWorld_HardFurry_Diffuser	SuplexAI	2024-03-01T22:01:09Z	0	1	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-01T21:54:25Z	--- license: creativeml-openrail-m ---
furrutiav/bert_qa_extractor_cockatiel_2022_aptus_it_148	furrutiav	2024-03-01T21:56:46Z	4	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-29T22:28:58Z	--- 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. 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]
NightFox/saiga_gemma_9b_GGUF	NightFox	2024-03-01T21:56:45Z	13	5	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-02-28T16:30:00Z	GGUF for https://huggingface.co/IlyaGusev/saiga_gemma_9b `bfloat16->f32->[Q8_0, Q6_K, Q5_K_M]`
kajol/mistral_song_writter_v01	kajol	2024-03-01T21:52:09Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-03-01T21:51:02Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
azizksar/outputs	azizksar	2024-03-01T21:52:08Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-03-01T21:15:50Z	--- library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0542 ## 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: 2 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1604 \| 9.92 \| 310 \| 2.0542 \| ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
nninjun/squad-llama-2-7b	nninjun	2024-03-01T21:40:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:52:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
lucyknada/Eric111_Mayo-exl2-6bpw	lucyknada	2024-03-01T21:24:17Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T20:59:17Z	### exl2 quant (measurement.json included) --- ### original readme below --- --- license: apache-2.0 tags: - merge - mergekit - lazymergekit - mlabonne/NeuralBeagle14-7B - openchat/openchat-3.5-0106 --- # Mayo Mayo is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) Acknowledgements: https://github.com/mlabonne/llm-course ## 🧩 Configuration ```yaml slices: - sources: - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] - model: openchat/openchat-3.5-0106 layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralBeagle14-7B 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 ```
Hired/llama7b-headlines	Hired	2024-03-01T21:22:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-20T20:11:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. 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mHossain/afrikans_sum_v1	mHossain	2024-03-01T21:17:05Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T20:35:35Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: afrikans_sum_v1 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. --> # afrikans_sum_v1 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5899 - Rouge1: 13.343 - Rouge2: 5.1675 - Rougel: 11.6215 - Rougelsum: 12.7283 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 3.1323 \| 1.0 \| 1250 \| 2.5899 \| 13.343 \| 5.1675 \| 11.6215 \| 12.7283 \| 19.0 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Baktashans/NLP_HF_Workshop	Baktashans	2024-03-01T21:16:11Z	6	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T21:12:20Z	--- pipeline_tag: text-classification ---
inkasaras/Mistral_ecommerce	inkasaras	2024-03-01T21:15:28Z	7	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-01T21:13:07Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
davepower/pegasus-samsum	davepower	2024-03-01T21:08:08Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-cnn_dailymail", "base_model:finetune:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T19:21:25Z	--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4095 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5156 \| 0.54 \| 500 \| 1.4095 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
mlx-community/starcoder2-3b-4bit	mlx-community	2024-03-01T21:06:09Z	21	0	transformers	[ "transformers", "safetensors", "starcoder2", "text-generation", "code", "mlx", "dataset:bigcode/the-stack-v2-train", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T20:59:16Z	--- license: bigcode-openrail-m library_name: transformers tags: - code - mlx datasets: - bigcode/the-stack-v2-train pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python model-index: - name: starcoder2-3b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 32.7 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 25.0 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 27.7 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 27.4 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 31.7 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 71.19 --- # mlx-community/starcoder2-3b-4bit This model was converted to MLX format from [`bigcode/starcoder2-3b`](). Refer to the [original model card](https://huggingface.co/bigcode/starcoder2-3b) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/starcoder2-3b-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
bfwggggg/datadome-slider-b0-finetuned-segments-missing-puzzle-mar-1	bfwggggg	2024-03-01T20:58:59Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2024-03-01T19:13:26Z	--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: datadome-slider-b0-finetuned-segments-missing-puzzle-mar-1 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. --> # datadome-slider-b0-finetuned-segments-missing-puzzle-mar-1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the bfwggggg/image-with-puzzle dataset. It achieves the following results on the evaluation set: - Loss: 0.2458 - Mean Iou: 0.0 - Mean Accuracy: nan - Overall Accuracy: nan - Accuracy Missing-puzzle: nan - Iou Missing-puzzle: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - 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 \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Missing-puzzle \| Iou Missing-puzzle \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------:\|:------------------:\| \| 0.4987 \| 5.0 \| 20 \| 0.6323 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.388 \| 10.0 \| 40 \| 0.4734 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.3014 \| 15.0 \| 60 \| 0.3631 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.3179 \| 20.0 \| 80 \| 0.3405 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.2963 \| 25.0 \| 100 \| 0.2696 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.2458 \| 30.0 \| 120 \| 0.2803 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.2757 \| 35.0 \| 140 \| 0.2635 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.2519 \| 40.0 \| 160 \| 0.2522 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.2349 \| 45.0 \| 180 \| 0.2474 \| 0.0 \| nan \| nan \| nan \| 0.0 \| \| 0.212 \| 50.0 \| 200 \| 0.2458 \| 0.0 \| nan \| nan \| nan \| 0.0 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
sanchit-gandhi/gemma-2b-openassistant-guanaco	sanchit-gandhi	2024-03-01T20:58:13Z	24	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:other", "region:us" ]	null	2024-03-01T17:52:37Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b model-index: - name: gemma-2b-fine-tuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-2b-fine-tuned This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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: 0.001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.39.0.dev0 - Pytorch 2.3.0.dev20240118+cu121 - Datasets 2.16.2.dev0 - Tokenizers 0.15.0
influenceia15/generator.image	influenceia15	2024-03-01T20:54:35Z	0	0	null	[ "region:us" ]	null	2024-03-01T20:53:42Z	!pip install pygit2==1.12.2 %cd /content !git clone https://github.com/lllyasviel/Fooocus.git %cd /content/Fooocus !python entry_with_update.py --share
Mantis-VL/mfuyu_v2_8192_720p-9000	Mantis-VL	2024-03-01T20:41:41Z	6	0	transformers	[ "transformers", "safetensors", "fuyu", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T01:43:31Z	--- 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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spotify/Mixtral-8x7B-Instruct-v0.1-HIReview-v0.1.8	spotify	2024-03-01T20:41:29Z	5	0	peft	[ "peft", "safetensors", "mixtral", "arxiv:1910.09700", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "region:us" ]	null	2024-03-01T20:19:22Z	--- library_name: peft base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 --- # 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 0.8.2
Mantis-VL/mfuyu_v2_3072_480p-final	Mantis-VL	2024-03-01T20:41:29Z	7	0	transformers	[ "transformers", "safetensors", "fuyu", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T01:40:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
numen-tech/stablelm-zephyr-3b-w4a16g128asym	numen-tech	2024-03-01T20:40:17Z	0	0	null	[ "arxiv:2308.13137", "license:other", "region:us" ]	null	2024-03-01T20:36:52Z	--- license: other --- 4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b).
sujayC66/mistral_finetune_docker	sujayC66	2024-03-01T20:39:33Z	2	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:other", "region:us" ]	null	2024-03-01T20:30:13Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: train_2024-03-01-19-26-33 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. --> # train_2024-03-01-19-26-33 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the docker_NL 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
GlycerinLOL/LLM_Teached_Bart_50k	GlycerinLOL	2024-03-01T20:30:43Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T17:23:12Z	--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge - precision - recall - f1 model-index: - name: LLM_Teached_Bart_50k 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. --> # LLM_Teached_Bart_50k This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5590 - Rouge1: 0.4909 - Rouge2: 0.2303 - Rougel: 0.3967 - Rougelsum: 0.3965 - Gen Len: 38.2287 - Precision: 0.9063 - Recall: 0.9187 - F1: 0.9123 ## 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: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 390 \| 1.6214 \| 0.4804 \| 0.2218 \| 0.3873 \| 0.3873 \| 38.3549 \| 0.9049 \| 0.9166 \| 0.9106 \| \| 1.5842 \| 2.0 \| 781 \| 1.5548 \| 0.4874 \| 0.2283 \| 0.3945 \| 0.3945 \| 37.8604 \| 0.9059 \| 0.9171 \| 0.9113 \| \| 1.3014 \| 3.0 \| 1172 \| 1.5461 \| 0.49 \| 0.2294 \| 0.3975 \| 0.3974 \| 37.7564 \| 0.9064 \| 0.918 \| 0.912 \| \| 1.18 \| 3.99 \| 1560 \| 1.5590 \| 0.4909 \| 0.2303 \| 0.3967 \| 0.3965 \| 38.2287 \| 0.9063 \| 0.9187 \| 0.9123 \| ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.15.0
lucyknada/EmbeddedLLM_Mistral-7B-Merge-14-v0.5-exl2-6bpw	lucyknada	2024-03-01T20:29:58Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T19:43:26Z	### exl2 quant (measurement.json included) --- ### original readme below --- --- license: cc-by-nc-4.0 language: - en tags: - merge base_model: - EmbeddedLLM/Mistral-7B-Merge-14-v0.3 - Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp - openchat/openchat-3.5-0106 - mlabonne/NeuralMarcoro14-7B --- # Update 2024-01-21 Due to [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) updating its license to CC-BY-NC, our license will follow suit. # Model Description This is an experiment to test merging 14 models using DARE TIES 🦙 1. We first merge 14 models to produce [EmbeddedLLM/Mistral-7B-Merge-14-v0.3](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.3). 2. The model is merged again using DARE TIES with: - [Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp) - [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) - [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) ## Open LLM Leaderboard \| Average \| 71.96 \| \|------------\|-------\| \| ARC \| 68.69 \| \| HellaSwag \| 86.45 \| \| MMLU \| 65.65 \| \| TruthfulQA \| 59.12 \| \| Winogrande \| 80.66 \| \| GSM8K \| 71.19 \| ## Chat Template Either ChatML or Llama-2 chat template. ## Merge Configuration The merge config file for this model is here: ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.3 parameters: weight: 0.3 density: 0.5 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp parameters: weight: 0.2 density: 0.5 - model: openchat/openchat-3.5-0106 parameters: weight: 0.2 density: 0.5 - model: mlabonne/NeuralMarcoro14-7B parameters: weight: 0.3 density: 0.5 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true tokenizer_source: union dtype: bfloat16 ```
