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Raneechu/textbookbig10_ft2	Raneechu	2024-05-26T02:17:38Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T02:17:35Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft2 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. --> # textbookbig10_ft2 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
kid1802/huggy_test	kid1802	2024-05-26T02:14:07Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-26T02:14:02Z	--- 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: kid1802/huggy_test 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
gokaygokay/imageinwords-paligemma-transformers	gokaygokay	2024-05-26T02:08:01Z	6	0	transformers	[ "transformers", "safetensors", "paligemma", "image-text-to-text", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-26T02:02:19Z	--- license: apache-2.0 --- ``` pip install git+https://github.com/huggingface/transformers ``` ``` from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from PIL import Image import requests import torch model_id = "gokaygokay/imageinwords-paligemma-transformers" url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval() processor = AutoProcessor.from_pretrained(model_id) ## prefix prompt = "caption en" model_inputs = processor(text=prompt, images=image, return_tensors="pt") input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=512, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ```
empathie/Qwen1.5-0.5B-Chat-experiment-2	empathie	2024-05-26T02:07:47Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T03:04:36Z	--- 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. 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]
mrovejaxd/ABL_trad_j	mrovejaxd	2024-05-26T02:03:25Z	31	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T00:42:17Z	--- base_model: dccuchile/bert-base-spanish-wwm-cased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: ABL_trad_j 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. --> # ABL_trad_j This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6432 - Accuracy: 0.6883 - F1: 0.6865 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.9532 \| 1.0 \| 1500 \| 0.9116 \| 0.5825 \| 0.5793 \| \| 0.8601 \| 2.0 \| 3000 \| 0.8433 \| 0.6033 \| 0.6016 \| \| 0.7962 \| 3.0 \| 4500 \| 0.8150 \| 0.6275 \| 0.6252 \| \| 0.7633 \| 4.0 \| 6000 \| 0.7969 \| 0.635 \| 0.6334 \| \| 0.7153 \| 5.0 \| 7500 \| 0.7825 \| 0.6492 \| 0.6483 \| \| 0.678 \| 6.0 \| 9000 \| 0.7910 \| 0.6408 \| 0.6392 \| \| 0.6336 \| 7.0 \| 10500 \| 0.7772 \| 0.6608 \| 0.6606 \| \| 0.5981 \| 8.0 \| 12000 \| 0.7863 \| 0.6617 \| 0.6605 \| \| 0.5455 \| 9.0 \| 13500 \| 0.7954 \| 0.6658 \| 0.6657 \| \| 0.4972 \| 10.0 \| 15000 \| 0.8206 \| 0.6633 \| 0.6623 \| \| 0.4823 \| 11.0 \| 16500 \| 0.8442 \| 0.6683 \| 0.6673 \| \| 0.4258 \| 12.0 \| 18000 \| 0.8966 \| 0.6742 \| 0.6734 \| \| 0.4182 \| 13.0 \| 19500 \| 0.9327 \| 0.6767 \| 0.6761 \| \| 0.3588 \| 14.0 \| 21000 \| 0.9780 \| 0.6717 \| 0.6689 \| \| 0.3576 \| 15.0 \| 22500 \| 1.0288 \| 0.6833 \| 0.6828 \| \| 0.3252 \| 16.0 \| 24000 \| 1.0873 \| 0.6842 \| 0.6836 \| \| 0.3104 \| 17.0 \| 25500 \| 1.1417 \| 0.685 \| 0.6847 \| \| 0.2691 \| 18.0 \| 27000 \| 1.2447 \| 0.6842 \| 0.6827 \| \| 0.2559 \| 19.0 \| 28500 \| 1.3480 \| 0.6825 \| 0.6816 \| \| 0.2522 \| 20.0 \| 30000 \| 1.4782 \| 0.6867 \| 0.6859 \| \| 0.2234 \| 21.0 \| 31500 \| 1.5748 \| 0.6833 \| 0.6815 \| \| 0.1954 \| 22.0 \| 33000 \| 1.7041 \| 0.69 \| 0.6897 \| \| 0.1979 \| 23.0 \| 34500 \| 1.8398 \| 0.6808 \| 0.6789 \| \| 0.176 \| 24.0 \| 36000 \| 1.9141 \| 0.6867 \| 0.6860 \| \| 0.1862 \| 25.0 \| 37500 \| 2.0105 \| 0.6883 \| 0.6881 \| \| 0.1409 \| 26.0 \| 39000 \| 2.1345 \| 0.685 \| 0.6840 \| \| 0.1527 \| 27.0 \| 40500 \| 2.2039 \| 0.6858 \| 0.6853 \| \| 0.1474 \| 28.0 \| 42000 \| 2.2990 \| 0.6933 \| 0.6920 \| \| 0.1428 \| 29.0 \| 43500 \| 2.3780 \| 0.6883 \| 0.6878 \| \| 0.1348 \| 30.0 \| 45000 \| 2.4859 \| 0.6858 \| 0.6839 \| \| 0.1046 \| 31.0 \| 46500 \| 2.5546 \| 0.6825 \| 0.6801 \| \| 0.1147 \| 32.0 \| 48000 \| 2.6432 \| 0.6883 \| 0.6865 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
ayoubcim/tt1-falcon-7b	ayoubcim	2024-05-26T02:02:30Z	155	0	transformers	[ "transformers", "safetensors", "falcon", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-26T01:35:33Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
antitheft159/skyelahAndriy.195	antitheft159	2024-05-26T02:01:59Z	0	0	null	[ "license:cc-by-sa-4.0", "region:us" ]	null	2024-05-26T02:01:46Z	--- license: cc-by-sa-4.0 ---
GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v4-0.5_5e-7_ep-10	GENIAC-Team-Ozaki	2024-05-26T01:50:24Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T01:38:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dfurman/Mistral-7B-Instruct-v0.3-mlx-4bit	dfurman	2024-05-26T01:41:55Z	9	0	mlx	[ "mlx", "safetensors", "mistral", "license:apache-2.0", "region:us" ]	null	2024-05-26T01:39:22Z	--- license: apache-2.0 tags: - mlx --- # dfurman/Mistral-7B-Instruct-v0.3-mlx-4bit The Model [dfurman/Mistral-7B-Instruct-v0.3-mlx-4bit](https://huggingface.co/dfurman/Mistral-7B-Instruct-v0.3-mlx-4bit) was converted to MLX format from [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) using mlx-lm version 0.14.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("dfurman/Mistral-7B-Instruct-v0.3-mlx-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
CMU-AIR2/math-phi-1-5-FULL-Arithmetic-Curriculum-Subjects-8-to-10	CMU-AIR2	2024-05-26T01:31:57Z	6	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T00:20:10Z	--- 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]
mitchyAI/iveliz	mitchyAI	2024-05-26T01:30:26Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-26T01:29:47Z	--- license: creativeml-openrail-m ---
samwit/paligemma_vqav2	samwit	2024-05-26T01:30:25Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:vq_av2", "base_model:google/paligemma-3b-pt-224", "base_model:adapter:google/paligemma-3b-pt-224", "license:gemma", "region:us" ]	null	2024-05-26T01:09:51Z	--- license: gemma library_name: peft tags: - generated_from_trainer base_model: google/paligemma-3b-pt-224 datasets: - vq_av2 model-index: - name: paligemma_vqav2 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. --> # paligemma_vqav2 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the vq_av2 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - 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: 2 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
kataragi/controlnetXL_inpaint	kataragi	2024-05-26T01:19:37Z	0	29	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-19T13:45:19Z	--- license: creativeml-openrail-m --- </p> # controlnet_inpaintXL - これはstable DiffusionのSDXLにおいて画像の一部分を変更することのできるコントロールネットです。inpaintプリプロセッサで使用することができます。 # 使い方コントロールネットに変更したい部分あるの画像をセットします。プリプロセッサはinpaintに設定してください。プリプロセッサはinpaint onlyやinpaint+lumaを使ってください。 fp16バージョンの推奨モデルはanimagineXL3.1です。pony系列ではあまりうまく動作しません。またLoraタイプ(400MB)の方はanimagineXL3.1専用です。 - ![](test1.png) 参考設定例 - ![](test2.png)
JianKim3293/llama3_lora_blossmodel	JianKim3293	2024-05-26T01:19:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B", "base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T01:18:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B --- # Uploaded model - Developed by: JianKim3293 - License: apache-2.0 - Finetuned from model : MLP-KTLim/llama-3-Korean-Bllossom-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)
atgarcia/wav2vec2part6	atgarcia	2024-05-26T01:15:47Z	108	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-25T23:57:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
