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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 18:27:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 18:23:41	card stringlengths 11 1.01M
mlx-community/granite-20b-code-base-8bit	mlx-community	2024-05-14T17:17:01Z	11	0	transformers	[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "code", "granite", "mlx", "dataset:codeparrot/github-code-clean", "dataset:bigcode/starcoderdata", "dataset:open-web-math/open-web-math", "dataset:math-ai/StackMathQA", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T21:39:50Z	--- license: apache-2.0 library_name: transformers tags: - code - granite - mlx datasets: - codeparrot/github-code-clean - bigcode/starcoderdata - open-web-math/open-web-math - math-ai/StackMathQA metrics: - code_eval pipeline_tag: text-generation inference: true model-index: - name: granite-20b-code-base results: - task: type: text-generation dataset: name: MBPP type: mbpp metrics: - type: pass@1 value: 43.8 name: pass@1 - task: type: text-generation dataset: name: MBPP+ type: evalplus/mbppplus metrics: - type: pass@1 value: 51.6 name: pass@1 - task: type: text-generation dataset: name: HumanEvalSynthesis(Python) type: bigcode/humanevalpack metrics: - type: pass@1 value: 48.2 name: pass@1 - type: pass@1 value: 50.0 name: pass@1 - type: pass@1 value: 59.1 name: pass@1 - type: pass@1 value: 32.3 name: pass@1 - type: pass@1 value: 40.9 name: pass@1 - type: pass@1 value: 35.4 name: pass@1 - type: pass@1 value: 17.1 name: pass@1 - type: pass@1 value: 18.3 name: pass@1 - type: pass@1 value: 23.2 name: pass@1 - type: pass@1 value: 10.4 name: pass@1 - type: pass@1 value: 25.6 name: pass@1 - type: pass@1 value: 18.3 name: pass@1 - type: pass@1 value: 23.2 name: pass@1 - type: pass@1 value: 23.8 name: pass@1 - type: pass@1 value: 14.6 name: pass@1 - type: pass@1 value: 26.2 name: pass@1 - type: pass@1 value: 15.2 name: pass@1 - type: pass@1 value: 3.0 name: pass@1 --- # mlx-community/granite-20b-code-base-8bit The Model [mlx-community/granite-20b-code-base-8bit](https://huggingface.co/mlx-community/granite-20b-code-base-8bit) was converted to MLX format from [ibm-granite/granite-20b-code-base](https://huggingface.co/ibm-granite/granite-20b-code-base) using mlx-lm version 0.13.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/granite-20b-code-base-8bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
sgarrett/test	sgarrett	2024-05-14T17:16:20Z	146	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:nferruz/ProtGPT2", "base_model:finetune:nferruz/ProtGPT2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T17:04:45Z	--- license: apache-2.0 base_model: nferruz/ProtGPT2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: output 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. --> # output This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 17.4453 - Accuracy: 0.0333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
bryanlimy/ViV1T	bryanlimy	2024-05-14T17:15:52Z	0	0	null	[ "neuroai", "neuro-ai", "visual-response-prediction", "en", "license:mit", "region:us" ]	null	2024-05-06T18:46:00Z	--- license: mit language: - en tags: - neuroai - neuro-ai - visual-response-prediction --- # ViV1T model checkpoint Model checkpoints used in the ViV1T (team `dunedin`) submission to the [NeurIPS Sensorium 2023 challenge](https://www.sensorium-competition.net/) which came 🥉 place overall. The checkpoint and training log from 5 ViV1T models, each trained with a different seed, are available. Please check [github.com/bryanlimy/ViV1T](https://github.com/bryanlimy/ViV1T) for more information and example code.
CowCowC/Adu_mod_id_img	CowCowC	2024-05-14T17:11:20Z	192	0	transformers	[ "transformers", "onnx", "safetensors", "vit", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-14T16:35:49Z	--- model-index: - name: adult-content-classifier-image results: [] pipeline_tag: image-classification --- # adult-content-identify-image (text version [here](https://huggingface.co/jiechau/adult-content-identify-text) 文字版本請參考 [這裡](https://huggingface.co/jiechau/adult-content-identify-text)) Determine whether online sales products are adult content. Input: image content, Output results: 0 Unknown, 1 Adult Content, 2 General Merchandise. 判斷網路銷售商品是否屬於成人內容。輸入圖片內容，輸出結果: 0 未知, 1 成人內容, 2 一般商品。 # use transformers pipeline ```python from transformers import pipeline, AutoConfig pipe = pipeline("image-classification", model="jiechau/adult-content-identify-image") config = AutoConfig.from_pretrained("jiechau/adult-content-identify-image") label2id = config.label2id id2label = config.id2label q = 'https://xxx.xxx.xxx/images/xxx/xxx.webp' q = 'https://xxx.xxx.xxx/images/xxx/xxx.jpg' result = pipe(q) print(result) print(label2id[result[0]['label']]) # [{'label': 'adult_成人商品', 'score': 0.7516837120056152}, {'label': 'regular_一般商品', 'score': 0.2475457787513733}, {'label': 'unknown', 'score': 0.0007705678581260145}] # 1 ```
VanCan23/SFTDPO_1epoch_adapter	VanCan23	2024-05-14T17:08:17Z	0	0	null	[ "tensorboard", "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-09T17:09:35Z	--- license: apache-2.0 ---
SakuraLLM/Sakura-32B-Qwen2beta-v0.9-GGUF	SakuraLLM	2024-05-14T17:06:32Z	304	8	null	[ "gguf", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-08T12:58:05Z	--- license: cc-by-nc-sa-4.0 ---
PrawitK/llama3_8b_han_16bit	PrawitK	2024-05-14T17:04:25Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T17:04:24Z	--- library_name: transformers tags: - unsloth --- # 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]
PrawitK/llama3_8b_han_1	PrawitK	2024-05-14T17:04:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T17:04:12Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: PrawitK - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fine-tuned/dutch-legal-c-64-24	fine-tuned	2024-05-14T16:54:08Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Law", "Legislation", "Netherlands", "Policy", "Support", "custom_code", "en", "dataset:fine-tuned/dutch-legal-c-64-24", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-14T16:53:53Z	--- license: apache-2.0 datasets: - fine-tuned/dutch-legal-c-64-24 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Law - Legislation - Netherlands - Policy - Support --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: legal document search for Dutch legislation ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/dutch-legal-c-64-24', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
keyoae/MBBkeyo	keyoae	2024-05-14T16:53:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-14T16:48:56Z	--- license: apache-2.0 ---
erwinyonata/distilbert-base-uncased-lora-text-classification	erwinyonata	2024-05-14T16:53:03Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-05-14T16:39:48Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: distilbert-base-uncased metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7392 - Accuracy: {'accuracy': 0.901} ## 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: 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\| \| No log \| 1.0 \| 125 \| 0.2589 \| {'accuracy': 0.896} \| \| No log \| 2.0 \| 250 \| 0.4331 \| {'accuracy': 0.868} \| \| No log \| 3.0 \| 375 \| 0.3884 \| {'accuracy': 0.901} \| \| 0.2587 \| 4.0 \| 500 \| 0.4673 \| {'accuracy': 0.895} \| \| 0.2587 \| 5.0 \| 625 \| 0.6184 \| {'accuracy': 0.899} \| \| 0.2587 \| 6.0 \| 750 \| 0.6478 \| {'accuracy': 0.902} \| \| 0.2587 \| 7.0 \| 875 \| 0.7249 \| {'accuracy': 0.899} \| \| 0.0338 \| 8.0 \| 1000 \| 0.7446 \| {'accuracy': 0.893} \| \| 0.0338 \| 9.0 \| 1125 \| 0.7290 \| {'accuracy': 0.9} \| \| 0.0338 \| 10.0 \| 1250 \| 0.7392 \| {'accuracy': 0.901} \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
HariprasathSB/whisper-peft2	HariprasathSB	2024-05-14T16:51:41Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:vasista22/whisper-tamil-medium", "base_model:adapter:vasista22/whisper-tamil-medium", "region:us" ]	null	2024-05-13T20:23:43Z	--- library_name: peft base_model: vasista22/whisper-tamil-medium --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0
Mag0g/Ezekiel27_5	Mag0g	2024-05-14T16:51:35Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T16:50:12Z	--- 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. 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]
Pavithira9112/rare-puppers	Pavithira9112	2024-05-14T16:48:44Z	197	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-14T16:48:39Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9464285969734192 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### apple ![apple](images/apple.jpg) #### mango ![mango](images/mango.jpg) #### papaya ![papaya](images/papaya.jpg) #### pineapple ![pineapple](images/pineapple.jpg) #### watermelon ![watermelon](images/watermelon.jpg)
tsavage68/Transaminitis_L3_1000steps_1e8rate_01beta_CSFTDPO	tsavage68	2024-05-14T16:48:35Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT", "base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T16:32:13Z	--- license: llama3 base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: Transaminitis_L3_1000steps_1e8rate_01beta_DPO 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. --> # Transaminitis_L3_1000steps_1e8rate_01beta_DPO This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6939 - Rewards/chosen: 0.0011 - Rewards/rejected: 0.0026 - Rewards/accuracies: 0.4100 - Rewards/margins: -0.0014 - Logps/rejected: -18.5291 - Logps/chosen: -18.5229 - Logits/rejected: -1.0656 - Logits/chosen: -1.0644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6937 \| 0.2 \| 25 \| 0.6931 \| 0.0001 \| 0.0001 \| 0.0100 \| 0.0000 \| -18.5542 \| -18.5333 \| -1.0657 \| -1.0646 \| \| 0.6937 \| 0.4 \| 50 \| 0.6931 \| 0.0014 \| 0.0012 \| 0.5400 \| 0.0002 \| -18.5426 \| -18.5205 \| -1.0657 \| -1.0645 \| \| 0.6937 \| 0.6 \| 75 \| 0.6938 \| 0.0004 \| 0.0017 \| 0.4600 \| -0.0013 \| -18.5374 \| -18.5302 \| -1.0653 \| -1.0643 \| \| 0.6941 \| 0.8 \| 100 \| 0.6929 \| 0.0003 \| -0.0003 \| 0.5 \| 0.0006 \| -18.5573 \| -18.5312 \| -1.0667 \| -1.0656 \| \| 0.6922 \| 1.0 \| 125 \| 0.6934 \| 0.0022 \| 0.0026 \| 0.4800 \| -0.0004 \| -18.5288 \| -18.5123 \| -1.0666 \| -1.0654 \| \| 0.6945 \| 1.2 \| 150 \| 0.6937 \| 0.0009 \| 0.0020 \| 0.4500 \| -0.0011 \| -18.5347 \| -18.5251 \| -1.0648 \| -1.0637 \| \| 0.6934 \| 1.4 \| 175 \| 0.6927 \| 0.0058 \| 0.0049 \| 0.5600 \| 0.0010 \| -18.5061 \| -18.4759 \| -1.0650 \| -1.0639 \| \| 0.6934 \| 1.6 \| 200 \| 0.6937 \| 0.0009 \| 0.0021 \| 0.4200 \| -0.0011 \| -18.5342 \| -18.5251 \| -1.0652 \| -1.0640 \| \| 0.6953 \| 1.8 \| 225 \| 0.6935 \| -0.0007 \| -0.0002 \| 0.4700 \| -0.0006 \| -18.5563 \| -18.5415 \| -1.0650 \| -1.0638 \| \| 0.6906 \| 2.0 \| 250 \| 0.6935 \| 0.0008 \| 0.0014 \| 0.4900 \| -0.0006 \| -18.5411 \| -18.5264 \| -1.0657 \| -1.0645 \| \| 0.693 \| 2.2 \| 275 \| 0.6935 \| 0.0028 \| 0.0035 \| 0.5100 \| -0.0007 \| -18.5196 \| -18.5059 \| -1.0662 \| -1.0650 \| \| 0.6945 \| 2.4 \| 300 \| 0.6934 \| 0.0013 \| 0.0018 \| 0.5300 \| -0.0005 \| -18.5368 \| -18.5211 \| -1.0658 \| -1.0646 \| \| 0.6934 \| 2.6 \| 325 \| 0.6933 \| 0.0002 \| 0.0005 \| 0.5 \| -0.0002 \| -18.5500 \| -18.5320 \| -1.0657 \| -1.0646 \| \| 0.6914 \| 2.8 \| 350 \| 0.6933 \| -0.0038 \| -0.0036 \| 0.4900 \| -0.0003 \| -18.5903 \| -18.5727 \| -1.0655 \| -1.0643 \| \| 0.6914 \| 3.0 \| 375 \| 0.6935 \| 0.0004 \| 0.0011 \| 0.4900 \| -0.0007 \| -18.5435 \| -18.5301 \| -1.0665 \| -1.0654 \| \| 0.6914 \| 3.2 \| 400 \| 0.6927 \| 0.0048 \| 0.0038 \| 0.4900 \| 0.0009 \| -18.5165 \| -18.4865 \| -1.0655 \| -1.0643 \| \| 0.6949 \| 3.4 \| 425 \| 0.6933 \| 0.0020 \| 0.0023 \| 0.4900 \| -0.0003 \| -18.5321 \| -18.5146 \| -1.0660 \| -1.0649 \| \| 0.6922 \| 3.6 \| 450 \| 0.6937 \| -0.0020 \| -0.0009 \| 0.5 \| -0.0011 \| -18.5634 \| -18.5540 \| -1.0653 \| -1.0642 \| \| 0.6926 \| 3.8 \| 475 \| 0.6927 \| 0.0040 \| 0.0030 \| 0.4800 \| 0.0010 \| -18.5242 \| -18.4937 \| -1.0656 \| -1.0645 \| \| 0.693 \| 4.0 \| 500 \| 0.6942 \| 0.0022 \| 0.0042 \| 0.4400 \| -0.0020 \| -18.5124 \| -18.5118 \| -1.0658 \| -1.0646 \| \| 0.693 \| 4.2 \| 525 \| 0.6932 \| 0.0030 \| 0.0031 \| 0.4500 \| -0.0000 \| -18.5239 \| -18.5038 \| -1.0662 \| -1.0649 \| \| 0.6922 \| 4.4 \| 550 \| 0.6936 \| 0.0028 \| 0.0036 \| 0.5100 \| -0.0009 \| -18.5182 \| -18.5066 \| -1.0651 \| -1.0640 \| \| 0.6934 \| 4.6 \| 575 \| 0.6938 \| 0.0014 \| 0.0027 \| 0.4800 \| -0.0013 \| -18.5278 \| -18.5202 \| -1.0656 \| -1.0645 \| \| 0.6937 \| 4.8 \| 600 \| 0.6941 \| 0.0023 \| 0.0041 \| 0.4500 \| -0.0019 \| -18.5132 \| -18.5113 \| -1.0653 \| -1.0642 \| \| 0.691 \| 5.0 \| 625 \| 0.6936 \| 0.0024 \| 0.0033 \| 0.5100 \| -0.0009 \| -18.5219 \| -18.5103 \| -1.0654 \| -1.0642 \| \| 0.6926 \| 5.2 \| 650 \| 0.6942 \| 0.0006 \| 0.0027 \| 0.4100 \| -0.0021 \| -18.5279 \| -18.5280 \| -1.0655 \| -1.0643 \| \| 0.6953 \| 5.4 \| 675 \| 0.6938 \| 0.0027 \| 0.0040 \| 0.4400 \| -0.0013 \| -18.5149 \| -18.5071 \| -1.0656 \| -1.0645 \| \| 0.6937 \| 5.6 \| 700 \| 0.6930 \| 0.0042 \| 0.0038 \| 0.5 \| 0.0004 \| -18.5169 \| -18.4921 \| -1.0657 \| -1.0645 \| \| 0.693 \| 5.8 \| 725 \| 0.6935 \| 0.0022 \| 0.0027 \| 0.4600 \| -0.0006 \| -18.5272 \| -18.5127 \| -1.0656 \| -1.0644 \| \| 0.6937 \| 6.0 \| 750 \| 0.6935 \| 0.0014 \| 0.0022 \| 0.4400 \| -0.0008 \| -18.5327 \| -18.5198 \| -1.0656 \| -1.0645 \| \| 0.6918 \| 6.2 \| 775 \| 0.6936 \| 0.0017 \| 0.0024 \| 0.4300 \| -0.0008 \| -18.5303 \| -18.5175 \| -1.0655 \| -1.0644 \| \| 0.6934 \| 6.4 \| 800 \| 0.6938 \| 0.0008 \| 0.0021 \| 0.4200 \| -0.0013 \| -18.5333 \| -18.5261 \| -1.0655 \| -1.0644 \| \| 0.6902 \| 6.6 \| 825 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.6937 \| 6.8 \| 850 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.6949 \| 7.0 \| 875 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.693 \| 7.2 \| 900 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.6941 \| 7.4 \| 925 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.6937 \| 7.6 \| 950 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.6926 \| 7.8 \| 975 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| \| 0.6918 \| 8.0 \| 1000 \| 0.6939 \| 0.0011 \| 0.0026 \| 0.4100 \| -0.0014 \| -18.5291 \| -18.5229 \| -1.0656 \| -1.0644 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