fatcatmilo/Text2RDF	fatcatmilo	2024-03-01T20:26:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/pythia-1b-multiplication-first	EleutherAI	2024-03-01T20:26:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:49Z	--- 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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EleutherAI/pythia-1b-subtraction-first	EleutherAI	2024-03-01T20:26:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:43Z	--- 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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EleutherAI/pythia-1b-authors-first	EleutherAI	2024-03-01T20:26:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:36Z	--- 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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EleutherAI/pythia-1b-nli-first	EleutherAI	2024-03-01T20:26:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:28Z	--- 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. 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EleutherAI/pythia-1b-sentiment-first	EleutherAI	2024-03-01T20:26:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:25Z	--- 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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EleutherAI/pythia-1b-population-first	EleutherAI	2024-03-01T20:26:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:19Z	--- 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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EleutherAI/pythia-1b-hemisphere-first	EleutherAI	2024-03-01T20:26:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/pythia-410m-squaring-first	EleutherAI	2024-03-01T20:26:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:26:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/pythia-410m-multiplication-first	EleutherAI	2024-03-01T20:25:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:25:54Z	--- 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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EleutherAI/pythia-410m-subtraction-first	EleutherAI	2024-03-01T20:25:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:25:49Z	--- 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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EleutherAI/pythia-410m-addition-first	EleutherAI	2024-03-01T20:25:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:25:43Z	--- 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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EleutherAI/pythia-410m-nli-first	EleutherAI	2024-03-01T20:25:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:25:32Z	--- 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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EleutherAI/pythia-410m-sciq-first	EleutherAI	2024-03-01T20:25:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:25:17Z	--- 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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EleutherAI/pythia-410m-population-first	EleutherAI	2024-03-01T20:25:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T20:25:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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arcee-ai/gemma-7b-zephyr-alpaca-it-ties	arcee-ai	2024-03-01T20:23:37Z	13	2	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "merge", "mergekit", "google/gemma-7b-it", "HuggingFaceH4/zephyr-7b-gemma-v0.1", "mlabonne/Gemmalpaca-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T20:19:14Z	--- license: apache-2.0 tags: - merge - mergekit - google/gemma-7b-it - HuggingFaceH4/zephyr-7b-gemma-v0.1 - mlabonne/Gemmalpaca-7B --- # gemma-7b-zephyr-alpaca-it-ties gemma-7b-zephyr-alpaca-it-ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) * [HuggingFaceH4/zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1) * [mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B) ## 🧩 Configuration ```yaml models: - model: google/gemma-7b-it parameters: density: 0.5 weight: 0.3 - model: HuggingFaceH4/zephyr-7b-gemma-v0.1 parameters: density: 0.5 weight: 0.3 # weight gradient - model: mlabonne/Gemmalpaca-7B parameters: density: 0.5 weight: 0.3 # weight gradient merge_method: dare_ties base_model: google/gemma-7b parameters: normalize: true int8_mask: true dtype: float16 ```
garrettallen/my-awesome-model	garrettallen	2024-03-01T20:19:39Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-01T20:17:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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numen-tech/Mistral-7B-Instruct-v0.2-w3a16g40sym	numen-tech	2024-03-01T20:00:33Z	0	0	null	[ "arxiv:2308.13137", "license:apache-2.0", "region:us" ]	null	2024-03-01T19:51:06Z	--- license: apache-2.0 --- 3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
numen-tech/OpenHermes-2.5-Mistral-7B-w3a16g40sym	numen-tech	2024-03-01T20:00:13Z	0	0	null	[ "arxiv:2308.13137", "license:apache-2.0", "region:us" ]	null	2024-03-01T19:51:23Z	--- license: apache-2.0 --- 3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B).
BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch	BlackSamorez	2024-03-01T19:27:16Z	16	3	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "arxiv:2401.06118", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "aqlm", "region:us" ]	text-generation	2024-02-08T13:08:06Z	<span style="color:red">WARNING:</span> this checkpoint might be obsolete. Please use the [updated Mixtral checkpoint](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf). Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `mistralai/Mixtral-8x7B-v0.1`.
numen-tech/spicyboros-7b-2.2-w3a16g40sym	numen-tech	2024-03-01T19:26:58Z	0	0	null	[ "arxiv:2308.13137", "license:llama2", "region:us" ]	null	2024-03-01T19:25:26Z	--- license: llama2 --- 3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [spicyboros-7b-2.2](https://huggingface.co/jondurbin/spicyboros-7b-2.2).
OmarHaroon01/t5_small_finetune_ag_news_main_model	OmarHaroon01	2024-03-01T19:25:53Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T19:25:34Z	--- 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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adenhaus/mt5-small-tata	adenhaus	2024-03-01T19:23:14Z	5	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-04T15:35:48Z	--- license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer metrics: - rouge - sacrebleu - chrf model-index: - name: mt5-small-tata-finetuned results: [] inference: parameters: max_length: 100 widget: - text: "Planning Status of Births \| Percent \| (Wanted then, 0.57) (Unwanted, 0.17) (Wanted later, 0.26)" example_title: "English" ---
imvladikon/mpnet-base-nli-matryoshka	imvladikon	2024-03-01T19:19:40Z	6	0	transformers	[ "transformers", "safetensors", "mpnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T19:06:41Z	--- 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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Owhslp/nous_researcher_tuning_2_1	Owhslp	2024-03-01T19:19:28Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T18:59:19Z	--- 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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maksimmezentsev/phi-2-trained-v0.1	maksimmezentsev	2024-03-01T19:10:47Z	5	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-01T19:08:28Z	--- 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. 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PulseWave/REMITTANCE-ADVICE	PulseWave	2024-03-01T19:08:15Z	4	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "region:us" ]	text-classification	2024-03-01T19:05:29Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: [] pipeline_tag: text-classification inference: true --- # SetFit This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit <!-- - Sentence Transformer: [Unknown](https://huggingface.co/unknown) --> - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("setfit_model_id") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.11.7 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.2 - PyTorch: 2.2.0 - Datasets: 2.16.1 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
kawthergb/whisper-small-hi	kawthergb	2024-03-01T19:03:31Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-01T14:02:31Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Arabic results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: ar split: None args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 64.7673314339981 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Arabic This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5022 - Wer: 64.7673 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3088 \| 0.57 \| 1000 \| 0.5616 \| 62.7730 \| \| 0.1708 \| 1.13 \| 2000 \| 0.5087 \| 53.8462 \| \| 0.1587 \| 1.7 \| 3000 \| 0.4995 \| 58.9744 \| \| 0.0957 \| 2.27 \| 4000 \| 0.5022 \| 64.7673 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
arcee-ai/gemma-7b-alpaca-it-ties	arcee-ai	2024-03-01T19:02:06Z	12	1	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "merge", "mergekit", "google/gemma-7b-it", "mlabonne/Gemmalpaca-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T18:57:56Z	--- license: apache-2.0 tags: - merge - mergekit - google/gemma-7b-it - mlabonne/Gemmalpaca-7B --- # gemma-7b-alpaca-it-ties gemma-7b-alpaca-it-ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) * [mlabonne/Gemmalpaca-7B](https://huggingface.co/mlabonne/Gemmalpaca-7B) ## 🧩 Configuration ```yaml models: - model: google/gemma-7b-it parameters: density: 0.5 weight: 0.5 - model: mlabonne/Gemmalpaca-7B parameters: density: 0.5 weight: 0.5 # weight gradient merge_method: ties base_model: google/gemma-7b parameters: normalize: true int8_mask: true dtype: float16 ```