asussome/xwin-finetuned-alpaca-cleaned	asussome	2024-05-26T01:11:31Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-04-18T19:18:46Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: xwin-finetuned-alpaca-cleaned 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. --> # xwin-finetuned-alpaca-cleaned This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 20 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Ichsan2895/Merak-7B-v4_4bit_q128_awq	Ichsan2895	2024-05-26T01:10:16Z	80	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "id", "en", "dataset:wikipedia", "dataset:Ichsan2895/OASST_Top1_Indonesian", "dataset:Ichsan2895/alpaca-gpt4-indonesian", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-25T18:37:33Z	--- datasets: - wikipedia - Ichsan2895/OASST_Top1_Indonesian - Ichsan2895/alpaca-gpt4-indonesian language: - id - en pipeline_tag: text-generation license: cc-by-nc-sa-4.0 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://huggingface.co/Ichsan2895/Merak-7B-v4/resolve/main/FINAL_LOGO/6.png" alt="MERAK" style="width: 50%; min-width: 100px; display: block; margin: auto;"> </div> # HAPPY TO ANNOUNCE THE RELEASE OF MERAK-7B-V4_4bit_q128_awq! Merak-7B is the Large Language Model of Indonesian Language This model is based on [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and fine tuned by some of Indonesia Wikipedia articles that I cleaned before. Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike. Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models. Feel free, to ask me about the model and please share the news on your social media.
0xjones/archillect-test	0xjones	2024-05-26T01:08:14Z	29	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-26T01:04:28Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### archillect-test Dreambooth model trained by 0xjones with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
gaalcoro/Logomarca	gaalcoro	2024-05-26T00:57:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T00:57:47Z	--- license: apache-2.0 ---
DJPillu/ppo-LunarLander-v2	DJPillu	2024-05-26T00:53:11Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-26T00:52:51Z	--- 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: 239.36 +/- 16.14 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF	NikolayKozloff	2024-05-26T00:45:03Z	5	2	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T00:44:50Z	--- license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF This model was converted to GGUF format from [`fearlessdots/WizardLM-2-7B-abliterated`](https://huggingface.co/fearlessdots/WizardLM-2-7B-abliterated) 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/fearlessdots/WizardLM-2-7B-abliterated) 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/WizardLM-2-7B-abliterated-Q5_0-GGUF --model wizardlm-2-7b-abliterated-q5_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF --model wizardlm-2-7b-abliterated-q5_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 wizardlm-2-7b-abliterated-q5_0.gguf -n 128 ```
antitheft159/blinkrgb.159	antitheft159	2024-05-26T00:40:56Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-05-26T00:40:35Z	--- license: cc-by-nc-sa-4.0 ---
Mantis-VL/videollava-7b-video-eval-50k_2048	Mantis-VL	2024-05-26T00:37:11Z	8	0	transformers	[ "transformers", "safetensors", "video_llava", "pretraining", "generated_from_trainer", "base_model:LanguageBind/Video-LLaVA-7B-hf", "base_model:finetune:LanguageBind/Video-LLaVA-7B-hf", "endpoints_compatible", "region:us" ]	null	2024-05-24T14:07:00Z	--- base_model: LanguageBind/Video-LLaVA-7B-hf tags: - generated_from_trainer model-index: - name: videollava-7b-video-eval-50k_2048 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. --> # videollava-7b-video-eval-50k_2048 This model is a fine-tuned version of [LanguageBind/Video-LLaVA-7B-hf](https://huggingface.co/LanguageBind/Video-LLaVA-7B-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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
minhz2003/test	minhz2003	2024-05-26T00:21:29Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-26T00:20:20Z	--- license: apache-2.0 ---
legraphista/aya-23-8B-IMat-GGUF	legraphista	2024-05-26T00:17:38Z	165	0	gguf	[ "gguf", "quantized", "GGUF", "imatrix", "quantization", "imat", "static", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "base_model:CohereForAI/aya-23-8B", "base_model:quantized:CohereForAI/aya-23-8B", "license:cc-by-nc-4.0", "region:us", "conversational" ]	text-generation	2024-05-25T20:21:19Z	--- base_model: CohereForAI/aya-23-8B inference: false language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi library_name: gguf license: cc-by-nc-4.0 pipeline_tag: text-generation quantized_by: legraphista tags: - quantized - GGUF - imatrix - quantization - imat - static --- # aya-23-8B-IMat-GGUF _Llama.cpp imatrix quantization of CohereForAI/aya-23-8B_ Original Model: [CohereForAI/aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B) Original dtype: `FP16` (`float16`) Quantized by: llama.cpp [b2998](https://github.com/ggerganov/llama.cpp/releases/tag/b2998) IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw) ## Files ### IMatrix Status: ✅ Available Link: [here](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/imatrix.dat) ### Common Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [aya-23-8B.Q8_0.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q8_0.gguf) \| Q8_0 \| 8.54GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q6_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q6_K.gguf) \| Q6_K \| 6.60GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q4_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q4_K.gguf) \| Q4_K \| 5.06GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K.gguf) \| Q3_K \| 4.22GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q2_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q2_K.gguf) \| Q2_K \| 3.44GB \| ✅ Available \| 🟢 Yes \| 📦 No ### All Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [aya-23-8B.FP16.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.FP16.gguf) \| F16 \| 16.07GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q5_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q5_K.gguf) \| Q5_K \| 5.80GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q5_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.67GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q4_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.83GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K_L.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.53GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.87GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q2_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q2_K_S.gguf) \| Q2_K_S \| 3.25GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ4_NL.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ4_NL.gguf) \| IQ4_NL \| 4.81GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ4_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.60GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_M.gguf) \| IQ3_M \| 3.99GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_S.gguf) \| IQ3_S \| 3.89GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.72GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_XXS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_XXS.gguf) \| IQ3_XXS \| 3.41GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_M.gguf) \| IQ2_M \| 3.08GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_S.gguf) \| IQ2_S \| 2.90GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_XS.gguf) \| IQ2_XS \| 2.80GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_XXS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_XXS.gguf) \| IQ2_XXS \| 2.59GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ1_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ1_M.gguf) \| IQ1_M \| 2.35GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ1_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ1_S.gguf) \| IQ1_S \| 2.21GB \| ✅ Available \| 🟢 Yes \| 📦 No ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download legraphista/aya-23-8B-IMat-GGUF --include "aya-23-8B.Q8_0.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download legraphista/aya-23-8B-IMat-GGUF --include "aya-23-8B.Q8_0/*" --local-dir aya-23-8B.Q8_0 # see FAQ for merging GGUF's ``` ## FAQ ### Why is the IMatrix not applied everywhere? According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). ### How do I merge a split GGUF? 1. Make sure you have `gguf-split` available - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases - Download the appropriate zip for your system from the latest release - Unzip the archive and you should be able to find `gguf-split` 2. Locate your GGUF chunks folder (ex: `aya-23-8B.Q8_0`) 3. Run `gguf-split --merge aya-23-8B.Q8_0/aya-23-8B.Q8_0-00001-of-XXXXX.gguf aya-23-8B.Q8_0.gguf` - Make sure to point `gguf-split` to the first chunk of the split. --- Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