saransh03sharma/mintrec2-mistral-2-7b-50-1	saransh03sharma	2024-05-14T16:47:07Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T16:41:25Z	--- 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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ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_8_0.0002	ferrazzipietro	2024-05-14T16:45:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T13:12:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MaziyarPanahi/Goku-8x22B-v0.1	MaziyarPanahi	2024-05-14T16:45:35Z	30	8	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "sharegpt", "axolotl", "conversational", "fr", "it", "de", "es", "en", "dataset:philschmid/guanaco-sharegpt-style", "base_model:v2ray/Mixtral-8x22B-v0.1", "base_model:finetune:v2ray/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-04-12T10:48:25Z	--- license: apache-2.0 language: - fr - it - de - es - en tags: - moe - mixtral - sharegpt - axolotl library_name: transformers base_model: v2ray/Mixtral-8x22B-v0.1 inference: false model_creator: MaziyarPanahi model_name: Goku-8x22B-v0.1 pipeline_tag: text-generation quantized_by: MaziyarPanahi datasets: - philschmid/guanaco-sharegpt-style --- <img src="./Goku-8x22b-v0.1.webp" alt="Goku 8x22B v0.1 Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Goku-8x22B-v0.1 (Goku 141b-A35b) A fine-tuned version of [v2ray/Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) model on the `philschmid/guanaco-sharegpt-style` dataset. This model has a total of 141b parameters with 35b only active. ## How to use it Use a pipeline as a high-level helper: ```python from transformers import pipeline pipe = pipeline("text-generation", model="MaziyarPanahi/Goku-8x22B-v0.1") ``` Load model directly: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.1") model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.1") ``` Load via Adapter: You can also use PEFT to just load the adapter if you already have one of these models downloaded: [v2ray/Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) or [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) (they are the same) ```python # assuming you have already downloaded the # resizing the vocab import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id="v2ray/Mixtral-8x22B-v0.1" peft_model_id = "~/.cache/huggingface/hub/models--MaziyarPanahi--Goku-8x22B-v0.1/adapter" tokenizer = AutoTokenizer. from_pretrained (peft_model_id) model = AutoModelForCausalLM. from_pretrained (model_id) # I have added 2 new tokens for ChatML template # this step is required if you are using PEFT/Adapter model.resize_token_embeddings (len (tokenizer)) model.load_adapter(peft_model_id) # you can even have TextStreamer and a text-generation pipeline with your adapter streamer = TextStreamer(tokenizer) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=750, temperature=0.6, do_sample=True, top_k=50, top_p=0.95, repetition_penalty=1.1, return_full_text=False, add_special_tokens=False, streamer=streamer ) ``` ## Examples `Goku-8x22B-v0.1` has been tested in generating text, answering questions based on long context, coding, and some reasoning. In the next version I will use more `math` and `coding` related datasets. This is a sample story written by [MaziyarPanahi/Goku-8x22B-v0.1](https://huggingface.co/MaziyarPanahi/Goku-8x22B-v0.1/) ``` Goku had heard a commotion from his house but when he went to check he saw nothing. He thought to himself, "I'll let it go, it was probably just a bird or something. I'm sure it will be fine." But that was when he heard the commotion again, so he went outside and this time he saw two figures on the horizon. One of the figures was a giant pinkish-purple creature, while the other was small, pink, ball-shaped thing. As the figures approached, Goku realized the large creature was his former enemy, the powerful Majin Buu. And the smaller creature was Kirby, a powerful Star Warrior from the planet Popstar. Goku couldn't believe his eyes. The two creatures approached Goku menacingly. "Kirby and I have teamed up," said Majin Buu. "We're going to destroy the world!" Goku was taken aback by the statement. He had never considered the possibility of these two powerful creatures joining forces. He knew he had to put a stop to them, before they could cause any more damage. He took a deep breath and faced the two creatures. "You two won't get away with this," Goku said firmly. "I won't let you destroy the world." Majin Buu scoffed, "You can't stop us! Kirby and I are too powerful!" Goku quickly formed an energy ball in his hands and faced the two creatures. "We'll see about that," he said. The battle that ensued was intense. The two creatures worked together, using their powerful energy attacks to try to overcome Goku. But Goku kept fighting, using his own powerful energy attacks to counter their moves. After what seemed like an eternity, Goku managed to get the upper hand. He used a powerful energy attack to defeat the two creatures. After they were defeated, Goku looked around and saw the damage that had been caused by the battle. He knew he still had a lot of work ahead of him in order to prevent any further destruction, but he was determined to do his best. He summoned all of his power and focused it into a powerful energy attack. The energy spread throughout his body and he felt his power grow stronger. With a battle cry, he launched the attack at the two creatures. The energy hit them both, sending them flying back, stunned for a moment. Goku continued to pressure them with his energy attacks, but they soon recovered and began to counter-attack with their own energy blasts. Goku knew he had to act quickly if he was going to defeat them. He focused his energy into one powerful attack, and launched it at Kirby. The attack hit and the Star Warrior was sent flying away. Goku then focused his attention on Majin Buu. He launched a series of energy attacks, using his signature technique, the Kamehameha, and managed to defeat the powerful creature. After the battle, Goku looked around at the destruction that had been caused by the two creatures. He knew he still had a lot of work ahead of him in order to prevent any further destruction, but he was determined to do his best. With the two creatures defeated, Goku knew he still had a job to do. He took a deep breath and set out to repair the damage that had been caused by the two powerful creatures. He worked for hours, using his energy to put everything back in order and ensuring that the world was safe once again. Goku's journey was long and hard but, in the end, he was successful. He defeated two powerful enemies and saved the world from destroyers. Thanks to his hard work, the world was able to heal and once again become a place of peace and prosperity. ```
NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF	NikolayKozloff	2024-05-14T16:42:43Z	1	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ms", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T16:42:19Z	--- language: - ms tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF This model was converted to GGUF format from [`mesolitica/malaysian-llama-3-8b-instruct-16k`](https://huggingface.co/mesolitica/malaysian-llama-3-8b-instruct-16k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mesolitica/malaysian-llama-3-8b-instruct-16k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF --model malaysian-llama-3-8b-instruct-16k.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF --model malaysian-llama-3-8b-instruct-16k.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m malaysian-llama-3-8b-instruct-16k.Q8_0.gguf -n 128 ```
tsavage68/Transaminitis_L3_1000steps_1e5rate_01beta_CSFTDPO	tsavage68	2024-05-14T16:40:24Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT", "base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T16:34:17Z	--- license: llama3 base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: Transaminitis_L3_1000steps_1e5rate_01beta_CSFTDPO 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. --> # Transaminitis_L3_1000steps_1e5rate_01beta_CSFTDPO This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2656 - Rewards/chosen: -5.8394 - Rewards/rejected: -13.5464 - Rewards/accuracies: 0.9500 - Rewards/margins: 7.7070 - Logps/rejected: -154.0191 - Logps/chosen: -76.9285 - Logits/rejected: -1.0971 - Logits/chosen: -1.0952 ## 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: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.8157 \| 0.2 \| 25 \| 0.7130 \| -1.6812 \| -1.6422 \| 0.2000 \| -0.0390 \| -34.9765 \| -35.3459 \| -1.0074 \| -1.0078 \| \| 0.6531 \| 0.4 \| 50 \| 0.5572 \| 1.4920 \| 1.0999 \| 0.5400 \| 0.3921 \| -7.5562 \| -3.6147 \| -0.6331 \| -0.6288 \| \| 0.0069 \| 0.6 \| 75 \| 0.0638 \| 1.5026 \| -8.0172 \| 0.9900 \| 9.5198 \| -98.7265 \| -3.5080 \| -1.0032 \| -0.9076 \| \| 1.4987 \| 0.8 \| 100 \| 0.7768 \| -3.4746 \| -3.5322 \| 0.5400 \| 0.0576 \| -53.8765 \| -53.2803 \| -0.4138 \| -0.4136 \| \| 0.7987 \| 1.0 \| 125 \| 0.7220 \| -3.4829 \| -3.5110 \| 0.5400 \| 0.0281 \| -53.6649 \| -53.3632 \| -0.7087 \| -0.7087 \| \| 0.7438 \| 1.2 \| 150 \| 0.7114 \| -3.2843 \| -3.2535 \| 0.4600 \| -0.0308 \| -51.0900 \| -51.3775 \| -1.0310 \| -1.0310 \| \| 0.6949 \| 1.4 \| 175 \| 0.7051 \| -3.3085 \| -3.2855 \| 0.4000 \| -0.0230 \| -51.4100 \| -51.6195 \| -0.7593 \| -0.7593 \| \| 0.7 \| 1.6 \| 200 \| 0.7007 \| -3.3122 \| -3.2981 \| 0.4400 \| -0.0141 \| -51.5352 \| -51.6561 \| -0.7261 \| -0.7261 \| \| 0.7004 \| 1.8 \| 225 \| 0.7092 \| -3.5268 \| -3.5014 \| 0.4600 \| -0.0254 \| -53.5688 \| -53.8022 \| -1.0639 \| -1.0640 \| \| 0.7056 \| 2.0 \| 250 \| 0.7048 \| -3.3574 \| -3.3377 \| 0.4800 \| -0.0197 \| -51.9312 \| -52.1080 \| -0.8329 \| -0.8329 \| \| 0.6829 \| 2.2 \| 275 \| 0.6964 \| -3.4182 \| -3.4152 \| 0.5400 \| -0.0030 \| -52.7066 \| -52.7166 \| -1.0186 \| -1.0187 \| \| 0.7101 \| 2.4 \| 300 \| 0.6992 \| -4.3808 \| -4.3804 \| 0.5400 \| -0.0003 \| -62.3591 \| -62.3421 \| -1.3638 \| -1.3638 \| \| 0.7107 \| 2.6 \| 325 \| 0.7081 \| -4.1483 \| -4.1266 \| 0.4600 \| -0.0217 \| -59.8212 \| -60.0177 \| -1.3589 \| -1.3589 \| \| 0.7035 \| 2.8 \| 350 \| 0.6913 \| -3.0909 \| -3.0966 \| 0.2900 \| 0.0058 \| -49.5212 \| -49.4432 \| -0.7017 \| -0.7017 \| \| 0.7112 \| 3.0 \| 375 \| 0.7096 \| -4.4207 \| -4.3939 \| 0.4600 \| -0.0268 \| -62.4938 \| -62.7416 \| -1.3752 \| -1.3752 \| \| 0.659 \| 3.2 \| 400 \| 0.7992 \| -4.2280 \| -4.1290 \| 0.5200 \| -0.0990 \| -59.8449 \| -60.8146 \| -1.0809 \| -1.0815 \| \| 0.6253 \| 3.4 \| 425 \| 0.9164 \| -4.3837 \| -4.1124 \| 0.5200 \| -0.2713 \| -59.6787 \| -62.3715 \| -0.7324 \| -0.7317 \| \| 0.956 \| 3.6 \| 450 \| 0.5266 \| -3.8419 \| -5.4570 \| 0.6800 \| 1.6151 \| -73.1246 \| -56.9532 \| -0.3747 \| -0.3742 \| \| 0.5604 \| 3.8 \| 475 \| 0.6506 \| -3.5933 \| -6.2168 \| 0.7000 \| 2.6234 \| -80.7223 \| -54.4675 \| -0.1960 \| -0.1952 \| \| 0.8776 \| 4.0 \| 500 \| 0.5657 \| -3.9281 \| -7.0564 \| 0.8400 \| 3.1284 \| -89.1191 \| -57.8147 \| -0.6674 \| -0.6680 \| \| 0.4978 \| 4.2 \| 525 \| 0.6285 \| -4.8602 \| -10.3518 \| 0.8800 \| 5.4916 \| -122.0728 \| -67.1361 \| -0.9244 \| -0.9236 \| \| 1.0258 \| 4.4 \| 550 \| 0.6966 \| -5.0528 \| -8.7895 \| 0.8000 \| 3.7367 \| -106.4495 \| -69.0625 \| -0.6216 \| -0.6205 \| \| 0.3559 \| 4.6 \| 575 \| 0.6527 \| -5.5366 \| -9.7092 \| 0.8100 \| 4.1726 \| -115.6466 \| -73.9002 \| -1.1615 \| -1.1603 \| \| 0.2236 \| 4.8 \| 600 \| 0.3743 \| -5.2783 \| -10.8881 \| 0.9100 \| 5.6099 \| -127.4360 \| -71.3169 \| -1.0731 \| -1.0714 \| \| 0.0995 \| 5.0 \| 625 \| 0.1816 \| -4.6140 \| -10.2504 \| 0.9500 \| 5.6364 \| -121.0588 \| -64.6745 \| -1.0550 \| -1.0504 \| \| 0.4954 \| 5.2 \| 650 \| 0.2771 \| -4.9474 \| -10.6256 \| 0.9000 \| 5.6781 \| -124.8103 \| -68.0087 \| -0.9020 \| -0.9007 \| \| 0.2031 \| 5.4 \| 675 \| 0.2731 \| -5.6955 \| -12.6949 \| 0.9600 \| 6.9994 \| -145.5037 \| -75.4888 \| -1.0406 \| -1.0388 \| \| 0.3665 \| 5.6 \| 700 \| 0.2912 \| -5.5615 \| -11.9434 \| 0.9300 \| 6.3819 \| -137.9883 \| -74.1489 \| -0.9311 \| -0.9288 \| \| 0.132 \| 5.8 \| 725 \| 0.2410 \| -6.2707 \| -13.3387 \| 0.9400 \| 7.0680 \| -151.9420 \| -81.2413 \| -1.0742 \| -1.0720 \| \| 0.1044 \| 6.0 \| 750 \| 0.2450 \| -6.0942 \| -13.2397 \| 0.9500 \| 7.1455 \| -150.9520 \| -79.4765 \| -1.0715 \| -1.0693 \| \| 0.1984 \| 6.2 \| 775 \| 0.2646 \| -6.1961 \| -13.4718 \| 0.9500 \| 7.2757 \| -153.2727 \| -80.4953 \| -1.0771 \| -1.0748 \| \| 0.0156 \| 6.4 \| 800 \| 0.3140 \| -6.1100 \| -13.6377 \| 0.9500 \| 7.5277 \| -154.9315 \| -79.6341 \| -1.1101 \| -1.1082 \| \| 0.2682 \| 6.6 \| 825 \| 0.2528 \| -5.9327 \| -13.5268 \| 0.9600 \| 7.5942 \| -153.8231 \| -77.8608 \| -1.0893 \| -1.0873 \| \| 0.0011 \| 6.8 \| 850 \| 0.2762 \| -5.9315 \| -13.5461 \| 0.9500 \| 7.6146 \| -154.0158 \| -77.8491 \| -1.0916 \| -1.0895 \| \| 0.1031 \| 7.0 \| 875 \| 0.2613 \| -5.8587 \| -13.5305 \| 0.9500 \| 7.6718 \| -153.8600 \| -77.1214 \| -1.0933 \| -1.0913 \| \| 0.0034 \| 7.2 \| 900 \| 0.2675 \| -5.8590 \| -13.5490 \| 0.9500 \| 7.6900 \| -154.0449 \| -77.1244 \| -1.0975 \| -1.0955 \| \| 0.1314 \| 7.4 \| 925 \| 0.2662 \| -5.8482 \| -13.5520 \| 0.9500 \| 7.7038 \| -154.0743 \| -77.0162 \| -1.0978 \| -1.0958 \| \| 0.3318 \| 7.6 \| 950 \| 0.2651 \| -5.8403 \| -13.5464 \| 0.9500 \| 7.7060 \| -154.0184 \| -76.9377 \| -1.0974 \| -1.0954 \| \| 0.1093 \| 7.8 \| 975 \| 0.2653 \| -5.8449 \| -13.5488 \| 0.9500 \| 7.7039 \| -154.0427 \| -76.9835 \| -1.0977 \| -1.0957 \| \| 0.1808 \| 8.0 \| 1000 \| 0.2656 \| -5.8394 \| -13.5464 \| 0.9500 \| 7.7070 \| -154.0191 \| -76.9285 \| -1.0971 \| -1.0952 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
Litzy619/O0513MA	Litzy619	2024-05-14T16:39:10Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "base_model:finetune:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]	null	2024-05-14T04:28:24Z	--- license: apache-2.0 base_model: allenai/OLMo-1B tags: - generated_from_trainer model-index: - name: O0513MA 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. --> # O0513MA This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1456 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 80 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.2491 \| 0.09 \| 10 \| 1.4949 \| \| 0.7419 \| 0.18 \| 20 \| 0.2008 \| \| 0.1742 \| 0.27 \| 30 \| 0.1637 \| \| 0.1554 \| 0.36 \| 40 \| 0.1571 \| \| 0.1519 \| 0.45 \| 50 \| 0.1515 \| \| 0.1531 \| 0.54 \| 60 \| 0.1494 \| \| 0.1497 \| 0.63 \| 70 \| 0.1489 \| \| 0.1488 \| 0.73 \| 80 \| 0.1584 \| \| 0.148 \| 0.82 \| 90 \| 0.1510 \| \| 0.1476 \| 0.91 \| 100 \| 0.1509 \| \| 0.1499 \| 1.0 \| 110 \| 0.1486 \| \| 0.1456 \| 1.09 \| 120 \| 0.1507 \| \| 0.1447 \| 1.18 \| 130 \| 0.1518 \| \| 0.1472 \| 1.27 \| 140 \| 0.1486 \| \| 0.148 \| 1.36 \| 150 \| 0.1490 \| \| 0.1455 \| 1.45 \| 160 \| 0.1487 \| \| 0.1463 \| 1.54 \| 170 \| 0.1473 \| \| 0.1475 \| 1.63 \| 180 \| 0.1475 \| \| 0.1479 \| 1.72 \| 190 \| 0.1505 \| \| 0.1454 \| 1.81 \| 200 \| 0.1487 \| \| 0.1499 \| 1.9 \| 210 \| 0.1480 \| \| 0.1474 \| 1.99 \| 220 \| 0.1498 \| \| 0.1464 \| 2.08 \| 230 \| 0.1472 \| \| 0.1401 \| 2.18 \| 240 \| 0.1462 \| \| 0.1419 \| 2.27 \| 250 \| 0.1483 \| \| 0.1426 \| 2.36 \| 260 \| 0.1477 \| \| 0.141 \| 2.45 \| 270 \| 0.1461 \| \| 0.1402 \| 2.54 \| 280 \| 0.1468 \| \| 0.1393 \| 2.63 \| 290 \| 0.1469 \| \| 0.1426 \| 2.72 \| 300 \| 0.1455 \| \| 0.1417 \| 2.81 \| 310 \| 0.1454 \| \| 0.1408 \| 2.9 \| 320 \| 0.1456 \| \| 0.1424 \| 2.99 \| 330 \| 0.1456 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.0