lucyknada/ChaoticNeutrals_Prodigy_7B-exl2-6bpw	lucyknada	2024-03-01T19:00:54Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T18:36:06Z	### exl2 quant (measurement.json included) --- ### original readme below --- base_model: - macadeliccc/WestLake-7B-v2-laser-truthy-dpo - ChaoticNeutrals/This_is_fine_7B library_name: transformers tags: - mergekit - merge license: other --- # Wing GGUF available here: https://huggingface.co/Lewdiculous/Prodigy_7B-GGUF-Imatrix Big thanks to https://huggingface.co/Lewdiculous ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/S-E_CADzfAg3xaVX01rdx.jpeg) 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: * [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo) * [ChaoticNeutrals/This_is_fine_7B](https://huggingface.co/ChaoticNeutrals/This_is_fine_7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: ChaoticNeutrals/This_is_fine_7B layer_range: [0, 32] - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo layer_range: [0, 32] merge_method: slerp base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo 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: float16 ```
PulseWave/TAX-EXEMPT	PulseWave	2024-03-01T18:59:43Z	4	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "region:us" ]	text-classification	2024-03-01T18:57:03Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: [] pipeline_tag: text-classification inference: true --- # SetFit This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit <!-- - Sentence Transformer: [Unknown](https://huggingface.co/unknown) --> - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("setfit_model_id") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.11.7 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.2 - PyTorch: 2.2.0 - Datasets: 2.16.1 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
ritwikraha/khabib_sketch_LoRA	ritwikraha	2024-03-01T18:51:34Z	8	2	diffusers	[ "diffusers", "tensorboard", "art", "code", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "en", "license:cc-by-2.0", "region:us" ]	text-to-image	2024-03-01T15:47:25Z	--- license: cc-by-2.0 language: - en library_name: diffusers tags: - art - code - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora --- # Khabib Sketch SDXL LoRA A LoRA adaptation of SDXL to produce sketches of the MMA fighter and G.O.A.T Khabib. <figure> <img src="https://i.imgur.com/eIn5oqJ.png" alt="Khabib" width="256" height="256"> <figcaption>Sketch of Khabib fighting a Bengal Tiger</figcaption> </figure> These are LoRA adaption weights for `stabilityai/stable-diffusion-xl-base-1.0`. The weights were trained on sketches of Khabib by [ritwikraha](https://www.ritwikraha.com/) using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. DataSet: custom hand-drawn sketches by [ritwikraha](https://www.ritwikraha.com/) ## Usage ``` !pip install diffusers accelerate -q import torch from PIL import Image from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe.load_lora_weights('ritwikraha/khabib_sketch_LoRA') _ = pipe.to("cuda") prompt = "a sketch of TOK khabib pointing at another khabib like the spiderman meme, monchrome, pen sketch" negative_prompt ="ugly face, multiple bodies, bad anatomy, disfigured, extra fingers" image = pipe(prompt=prompt, negative_prompt=negative_prompt, guidance_scale=3, num_inference_steps=50).images[0] image ``` ## Examples \| Image 1 \| Image 2 \| \|---\|---\| \| ![khabib sketch example 1](./1.png) \| ![khabib sketch example 2](./2.png) \| \| Image 3 \| Image 4 \| \|---\|---\| \| ![khabib sketch example 3](./3.png) \| ![sks dog sample 4](./4.png) \| ## Tips - The examples are all sketches created in Procreate so prompts with words like sketch, and monochrome work best - Use a negative prompt and guidance scale for the model - Images at 1024X1024 will be better than other dimensions
arcee-ai/BioMistral-merged-zephyr	arcee-ai	2024-03-01T18:46:55Z	14	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2306.01708", "base_model:BioMistral/BioMistral-7B", "base_model:merge:BioMistral/BioMistral-7B", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:merge:HuggingFaceH4/zephyr-7b-beta", "base_model:mistralai/Mistral-7B-v0.1", "base_model:merge:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-27T23:56:55Z	--- base_model: - mistralai/Mistral-7B-v0.1 - BioMistral/BioMistral-7B - HuggingFaceH4/zephyr-7b-beta library_name: transformers tags: - mergekit - merge --- # BioMistral-merged-zephyr 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) * [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ## 🏆 Evaluation \|Pubmedqa\|Medmcqa\|USMLE\|arc_challenge\|Hellaswag\| \|---\|---\|---\|---\|---\| \|76.8\|47.21\|47.62\|55.89\|63.43\| ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-v0.1 - model: HuggingFaceH4/zephyr-7b-beta parameters: density: 0.5 weight: 0.5 - model: BioMistral/BioMistral-7B parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-v0.1 parameters: normalize: false int8_mask: true dtype: float16 ```
YusufTree/ppo-PyramidsRND	YusufTree	2024-03-01T18:46:38Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2024-03-01T18:46:36Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: YusufTree/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