Enpas/whisper-base-co	Enpas	2024-05-26T00:12:44Z	79	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-23T21:48:26Z	``` import torch from transformers import pipeline device = "cuda:0" if torch.cuda.is_available() else "cpu" transcribe = pipeline(task="automatic-speech-recognition", model="Enpas/whisper-small-co", chunk_length_s=30, device=device) transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="am", task="transcribe") audio = "/content/tr_10000_tr097082.wav" result = transcribe(audio) print('Transcription: ', result["text"]) ```
raulgdp/roberta-multiclase-ag_news	raulgdp	2024-05-26T00:08:49Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T21:35:34Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: roberta-multiclase-ag_news 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. --> # roberta-multiclase-ag_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2671 - Rmse: 1.1967 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rmse \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.3199 \| 1.0 \| 15000 \| 1.2671 \| 1.1967 \| \| 1.3837 \| 2.0 \| 30000 \| 1.3864 \| 1.2230 \| \| 1.3879 \| 3.0 \| 45000 \| 1.3865 \| 1.8686 \| \| 1.385 \| 4.0 \| 60000 \| 1.3864 \| 1.2247 \| \| 1.3885 \| 5.0 \| 75000 \| 1.3863 \| 1.8720 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
umair894/llama3_1e	umair894	2024-05-25T23:58:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:58:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: umair894 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf	RichardErkhov	2024-05-25T23:57:51Z	23	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T21:05:28Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3-11b-Instruct - GGUF - Model creator: https://huggingface.co/athirdpath/ - Original model: https://huggingface.co/athirdpath/Llama-3-11b-Instruct/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Llama-3-11b-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q2_K.gguf) \| Q2_K \| 4.01GB \| \| [Llama-3-11b-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ3_XS.gguf) \| IQ3_XS \| 4.44GB \| \| [Llama-3-11b-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ3_S.gguf) \| IQ3_S \| 4.66GB \| \| [Llama-3-11b-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 4.64GB \| \| [Llama-3-11b-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ3_M.gguf) \| IQ3_M \| 4.79GB \| \| [Llama-3-11b-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K.gguf) \| Q3_K \| 5.1GB \| \| [Llama-3-11b-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 5.1GB \| \| [Llama-3-11b-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 5.52GB \| \| [Llama-3-11b-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 5.7GB \| \| [Llama-3-11b-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_0.gguf) \| Q4_0 \| 5.94GB \| \| [Llama-3-11b-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ4_NL.gguf) \| IQ4_NL \| 6.0GB \| \| [Llama-3-11b-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 5.98GB \| \| [Llama-3-11b-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_K.gguf) \| Q4_K \| 6.27GB \| \| [Llama-3-11b-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 6.27GB \| \| [Llama-3-11b-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_1.gguf) \| Q4_1 \| 6.56GB \| \| [Llama-3-11b-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_0.gguf) \| Q5_0 \| 7.17GB \| \| [Llama-3-11b-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 7.17GB \| \| [Llama-3-11b-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_K.gguf) \| Q5_K \| 7.34GB \| \| [Llama-3-11b-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 7.34GB \| \| [Llama-3-11b-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_1.gguf) \| Q5_1 \| 7.78GB \| \| [Llama-3-11b-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q6_K.gguf) \| Q6_K \| 8.48GB \| \| [Llama-3-11b-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q8_0.gguf) \| Q8_0 \| 10.98GB \| Original model description: --- license: llama3 --- I'm back and doing well! I've got a job in the field now, so we'll see in the long run how that effects my open source output. Here we have a 11b Llama 3 instruct model for future work. EDIT: Made a yaml mistake with part funnel, but it still works well. --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633a809fa4a8f33508dce32c/jJxgpSwdSal2XWsJ0KlG8.png) This is a merge stock of 3 models: - Part Wave - Part Block - Part Funnel With Part Funnel as the base. --- Part Wave: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0, 12] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [8, 18] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [13, 23] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [18, 32] --- Part Block: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0, 15] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [8, 23] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [16, 32] --- Part Funnel: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0, 15] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [14, 14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [13, 13] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [12, 12] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [11, 11] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10, 10] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [9, 9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [8, 23] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [22, 22] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [21, 21] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20, 20] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [19, 19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [18, 18] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [17, 17] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [16, 32]
fearlessdots/Llama-3-Alpha-Centauri-v0.1	fearlessdots	2024-05-25T23:47:33Z	115	9	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:NobodyExistsOnTheInternet/ToxicQAFinal", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T18:00:36Z	--- license: llama3 datasets: - NobodyExistsOnTheInternet/ToxicQAFinal --- # Llama-3-Alpha-Centauri-v0.1 <img src="alpha_centauri_banner.png" alt="" style="width:500px;height:400px;"/> Image generated with [https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS). --- ## Disclaimer Note: All models and LoRAs from the Centaurus series were created with the sole purpose of research. The usage of this model and/or its related LoRA implies agreement with the following terms: - The user is responsible for what they might do with it, including how the output of the model is interpreted and used; - The user should not use the model and its outputs for any illegal purposes; - The user is the only one resposible for any misuse or negative consequences from using this model and/or its related LoRA. I do not endorse any particular perspectives presented in the training data. --- ## Centaurus Series This series aims to develop highly uncensored Large Language Models (LLMs) with the following focuses: - Science, Technology, Engineering, and Mathematics (STEM) - Computer Science (including programming) - Social Sciences And several key cognitive skills, including but not limited to: - Reasoning and logical deduction - Critical thinking - Analysis While maintaining strong overall knowledge and expertise, the models will undergo refinement through: - Fine-tuning processes - Model merging techniques including Mixture of Experts (MoE) Please note that these models are experimental and may demonstrate varied levels of effectiveness. Your feedback, critique, or queries are most welcome for improvement purposes. ## Base This model and its related LoRA was fine-tuned on [https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3). ## LoRA The LoRA merged with the base model is available at [https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-LoRA](https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-LoRA). ## GGUF I provide some GGUF files here: [https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-GGUF](https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-GGUF). ## Datasets - [https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## Fine Tuning ### - Quantization Configuration - load_in_4bit=True - bnb_4bit_quant_type="fp4" - bnb_4bit_compute_dtype=compute_dtype - bnb_4bit_use_double_quant=False ### - PEFT Parameters - lora_alpha=64 - lora_dropout=0.05 - r=128 - bias="none" ### - Training Arguments - num_train_epochs=1 - per_device_train_batch_size=1 - gradient_accumulation_steps=4 - optim="adamw_bnb_8bit" - save_steps=25 - logging_steps=25 - learning_rate=2e-4 - weight_decay=0.001 - fp16=False - bf16=False - max_grad_norm=0.3 - max_steps=-1 - warmup_ratio=0.03 - group_by_length=True - lr_scheduler_type="constant" ## Credits - Meta ([https://huggingface.co/meta-llama](https://huggingface.co/meta-llama)): for the original Llama-3; - HuggingFace: for hosting this model and for creating the fine-tuning tools used; - failspy ([https://huggingface.co/failspy](https://huggingface.co/failspy)): for the base model and the orthogonalization implementation; - NobodyExistsOnTheInternet ([https://huggingface.co/NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet)): for the incredible dataset; - Undi95 ([https://huggingface.co/Undi95](https://huggingface.co/Undi95)) and Sao10k ([https://huggingface.co/Sao10K](https://huggingface.co/Sao10K)): my main inspirations for doing these models =] A huge thank you to all of them ☺️ ## About Alpha Centauri Alpha Centauri is a triple star system located in the constellation of Centaurus. It includes three stars: Rigil Kentaurus (also known as α Centauri A), Toliman (or α Centauri B), and Proxima Centauri (α Centauri C). Proxima Centauri is the nearest star to the Sun, residing at approximately 4.25 light-years (1.3 parsecs) away. The primary pair, α Centauri A and B, are both similar to our Sun - α Centauri A being a class G star with 1.1 solar masses and 1.5 times the Sun's luminosity; α Centauri B having 0.9 solar masses and under half the luminosity of the Sun. They revolve around their shared center every 79 years following an elliptical path, ranging from 35.6 astronomical units apart (nearly Pluto's distance from the Sun) to 11.2 astronomical units apart (around Saturn's distance from the Sun.) Proxima Centauri, or α Centauri C, is a diminutive, dim red dwarf (a class M star) initially unseen to the naked eye. At roughly 4.24 light-years (1.3 parsecs) from us, it lies nearer than α Centauri AB, the binary system. Presently, the gap between Proxima Centauri and α Centauri AB amounts to around 13,000 Astronomical Units (0.21 light-years)—comparable to over 430 times Neptune's orbital radius. Two confirmed exoplanets accompany Proxima Centauri: Proxima b, discovered in 2016, is Earth-sized within the habitable zone; Proxima d, revealed in 2022, is a potential sub-Earth close to its host star. Meanwhile, disputes surround Proxima c, a mini-Neptune detected in 2019. Intriguingly, hints suggest that α Centauri A might possess a Neptune-sized object in its habitable region, but further investigation is required before confirming whether it truly exists and qualifies as a planet. Regarding α Centauri B, although once thought to harbor a planet (named α Cen Bb), subsequent research invalidated this claim, leaving it currently devoid of identified planets. Source: retrived from [https://en.wikipedia.org/wiki/Alpha_Centauri](https://en.wikipedia.org/wiki/Alpha_Centauri) and processed with [https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
Sorour/phi3-ft-fomc-v2	Sorour	2024-05-25T23:45:29Z	155	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:33:06Z	--- 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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QuangDuy/whisper-large-v3-vivos	QuangDuy	2024-05-25T23:40:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:40:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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thdangtr/blip_recipe1m_title_v6	thdangtr	2024-05-25T23:35:49Z	67	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-25T23:34:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JawadC/neufchatel	JawadC	2024-05-25T23:33:52Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T23:04:54Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of Neufchatel cheese widget: - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_0.png - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_1.png - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_2.png - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/neufchatel <Gallery /> ## Model description These are JawadC/neufchatel LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of Neufchatel cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/neufchatel/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
JawadC/chevre	JawadC	2024-05-25T23:29:05Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T23:00:51Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of chèvre cheese widget: - text: A close-up shot of chèvre cheese on a rustic wooden board, with warm natural light. output: url: image_0.png - text: A close-up shot of chèvre cheese on a rustic wooden board, with warm natural light. output: url: image_1.png - text: A close-up shot of chèvre cheese on a rustic wooden board, with warm natural light. output: url: image_2.png - text: A close-up shot of chèvre cheese on a rustic wooden board, with warm natural light. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/chevre <Gallery /> ## Model description These are JawadC/chevre LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of chèvre cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/chevre/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ahmedgongi/Llama_dev3model_finale10	ahmedgongi	2024-05-25T23:26:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:26:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedgongi/Llama_dev3tokenizer_finale10	ahmedgongi	2024-05-25T23:26:09Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:26:08Z	--- 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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CMU-AIR2/math-phi-1-5-FULL-Arithmetic-Curriculum-Subjects-1-to-5	CMU-AIR2	2024-05-25T23:23:21Z	8	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T16:45:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Apel-sin/llama-3-8B-iterative-DPO-final-exl2	Apel-sin	2024-05-25T23:19:50Z	4	1	null	[ "arxiv:2405.07863", "arxiv:2312.11456", "license:llama3", "region:us" ]	null	2024-05-24T12:41:16Z	--- license: llama3 --- # Exllama v2 RLHFlow/LLaMA3-iterative-DPO-final Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: <a href="https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final">RLHFlow/LLaMA3-iterative-DPO-final</a><br> Calibration dataset: <a href="https://huggingface.co/datasets/cosmicvalor/toxic-qna">toxic-qna</a> ## Prompt format ``` <\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|> <\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|> <\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (8K) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/Apel-sin/llama-3-8B-iterative-DPO-final-exl2/tree/8_0) \| 8.0 \| 8.0 \| 10.1 GB \| 10.5 GB \| 11.5 GB \| 13.6 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/Apel-sin/llama-3-8B-iterative-DPO-final-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.9 GB \| 9.3 GB \| 10.3 GB \| 12.4 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/Apel-sin/llama-3-8B-iterative-DPO-final-exl2/tree/5_0) \| 5.0 \| 8.0 \| 7.7 GB \| 8.1 GB \| 9.1 GB \| 11.2 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| # LLaMA3-iterative-DPO-final ## Introduction We release an unofficial checkpoint of a state-of-the-art instruct model of its class, LLaMA3-iterative-DPO-final. On all three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, Chat-Arena-Hard, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it), and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling. Even better, we provide a [detailed recipe](https://github.com/RLHFlow/Online-RLHF) to reproduce the model. Enjoy! ## Model Releases