MaziyarPanahi/Llama-3-70B-Instruct-v0.1	MaziyarPanahi	2024-05-14T16:38:13Z	23	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "finetune", "facebook", "meta", "pytorch", "llama-3", "chatml", "conversational", "en", "dataset:MaziyarPanahi/truthy-dpo-v0.1-axolotl", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-14T14:23:52Z	--- language: - en license: llama3 library_name: transformers tags: - axolotl - finetune - facebook - meta - pytorch - llama - llama-3 - chatml base_model: meta-llama/Meta-Llama-3-70B-Instruct datasets: - MaziyarPanahi/truthy-dpo-v0.1-axolotl model_name: Llama-3-70B-Instruct-v0.1 pipeline_tag: text-generation license_name: llama3 license_link: LICENSE inference: false model_creator: MaziyarPanahi quantized_by: MaziyarPanahi --- <img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # MaziyarPanahi/Llama-3-70B-Instruct-v0.1 This model is a fine-tune of `meta-llama/Meta-Llama-3-70B-Instruct` model. This version comes with `<\|im_start\|>` and `<\|im_end\|>` as extra tokens to avoid taking up extra tokens via ChatML prompt. # ⚡ Quantized GGUF All GGUF models are available here: [MaziyarPanahi/Llama-3-70B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-v0.1-GGUF) # 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) coming soon. # Prompt Template This model uses `ChatML` prompt template: ``` <\|im_start\|>system {System} <\|im_end\|> <\|im_start\|>user {User} <\|im_end\|> <\|im_start\|>assistant {Assistant} ```` # How to use You can use this model by using `MaziyarPanahi/Llama-3-70B-Instruct-v0.1` as the model name in Hugging Face's transformers library. ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer from transformers import pipeline import torch model_id = "MaziyarPanahi/Llama-3-70B-Instruct-v0.1" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, # attn_implementation="flash_attention_2" ) tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True ) streamer = TextStreamer(tokenizer) pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, model_kwargs={"torch_dtype": torch.bfloat16}, streamer=streamer ) # Then you can use the pipeline to generate text. messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=2048, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.95, ) print(outputs[0]["generated_text"][len(prompt):]) ```
SotirisLegkas/Llama3_ALL_BCE_translations_19_shuffled_special_tokens	SotirisLegkas	2024-05-14T16:28:10Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-14T16:27:29Z	--- license: llama3 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: Llama3_ALL_BCE_translations_19_shuffled_special_tokens 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. --> # Llama3_ALL_BCE_translations_19_shuffled_special_tokens This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4776 - F1 Macro 0.1: 0.0818 - F1 Macro 0.15: 0.0922 - F1 Macro 0.2: 0.1027 - F1 Macro 0.25: 0.1130 - F1 Macro 0.3: 0.1230 - F1 Macro 0.35: 0.1336 - F1 Macro 0.4: 0.1440 - F1 Macro 0.45: 0.1551 - F1 Macro 0.5: 0.1663 - F1 Macro 0.55: 0.1778 - F1 Macro 0.6: 0.1879 - F1 Macro 0.65: 0.1987 - F1 Macro 0.7: 0.2090 - F1 Macro 0.75: 0.2178 - F1 Macro 0.8: 0.2211 - F1 Macro 0.85: 0.2205 - F1 Macro 0.9: 0.2010 - F1 Macro 0.95: 0.1457 - Threshold 0: 0.65 - Threshold 1: 0.75 - Threshold 2: 0.7 - Threshold 3: 0.85 - Threshold 4: 0.8 - Threshold 5: 0.85 - Threshold 6: 0.8 - Threshold 7: 0.8 - Threshold 8: 0.85 - Threshold 9: 0.75 - Threshold 10: 0.85 - Threshold 11: 0.8 - Threshold 12: 0.85 - Threshold 13: 0.95 - Threshold 14: 0.85 - Threshold 15: 0.75 - Threshold 16: 0.85 - Threshold 17: 0.8 - Threshold 18: 0.9 - 0: 0.0619 - 1: 0.1388 - 2: 0.1978 - 3: 0.1328 - 4: 0.2961 - 5: 0.3489 - 6: 0.3179 - 7: 0.1268 - 8: 0.2043 - 9: 0.3668 - 10: 0.3216 - 11: 0.3669 - 12: 0.1276 - 13: 0.1205 - 14: 0.2264 - 15: 0.1576 - 16: 0.3078 - 17: 0.3722 - 18: 0.125 - Max F1: 0.2211 - Mean F1: 0.2273 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Macro 0.1 \| F1 Macro 0.15 \| F1 Macro 0.2 \| F1 Macro 0.25 \| F1 Macro 0.3 \| F1 Macro 0.35 \| F1 Macro 0.4 \| F1 Macro 0.45 \| F1 Macro 0.5 \| F1 Macro 0.55 \| F1 Macro 0.6 \| F1 Macro 0.65 \| F1 Macro 0.7 \| F1 Macro 0.75 \| F1 Macro 0.8 \| F1 Macro 0.85 \| F1 Macro 0.9 \| F1 Macro 0.95 \| Threshold 0 \| Threshold 1 \| Threshold 2 \| Threshold 3 \| Threshold 4 \| Threshold 5 \| Threshold 6 \| Threshold 7 \| Threshold 8 \| Threshold 9 \| Threshold 10 \| Threshold 11 \| Threshold 12 \| Threshold 13 \| Threshold 14 \| Threshold 15 \| Threshold 16 \| Threshold 17 \| Threshold 18 \| 0 \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 10 \| 11 \| 12 \| 13 \| 14 \| 15 \| 16 \| 17 \| 18 \| Max F1 \| Mean F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:-----------:\|:------------:\|:------------:\|:------------:\|:------------:\|:------------:\|:------------:\|:------------:\|:------------:\|:------------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:------:\|:-------:\| \| 3.3824 \| 1.0 \| 5595 \| 4.3847 \| 0.0700 \| 0.0761 \| 0.0818 \| 0.0877 \| 0.0936 \| 0.1000 \| 0.1064 \| 0.1134 \| 0.1196 \| 0.1265 \| 0.1327 \| 0.1381 \| 0.1432 \| 0.1483 \| 0.1465 \| 0.1417 \| 0.1291 \| 0.0836 \| 0.65 \| 0.9 \| 0.85 \| 0.9 \| 0.75 \| 0.6 \| 0.8 \| 0.75 \| 0.9 \| 0.9 \| 0.9 \| 0.85 \| 0.9 \| 0.0 \| 0.85 \| 0.75 \| 0.6 \| 0.6 \| 0.9 \| 0.0649 \| 0.0879 \| 0.1603 \| 0.0899 \| 0.2589 \| 0.2876 \| 0.2683 \| 0.1036 \| 0.1245 \| 0.2856 \| 0.2387 \| 0.3033 \| 0.0726 \| 0.0 \| 0.1779 \| 0.1109 \| 0.2192 \| 0.2743 \| 0.0641 \| 0.1483 \| 0.1680 \| \| 2.4859 \| 2.0 \| 11190 \| 1.7537 \| 0.0881 \| 0.0994 \| 0.1111 \| 0.1210 \| 0.1310 \| 0.1401 \| 0.1472 \| 0.1541 \| 0.1607 \| 0.1676 \| 0.1697 \| 0.1731 \| 0.1768 \| 0.1761 \| 0.1713 \| 0.1575 \| 0.1365 \| 0.0927 \| 0.55 \| 0.7 \| 0.85 \| 0.8 \| 0.4 \| 0.35 \| 0.95 \| 0.75 \| 0.7 \| 0.85 \| 0.8 \| 0.65 \| 0.8 \| 0.95 \| 0.8 \| 0.7 \| 0.85 \| 0.6 \| 0.75 \| 0.0534 \| 0.1241 \| 0.1924 \| 0.1020 \| 0.2738 \| 0.3163 \| 0.3072 \| 0.1109 \| 0.1793 \| 0.3414 \| 0.2889 \| 0.3332 \| 0.0831 \| 0.0870 \| 0.2137 \| 0.1305 \| 0.2881 \| 0.3396 \| 0.1254 \| 0.1768 \| 0.2048 \| \| 1.7561 \| 3.0 \| 16785 \| 1.4633 \| 0.0840 \| 0.0954 \| 0.1062 \| 0.1164 \| 0.1271 \| 0.1382 \| 0.1485 \| 0.1597 \| 0.1713 \| 0.1809 \| 0.1895 \| 0.1976 \| 0.2056 \| 0.2113 \| 0.2115 \| 0.1995 \| 0.1805 \| 0.1184 \| 0.6 \| 0.75 \| 0.75 \| 0.95 \| 0.8 \| 0.7 \| 0.9 \| 0.8 \| 0.8 \| 0.7 \| 0.8 \| 0.8 \| 0.9 \| 0.95 \| 0.75 \| 0.8 \| 0.7 \| 0.7 \| 0.8 \| 0.0581 \| 0.1395 \| 0.1946 \| 0.1235 \| 0.2818 \| 0.3391 \| 0.3151 \| 0.1202 \| 0.1997 \| 0.3656 \| 0.3056 \| 0.3630 \| 0.1340 \| 0.1087 \| 0.2272 \| 0.1482 \| 0.2953 \| 0.3589 \| 0.1233 \| 0.2115 \| 0.2211 \| \| 1.2709 \| 4.0 \| 22380 \| 1.4776 \| 0.0818 \| 0.0922 \| 0.1027 \| 0.1130 \| 0.1230 \| 0.1336 \| 0.1440 \| 0.1551 \| 0.1663 \| 0.1778 \| 0.1879 \| 0.1987 \| 0.2090 \| 0.2178 \| 0.2211 \| 0.2205 \| 0.2010 \| 0.1457 \| 0.65 \| 0.75 \| 0.7 \| 0.85 \| 0.8 \| 0.85 \| 0.8 \| 0.8 \| 0.85 \| 0.75 \| 0.85 \| 0.8 \| 0.85 \| 0.95 \| 0.85 \| 0.75 \| 0.85 \| 0.8 \| 0.9 \| 0.0619 \| 0.1388 \| 0.1978 \| 0.1328 \| 0.2961 \| 0.3489 \| 0.3179 \| 0.1268 \| 0.2043 \| 0.3668 \| 0.3216 \| 0.3669 \| 0.1276 \| 0.1205 \| 0.2264 \| 0.1576 \| 0.3078 \| 0.3722 \| 0.125 \| 0.2211 \| 0.2273 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
r4fall/Meta-Llama-3-8B-Instruct-pl	r4fall	2024-05-14T16:27:55Z	4	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-14T16:27:04Z	--- library_name: peft base_model: NousResearch/Meta-Llama-3-8B-Instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
DUAL-GPO/phi-2-gpo-v21-i1	DUAL-GPO	2024-05-14T16:23:44Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-gpo-new-i0", "base_model:adapter:DUAL-GPO/phi-2-gpo-new-i0", "license:mit", "region:us" ]	null	2024-05-14T15:31:00Z	--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo base_model: DUAL-GPO/phi-2-gpo-new-i0 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-gpo-v21-i1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-gpo-v21-i1 This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-new-i0](https://huggingface.co/DUAL-GPO/phi-2-gpo-new-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.2.1+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
stephenimm/my_awesome_eli5_mlm_model	stephenimm	2024-05-14T16:17:44Z	116	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "dataset:eli5_category", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-14T14:46:54Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: distilbert/distilroberta-base datasets: - eli5_category model-index: - name: my_awesome_eli5_mlm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 2.0168 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2612 \| 1.0 \| 1311 \| 2.0833 \| \| 2.1651 \| 2.0 \| 2622 \| 2.0288 \| \| 2.1274 \| 3.0 \| 3933 \| 2.0364 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cpu - Datasets 2.19.1 - Tokenizers 0.19.1
maisonmargela/gpt2_code_writer	maisonmargela	2024-05-14T16:14:36Z	147	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T16:13:07Z	--- 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]
wenshicheng97/no_board_history_with_sys_history_cicero_lr5e-5_batch10	wenshicheng97	2024-05-14T16:14:12Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	null	2024-05-14T05:48:50Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: no_board_history_with_sys_history_cicero_lr5e-5_batch10 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. --> # no_board_history_with_sys_history_cicero_lr5e-5_batch10 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. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.2 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
iony-mikler/q-Taxi-v3	iony-mikler	2024-05-14T16:13:03Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-14T15:38:45Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="iony-mikler/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
DucPhanBa/llama2-finetuned-qlora	DucPhanBa	2024-05-14T16:12:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T16:11:36Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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oasic/merged-4bit-tiny-llama-gc2	oasic	2024-05-14T16:11:39Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:quantized:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-14T16:10:26Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: oasic - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
veritober/clasificador-muchocine	veritober	2024-05-14T16:09:46Z	105	0	transformers	[ "transformers", "safetensors", "electra", "text-classification", "classification", "generated_from_trainer", "base_model:mrm8488/electricidad-base-discriminator", "base_model:finetune:mrm8488/electricidad-base-discriminator", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T16:09:28Z	--- base_model: mrm8488/electricidad-base-discriminator tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: clasificador-muchocine results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3716 - Accuracy: 0.4465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 388 \| 1.3597 \| 0.3794 \| \| 1.4242 \| 2.0 \| 776 \| 1.3048 \| 0.4374 \| \| 1.0638 \| 3.0 \| 1164 \| 1.3716 \| 0.4465 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Meziane/three_question	Meziane	2024-05-14T16:07:59Z	131	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "question-answering", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-14T16:02:48Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: three_question 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. --> # three_question This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 100 \| nan \| \| No log \| 2.0 \| 200 \| nan \| \| No log \| 3.0 \| 300 \| nan \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
PabloMiguelGarcia/clasificador-muchocine	PabloMiguelGarcia	2024-05-14T16:04:59Z	104	0	transformers	[ "transformers", "safetensors", "electra", "text-classification", "classification", "generated_from_trainer", "base_model:mrm8488/electricidad-base-discriminator", "base_model:finetune:mrm8488/electricidad-base-discriminator", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T16:04:08Z	--- base_model: mrm8488/electricidad-base-discriminator tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: clasificador-muchocine results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4110 - Accuracy: 0.4490 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 388 \| 1.3473 \| 0.3884 \| \| 1.3955 \| 2.0 \| 776 \| 1.3101 \| 0.4465 \| \| 1.0263 \| 3.0 \| 1164 \| 1.4110 \| 0.4490 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
BuroIdentidadDigital/Ine_FrontalV0	BuroIdentidadDigital	2024-05-14T16:01:01Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-14T15:51:40Z	--- 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]
Samael667/my-autotrain-llm	Samael667	2024-05-14T15:56:24Z	0	0	transformers	[ "transformers", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:56:08Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
DUAL-GPO/zephyr-7b-gpo-v5-i3	DUAL-GPO	2024-05-14T15:53:15Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO-2/zephyr-7b-irepo-new-i2", "base_model:adapter:DUAL-GPO-2/zephyr-7b-irepo-new-i2", "license:apache-2.0", "region:us" ]	null	2024-05-14T09:24:48Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO-2/zephyr-7b-irepo-new-i2 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-gpo-v5-i3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-gpo-v5-i3 This model is a fine-tuned version of [DUAL-GPO-2/zephyr-7b-irepo-new-i2](https://huggingface.co/DUAL-GPO-2/zephyr-7b-irepo-new-i2) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
jdumasleon/clasificador-muchocine	jdumasleon	2024-05-14T15:51:16Z	105	0	transformers	[ "transformers", "safetensors", "electra", "text-classification", "classification", "generated_from_trainer", "base_model:mrm8488/electricidad-base-discriminator", "base_model:finetune:mrm8488/electricidad-base-discriminator", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T15:50:56Z	--- base_model: mrm8488/electricidad-base-discriminator tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: clasificador-muchocine results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4260 - Accuracy: 0.4348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 388 \| 1.3400 \| 0.3923 \| \| 1.3934 \| 2.0 \| 776 \| 1.2767 \| 0.4503 \| \| 0.9927 \| 3.0 \| 1164 \| 1.4260 \| 0.4348 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
EyaZr/my_code_dataset	EyaZr	2024-05-14T15:51:02Z	145	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:46:51Z	--- 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. (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]