azizksar/FT_LLma_4bits_V2_2column	azizksar	2024-03-01T18:45:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T13:58:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vlada-v/whisper-small-en	vlada-v	2024-03-01T18:41:46Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "en", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-27T18:27:45Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Whisper Small En - Kids results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small En - Kids This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the PRG Dataset dataset. It achieves the following results on the evaluation set: - Loss: 8.4674 - Wer: 68.1178 ## 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: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ArchiveAI/WestLake-7B-v2	ArchiveAI	2024-03-01T18:40:59Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T18:40:59Z	--- license: apache-2.0 language: - en library_name: transformers --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ffb10eeafbd678d4b3fe/jnqnl8a_zYYMqJoBpX8yS.png) Update Notes: Version 2 trained 1 additional epoch cycle for 3 total # Westlake-7Bv2: Role-Play & Text Generation Specialist Model Welcome to the documentation of Westlake-7B, a cutting-edge language model designed for exceptional role-play and text generation tasks. This README file aims to provide an overview of our capabilities, usage guidelines, and potential applications. ## About Westlake-7Bv2 Westlake-7B is built upon a vast corpus of diverse texts, enabling it to generate contextually relevant responses in various scenarios. With its impressive size of 7 billion parameters, this model excels at understanding nuances in language and producing creative outputs. ### Key Features 1. Role-Play: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds. 2. Text Generation: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects. 3. Contextual Understanding: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications. 4. Continuous Learning: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication. ## Usage Guidelines To utilize Westlake-7Bv2 for your projects or experiments, follow these steps: 1. Prompting: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions. 2. Feedback Loop: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains. 3. Ethical Considerations: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities. ## Potential Applications Westlake-7Bv2's versatility makes it suitable for various applications across different industries: 1. Creative Writing: Assist authors in generating new ideas, expanding storylines, or even completing drafts by providing creative suggestions and textual content. 2. Education: Enhance language learning platforms with interactive role-play scenarios to improve students' communication skills and cultural understanding. 3. Gaming: Integrate Westlake-7B into game engines for dynamic non-player character interactions or generating unique questlines based on player choices. 4. Customer Support: Leverage the model's conversational abilities to create chatbots capable of handling complex queries and providing personalized assistance. 5. Social Media: Develop applications that generate engaging content such as captions, status updates, or even entire posts tailored to users' preferences and interests.
ArchiveAI/AlphaMonarch-7B	ArchiveAI	2024-03-01T18:40:36Z	3	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	2024-03-01T18:40:36Z	--- 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 * GPTQ: https://huggingface.co/LoneStriker/AlphaMonarch-7B-GPTQ * AWQ: https://huggingface.co/LoneStriker/AlphaMonarch-7B-AWQ * mlx: https://huggingface.co/mlx-community/AlphaMonarch-7B-mlx * EXL2: * https://huggingface.co/LoneStriker/AlphaMonarch-7B-3.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-4.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-5.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-6.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-8.0bpw-h6-exl2 ## 🏆 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"]) ```
crumb/minipile-111m	crumb	2024-03-01T18:40:17Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-29T07:41:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 1\|none \| 25\|acc \|0.1775\|± \|0.0112\| \| \| \|none \| 25\|acc_norm\|0.2065\|± \|0.0118\| \|truthfulqa_mc2\| 2\|none \| 0\|acc \|0.4633\|± \|0.0155\| \|winogrande\| 1\|none \| 5\|acc \|0.5075\|± \|0.0141\| \|hellaswag\| 1\|none \| 10\|acc \|0.2685\|± \|0.0044\| \| \| \|none \| 10\|acc_norm\|0.2746\|± \|0.0045\| \|gsm8k\| 3\|strict-match \| 5\|exact_match\|0.0023\|± \|0.0013\| \| \| \|flexible-extract\| 5\|exact_match\|0.0152\|± \|0.0034\| (0.26113333333333333, 0.004443523026985591) \| Tasks \|Version\|Filter\|n-shot\|Metric\|Value \| \|Stderr\| \|-----------------------------------\|------:\|------\|-----:\|------\|-----:\|---\|-----:\| \|world_religions \| 0\|none \| 5\|acc \|0.2047\|± \|0.0309\| \|virology \| 0\|none \| 5\|acc \|0.1807\|± \|0.0300\| \|us_foreign_policy \| 0\|none \| 5\|acc \|0.2700\|± \|0.0446\| \|sociology \| 0\|none \| 5\|acc \|0.2488\|± \|0.0306\| \|security_studies \| 0\|none \| 5\|acc \|0.3347\|± \|0.0302\| \|public_relations \| 0\|none \| 5\|acc \|0.2273\|± \|0.0401\| \|professional_psychology \| 0\|none \| 5\|acc \|0.2042\|± \|0.0163\| \|professional_medicine \| 0\|none \| 5\|acc \|0.4485\|± \|0.0302\| \|professional_law \| 0\|none \| 5\|acc \|0.2458\|± \|0.0110\| \|professional_accounting \| 0\|none \| 5\|acc \|0.2163\|± \|0.0246\| \|prehistory \| 0\|none \| 5\|acc \|0.2222\|± \|0.0231\| \|philosophy \| 0\|none \| 5\|acc \|0.2379\|± \|0.0242\| \|nutrition \| 0\|none \| 5\|acc \|0.2810\|± \|0.0257\| \|moral_scenarios \| 0\|none \| 5\|acc \|0.2659\|± \|0.0148\| \|moral_disputes \| 0\|none \| 5\|acc \|0.2428\|± \|0.0231\| \|miscellaneous \| 0\|none \| 5\|acc \|0.2375\|± \|0.0152\| \|medical_genetics \| 0\|none \| 5\|acc \|0.3000\|± \|0.0461\| \|marketing \| 0\|none \| 5\|acc \|0.1966\|± \|0.0260\| \|management \| 0\|none \| 5\|acc \|0.1553\|± \|0.0359\| \|machine_learning \| 0\|none \| 5\|acc \|0.3304\|± \|0.0446\| \|logical_fallacies \| 0\|none \| 5\|acc \|0.2331\|± \|0.0332\| \|jurisprudence \| 0\|none \| 5\|acc \|0.2407\|± \|0.0413\| \|international_law \| 0\|none \| 5\|acc \|0.3306\|± \|0.0429\| \|human_sexuality \| 0\|none \| 5\|acc \|0.2595\|± \|0.0384\| \|human_aging \| 0\|none \| 5\|acc \|0.2063\|± \|0.0272\| \|high_school_world_history \| 0\|none \| 5\|acc \|0.2658\|± \|0.0288\| \|high_school_us_history \| 0\|none \| 5\|acc \|0.2745\|± \|0.0313\| \|high_school_statistics \| 0\|none \| 5\|acc \|0.4722\|± \|0.0340\| \|high_school_psychology \| 0\|none \| 5\|acc \|0.2330\|± \|0.0181\| \|high_school_physics \| 0\|none \| 5\|acc \|0.3311\|± \|0.0384\| \|high_school_microeconomics \| 0\|none \| 5\|acc \|0.3403\|± \|0.0308\| \|high_school_mathematics \| 0\|none \| 5\|acc \|0.2630\|± \|0.0268\| \|high_school_macroeconomics \| 0\|none \| 5\|acc \|0.3205\|± \|0.0237\| \|high_school_government_and_politics\| 0\|none \| 5\|acc \|0.3679\|± \|0.0348\| \|high_school_geography \| 0\|none \| 5\|acc \|0.3283\|± \|0.0335\| \|high_school_european_history \| 0\|none \| 5\|acc \|0.2606\|± \|0.0343\| \|high_school_computer_science \| 0\|none \| 5\|acc \|0.2800\|± \|0.0451\| \|high_school_chemistry \| 0\|none \| 5\|acc \|0.2956\|± \|0.0321\| \|high_school_biology \| 0\|none \| 5\|acc \|0.3194\|± \|0.0265\| \|global_facts \| 0\|none \| 5\|acc \|0.1600\|± \|0.0368\| \|formal_logic \| 0\|none \| 5\|acc \|0.1825\|± \|0.0346\| \|elementary_mathematics \| 0\|none \| 5\|acc \|0.2487\|± \|0.0223\| \|electrical_engineering \| 0\|none \| 5\|acc \|0.2966\|± \|0.0381\| \|econometrics \| 0\|none \| 5\|acc \|0.2632\|± \|0.0414\| \|conceptual_physics \| 0\|none \| 5\|acc \|0.2553\|± \|0.0285\| \|computer_security \| 0\|none \| 5\|acc \|0.1800\|± \|0.0386\| \|college_physics \| 0\|none \| 5\|acc \|0.2451\|± \|0.0428\| \|college_medicine \| 0\|none \| 5\|acc \|0.2312\|± \|0.0321\| \|college_mathematics \| 0\|none \| 5\|acc \|0.3200\|± \|0.0469\| \|college_computer_science \| 0\|none \| 5\|acc \|0.3000\|± \|0.0461\| \|college_chemistry \| 0\|none \| 5\|acc \|0.1800\|± \|0.0386\| \|college_biology \| 0\|none \| 5\|acc \|0.2778\|± \|0.0375\| \|clinical_knowledge \| 0\|none \| 5\|acc \|0.2340\|± \|0.0261\| \|business_ethics \| 0\|none \| 5\|acc \|0.2100\|± \|0.0409\| \|astronomy \| 0\|none \| 5\|acc \|0.1776\|± \|0.0311\| \|anatomy \| 0\|none \| 5\|acc \|0.2296\|± \|0.0363\| \|abstract_algebra \| 0\|none \| 5\|acc \|0.2200\|± \|0.0416\| - 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MesozoicMetallurgist/nous-Callovian	MesozoicMetallurgist	2024-03-01T18:38:24Z	117	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T18:35:43Z	--- 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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lillybak/mistral7binstruct_summarize	lillybak	2024-03-01T18:37:19Z	8	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-02-28T06:20:07Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral7binstruct_summarize 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. --> # mistral7binstruct_summarize This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.4327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 125 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7572 \| 0.17 \| 20 \| 1.5089 \| \| 1.5374 \| 0.34 \| 40 \| 1.4566 \| \| 1.4774 \| 0.51 \| 60 \| 1.4456 \| \| 1.5517 \| 0.68 \| 80 \| 1.4398 \| \| 1.5103 \| 0.85 \| 100 \| 1.4347 \| \| 1.4976 \| 1.02 \| 120 \| 1.4327 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
SriramAditya/propaganda_model	SriramAditya	2024-03-01T18:34:35Z	105	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-01T18:05:03Z	--- 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. 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. (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]