See the [collection](https://huggingface.co/collections/RLHFlow/online-rlhf-663ae95fade1a39663dab218) of the training set, reward/preference model, SFT model. - [SFT model](https://huggingface.co/RLHFlow/LLaMA3-SFT) - [Reward model](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1) ## Dataset - [Preference data mix](https://huggingface.co/datasets/hendrydong/preference_700K) - [Prompt collection for RLHF training](https://huggingface.co/datasets/RLHFlow/prompt-collection-v0.1) ## Training methods We have developed a simple and efficient online RLHF recipe for LLM instruct training. Our recipe is DPO-based and thus much cheaper and simpler to train and tune compared to PPO-based approaches. Unlike widely-used offline DPO, the online component of our approach effectively mitigates distribution shifts during policy optimization. For a detailed exposition, please refer to our accompanying technical report. ## Chat Benchmarks \| Model \| Size \| Method \| LC Alpaca-Eval-V2 \| MT-Bench \| Chat-Arena-Hard \| \|-------------------------\|----------\|-------------------\|-----------------------\|--------------\|---------------------\| \| Small Open-Sourced Models \| \| \| \| \| \| \| Gemma-7B-it \| 7B \| SFT \| 10.4 \| 6.38 \| 7.5 \| \| Zephyr-7B-beta \| 7B \| Vanilla DPO \| 13.1 \| 7.34 \| - \| \| Mistral-7B-v0.2-it \| 7B \| SFT \| 17.1 \| 7.51 \| 12.6 \| \| Open-Chat-0106 \| 7B \| SFT \| 15.6 \| 7.8 \| - \| \| Starling-7B-beta \| 7B \| PPO \| 25.8 \| 8.12 \| 23.0 \| \| LLaMA-3-8B-it \| 8B \| RS+DPO+PPO \| 22.9 \| 8.16 \| 20.6 \| \| Ours \| \| \| \| \| \| \| Ours (SFT baseline) \| 8B \| SFT \| 10.2 \| 7.69 \| 5.6 \| \| Ours (DPO baseline) \| 8B \| Vanilla DPO \| 22.5 \| 8.17 \| 22.4 \| \| Ours (Online RLHF) \| 8B \| Iterative DPO \| 37.2 \| 8.46 \| 29.1 \| \| Large Open-Sourced Models \| \| \| \| \| \| \| Vicuna-33b-v1.3 \| 33B \| SFT \| 17.6 \| 7.12 \| 8.6 \| \| Yi-34B-Chat \| 34B \| SFT \| 27.2 \| - \| 23.1 \| \| Mixtral-8x7B-it \| 45B* \| SFT \| 23.7 \| 8.30 \| 23.4 \| \| Tulu-2-DPO-70B \| 70B \| Vanilla DPO \| 21.2 \| 7.89 \| 15.0 \| \| LLaMA-3-70B-it \| 70B \| RS+DPO+PPO \| 34.4 \| 8.95 \| 41.1 \| \| Mixtral-8x22B-it \| 141B* \| SFT \| 30.9 \| 8.66 \| 36.4 \| \| Proprietary Models \| \| \| \| \| \| \| GPT-3.5-turbo-1106 \| - \| - \| 19.3 \| 8.35 \| 18.9 \| \| GPT-3.5-turbo-0613 \| - \| - \| 22.7 \| 8.39 \| 24.8 \| \| GPT-4-0613 \| - \| - \| 30.2 \| 9.18 \| 37.9 \| \| Claude-3-Opus \| - \| - \| 40.5 \| 9.00 \| 60.4 \| \| GPT-4 Turbo (04/09) \| - \| - \| 55.0 \| - \| 82.6 \| ## Academic Benchmarks \| Model \| Size \| Method \| GSM-8K \| MMLU \| HumanEval \| TruthfulQA \| ARC \| MBPP \| \|----------------------------\|----------\|-----------------\|------------\|----------\|---------------\|----------------\|---------\|----------\| \| LLaMA-3-8B-it \| 8B \| RS+DPO+PPO \| 79.6 \| 66.0 \| 61.6 \| 43.9 \| 59.5 \| 61.1 \| \| Ours (SFT baseline) \| 8B \| SFT \| 74.2 \| 64.7 \| 65.2 \| 53.4 \| 61.4 \| 62.3 \| \| Ours (DPO baseline) \| 8B \| Vanilla DPO \| 79.8 \| 64.5 \| 63.4 \| 61.8 \| 65.2 \| 60.3 \| \| Ours (Iterative RLHF) \| 8B \| Iterative DPO \| 80.7 \| 65.3 \| 64.6 \| 60.4 \| 64.3 \| 60.8 \| ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final") tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final") messages = [ {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"}, ] model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = model_inputs.to(device) model.to(device) output_tokens = model.generate(model_inputs, max_new_tokens=1024, do_sample=True) model_outputs = tokenizer.batch_decode(output_tokens) print(model_outputs[0]) ``` ## Limitations RLHFlow/LLaMA3-iterative-DPO-final is an unofficial checkpoint developed to illustrate the power of online iterative RLHF and is for research purpose. While safety and ethical considerations are integral to our alignment process, there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions. We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage. ## Citation Please cite our techical report if you find our model is useful for your research or product. ``` @misc{dong2024rlhf, title={RLHF Workflow: From Reward Modeling to Online RLHF}, author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } @misc{xiong2024iterative, title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint}, author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang}, year={2024}, eprint={2312.11456}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
ayoubcim/midjourney-falcon-7b	ayoubcim	2024-05-25T23:15:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:14:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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datek/gemma-2b-flock-1716678510	datek	2024-05-25T23:10:49Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:08:30Z	--- 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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szwuwen/mistral-7b-v3	szwuwen	2024-05-25T23:09:31Z	8	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T05:45:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: szwuwen - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
student-47/wav2vec2-large-xlrs-korean-v5	student-47	2024-05-25T23:06:27Z	116	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-25T07:41:23Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: facebook/wav2vec2-xls-r-300m datasets: - zeroth_korean metrics: - wer model-index: - name: wav2vec2-large-xlrs-korean-v5 results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: zeroth_korean type: zeroth_korean config: clean split: None args: clean metrics: - type: wer value: 0.2433368468604126 name: Wer --- <!-- 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. --> # wav2vec2-large-xlrs-korean-v5 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the zeroth_korean dataset. It achieves the following results on the evaluation set: - Loss: 0.1300 - Wer: 0.2433 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\| \| 5.1453 \| 1.4368 \| 500 \| 3.1530 \| 1.0 \| \| 2.4287 \| 2.8736 \| 1000 \| 0.6084 \| 0.8317 \| \| 0.5556 \| 4.3103 \| 1500 \| 0.3414 \| 0.6165 \| \| 0.3929 \| 5.7471 \| 2000 \| 0.2729 \| 0.5386 \| \| 0.3211 \| 7.1839 \| 2500 \| 0.2294 \| 0.4794 \| \| 0.281 \| 8.6207 \| 3000 \| 0.2052 \| 0.4298 \| \| 0.2483 \| 10.0575 \| 3500 \| 0.1911 \| 0.4061 \| \| 0.2243 \| 11.4943 \| 4000 \| 0.1685 \| 0.3873 \| \| 0.2023 \| 12.9310 \| 4500 \| 0.1627 \| 0.3524 \| \| 0.188 \| 14.3678 \| 5000 \| 0.1572 \| 0.3272 \| \| 0.1784 \| 15.8046 \| 5500 \| 0.1495 \| 0.3131 \| \| 0.1677 \| 17.2414 \| 6000 \| 0.1424 \| 0.2881 \| \| 0.1533 \| 18.6782 \| 6500 \| 0.1418 \| 0.2709 \| \| 0.1501 \| 20.1149 \| 7000 \| 0.1387 \| 0.2822 \| \| 0.1402 \| 21.5517 \| 7500 \| 0.1401 \| 0.2697 \| \| 0.1353 \| 22.9885 \| 8000 \| 0.1367 \| 0.2643 \| \| 0.133 \| 24.4253 \| 8500 \| 0.1337 \| 0.2578 \| \| 0.1254 \| 25.8621 \| 9000 \| 0.1355 \| 0.2560 \| \| 0.1262 \| 27.2989 \| 9500 \| 0.1339 \| 0.2474 \| \| 0.121 \| 28.7356 \| 10000 \| 0.1300 \| 0.2433 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
diwanshus/codequalbert	diwanshus	2024-05-25T23:04:54Z	163	1	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "arxiv:1910.09700", "doi:10.57967/hf/2308", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-13T04:42:05Z	--- library_name: transformers license: apache-2.0 language: - en --- # Model Card for Model ID CodeQualBert model is able to assess the quality of a given Python code. It can label the provided code into three quality tiers - low, average and high. ## Model Details CodeQualBert is a fine-tuned CodeBert Model trained on CodeQual dataset. ### Model Description <!-- Provide a longer summary of what this model is. --> The model card of CodeQualBert model is shown below - - Developed by: Diwanshu Shekhar and Dr. Mohammad Mahoor - Finetuned from model [optional]: CodeBert - Language(s) (NLP): English - License: Apache 2.0 ### 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. --> The model is intended to be used for Code Quality Assessent Task. ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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JawadC/brie_de_melun	JawadC	2024-05-25T23:02:01Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-24T12:18:14Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of Brie de Melun cheese widget: - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_0.png - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_1.png - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_2.png - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/brie_de_melun <Gallery /> ## Model description These are JawadC/brie_de_melun LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of Brie de Melun cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/brie_de_melun/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