ashishkgpian/best_mistral_model	ashishkgpian	2024-05-14T15:48:49Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "astronomy", "conversational", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-14T15:43:42Z	--- library_name: transformers tags: - astronomy license: apache-2.0 language: - en pipeline_tag: text-generation --- # 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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Aryan0310/t5-small-finetuned-cnn-daily	Aryan0310	2024-05-14T15:47:52Z	14	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	2024-05-14T09:34:58Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-cnn-daily 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. --> # t5-small-finetuned-cnn-daily This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6925 - Rouge1: 24.4516 - Rouge2: 11.7206 - Rougel: 20.1946 - Rougelsum: 23.0597 - Gen Len: 18.9996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.8533 \| 1.0 \| 17945 \| 1.6925 \| 24.4516 \| 11.7206 \| 20.1946 \| 23.0597 \| 18.9996 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
MiVaCod/rotten	MiVaCod	2024-05-14T15:46:41Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T17:44:35Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: rotten results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rotten This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8598 - Accuracy: 0.8527 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.405 \| 1.0 \| 1067 \| 0.3657 \| 0.8546 \| \| 0.225 \| 2.0 \| 2134 \| 0.7075 \| 0.8433 \| \| 0.0711 \| 3.0 \| 3201 \| 0.8598 \| 0.8527 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
shull/whisper-small-finetuned-v5en	shull	2024-05-14T15:45:12Z	89	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "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	2024-05-14T06:45:44Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper small v5-en finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper small v5-en finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the my_audio_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1560 - Wer: 5.6915 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 0.1138 \| 5.8309 \| 1000 \| 0.1326 \| 6.3035 \| \| 0.004 \| 11.6618 \| 2000 \| 0.1507 \| 5.7015 \| \| 0.0014 \| 17.4927 \| 3000 \| 0.1560 \| 5.6915 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mradermacher/llama-3-spicy-8B-GGUF	mradermacher	2024-05-14T15:41:18Z	60	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:nbeerbower/llama-3-spicy-8B", "base_model:quantized:nbeerbower/llama-3-spicy-8B", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T14:39:17Z	--- base_model: nbeerbower/llama-3-spicy-8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/nbeerbower/llama-3-spicy-8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Trelis/OpenELM-450M-instruct-ORPO	Trelis	2024-05-14T15:40:06Z	160	0	transformers	[ "transformers", "safetensors", "openelm", "text-generation", "apple", "OpenELM", "conversational", "custom_code", "dataset:argilla/dpo-mix-7k", "arxiv:2404.14619", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2024-05-14T15:25:06Z	--- license: other license_name: apple-sample-code-license license_link: LICENSE datasets: - argilla/dpo-mix-7k tags: - apple - OpenELM --- # OpenELM These are ORPO fine-tunes, done using the Argilla/dpo-mix-7k dataset: - [270M fine-tune](https://huggingface.co/Trelis/OpenELM-270M-instruct-ORPO) - [450M fine-tune](https://huggingface.co/Trelis/OpenELM-450M-instruct-ORPO) ## Performance notes OpenELM models are quite weak. - OpenELM 270M is uniquely small, but weak. - OpenELM 450M improves a little over the 270M model, but remains weak on accuracy and hallucinates strongly. - Qwen 1.5 0.5B is stronger than the OpenELM model. - TinyLlama is stronger than OpenELM 1B. - Models like Phi-3 are stronger than OpenELM 3B. ## Usage Notes - Flash attention is not supported - Making GGUFs is not [yet supported](https://github.com/ggerganov/llama.cpp/issues/6868) ## Inference See [this Colab Notebook](https://colab.research.google.com/drive/1vFMRhHdPyUxbZAlRWwyl79NwnrSz_yQL?usp=sharing) ~~~ The original model card follows below. ~~~ Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari We introduce OpenELM, a family of Open Efficient Language Models. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters. Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them. ## Usage We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`. You can try the model by running the following command: ``` python generate_openelm.py --model apple/OpenELM-450M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 ``` Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token. Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows: ``` python generate_openelm.py --model apple/OpenELM-450M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10 ``` Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example: ``` python generate_openelm.py --model apple/OpenELM-450M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL] ``` ## Main Results ### Zero-Shot \| Model Size \| ARC-c \| ARC-e \| BoolQ \| HellaSwag \| PIQA \| SciQ \| WinoGrande \| Average \| \|-----------------------------------------------------------------------------\|-----------\|-----------\|-----------\|---------------\|-----------\|-----------\|----------------\|-------------\| \| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) \| 26.45 \| 45.08 \| 53.98 \| 46.71 \| 69.75 \| 84.70 \| 53.91 \| 54.37 \| \| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) \| 30.55 \| 46.68 \| 48.56 \| 52.07 \| 70.78 \| 84.40 \| 52.72 \| 55.11 \| \| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) \| 27.56 \| 48.06 \| 55.78 \| 53.97 \| 72.31 \| 87.20 \| 58.01 \| 57.56 \| \| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) \| 30.38 \| 50.00 \| 60.37 \| 59.34 \| 72.63 \| 88.00 \| 58.96 \| 59.95 \| \| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) \| 32.34 \| 55.43 \| 63.58 \| 64.81 \| 75.57 \| 90.60 \| 61.72 \| 63.44 \| \| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) \| 37.97 \| 52.23 \| 70.00 \| 71.20 \| 75.03 \| 89.30 \| 62.75 \| 65.50 \| \| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) \| 35.58 \| 59.89 \| 67.40 \| 72.44 \| 78.24 \| 92.70 \| 65.51 \| 67.39 \| \| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) \| 39.42 \| 61.74 \| 68.17 \| 76.36 \| 79.00 \| 92.50 \| 66.85 \| 69.15 \| ### LLM360 \| Model Size \| ARC-c \| HellaSwag \| MMLU \| TruthfulQA \| WinoGrande \| Average \| \|-----------------------------------------------------------------------------\|-----------\|---------------\|-----------\|----------------\|----------------\|-------------\| \| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) \| 27.65 \| 47.15 \| 25.72 \| 39.24 \| 53.83 \| 38.72 \| \| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) \| 32.51 \| 51.58 \| 26.70 \| 38.72 \| 53.20 \| 40.54 \| \| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) \| 30.20 \| 53.86 \| 26.01 \| 40.18 \| 57.22 \| 41.50 \| \| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) \| 33.53 \| 59.31 \| 25.41 \| 40.48 \| 58.33 \| 43.41 \| \| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) \| 36.69 \| 65.71 \| 27.05 \| 36.98 \| 63.22 \| 45.93 \| \| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) \| 41.55 \| 71.83 \| 25.65 \| 45.95 \| 64.72 \| 49.94 \| \| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) \| 42.24 \| 73.28 \| 26.76 \| 34.98 \| 67.25 \| 48.90 \| \| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) \| 47.70 \| 76.87 \| 24.80 \| 38.76 \| 67.96 \| 51.22 \| ### OpenLLM Leaderboard \| Model Size \| ARC-c \| CrowS-Pairs \| HellaSwag \| MMLU \| PIQA \| RACE \| TruthfulQA \| WinoGrande \| Average \| \|-----------------------------------------------------------------------------\|-----------\|-----------------\|---------------\|-----------\|-----------\|-----------\|----------------\|----------------\|-------------\| \| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) \| 27.65 \| 66.79 \| 47.15 \| 25.72 \| 69.75 \| 30.91 \| 39.24 \| 53.83 \| 45.13 \| \| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) \| 32.51 \| 66.01 \| 51.58 \| 26.70 \| 70.78 \| 33.78 \| 38.72 \| 53.20 \| 46.66 \| \| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) \| 30.20 \| 68.63 \| 53.86 \| 26.01 \| 72.31 \| 33.11 \| 40.18 \| 57.22 \| 47.69 \| \| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) \| 33.53 \| 67.44 \| 59.31 \| 25.41 \| 72.63 \| 36.84 \| 40.48 \| 58.33 \| 49.25 \| \| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) \| 36.69 \| 71.74 \| 65.71 \| 27.05 \| 75.57 \| 36.46 \| 36.98 \| 63.22 \| 51.68 \| \| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) \| 41.55 \| 71.02 \| 71.83 \| 25.65 \| 75.03 \| 39.43 \| 45.95 \| 64.72 \| 54.40 \| \| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) \| 42.24 \| 73.29 \| 73.28 \| 26.76 \| 78.24 \| 38.76 \| 34.98 \| 67.25 \| 54.35 \| \| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) \| 47.70 \| 72.33 \| 76.87 \| 24.80 \| 79.00 \| 38.47 \| 38.76 \| 67.96 \| 55.73 \| See the technical report for more results and comparison. ## Evaluation ### Setup Install the following dependencies: ```bash # install public lm-eval-harness harness_repo="public-lm-eval-harness" git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo} cd ${harness_repo} # use main branch on 03-15-2024, SHA is dc90fec git checkout dc90fec pip install -e . cd .. # 66d6242 is the main branch on 2024-04-01 pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242 pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0 ``` ### Evaluate OpenELM ```bash # OpenELM-450M-Instruct hf_model=apple/OpenELM-450M-Instruct # this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True tokenizer=meta-llama/Llama-2-7b-hf add_bos_token=True batch_size=1 mkdir lm_eval_output shot=0 task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2 lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=5 task=mmlu,winogrande lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=25 task=arc_challenge,crows_pairs_english lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=10 task=hellaswag lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 \| tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log ``` ## Bias, Risks, and Limitations The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements. ## Citation If you find our work useful, please cite: ```BibTex @article{mehtaOpenELMEfficientLanguage2024, title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open} {Training} and {Inference} {Framework}}, shorttitle = {{OpenELM}}, url = {https://arxiv.org/abs/2404.14619v1}, language = {en}, urldate = {2024-04-24}, journal = {arXiv.org}, author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad}, month = apr, year = {2024}, } @inproceedings{mehta2022cvnets, author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad}, title = {CVNets: High Performance Library for Computer Vision}, year = {2022}, booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, series = {MM '22} } ```
reemmasoud/idv_vs_col_llama-3_PromptTuning_CAUSAL_LM_gradient_descent_v1	reemmasoud	2024-05-14T15:39:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T15:39:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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terry69/mistral_poe_10-full	terry69	2024-05-14T15:31:35Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:29:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF	mradermacher	2024-05-14T15:30:55Z	21	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mergekit-community/LLaMa-3-Base-Zeroed-13B", "base_model:quantized:mergekit-community/LLaMa-3-Base-Zeroed-13B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T14:39:16Z	--- base_model: mergekit-community/LLaMa-3-Base-Zeroed-13B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mergekit-community/LLaMa-3-Base-Zeroed-13B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q2_K.gguf) \| Q2_K \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ3_XS.gguf) \| IQ3_XS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q3_K_S.gguf) \| Q3_K_S \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ3_M.gguf) \| IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q3_K_L.gguf) \| Q3_K_L \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.IQ4_XS.gguf) \| IQ4_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q5_K_M.gguf) \| Q5_K_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LLaMa-3-Base-Zeroed-13B-GGUF/resolve/main/LLaMa-3-Base-Zeroed-13B.Q8_0.gguf) \| Q8_0 \| 14.0 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
kyl23/hw3_SST2_lora_1e-4_r16	kyl23	2024-05-14T15:29:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T15:29:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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terry69/mistral_poe_20-full	terry69	2024-05-14T15:27:54Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:25:47Z	--- 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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terry69/mistral_poe_add-full	terry69	2024-05-14T15:23:42Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:10:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mjavadf/whisper-small-dv	mjavadf	2024-05-14T15:21:12Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-14T13:19:41Z	--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.60712174427096 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1733 - Wer Ortho: 62.6715 - Wer: 13.6071 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1198 \| 1.6287 \| 500 \| 0.1733 \| 62.6715 \| 13.6071 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Antonio49/ModeloCanal	Antonio49	2024-05-14T15:20:20Z	113	2	transformers	[ "transformers", "safetensors", "bert", "question-answering", "es", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2024-04-07T08:20:05Z	--- title: Antonio.BERT.Canal emoji: 🐠 colorFrom: red colorTo: blue sdk: gradio sdk_version: 3.33.1 app_file: app.py pinned: false license: mit language: - es --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed]