ManishThota/Thota	ManishThota	2024-03-01T18:25:44Z	119	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "tinyllama", "conversational", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-18T00:55:34Z	--- license: openrail tags: - tinyllama ---
arcee-ai/Mistral-Hermes-Support-Ties	arcee-ai	2024-03-01T18:25:03Z	12	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "mistralai/Mistral-7B-v0.1+predibase/customer_support", "NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T18:21:03Z	--- license: apache-2.0 tags: - merge - mergekit - mistralai/Mistral-7B-v0.1+predibase/customer_support - NousResearch/Nous-Hermes-2-Mistral-7B-DPO --- # Mistral-Hermes-Support-Ties Mistral-Hermes-Support-Ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [mistralai/Mistral-7B-v0.1+predibase/customer_support](https://huggingface.co/mistralai/Mistral-7B-v0.1+predibase/customer_support) * [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO) ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-Instruct-v0.2 #no parameters necessary for base model - model: mistralai/Mistral-7B-v0.1+predibase/customer_support parameters: density: 0.5 weight: 0.5 - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-Instruct-v0.2 parameters: normalize: false int8_mask: true dtype: float16 ```
kaouthar1223/whisper-small-ar	kaouthar1223	2024-03-01T18:19:10Z	86	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ar", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-01T14:03:10Z	--- language: - ar license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: 'Whisper Small arabic From Ensia Student Team3 ' results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: ar split: None args: 'config: ar, split: test' metrics: - name: Wer type: wer value: 63.90977443609023 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small arabic From Ensia Student Team3 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.2919 - Wer: 63.9098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.0003 \| 41.67 \| 1000 \| 1.1397 \| 63.1579 \| \| 0.0001 \| 83.33 \| 2000 \| 1.2204 \| 62.9699 \| \| 0.0001 \| 125.0 \| 3000 \| 1.2751 \| 64.0977 \| \| 0.0001 \| 166.67 \| 4000 \| 1.2919 \| 63.9098 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ImperialIndians23/RobertaBaseProcessedDownsampled	ImperialIndians23	2024-03-01T18:18:17Z	106	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T18:10:01Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: RobertaBaseProcessedDownsampled 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. --> # RobertaBaseProcessedDownsampled This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2432 - Accuracy: 0.8949 - F1: 0.5045 - Precision: 0.4553 - Recall: 0.5657 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: inverse_sqrt - lr_scheduler_warmup_steps: 500 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.474 \| 1.0 \| 297 \| 0.5854 \| 0.6371 \| 0.3214 \| 0.1952 \| 0.9091 \| \| 0.3652 \| 2.0 \| 595 \| 0.2432 \| 0.8949 \| 0.5045 \| 0.4553 \| 0.5657 \| \| 0.3958 \| 3.0 \| 893 \| 0.3959 \| 0.8185 \| 0.4663 \| 0.3230 \| 0.8384 \| \| 0.273 \| 3.99 \| 1188 \| 0.6534 \| 0.7564 \| 0.4056 \| 0.2636 \| 0.8788 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Zen1t/WordWeaver-AI	Zen1t	2024-03-01T18:17:33Z	1	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-29T17:02:04Z	--- license: apache-2.0 language: - ru --- ## How to use it ``` python # pip install transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Zen1t/WordWeaver-AI" prompt = "Сегодня он собирался удивить Аню сюрпризом — отвезти её в ресторан, заслуживающий особого внимания из-за своего расположения. А располагался тот на двадцать пятом этаже здания «Российской Академии Наук». Из его окон, судя по описанию, открывался изумительный вид на город, особенно ночью." prompt = f"<s>[INST] {prompt} [/INST]" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( prompt, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(seq['generated_text'][len(prompt):]) ``` Output ``` Сегодня он собирался удивить Аню сюрпризом — отвезти ее в ресторан, заслуживающий особого внимания из-за своего расположения на двадцать пятом этаже здания «Российской Академии Наук». Из его окон, судя по описанию, открывался изумительный вид на город, особенно ночью. ```
savinda99/distilbert-base-uncased-finetuned-lgbt	savinda99	2024-03-01T18:16:36Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-01T18:09:11Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-lgbt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-lgbt This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3478 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.3671 \| 1.0 \| 752 \| 2.3912 \| \| 2.3669 \| 2.0 \| 1504 \| 2.3650 \| \| 2.3922 \| 3.0 \| 2256 \| 2.3519 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

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