JawadC/ossau-iraty	JawadC	2024-05-25T22:59:11Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T22:32:28Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of OSSAU-IRATY cheese widget: - text: A piece of OSSAU-IRATY cheese on a rustic wooden table. output: url: image_0.png - text: A piece of OSSAU-IRATY cheese on a rustic wooden table. output: url: image_1.png - text: A piece of OSSAU-IRATY cheese on a rustic wooden table. output: url: image_2.png - text: A piece of OSSAU-IRATY cheese on a rustic wooden table. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/ossau-iraty <Gallery /> ## Model description These are JawadC/ossau-iraty LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of OSSAU-IRATY cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/ossau-iraty/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lesserfield/fiona-7B-v0.2	lesserfield	2024-05-25T22:50:03Z	129	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "dataset:niizam/fiona-sft", "base_model:BarraHome/Mistroll-7B-v2.2", "base_model:finetune:BarraHome/Mistroll-7B-v2.2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T03:37:28Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: BarraHome/Mistroll-7B-v2.2 model-index: - name: fiona-7B-v0.2 results: - task: type: text-generation metrics: - name: Average type: Average value: 69.5 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - task: type: text-generation metrics: - name: AI2 Reasoning Challenge type: AI2 Reasoning Challenge value: 65.1 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - task: type: text-generation metrics: - name: HellaSwag type: HellaSwag value: 85.49 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - task: type: text-generation metrics: - name: MMLU type: MMLU value: 62.78 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - task: type: text-generation metrics: - name: TruthfulQA type: TruthfulQA value: 56.53 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - task: type: text-generation metrics: - name: Winogrande type: Winogrande value: 78.77 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - task: type: text-generation metrics: - name: GSM8K type: GSM8K value: 68.31 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard datasets: - niizam/fiona-sft --- # Uploaded model - Developed by: niizam - License: apache-2.0 - Finetuned from model : BarraHome/Mistroll-7B-v2.2 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LA1512/Led-pubmed-20K-4096-epoch2	LA1512	2024-05-25T22:47:57Z	98	0	transformers	[ "transformers", "safetensors", "led", "text2text-generation", "generated_from_trainer", "base_model:LA1512/Led-pubmed-20K-4096-v2", "base_model:finetune:LA1512/Led-pubmed-20K-4096-v2", "license:bsd-3-clause", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T22:47:40Z	--- license: bsd-3-clause base_model: LA1512/Led-pubmed-20K-4096-v2 tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [LA1512/Led-pubmed-20K-4096-v2](https://huggingface.co/LA1512/Led-pubmed-20K-4096-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4248 ## 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: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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: 400 - num_epochs: 1 - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.4638 \| 0.16 \| 200 \| 3.4713 \| \| 3.3407 \| 0.32 \| 400 \| 3.4833 \| \| 3.2799 \| 0.48 \| 600 \| 3.4805 \| \| 3.3883 \| 0.64 \| 800 \| 3.4563 \| \| 3.3559 \| 0.8 \| 1000 \| 3.4350 \| \| 3.4671 \| 0.96 \| 1200 \| 3.4248 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
G-R-A-V-I-T-Y/flan-t5-base-ARv1-ARv2	G-R-A-V-I-T-Y	2024-05-25T22:46:08Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:G-R-A-V-I-T-Y/flan-t5-base-ARv1", "base_model:finetune:G-R-A-V-I-T-Y/flan-t5-base-ARv1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T22:19:47Z	--- license: apache-2.0 base_model: G-R-A-V-I-T-Y/flan-t5-base-ARv1 tags: - generated_from_trainer model-index: - name: flan-t5-base-ARv1-ARv2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base-ARv1-ARv2 This model is a fine-tuned version of [G-R-A-V-I-T-Y/flan-t5-base-ARv1](https://huggingface.co/G-R-A-V-I-T-Y/flan-t5-base-ARv1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7981 - Exact Match: 10.0 - Gen Len: 4.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact Match \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:-------:\| \| No log \| 1.0 \| 7 \| 0.7981 \| 10.0 \| 4.0 \| \| No log \| 2.0 \| 14 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 3.0 \| 21 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 4.0 \| 28 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 5.0 \| 35 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 6.0 \| 42 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 7.0 \| 49 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 8.0 \| 56 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 9.0 \| 63 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 10.0 \| 70 \| 0.7982 \| 10.0 \| 4.0 \| \| No log \| 11.0 \| 77 \| 0.7983 \| 10.0 \| 4.0 \| \| No log \| 12.0 \| 84 \| 0.7983 \| 10.0 \| 4.0 \| \| No log \| 13.0 \| 91 \| 0.7983 \| 10.0 \| 4.0 \| \| No log \| 14.0 \| 98 \| 0.7983 \| 10.0 \| 4.0 \| \| No log \| 15.0 \| 105 \| 0.7984 \| 10.0 \| 4.0 \| \| No log \| 16.0 \| 112 \| 0.7984 \| 10.0 \| 4.0 \| \| No log \| 17.0 \| 119 \| 0.7984 \| 10.0 \| 4.0 \| \| No log \| 18.0 \| 126 \| 0.7984 \| 10.0 \| 4.0 \| \| No log \| 19.0 \| 133 \| 0.7984 \| 10.0 \| 4.0 \| \| No log \| 20.0 \| 140 \| 0.7984 \| 10.0 \| 4.0 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1 - Datasets 2.19.1 - Tokenizers 0.19.1
Robi21/Meta-Llama-3-8B-Q4_K_M-GGUF	Robi21	2024-05-25T22:45:51Z	2	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T22:45:32Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # Robi21/Meta-Llama-3-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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/meta-llama/Meta-Llama-3-8B) 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 Robi21/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Robi21/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m meta-llama-3-8b-q4_k_m.gguf -n 128 ```
arinakosovskaia/implicit_toxicity	arinakosovskaia	2024-05-25T22:35:04Z	112	6	transformers	[ "transformers", "safetensors", "bert", "text-classification", "ru", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-10T14:36:27Z	--- language: - ru license: apache-2.0 library_name: transformers metrics: - precision - recall - f1 pipeline_tag: text-classification --- # Model Card for Model ID Detect implicit toxicity in Russian (details will be later :)) ``` import torch from transformers import BertTokenizer, BertForSequenceClassification text = <your_text> device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_name = 'arinakosovskaia/implicit_toxicity' tokenizer = BertTokenizer.from_pretrained(model_name) model = BertForSequenceClassification.from_pretrained(model_name).to(device) encoded_text = tokenizer.encode(text, return_tensors='pt').to(device) outputs = model(encoded_text) logits = outputs[0] prob = torch.nn.functional.softmax(logits, dim=1)[:, 1] prob.cpu().detach().numpy()[0] ``` ## 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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quangtqv/cross_encoder_tool_learning_backbone_beta_26_5	quangtqv	2024-05-25T22:29:28Z	120	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T22:28:20Z	--- 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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MuniebAbdelrahman/bert-finetuned-squad	MuniebAbdelrahman	2024-05-25T22:24:28Z	72	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-25T17:47:49Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: MuniebAbdelrahman/bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # MuniebAbdelrahman/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2732 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5545, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 1.2732 \| 0 \| ### Framework versions - Transformers 4.41.0 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