terry69/mistral_poe_nores-full	terry69	2024-05-14T15:18:24Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:15:14Z	--- 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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PantagrueLLM/jargon-general-biomed	PantagrueLLM	2024-05-14T15:17:56Z	110	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T17:49:04Z	--- license: mit language: - fr library_name: transformers tags: - linformer - medical - RoBERTa - pytorch --- # Jargon-general-biomed [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \| \|-------------------------------------------------------------------------------------\|:-----------------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \| \| jargon-general-legal \| jargon-general-base \| \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \| \| jargon-legal \| scratch \| \| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) \| scratch \| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. ## Biomedical Benchmark Results averaged across five funs with varying random seeds. \| \|[FrenchMedMCQA](https://huggingface.co/datasets/qanastek/frenchmedmcqa)\|[MQC](https://aclanthology.org/2020.lrec-1.72/)\|[CAS-POS](https://clementdalloux.fr/?page_id=28)\|[ESSAI-POS](https://clementdalloux.fr/?page_id=28)\|[CAS-SG](https://aclanthology.org/W18-5614/)\|[MEDLINE](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[EMEA](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[E3C-NER](https://live.european-language-grid.eu/catalogue/corpus/7618)\|[CLISTER](https://aclanthology.org/2022.lrec-1.459/)\| \|-------------------------\|:-----------------------:\|:-----------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\| \| Task Type \| Sequence Classification \| Sequence Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| STS \| \| Metric \| EMR \| Accuracy \| Macro-F1 \| Macro-F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Spearman Correlation \| \| jargon-general-base \| 12.9 \| 76.7 \| 96.6 \| 96.0 \| 69.4 \| 81.7 \| 96.5 \| 91.9 \| 78.0 \| \| jargon-biomed \| 15.3 \| 91.1 \| 96.5 \| 95.6 \| 75.1 \| 83.7 \| 96.5 \| 93.5 \| 74.6 \| \| jargon-biomed-4096 \| 14.4 \| 78.9 \| 96.6 \| 95.9 \| 73.3 \| 82.3 \| 96.3 \| 92.5 \| 65.3 \| \| jargon-general-biomed \| 16.1 \| 69.7 \| 95.1 \| 95.1 \| 67.8 \| 78.2 \| 96.6 \| 91.3 \| 59.7 \| \| jargon-multidomain-base \| 14.9 \| 86.9 \| 96.3 \| 96.0 \| 70.6 \| 82.4 \| 96.6 \| 92.6 \| 74.8 \| \| jargon-NACHOS \| 13.3 \| 90.7 \| 96.3 \| 96.2 \| 75.0 \| 83.4 \| 96.8 \| 93.1 \| 70.9 \| \| jargon-NACHOS-4096 \| 18.4 \| 93.2 \| 96.2 \| 95.9 \| 74.9 \| 83.8 \| 96.8 \| 93.2 \| 74.9 \| For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with `jargon-general-biomed` using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-general-biomed", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-general-biomed", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
kishiyev/ppo-LunarLander-v2	kishiyev	2024-05-14T15:16:31Z	3	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-14T12:59:54Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 246.27 +/- 19.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub repo_id = "kishiyev/ppo-LunarLander-v2" # The repo_id filename = "ppo-LunarLander-v2.zip" # The model filename.zip custom_objects = { "learning_rate": 0.0, "lr_schedule": lambda _: 0.0, "clip_range": lambda _: 0.0, } checkpoint = load_from_hub(repo_id, filename) model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True) ... ```
emilykang/Phi_finetune_med	emilykang	2024-05-14T15:16:15Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-14T09:30:46Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_finetune_med results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_finetune_med This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
AlkQ/ppo-SnowballTarget	AlkQ	2024-05-14T15:13:57Z	13	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2024-05-14T15:13:54Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AlkQ/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
PantagrueLLM/jargon-NACHOS-4096	PantagrueLLM	2024-05-14T15:13:54Z	101	0	transformers	[ "transformers", "pytorch", "jargon", "fill-mask", "linformer", "medical", "RoBERTa", "custom_code", "fr", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-05-13T18:49:23Z	--- license: mit language: - fr library_name: transformers tags: - linformer - medical - RoBERTa - pytorch --- # Jargon-NACHOS-4096 [Jargon](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf) is an efficient transformer encoder LM for French, combining the LinFormer attention mechanism with the RoBERTa model architecture. Jargon is available in several versions with different context sizes and types of pre-training corpora. <!-- Provide a quick summary of what the model is/does. --> <!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). --> \| Model \| Initialised from... \|Training Data\| \|-------------------------------------------------------------------------------------\|:-----------------------:\|:----------------:\| \| [jargon-general-base](https://huggingface.co/PantagrueLLM/jargon-general-base) \| scratch \|8.5GB Web Corpus\| \| [jargon-general-biomed](https://huggingface.co/PantagrueLLM/jargon-general-biomed) \| jargon-general-base \|5.4GB Medical Corpus\| \| jargon-general-legal \| jargon-general-base \|18GB Legal Corpus \| [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) \| jargon-general-base \|Medical+Legal Corpora\| \| jargon-legal \| scratch \|18GB Legal Corpus\| \| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) \| scratch \|18GB Legal Corpus\| \| [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) \| scratch \|5.4GB Medical Corpus\| \| [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) \| scratch \|5.4GB Medical Corpus\| \| [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| \| [jargon-NACHOS-4096](https://huggingface.co/PantagrueLLM/jargon-NACHOS-4096) \| scratch \|[NACHOS](https://drbert.univ-avignon.fr/)\| ## Evaluation The Jargon models were evaluated on an range of specialized downstream tasks. ## Biomedical Benchmark Results averaged across five funs with varying random seeds. \| \|[FrenchMedMCQA](https://huggingface.co/datasets/qanastek/frenchmedmcqa)\|[MQC](https://aclanthology.org/2020.lrec-1.72/)\|[CAS-POS](https://clementdalloux.fr/?page_id=28)\|[ESSAI-POS](https://clementdalloux.fr/?page_id=28)\|[CAS-SG](https://aclanthology.org/W18-5614/)\|[MEDLINE](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[EMEA](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)\|[E3C-NER](https://live.european-language-grid.eu/catalogue/corpus/7618)\|[CLISTER](https://aclanthology.org/2022.lrec-1.459/)\| \|-------------------------\|:-----------------------:\|:-----------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\|:--------------------:\| \| Task Type \| Sequence Classification \| Sequence Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| Token Classification \| STS \| \| Metric \| EMR \| Accuracy \| Macro-F1 \| Macro-F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Weighted F1 \| Spearman Correlation \| \| jargon-general-base \| 12.9 \| 76.7 \| 96.6 \| 96.0 \| 69.4 \| 81.7 \| 96.5 \| 91.9 \| 78.0 \| \| jargon-biomed \| 15.3 \| 91.1 \| 96.5 \| 95.6 \| 75.1 \| 83.7 \| 96.5 \| 93.5 \| 74.6 \| \| jargon-biomed-4096 \| 14.4 \| 78.9 \| 96.6 \| 95.9 \| 73.3 \| 82.3 \| 96.3 \| 92.5 \| 65.3 \| \| jargon-general-biomed \| 16.1 \| 69.7 \| 95.1 \| 95.1 \| 67.8 \| 78.2 \| 96.6 \| 91.3 \| 59.7 \| \| jargon-multidomain-base \| 14.9 \| 86.9 \| 96.3 \| 96.0 \| 70.6 \| 82.4 \| 96.6 \| 92.6 \| 74.8 \| \| jargon-NACHOS \| 13.3 \| 90.7 \| 96.3 \| 96.2 \| 75.0 \| 83.4 \| 96.8 \| 93.1 \| 70.9 \| \| jargon-NACHOS-4096 \| 18.4 \| 93.2 \| 96.2 \| 95.9 \| 74.9 \| 83.8 \| 96.8 \| 93.2 \| 74.9 \| For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/). ## Using Jargon models with HuggingFace transformers You can get started with `jargon-NACHOS-4096` using the code snippet below: ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-NACHOS-4096", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-NACHOS-4096", trust_remote_code=True) jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer) output = jargon_maskfiller("Il est allé au <mask> hier") ``` You can also use the classes `AutoModel`, `AutoModelForSequenceClassification`, or `AutoModelForTokenClassification` to load Jargon models, depending on the downstream task in question. - Language(s): French - License: MIT - Developed by: Vincent Segonne - Funded by - GENCI-IDRIS (Grant 2022 A0131013801) - French National Research Agency: Pantagruel grant ANR-23-IAS1-0001 - MIAI@Grenoble Alpes ANR-19-P3IA-0003 - PROPICTO ANR-20-CE93-0005 - Lawbot ANR-20-CE38-0013 - Swiss National Science Foundation (grant PROPICTO N°197864) - Authors - Vincent Segonne - Aidan Mannion - Laura Cristina Alonzo Canul - Alexandre Audibert - Xingyu Liu - Cécile Macaire - Adrien Pupier - Yongxin Zhou - Mathilde Aguiar - Felix Herron - Magali Norré - Massih-Reza Amini - Pierrette Bouillon - Iris Eshkol-Taravella - Emmanuelle Esperança-Rodier - Thomas François - Lorraine Goeuriot - Jérôme Goulian - Mathieu Lafourcade - Benjamin Lecouteux - François Portet - Fabien Ringeval - Vincent Vandeghinste - Maximin Coavoux - Marco Dinarelli - Didier Schwab ## Citation If you use this model for your own research work, please cite as follows: ```bibtex @inproceedings{segonne:hal-04535557, TITLE = {{Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains}}, AUTHOR = {Segonne, Vincent and Mannion, Aidan and Alonzo Canul, Laura Cristina and Audibert, Alexandre and Liu, Xingyu and Macaire, C{\'e}cile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norr{\'e}, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperan{\c c}a-Rodier, Emmanuelle and Fran{\c c}ois, Thomas and Goeuriot, Lorraine and Goulian, J{\'e}r{\^o}me and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, Fran{\c c}ois and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier}, URL = {https://hal.science/hal-04535557}, BOOKTITLE = {{LREC-COLING 2024 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation}}, ADDRESS = {Turin, Italy}, YEAR = {2024}, MONTH = May, KEYWORDS = {Self-supervised learning ; Pretrained language models ; Evaluation benchmark ; Biomedical document processing ; Legal document processing ; Speech transcription}, PDF = {https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf}, HAL_ID = {hal-04535557}, HAL_VERSION = {v1}, } ``` <!-- - Finetuned from model [optional]: [More Information Needed] --> <!-- ### Model Sources [optional] <!-- Provide the basic links for the model. -->
Sarxsarkos/ppo-Huggy	Sarxsarkos	2024-05-14T15:13:28Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-14T14:54:03Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Sarxsarkos/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
terry69/mistral_poe_small-full	terry69	2024-05-14T15:11:11Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:09:05Z	--- 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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kyl23/hw3_SST2_lora_1e-3	kyl23	2024-05-14T15:11:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T15:10:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF	mradermacher	2024-05-14T15:09:07Z	119	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn", "base_model:quantized:asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn", "endpoints_compatible", "region:us" ]	null	2024-05-14T14:39:15Z	--- base_model: asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/asiansoul/Joah-Remix-Llama-3-KoEn-8B-Reborn <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Joah-Remix-Llama-3-KoEn-8B-Reborn-GGUF/resolve/main/Joah-Remix-Llama-3-KoEn-8B-Reborn.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
GenTrendGPT/OS-Test-Mark-GEN-IA	GenTrendGPT	2024-05-14T15:07:00Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:Nexusflow/Starling-LM-7B-beta", "base_model:merge:Nexusflow/Starling-LM-7B-beta", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T15:02:04Z	--- base_model: - Nexusflow/Starling-LM-7B-beta - meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge --- # merge 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: passthrough dtype: bfloat16 slices: - sources: - model: meta-llama/Meta-Llama-3-8B-Instruct layer_range: [0, 32] - sources: - model: Nexusflow/Starling-LM-7B-beta layer_range: [0, 32] merge_method: passthrough ```
NikolayKozloff/phi-3-portuguese-tom-cat-4k-instruct-Q8_0-GGUF	NikolayKozloff	2024-05-14T15:06:48Z	5	1	transformers	[ "transformers", "gguf", "portugues", "portuguese", "QA", "instruct", "phi", "llama-cpp", "gguf-my-repo", "text-generation", "pt", "dataset:rhaymison/superset", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T15:06:37Z	--- language: - pt license: apache-2.0 library_name: transformers tags: - portugues - portuguese - QA - instruct - phi - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-mini-4k-instruct datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: phi-3-portuguese-tom-cat-4k-instruct results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 61.58 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 50.63 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 43.69 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 91.54 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 75.27 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 47.46 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 83.01 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 70.19 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 57.78 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard --- # NikolayKozloff/phi-3-portuguese-tom-cat-4k-instruct-Q8_0-GGUF This model was converted to GGUF format from [`rhaymison/phi-3-portuguese-tom-cat-4k-instruct`](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/phi-3-portuguese-tom-cat-4k-instruct-Q8_0-GGUF --model phi-3-portuguese-tom-cat-4k-instruct.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/phi-3-portuguese-tom-cat-4k-instruct-Q8_0-GGUF --model phi-3-portuguese-tom-cat-4k-instruct.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi-3-portuguese-tom-cat-4k-instruct.Q8_0.gguf -n 128 ```
Gigax/NPC-LLM-7B-GGUF	Gigax	2024-05-14T15:06:18Z	6	4	null	[ "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-13T10:54:07Z	--- license: apache-2.0 language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Mistral-7B, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from llama_cpp import Llama # Download model from the Hugging Face Gigax Hub before run this code # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format llm = Llama.from_pretrained( repo_id="Gigax/NPC-LLM-7B-GGUF", filename="npc-llm-7B.gguf" # n_gpu_layers=-1, # Uncomment to use GPU acceleration # seed=1337, # Uncomment to set a specific seed # n_ctx=2048, # Uncomment to increase the context window ) model = models.LlamaCpp(llm) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Mistral-7B-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-7B-GGUF, url={[https://huggingface.co/Gigax/NPC-LLM-7B-GGUF](https://huggingface.co/Gigax/NPC-LLM-7B-GGUF)}, title={NPC-LLM-7B-GGUF}, author={Gigax team} } ```
Gigax/NPC-LLM-7B	Gigax	2024-05-14T15:05:43Z	79	11	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-26T15:34:34Z	--- license: apache-2.0 language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Mistral-7B, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper # Download model from the Hub model_name = "Gigax/NPC-LLM-7B" llm = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format model = models.Transformers(llm, tokenizer) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Mistral-7B-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-7B, url={[https://huggingface.co/Gigax/NPC-LLM-7B](https://huggingface.co/Gigax/NPC-LLM-7B)}, title={NPC-LLM-7B}, author={Gigax team} } ```
Gigax/NPC-LLM-3_8B-GGUF	Gigax	2024-05-14T15:05:13Z	32	1	null	[ "gguf", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T11:37:15Z	--- license: mit language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Phi-3, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper from llama_cpp import Llama # Download model from the Hugging Face Gigax Hub before run this code # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format llm = Llama.from_pretrained( repo_id="Gigax/NPC-LLM-3_8B-GGUF", filename="npc-llm-3_8B.gguf" # n_gpu_layers=-1, # Uncomment to use GPU acceleration # seed=1337, # Uncomment to set a specific seed # n_ctx=2048, # Uncomment to increase the context window ) model = models.LlamaCpp(llm) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-3_8B-GGUF, url={[https://huggingface.co/Gigax/NPC-LLM-3_8B-GGUF-](https://huggingface.co/Gigax/NPC-LLM-3_8B-GGUF)}, title={NPC-LLM-3_8B-GGUF}, author={Gigax team} } ```
Gigax/NPC-LLM-3_8B-128k-GGUF	Gigax	2024-05-14T15:04:28Z	4	2	null	[ "gguf", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T11:55:47Z	--- license: mit language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Phi-3-128k, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper from llama_cpp import Llama # Download model from the Hugging Face Gigax Hub before run this code # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format llm = Llama.from_pretrained( repo_id="Gigax/NPC-LLM-3_8B-128k-GGUF", filename="npc-llm-3_8B-128k.gguf" # n_gpu_layers=-1, # Uncomment to use GPU acceleration # seed=1337, # Uncomment to set a specific seed # n_ctx=2048, # Uncomment to increase the context window ) model = models.LlamaCpp(llm) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-3_8B-128k-GGUF, url={[https://huggingface.co/Gigax/NPC-LLM-3_8B-128k-GGUF](https://huggingface.co/Gigax/NPC-LLM-3_8B-128k-GGUF)}, title={NPC-LLM-3_8B-128k-GGUF}, author={Gigax team} } ```
NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF	NikolayKozloff	2024-05-14T15:03:48Z	6	1	transformers	[ "transformers", "gguf", "portugues", "portuguese", "QA", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "pt", "dataset:rhaymison/superset", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T15:03:30Z	--- language: - pt license: apache-2.0 library_name: transformers tags: - portugues - portuguese - QA - instruct - llama-cpp - gguf-my-repo base_model: meta-llama/Meta-Llama-3-8B-Instruct datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: Llama-3-portuguese-Tom-cat-8b-instruct results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 70.4 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 58.0 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 51.07 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 90.91 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 75.4 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 76.05 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 86.99 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 60.39 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 65.92 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct name: Open Portuguese LLM Leaderboard --- # NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF This model was converted to GGUF format from [`rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct`](https://huggingface.co/rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF --model llama-3-portuguese-tom-cat-8b-instruct.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-portuguese-Tom-cat-8b-instruct-Q6_K-GGUF --model llama-3-portuguese-tom-cat-8b-instruct.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-portuguese-tom-cat-8b-instruct.Q6_K.gguf -n 128 ```
Gigax/NPC-LLM-3_8B-128k	Gigax	2024-05-14T15:03:43Z	152	5	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-02T11:20:47Z	--- license: mit language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Phi-3-128k, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper # Download model from the Hub model_name = "Gigax/NPC-LLM-3_8B-128k" llm = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format model = models.Transformers(llm, tokenizer) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-3_8B-128k, url={[https://huggingface.co/Gigax/NPC-LLM-3_8B-128k](https://huggingface.co/Gigax/NPC-LLM-3_8B-128k)}, title={NPC-LLM-3_8B-128k}, author={Gigax team} } ```
SKLxAiforia/FriendV4.1	SKLxAiforia	2024-05-14T15:02:11Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T12:54:14Z	--- library_name: transformers tags: [] --- # Small intro about model В базовом промпте модели есть 5 основных блоков, разделенных символом `\n`. ``` Friend name: {friend_name}\n Friend description: {friend_description}\n Friend intention_of_friend: {intention_of_friend}\n Person name: {person_name}\n Person description: {person_description}\n {dialogue} ``` Диалог представляет собой последовательность реплик в следующем порядке, разделенных `\n`: ``` {user_name}: {user_reply_1}\n {bot_name}: {bot_reply_1}\n {user_name}: {user_reply_2}\n ... ``` Контекст - 20 сообщений. Возможно модель сможет общаться и при бОльшем кол-ве сообщений, но такой функционал не тестился. Не рекомендуется не заполнять какие-то блоки в базовом промпте, если информации нет - можно написать что-то совсем общее и базовое. # Example of usage ```python import requests import json URL = "http://35.209.126.102:7727/generate" #"https://3a31-34-170-161-27.ngrok-free.app/generate" MAX_CONTEXT_LENGTH = 20 BOT_PROMPT = "Jamie" USER_PROMPT = "Blake" NARRATIVE = "\n".join([ "Friend name: Jamie", "Friend description: Jamie is an ever-curious soul with a penchant for photography and volunteering at animal shelters. They were born in Melbourne and find joy in spontaneous road trips and outdoor adventures. Jamie, at 26 years old, carries an air of comforting assurance with an eclectic taste in indie music." "Friend intention_of_friend: Jamie's intention is to provide a safe space for Person to share their feelings. By engaging in meaningful dialogue, Jamie seeks to help Person recognize their own strengths and feel less isolated.", "Person name: Blake", "Person description: Blake, a reserved 23-year-old software engineer from Toronto, has a particular fondness for classic literature and chess. They appear indifferent on the surface but beneath lies a depth shaped by a recent breakup and a demanding career.", ]) SEPARATOR = "\n" def generate( prompt: str, url: str = URL, ) -> str: req_data = json.dumps({ "inputs": prompt, "parameters": { "max_new_tokens": 30, "stop": ["\n", " \n", ".\n", "?\n"], "top_p": 0.9, "temperature": 0.95, "top_k": 50, "do_sample": True, } }) headers = { 'Content-Type': 'application/json' } response = requests.post(url=url, data=req_data, headers=headers).json() response_text = response["generated_text"].strip() if '\n' in response_text: response_text = response_text.split('\n')[0] return response_text def make_prompt(context: list[str]) -> str: return SEPARATOR.join( [NARRATIVE] + context[-MAX_CONTEXT_LENGTH:] + [f"{BOT_PROMPT}:"] ) if __name__ == "__main__": messages = [] while True: user_phrase = input("You: ") messages.append(f"{USER_PROMPT}: {user_phrase}") model_prompt = make_prompt(context=messages) generated_response = generate(model_prompt) bot_phrase = f"{BOT_PROMPT}: {generated_response}" messages.append(bot_phrase) print(bot_phrase) ``` # Prompt Examples ```Friend name: Sam\nFriend description: Sam is a life coach and yoga enthusiast from San Francisco, aged 29, who thrives in assisting others to find their path. They have a past filled with overcoming personal obstacles, which they openly share to inspire resilience in others. They love experimenting with vegan recipes.\nFriend intention_of_friend: Sam intends to help Person build self-esteem and introduce healthy routines into their life. Through their conversation, Sam plans to motivate Person to practice self-care and mindfulness.\nPerson name: Cameron\nPerson description: Cameron, age 31, is a jaded musician living in New Orleans. Once hopeful and lively, recent setbacks in their career have led to disillusionment. Known for a sharp wit, they nonetheless retain a deep love for live jazz and rainy afternoons.\n``` ```Friend name: Taylor\nFriend description: Taylor, a charismatic event planner from New Orleans, 27, often feels energized by the dynamic bustle of city life. Their genuine care for others shines through in their active volunteer work. Taylor's personal journey includes a powerful narrative of self-discovery after college.\nFriend intention_of_friend: Taylor's intention is to uplift Person by getting them involved in local community events to foster a sense of belonging and purpose, something Taylor believes in strongly.\nPerson name: Harper\nPerson description: Harper, a 22-year-old aspiring writer from Dublin, harbors a zest for historical novels and boxing. Though typically cold and standoffish, they dream of authentic connections and a break from the monotony of their daily routine.\n``` ```Friend name: Riley\nFriend description: Riley, a world-wise traveler, 34, hails from a small coastal town in Iceland. They are a documentary filmmaker with an impressive collection of folk music records. Despite their accomplished life, they remain grounded and relatable, always seeking new friendships.\nFriend intention_of_friend: Riley wants to help Person discover the enriching experience of embracing different cultures. By sharing travel stories, Riley aims to spark an interest in Person to see the world from a fresh perspective.\nPerson name: Jordan\nPerson description: Jordan, an introverted postgrad student in philosophy from New York City, values solitude and reflection. At 25, they frequently grapple with existential questions, which can overshadow daily joys. Their analytical mind enjoys puzzles, but Jordan often struggles to connect with others.\n``` ```Friend name: Alex\nFriend description: Alex, age 28, is a free spirit originally from Portland, operating a cozy bookstore caf\u00e9. They have a fascination with culinary arts and a storied history in dance. A compassionate listener, Alex's vibrancy is contagious, and they find beauty in candid conversations.\nFriend intention_of_friend: Alex's goal is to encourage Person to explore and embrace their creative side. They believe creativity can be a therapeutic outlet and want to help Person find a passion to pursue.\nPerson name: Morgan\nPerson description: Morgan, a skeptical graphic designer from a small town in Italy, is 30 years old with a brave face masking their apprehension towards new relationships. A methodical thinker, they enjoy strategy games and have a bittersweet relationship with the fast-paced digital world.\n``` ```Friend name: Casey\nFriend description: Casey is a compassionate nurse from a cozy Colorado mountain town, 32. They balance their intense career with a passion for rock climbing and a dedication to living sustainably. Casey values authenticity and never shies away from showing empathy to both patients and strangers alike.\nFriend intention_of_friend: Casey aims to guide Person toward embracing outdoor activities for their therapeutic benefits and to inspire Person to nurture a connection with nature.\nPerson name: Quinn\nPerson description: Quinn, a 35-year-old real estate agent from Miami, is known for a sharp business acumen and a no-nonsense attitude. Beneath this fa\u00e7ade, Quinn has a surprisingly deep appreciation for poetry and solitude, often reflecting on the ephemerality of success.\n```
Gigax/NPC-LLM-3_8B	Gigax	2024-05-14T15:02:02Z	69	24	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-28T19:38:52Z	--- license: mit language: - en --- # NPC Model This repo contains the domain-specific NPC model we've fined-tuned from Phi-3, using LoRA. This model parses a text description of a game scene, and outputs commands like: * `say <player1> "Hello Adventurer, care to join me on a quest?` * `greet <player1>` * `attack <player1>` * Any other `<action> <param>` you add to the prompt! (We call these "skills"!) ⚠️ This model has been trained to overfit on our input prompt format. Follow it closely to reach optimal performance ⚠️ ## Usage Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax) * Instantiating the model using outlines: ```py from outlines import models from gigax.step import NPCStepper # Download model from the Hub model_name = "Gigax/NPC-LLM-3_8B" llm = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Our stepper takes in a Outlines model to enable guided generation # This forces the model to follow our output format model = models.Transformers(llm, tokenizer) # Instantiate a stepper: handles prompting + output parsing stepper = NPCStepper(model=model) ``` * Calling the model on your game's data: ```py from gigax.parse import CharacterAction from gigax.scene import ( Character, Item, Location, ProtagonistCharacter, ProtagonistCharacter, Skill, ParameterType, ) # Use sample data context = "Medieval world" current_location = Location(name="Old Town", description="A quiet and peaceful town.") locations = [current_location] # you can add more locations to the scene NPCs = [ Character( name="John the Brave", description="A fearless warrior", current_location=current_location, ) ] protagonist = ProtagonistCharacter( name="Aldren", description="Brave and curious", current_location=current_location, memories=["Saved the village", "Lost a friend"], quests=["Find the ancient artifact", "Defeat the evil warlock"], skills=[ Skill( name="Attack", description="Deliver a powerful blow", parameter_types=[ParameterType.character], ) ], psychological_profile="Determined and compassionate", ) items = [Item(name="Sword", description="A sharp blade")] events = [ CharacterAction( command="Say", protagonist=protagonist, parameters=[items[0], "What a fine sword!"], ) ] action = stepper.get_action( context=context, locations=locations, NPCs=NPCs, protagonist=protagonist, items=items, events=events, ) ``` ## Input prompt Here's a sample input prompt, showing you the format on which the model has been trained: ```txt - WORLD KNOWLEDGE: A vast open world full of mystery and adventure. - KNOWN LOCATIONS: Old Town - NPCS: John the Brave - CURRENT LOCATION: Old Town: A quiet and peaceful town. - CURRENT LOCATION ITEMS: Sword - LAST EVENTS: Aldren: Say Sword What a fine sword! - PROTAGONIST NAME: Aldren - PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious - PROTAGONIST MEMORIES: Saved the village Lost a friend - PROTAGONIST PENDING QUESTS: Find the ancient artifact Defeat the evil warlock - PROTAGONIST ALLOWED ACTIONS: Attack <character> : Deliver a powerful blow Aldren: ``` ### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗 ## Model info - Developed by: Gigax - Language(s) (NLP): English - Finetuned from model [optional]: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) - Contact: Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more! ## How to Cite ```bibtex @misc{NPC-LLM-3_8B, url={[https://huggingface.co/Gigax/NPC-LLM-3_8B](https://huggingface.co/Gigax/NPC-LLM-3_8B)}, title={NPC-LLM-3_8B}, author={Gigax team} } ```