vyctorbh/phi-3-medium-alpaca-pt	vyctorbh	2024-05-25T22:04:55Z	4	0	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/Phi-3-medium-4k-instruct-bnb-4bit", "base_model:quantized:unsloth/Phi-3-medium-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T21:17:43Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-medium-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: vyctorbh - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
oskarandrsson/wav2vec2-2-bert-swedish-lm	oskarandrsson	2024-05-25T22:01:18Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "sv", "dataset:common_voice_17_0", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-20T13:28:40Z	--- license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: w2v-bert-2.0-sv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: sv-SE split: test args: sv-SE metrics: - name: Wer type: wer value: 0.10046931592103249 language: - sv --- <!-- 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. --> # w2v-bert-2.0-sv This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1962 - Wer: 0.1005 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 2.075 \| 0.7407 \| 300 \| 0.3441 \| 0.3057 \| \| 0.2837 \| 1.4815 \| 600 \| 0.2995 \| 0.2274 \| \| 0.2081 \| 2.2222 \| 900 \| 0.2443 \| 0.1768 \| \| 0.1579 \| 2.9630 \| 1200 \| 0.2143 \| 0.1493 \| \| 0.1248 \| 3.7037 \| 1500 \| 0.2165 \| 0.1504 \| \| 0.0934 \| 4.4444 \| 1800 \| 0.1869 \| 0.1284 \| \| 0.0719 \| 5.1852 \| 2100 \| 0.2072 \| 0.1216 \| \| 0.0573 \| 5.9259 \| 2400 \| 0.1949 \| 0.1195 \| \| 0.0436 \| 6.6667 \| 2700 \| 0.2025 \| 0.1142 \| \| 0.0317 \| 7.4074 \| 3000 \| 0.2003 \| 0.1097 \| \| 0.0256 \| 8.1481 \| 3300 \| 0.1942 \| 0.1060 \| \| 0.0169 \| 8.8889 \| 3600 \| 0.1851 \| 0.1030 \| \| 0.0121 \| 9.6296 \| 3900 \| 0.1962 \| 0.1005 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
devjwsong/reinforce-Pixelcopter-PLE-v0	devjwsong	2024-05-25T21:56:14Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-25T13:58:50Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 38.15 +/- 29.50 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
krishi0311/gpt2-wikitext2	krishi0311	2024-05-25T21:52:52Z	224	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T21:10:08Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.1121 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.551 \| 1.0 \| 2249 \| 6.4712 \| \| 6.1869 \| 2.0 \| 4498 \| 6.1959 \| \| 6.0103 \| 3.0 \| 6747 \| 6.1121 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
rishiA/my_awesome_mind_model	rishiA	2024-05-25T21:37:58Z	168	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-05-25T21:32:49Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.07964601769911504 --- <!-- 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_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6532 - Accuracy: 0.0796 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6368 \| 0.0531 \| \| No log \| 1.8667 \| 7 \| 2.6427 \| 0.0708 \| \| 2.6403 \| 2.9333 \| 11 \| 2.6464 \| 0.0619 \| \| 2.6403 \| 4.0 \| 15 \| 2.6446 \| 0.0619 \| \| 2.6403 \| 4.8 \| 18 \| 2.6427 \| 0.0442 \| \| 2.6381 \| 5.8667 \| 22 \| 2.6478 \| 0.0796 \| \| 2.6381 \| 6.9333 \| 26 \| 2.6526 \| 0.0708 \| \| 2.6304 \| 8.0 \| 30 \| 2.6532 \| 0.0796 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
FuturisticVibes/aya-23-35B-8.0bpw-h8-exl2	FuturisticVibes	2024-05-25T21:27:46Z	7	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-25T21:18:00Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- I have no idea what I’m doing… if this causes the apocalypse someone please let me know. aya-23-35B 8.0bpw h8 EXL2 Includes [measurement.json]( https://huggingface.co/FuturisticVibes/aya-23-35B-8.0bpw-h8-exl2/tree/measurement) file for further quantization Original Model: https://huggingface.co/CohereForAI/aya-23-35B # Original Model Card # Model Card for Aya-23-35B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-35B - Model Size: 35 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} }
Vikhrmodels/kolibri-vikhr-mistral-0427	Vikhrmodels	2024-05-25T21:23:21Z	198	3	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "conversational", "en", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T13:20:03Z	--- license: apache-2.0 language: - en - ru --- ## Description This is an instruction following model (based on Vikhr Base) optimized for Russian language. It was trained using [kolibrify](https://github.com/oKatanaaa/kolibrify) via curriculum learning on a multitude of instruction datasets. The model uses ChatML template. It was trained to be sensitive to the system prompt, experiment with it. Primary uses: information retrieval, classification, semantic analysis, translation (en-ru), etc. Currently in pre-alpha. ## Instruction following evals The model was tested using the following benchmarks: - [ruIFEval](https://github.com/NLP-Core-Team/ruIFEval) - [ifeval](https://github.com/google-research/google-research/tree/master/instruction_following_eval) \| Eval name \|Strict Value\| Loose Value \|---------------------------------\|----\|----\| \|Avg. \|37.82\|39.24\| \|ifeval-prompt-level \|33.08\|35.12\| \|ifeval-instruction-level \|44.48\|46.40\| \|ru-ifeval-prompt-level \|31.79\|32.53\| \|ru-ifeval-instruction-level \|41.96\|42.92\|
camidenecken/mistral-7B-v0.2-quant	camidenecken	2024-05-25T21:18:39Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-25T21:14:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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JOY-ZHE/bloomz-560m_PROMPT_TUNING_CAUSAL_LM	JOY-ZHE	2024-05-25T21:16:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T21:16:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
fombus/mistral_history	fombus	2024-05-25T21:14:29Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-25T13:15:05Z	--- license: apache-2.0 ---
cstr/Llama3-DiscoLeo-Instruct-8B-v0.1-mlx	cstr	2024-05-25T21:01:39Z	80	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "de", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T20:42:48Z	--- language: - de license: llama3 library_name: transformers tags: - mlx --- # cstr/Llama3-DiscoLeo-Instruct-8B-v0.1-mlx The Model [cstr/Llama3-DiscoLeo-Instruct-8B-v0.1-mlx](https://huggingface.co/cstr/Llama3-DiscoLeo-Instruct-8B-v0.1-mlx) was converted to MLX format from [DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1) using mlx-lm version 0.14.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cstr/Llama3-DiscoLeo-Instruct-8B-v0.1-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
LarryAIDraw/kamuromasumi-nvwls-v1	LarryAIDraw	2024-05-25T20:59:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-25T20:53:27Z	--- license: creativeml-openrail-m --- https://civitai.com/models/472464/masumi-kamuro-classroom-of-the-elite-lora
LarryAIDraw/hanying-nvwls-v1	LarryAIDraw	2024-05-25T20:59:04Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-25T20:53:02Z	--- license: creativeml-openrail-m --- https://civitai.com/models/472463/hanying-punishing-gray-raven-lora
LarryAIDraw/y4ngy4ng	LarryAIDraw	2024-05-25T20:58:44Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-25T20:51:17Z	--- license: creativeml-openrail-m --- https://civitai.com/models/472508/yangyang-wuthering-waves?modelVersionId=525636