farenassr/autotrain-autotrain-my-custom-diversity	farenassr	2024-05-14T15:00:05Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:59:36Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
quocanh944/viT5-med-qa	quocanh944	2024-05-14T14:59:05Z	163	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T14:57:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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blackhole33/uzbek-speaker-verification-v3	blackhole33	2024-05-14T14:56:39Z	2	0	nemo	[ "nemo", "pytorch", "NeMo", "license:cc-by-4.0", "region:us" ]	null	2024-05-14T13:36:12Z	--- license: cc-by-4.0 library_name: nemo tags: - pytorch - NeMo --- # Uzbek-speaker-verification-v3 Put a short model description here. ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model NOTE: Please update the model class below to match the class of the model being uploaded. ```python import nemo.core import ModelPT model = ModelPT.from_pretrained("ai-nightcoder/uzbek-speaker-verification-v3") ``` ### NOTE Add some information about how to use the model here. An example is provided for ASR inference below. ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="ai-nightcoder/uzbek-speaker-verification-v3" audio_dir="" ``` ### Input Add some information about what are the inputs to this model ### Output Add some information about what are the outputs of this model ## Model Architecture Add information here discussing architectural details of the model or any comments to users about the model. ## Training Add information here about how the model was trained. It should be as detailed as possible, potentially including the the link to the script used to train as well as the base config used to train the model. If extraneous scripts are used to prepare the components of the model, please include them here. ### NOTE An example is provided below for ASR The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml). The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ### Datasets Try to provide as detailed a list of datasets as possible. If possible, provide links to the datasets on HF by adding it to the manifest section at the top of the README (marked by ---). ### NOTE An example for the manifest section is provided below for ASR datasets datasets: - librispeech_asr - fisher_corpus - Switchboard-1 - WSJ-0 - WSJ-1 - National-Singapore-Corpus-Part-1 - National-Singapore-Corpus-Part-6 - vctk - voxpopuli - europarl - multilingual_librispeech - mozilla-foundation/common_voice_8_0 - MLCommons/peoples_speech The corresponding text in this section for those datasets is stated below - The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams. The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets: - Librispeech 960 hours of English speech - Fisher Corpus - Switchboard-1 Dataset - WSJ-0 and WSJ-1 - National Speech Corpus (Part 1, Part 6) - VCTK - VoxPopuli (EN) - Europarl-ASR (EN) - Multilingual Librispeech (MLS EN) - 2,000 hour subset - Mozilla Common Voice (v7.0) - People's Speech - 12,000 hour subset ## Performance Add information here about the performance of the model. Discuss what is the metric that is being used to evaluate the model and if there are external links explaning the custom metric, please link to it. ### NOTE An example is provided below for ASR metrics list that can be added to the top of the README model-index: - name: PUT_MODEL_NAME results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: AMI (Meetings test) type: edinburghcstr/ami config: ihm split: test args: language: en metrics: - name: Test WER type: wer value: 17.10 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Earnings-22 type: revdotcom/earnings22 split: test args: language: en metrics: - name: Test WER type: wer value: 14.11 Provide any caveats about the results presented in the top of the discussion so that nuance is not lost. It should ideally be in a tabular format (you can use the following website to make your tables in markdown format - https://www.tablesgenerator.com/markdown_tables) ## Limitations Discuss any practical limitations to the model when being used in real world cases. They can also be legal disclaimers, or discussion regarding the safety of the model (particularly in the case of LLMs). ### Note An example is provided below Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
NikolayKozloff/Phi-3-mini-4k-instruct-dansk-Q8_0-GGUF	NikolayKozloff	2024-05-14T14:56:16Z	7	1	null	[ "gguf", "trl", "sft", "generated_from_trainer", "danish", "llama-cpp", "gguf-my-repo", "dataset:kobprof/skolegpt-instruct", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T14:56:03Z	--- license: mit tags: - trl - sft - generated_from_trainer - danish - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-mini-4k-instruct datasets: - kobprof/skolegpt-instruct model-index: - name: Phi-3-mini-4k-instruct-dansk results: [] --- # NikolayKozloff/Phi-3-mini-4k-instruct-dansk-Q8_0-GGUF This model was converted to GGUF format from [`emillykkejensen/Phi-3-mini-4k-instruct-dansk`](https://huggingface.co/emillykkejensen/Phi-3-mini-4k-instruct-dansk) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/emillykkejensen/Phi-3-mini-4k-instruct-dansk) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Phi-3-mini-4k-instruct-dansk-Q8_0-GGUF --model phi-3-mini-4k-instruct-dansk.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Phi-3-mini-4k-instruct-dansk-Q8_0-GGUF --model phi-3-mini-4k-instruct-dansk.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi-3-mini-4k-instruct-dansk.Q8_0.gguf -n 128 ```
kyl23/hw3_SST2_bitfit_1e-5	kyl23	2024-05-14T14:55:25Z	180	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-14T14:54:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TSingye/DYG_DistillGPT-2	TSingye	2024-05-14T14:52:45Z	145	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:48:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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. 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terry69/mistral_poe_nores	terry69	2024-05-14T14:49:13Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-14T13:09:29Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: mistral_poe_nores results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_poe_nores This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0 \| 1.0 \| 325 \| nan \| ### Framework versions - PEFT 0.7.1 - Transformers 4.39.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
emilykang/Gemma_finetune_med	emilykang	2024-05-14T14:48:35Z	7	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-14T12:23:43Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_finetune_med results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_finetune_med This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.0.1+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF	Recaru	2024-05-14T14:48:20Z	2	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ko", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-14T14:47:59Z	--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko - llama-cpp - gguf-my-repo pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- # Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF This model was converted to GGUF format from [`beomi/Llama-3-KoEn-8B-Instruct-preview`](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF --model llama-3-koen-8b-instruct-preview.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Recaru/Llama-3-KoEn-8B-Instruct-preview-Q5_K_M-GGUF --model llama-3-koen-8b-instruct-preview.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-koen-8b-instruct-preview.Q5_K_M.gguf -n 128 ```
Litzy619/G0513HMAB2	Litzy619	2024-05-14T14:42:52Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-14T08:42:47Z	--- license: gemma base_model: google/gemma-2b tags: - generated_from_trainer model-index: - name: G0513HMAB2 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. --> # G0513HMAB2 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1364 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 80 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9285 \| 0.09 \| 10 \| 1.9193 \| \| 1.9268 \| 0.18 \| 20 \| 1.9150 \| \| 1.9047 \| 0.27 \| 30 \| 1.8833 \| \| 1.8501 \| 0.36 \| 40 \| 1.7905 \| \| 1.7172 \| 0.45 \| 50 \| 1.6083 \| \| 1.4992 \| 0.54 \| 60 \| 1.3297 \| \| 1.1821 \| 0.63 \| 70 \| 0.9550 \| \| 0.748 \| 0.73 \| 80 \| 0.5145 \| \| 0.3913 \| 0.82 \| 90 \| 0.2609 \| \| 0.2021 \| 0.91 \| 100 \| 0.1661 \| \| 0.1594 \| 1.0 \| 110 \| 0.1513 \| \| 0.1462 \| 1.09 \| 120 \| 0.1484 \| \| 0.1441 \| 1.18 \| 130 \| 0.1473 \| \| 0.1453 \| 1.27 \| 140 \| 0.1458 \| \| 0.1485 \| 1.36 \| 150 \| 0.1448 \| \| 0.1407 \| 1.45 \| 160 \| 0.1455 \| \| 0.1417 \| 1.54 \| 170 \| 0.1428 \| \| 0.1421 \| 1.63 \| 180 \| 0.1416 \| \| 0.1428 \| 1.72 \| 190 \| 0.1438 \| \| 0.1398 \| 1.81 \| 200 \| 0.1403 \| \| 0.1399 \| 1.9 \| 210 \| 0.1392 \| \| 0.141 \| 1.99 \| 220 \| 0.1394 \| \| 0.1377 \| 2.08 \| 230 \| 0.1379 \| \| 0.1363 \| 2.18 \| 240 \| 0.1374 \| \| 0.1352 \| 2.27 \| 250 \| 0.1375 \| \| 0.1394 \| 2.36 \| 260 \| 0.1375 \| \| 0.1362 \| 2.45 \| 270 \| 0.1373 \| \| 0.1324 \| 2.54 \| 280 \| 0.1369 \| \| 0.1317 \| 2.63 \| 290 \| 0.1367 \| \| 0.133 \| 2.72 \| 300 \| 0.1365 \| \| 0.1341 \| 2.81 \| 310 \| 0.1364 \| \| 0.1346 \| 2.9 \| 320 \| 0.1364 \| \| 0.1365 \| 2.99 \| 330 \| 0.1364 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.0
QinLiuNLP/mistral-poe-10p-detach	QinLiuNLP	2024-05-14T14:31:09Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-14T08:34:11Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-poe-10p-detach results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-poe-10p-detach This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7345 \| 1.0 \| 3898 \| nan \| ### Framework versions - PEFT 0.7.1 - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2
AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid	AhmetAytar	2024-05-14T14:30:17Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-14T14:26:27Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook_grobid) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 160 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 32, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
cimphony-ai-admin/Cimphony-Mistral-Law-7B	cimphony-ai-admin	2024-05-14T14:28:04Z	30	3	peft	[ "peft", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "text-generation", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "model-index", "region:us" ]	text-generation	2024-05-10T18:58:33Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: Cimphony-Mistral-Law-7B results: - task: type: text-generation dataset: type: cais/mmlu name: MMLU metrics: - name: International Law type: accuracy value: 0.802 verified: false - task: type: text-generation dataset: type: cais/mmlu name: MMLU metrics: - name: Jurisprudence type: accuracy value: 0.704 verified: false - task: type: text-generation dataset: type: cais/mmlu name: MMLU metrics: - name: Professional Law type: accuracy value: 0.416 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: ECtHR A type: balanced accuracy value: 0.631 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: LEDGAR type: balanced accuracy value: 0.741 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: CaseHOLD type: accuracy value: 0.776 verified: false - task: type: text-generation dataset: type: coastalcph/lex_glue name: LexGLUE metrics: - name: Unfair-ToS type: balanced accuracy value: 0.809 verified: false pipeline_tag: text-generation --- # Cimphony-Mistral-Law-7B We introduce Cimphony-Mistral-Law-7B, a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). Cimphony’s LLMs present state-of-the-art performance on legal benchmarks, suppressing models trained on a much larger corpus with significantly more resources, even GPT-4, OpenAI’s flagship model. Checkout and register on our [https://cimphony.ai](https://app.cimphony.ai/signup?callbackUrl=https://app.cimphony.ai/) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657d36d3647c0211e7746ed9/Yjx96bC58SPgNwmDxx_yx.png) ## Model description The model was trained on 600M tokens. We use novel methods to expose the model to this corpus during training, blending a variety of legal reading comprehension tasks, as well as general language data. ## Legal Evaluation Results We evaluate on the legal splits of the MMLU benchmark, as well as LexGLUE. While both are multiple option benchmarks, prompts were adapted so that the models output a single answer. In some cases, additional post-processing was required. Benchmarks for which the labels were A-E multiple-choice options use an accuracy mertic. Benchmarks that have a closed list of options (e.g. Unfair-ToS) use a balanced-accuracy metric, as classes may not be balanced. \| Model / Benchmark \| International Law (MMLU) \| Jurisprudence (MMLU) \| Professional law (MMLU) \| ECtHR A (LexGlue) \| LEDGAR (LexGlue) \| CaseHOLD (LexGlue) \| Unfair-ToS (LexGlue) \| \|:-----------------------------------\|:--------------------------\|:----------------------\|:-------------------------\|:-------------------\|:------------------\|:--------------------\|:-----------------------\| \| Mistral-7B-Instruct-v0.2 \| 73.6% \| 69.4% \| 41.2% \| 67.5% \| 50.6% \| 56.3% \| 36.6% \| \| AdaptLLM \| 57.0% \| 52.8% \| 36.1% \| 51.9% \| 46.3% \| 50.0% \| 51.3% \| \| Saul-7B \| 69.4% \| 63.0% \| 43.2% \| 71.2% \| 55.9% \| 65.8% \| 80.3% \| \|<tr style="background-color:yellow;"><td>Cimphony-7B</td><td>80.2%</td><td>70.4%</td><td>41.6%</td><td>63.1%</td><td>74.1%</td><td>77.6%</td><td>80.9%</td></tr>\| ## Training and evaluation data Following the framework presented in [AdaptLLM](https://huggingface.co/AdaptLLM/law-chat), we convert the raw legal text into reading comprehension. Taking inspiration from human learning via reading comprehension - practice after reading improves the ability to answer questions based on the learned knowledge. We developed a high-quality prompt database, considering the capabilities we’d like the model to possess. LLMs were prompt with the raw text and a collection of prompts, and it returned answers, additional questions, and transformations relevant to the input data. With further post-processing of these outputs, we created our legal reading comprehension dataset. \| Domain \| Dataset \| Tokens \| License \| \|:-------------------\|:--------------------\|:------:\|:------------\| \| Legal \| The Pile (FreeLaw) \| 180M \| MIT \| \| Legal \| LexGlue (train split only) \| 108M \| CC-BY-4.0 \| \| Legal \| USClassActions \| 12M \| GPL-3.0 \| \| Math (CoT) \| AQUA-RAT \| 3M \| Apache-2.0 \| \| Commonsense (CoT) \| ECQA \| 2.4M \| Apache-2.0 \| \| Reasoning (CoT) \| EntailmentBank \| 1.8M \| Apache-2.0 \| \| Chat \| UltraChat \| 90M \| MIT \| \| Code \| Code-Feedback \| 36M \| Apache-2.0 \| \| Instruction \| OpenOrca \| 180M \| MIT \| ## Intended uses & limitations This model can be used for use cases involving legal domain text generation. As with any language model, users must not solely relay on model generations. This model has not gone through a human-feedback alignment (RLHF). The model may generate responses containing hallucinations and biases. Example use: ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained("cimphonyadmin/Cimphony-Mistral-Law-7B") model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(model, "cimphonyadmin/Cimphony-Mistral-Law-7B") # Put your input here: user_input = '''What can you tell me about ex post facto laws?''' # Apply the prompt template prompt = tokenizer.apply_chat_template(user_input, tokenize=False) inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 24 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
crrodrvi/First_Order_Motion	crrodrvi	2024-05-14T14:27:37Z	0	0	null	[ "arxiv:2104.11280", "region:us" ]	null	2024-05-11T21:47:57Z	<b>!!! Check out our new [paper](https://arxiv.org/pdf/2104.11280.pdf) and [framework](https://github.com/snap-research/articulated-animation) improved for articulated objects</b> # First Order Motion Model for Image Animation This repository contains the source code for the paper [First Order Motion Model for Image Animation](https://papers.nips.cc/paper/8935-first-order-motion-model-for-image-animation) by Aliaksandr Siarohin, [Stéphane Lathuilière](http://stelat.eu), [Sergey Tulyakov](http://stulyakov.com), [Elisa Ricci](http://elisaricci.eu/) and [Nicu Sebe](http://disi.unitn.it/~sebe/). [Hugging Face Spaces](https://huggingface.co/spaces/abhishek/first-order-motion-model) ## Example animations The videos on the left show the driving videos. The first row on the right for each dataset shows the source videos. The bottom row contains the animated sequences with motion transferred from the driving video and object taken from the source image. We trained a separate network for each task. ### VoxCeleb Dataset ![Screenshot](sup-mat/vox-teaser.gif) ### Fashion Dataset ![Screenshot](sup-mat/fashion-teaser.gif) ### MGIF Dataset ![Screenshot](sup-mat/mgif-teaser.gif) ### Installation We support ```python3```. To install the dependencies run: ``` pip install -r requirements.txt ``` ### YAML configs There are several configuration (```config/dataset_name.yaml```) files one for each `dataset`. See ```config/taichi-256.yaml``` to get description of each parameter. ### Pre-trained checkpoint Checkpoints can be found under following link: [google-drive](https://drive.google.com/open?id=1PyQJmkdCsAkOYwUyaj_l-l0as-iLDgeH) or [yandex-disk](https://yadi.sk/d/lEw8uRm140L_eQ). ### Animation Demo To run a demo, download checkpoint and run the following command: ``` python demo.py --config config/dataset_name.yaml --driving_video path/to/driving --source_image path/to/source --checkpoint path/to/checkpoint --relative --adapt_scale ``` The result will be stored in ```result.mp4```. The driving videos and source images should be cropped before it can be used in our method. To obtain some semi-automatic crop suggestions you can use ```python crop-video.py --inp some_youtube_video.mp4```. It will generate commands for crops using ffmpeg. In order to use the script, face-alligment library is needed: ``` git clone https://github.com/1adrianb/face-alignment cd face-alignment pip install -r requirements.txt python setup.py install ``` ### Animation demo with Docker If you are having trouble getting the demo to work because of library compatibility issues, and you're running Linux, you might try running it inside a Docker container, which would give you better control over the execution environment. Requirements: Docker 19.03+ and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) installed and able to successfully run the `nvidia-docker` usage tests. We'll first build the container. ``` docker build -t first-order-model . ``` And now that we have the container available locally, we can use it to run the demo. ``` docker run -it --rm --gpus all \ -v $HOME/first-order-model:/app first-order-model \ python3 demo.py --config config/vox-256.yaml \ --driving_video driving.mp4 \ --source_image source.png \ --checkpoint vox-cpk.pth.tar \ --result_video result.mp4 \ --relative --adapt_scale ``` ### Colab Demo [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb) @graphemecluster prepared a GUI demo for the Google Colab. It also works in Kaggle. For the source code, see [```demo.ipynb```](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb). For the old demo, see [```old_demo.ipynb```](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/old_demo.ipynb). ### Face-swap It is possible to modify the method to perform face-swap using supervised segmentation masks. ![Screenshot](sup-mat/face-swap.gif) For both unsupervised and supervised video editing, such as face-swap, please refer to [Motion Co-Segmentation](https://github.com/AliaksandrSiarohin/motion-cosegmentation). ### Training To train a model on specific dataset run: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 python run.py --config config/dataset_name.yaml --device_ids 0,1,2,3 ``` The code will create a folder in the log directory (each run will create a time-stamped new directory). Checkpoints will be saved to this folder. To check the loss values during training see ```log.txt```. You can also check training data reconstructions in the ```train-vis``` subfolder. By default the batch size is tunned to run on 2 or 4 Titan-X gpu (appart from speed it does not make much difference). You can change the batch size in the train_params in corresponding ```.yaml``` file. ### Evaluation on video reconstruction To evaluate the reconstruction performance run: ``` CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode reconstruction --checkpoint path/to/checkpoint ``` You will need to specify the path to the checkpoint, the ```reconstruction``` subfolder will be created in the checkpoint folder. The generated video will be stored to this folder, also generated videos will be stored in ```png``` subfolder in loss-less '.png' format for evaluation. Instructions for computing metrics from the paper can be found: https://github.com/AliaksandrSiarohin/pose-evaluation. ### Image animation In order to animate videos run: ``` CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode animate --checkpoint path/to/checkpoint ``` You will need to specify the path to the checkpoint, the ```animation``` subfolder will be created in the same folder as the checkpoint. You can find the generated video there and its loss-less version in the ```png``` subfolder. By default video from test set will be randomly paired, but you can specify the "source,driving" pairs in the corresponding ```.csv``` files. The path to this file should be specified in corresponding ```.yaml``` file in pairs_list setting. There are 2 different ways of performing animation: by using absolute keypoint locations or by using relative keypoint locations. 1) <i>Animation using absolute coordinates:</i> the animation is performed using the absolute postions of the driving video and appearance of the source image. In this way there are no specific requirements for the driving video and source appearance that is used. However this usually leads to poor performance since unrelevant details such as shape is transfered. Check animate parameters in ```taichi-256.yaml``` to enable this mode. <img src="sup-mat/absolute-demo.gif" width="512"> 2) <i>Animation using relative coordinates:</i> from the driving video we first estimate the relative movement of each keypoint, then we add this movement to the absolute position of keypoints in the source image. This keypoint along with source image is used for animation. This usually leads to better performance, however this requires that the object in the first frame of the video and in the source image have the same pose <img src="sup-mat/relative-demo.gif" width="512"> ### Datasets 1) Bair. This dataset can be directly [downloaded](https://yadi.sk/d/Rr-fjn-PdmmqeA). 2) Mgif. This dataset can be directly [downloaded](https://yadi.sk/d/5VdqLARizmnj3Q). 3) Fashion. Follow the instruction on dataset downloading [from](https://vision.cs.ubc.ca/datasets/fashion/). 4) Taichi. Follow the instructions in [data/taichi-loading](data/taichi-loading/README.md) or instructions from https://github.com/AliaksandrSiarohin/video-preprocessing. 5) Nemo. Please follow the [instructions](https://www.uva-nemo.org/) on how to download the dataset. Then the dataset should be preprocessed using scripts from https://github.com/AliaksandrSiarohin/video-preprocessing. 6) VoxCeleb. Please follow the instruction from https://github.com/AliaksandrSiarohin/video-preprocessing. ### Training on your own dataset 1) Resize all the videos to the same size e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images. We recommend the later, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance. 2) Create a folder ```data/dataset_name``` with 2 subfolders ```train``` and ```test```, put training videos in the ```train``` and testing in the ```test```. 3) Create a config ```config/dataset_name.yaml```, in dataset_params specify the root dir the ```root_dir: data/dataset_name```. Also adjust the number of epoch in train_params. #### Additional notes Citation: ``` @InProceedings{Siarohin_2019_NeurIPS, author={Siarohin, Aliaksandr and Lathuilière, Stéphane and Tulyakov, Sergey and Ricci, Elisa and Sebe, Nicu}, title={First Order Motion Model for Image Animation}, booktitle = {Conference on Neural Information Processing Systems (NeurIPS)}, month = {December}, year = {2019} } ```
eventdata-utd/conflibert-satp-binary-classification	eventdata-utd	2024-05-14T14:26:47Z	120	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-14T06:41:16Z	--- 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. 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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Danieljacobsen/Helsinki-DA-SV-v6	Danieljacobsen	2024-05-14T14:25:37Z	106	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T11:24:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF	NikolayKozloff	2024-05-14T14:24:43Z	2	1	transformers	[ "transformers", "gguf", "text-generation-inference", "trl", "sft", "phi-3", "phi-3-mini", "italian", "llama-cpp", "gguf-my-repo", "it", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-14T14:24:32Z	--- language: - it license: mit tags: - text-generation-inference - transformers - trl - sft - phi-3 - phi-3-mini - italian - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-mini-4k-instruct --- # NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF This model was converted to GGUF format from [`e-palmisano/Phi3-ITA-mini-4K-instruct`](https://huggingface.co/e-palmisano/Phi3-ITA-mini-4K-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/e-palmisano/Phi3-ITA-mini-4K-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF --model phi3-ita-mini-4k-instruct.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Phi3-ITA-mini-4K-instruct-Q8_0-GGUF --model phi3-ita-mini-4k-instruct.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi3-ita-mini-4k-instruct.Q8_0.gguf -n 128 ```
stablediffusionapi/realistic-vision-v6.0-b1-inpaint	stablediffusionapi	2024-05-14T14:20:11Z	965	2	diffusers	[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-04-25T12:51:24Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference ![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "realistic-vision-v6.0-b1-inpaint" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/realistic-vision-v6.0-b1-inpaint) Model link: [View model](https://modelslab.com/models/realistic-vision-v6.0-b1-inpaint) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "realistic-vision-v6.0-b1-inpaint", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
SABR22/unsloth-llama-3-8b-sql	SABR22	2024-05-14T14:19:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-14T14:19:26Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: SABR22 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ankesh1234/gemma-medical_qa-Finetune	Ankesh1234	2024-05-14T14:18:05Z	142	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:11:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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gutsartificial/hermes-2-pro-llama3-entity-mapping	gutsartificial	2024-05-14T14:17:11Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:finetune:NousResearch/Hermes-2-Pro-Llama-3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T13:47:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: NousResearch/Hermes-2-Pro-Llama-3-8B --- # Uploaded model - Developed by: gutsartificial - License: apache-2.0 - Finetuned from model : NousResearch/Hermes-2-Pro-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Huseyin/checkpoint-1000	Huseyin	2024-05-14T14:16:08Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-14T14:09:18Z	--- language: - tr license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: Whisper Medium Tr - Huseyin Ates results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 config: tr split: test args: 'config: tr, split: test' metrics: - name: Wer type: wer value: 19.615089840756195 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Medium Tr - Huseyin Ates This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2422 - Wer: 19.6151 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.1504 \| 0.1724 \| 1000 \| 0.2422 \| 19.6151 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kyl23/hw3_RTE_lora_1e-4_r4	kyl23	2024-05-14T14:15:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T14:15:46Z	--- 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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ksaito2omr/synth_doc_model	ksaito2omr	2024-05-14T14:10:50Z	49	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-14T14:05:41Z	--- 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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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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(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]
Tonnytempus/tempusdonum	Tonnytempus	2024-05-14T14:09:17Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:02:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ZcepZtar/DaToSw_V1	ZcepZtar	2024-05-14T14:06:27Z	114	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T14:06:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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EthanRhys/Dr-Crygor-Current	EthanRhys	2024-05-14T14:05:51Z	0	0	null	[ "license:openrail++", "region:us" ]	null	2024-05-14T14:05:04Z	--- license: openrail++ ---

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