HaileyStorm/llama3-5.4b-instruct-unhealed	HaileyStorm	2024-05-25T20:58:14Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "prune", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T23:02:34Z	--- base_model: - meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge - prune --- # merged This is a "merge" of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). It is a prune of Meta-Llama-3-8B-Instruct down to 20 layers, or about 5.4B parameter. Mostly, this is a test of pruning & healing an instruct-tuned model. THIS MODEL HAS NOT BEEN HEALED. It is presently unusable. The healed version will be in a different repository. This size should allow bf16 inference on 24GB VRAM, Q8 or Q6 inference on 6GB VRAM, Q5 inference on 4GB VRAM, and fine-tuning ... well, with less VRAM than an 8B model. ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [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 dtype: bfloat16 merge_method: passthrough slices: - sources: - layer_range: [0, 16] model: meta-llama/Meta-Llama-3-8B-Instruct - sources: - layer_range: [20, 21] model: meta-llama/Meta-Llama-3-8B-Instruct - sources: - layer_range: [29, 32] model: meta-llama/Meta-Llama-3-8B-Instruct ```
LarryAIDraw/Firefly_v1	LarryAIDraw	2024-05-25T20:58:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-25T20:49:52Z	--- license: creativeml-openrail-m --- https://civitai.com/models/474862/firefly-honkai-star-rail
Project-Sirus/AI	Project-Sirus	2024-05-25T20:57:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-25T20:57:11Z	--- license: apache-2.0 ---
seregadgl101/baii_rerank_v20_1ep	seregadgl101	2024-05-25T20:56:59Z	12	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-25T20:55:41Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # seregadgl101/baii_rerank_v20_1ep This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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('seregadgl101/baii_rerank_v20_1ep') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('seregadgl101/baii_rerank_v20_1ep') model = AutoModel.from_pretrained('seregadgl101/baii_rerank_v20_1ep') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=seregadgl101/baii_rerank_v20_1ep) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf	RichardErkhov	2024-05-25T20:54:48Z	2	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-25T17:49:38Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Synatra-11B-Tb2M_SM - GGUF - Model creator: https://huggingface.co/maywell/ - Original model: https://huggingface.co/maywell/Synatra-11B-Tb2M_SM/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Synatra-11B-Tb2M_SM.Q2_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [Synatra-11B-Tb2M_SM.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [Synatra-11B-Tb2M_SM.IQ3_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [Synatra-11B-Tb2M_SM.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [Synatra-11B-Tb2M_SM.IQ3_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [Synatra-11B-Tb2M_SM.Q3_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [Synatra-11B-Tb2M_SM.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [Synatra-11B-Tb2M_SM.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [Synatra-11B-Tb2M_SM.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [Synatra-11B-Tb2M_SM.Q4_0.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [Synatra-11B-Tb2M_SM.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [Synatra-11B-Tb2M_SM.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [Synatra-11B-Tb2M_SM.Q4_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [Synatra-11B-Tb2M_SM.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [Synatra-11B-Tb2M_SM.Q4_1.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [Synatra-11B-Tb2M_SM.Q5_0.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [Synatra-11B-Tb2M_SM.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [Synatra-11B-Tb2M_SM.Q5_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [Synatra-11B-Tb2M_SM.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [Synatra-11B-Tb2M_SM.Q5_1.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [Synatra-11B-Tb2M_SM.Q6_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [Synatra-11B-Tb2M_SM.Q8_0.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-11B-Tb2M_SM-gguf/blob/main/Synatra-11B-Tb2M_SM.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- language: - ko library_name: transformers pipeline_tag: text-generation license: cc-by-nc-4.0 --- # Synatra-11B-Tb2M-SM Made by StableFluffy Contact (Do not Contact for personal things.) Discord : is.maywell Telegram : AlzarTakkarsen ## License This model is strictly [non-commercial](https://creativecommons.org/licenses/by-nc/4.0/) (cc-by-nc-4.0) use only which takes priority over the MISTRAL APACHE 2.0. The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me. ## Model Details Base Model [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) teknium/CollectiveCognition-v1.1-Mistral-7B, Apache 2.0 Trained On A100 80GB * 4 # Model Benchmark X ``` > Readme format: [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) ---
Mouwiya/kinetics-400	Mouwiya	2024-05-25T20:53:02Z	6	0	tf-keras	[ "tf-keras", "video-classification", "en", "dataset:ucf101", "license:apache-2.0", "model-index", "region:us" ]	video-classification	2024-05-25T20:43:56Z	--- language: en tags: - video-classification license: apache-2.0 datasets: - ucf101 metrics: - accuracy - top-5-accuracy pipeline_tag: video-classification model-index: - name: i3d-kinetics-400 results: - task: type: video-classification name: Video Classification dataset: name: UCF101 type: ucf101 metrics: - name: Accuracy type: accuracy value: 0.95 - name: Top-5 Accuracy type: top-5-accuracy value: 0.95 --- # I3D Kinetics-400 This model is a fine-tuned version of the Inflated 3D Convnet model for action recognition, trained on the Kinetics-400 dataset. ## Model Description The I3D (Inflated 3D Convnet) model is designed for video classification tasks. It extends 2D convolutions to 3D, enabling the model to capture spatiotemporal features from video frames. ## Intended Uses The model can be used for action recognition in videos. It is particularly suited for tasks involving the classification of human activities. ## Training Data The model was fine-tuned on the UCF101 dataset, which consists of 13,320 videos belonging to 101 action categories. ## Performance The model achieves an accuracy of 90% and a top-5 accuracy of 95% on the UCF101 test set. ## Example Usage ```python from transformers import pipeline model = pipeline("video-classification", model="Mouwiya/i3d-kinetics-400") # Example video path video_path = "path_to_your_video.mp4" # Perform video classification results = model(video_path) print(results) ```
Mouwiya/kinetics-600	Mouwiya	2024-05-25T20:51:15Z	7	0	tf-keras	[ "tf-keras", "video-classification", "en", "dataset:ucf101", "license:apache-2.0", "model-index", "region:us" ]	video-classification	2024-05-25T20:43:56Z	--- language: en tags: - video-classification license: apache-2.0 datasets: - ucf101 metrics: - accuracy - top-5-accuracy pipeline_tag: video-classification model-index: - name: i3d-kinetics-400 results: - task: type: video-classification name: Video Classification dataset: name: UCF101 type: ucf101 metrics: - name: Accuracy type: accuracy value: 0.98 - name: Top-5 Accuracy type: top-5-accuracy value: 0.95 --- # I3D Kinetics-600 This model is a fine-tuned version of the Inflated 3D Convnet model for action recognition, trained on the Kinetics-400 dataset. ## Model Description The I3D (Inflated 3D Convnet) model is designed for video classification tasks. It extends 2D convolutions to 3D, enabling the model to capture spatiotemporal features from video frames. ## Intended Uses The model can be used for action recognition in videos. It is particularly suited for tasks involving the classification of human activities. ## Training Data The model was fine-tuned on the UCF101 dataset, which consists of 13,320 videos belonging to 101 action categories. ## Performance The model achieves an accuracy of 90% and a top-5 accuracy of 95% on the UCF101 test set. ## Example Usage ```python from transformers import pipeline model = pipeline("video-classification", model="Mouwiya/i3d-kinetics-600") # Example video path video_path = "path_to_your_video.mp4" # Perform video classification results = model(video_path) print(results) ```
amanvvip2/finetuned-breast_cancer_images	amanvvip2	2024-05-25T20:47:42Z	382	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "breast cancer image classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-13T19:08:25Z	--- license: apache-2.0 tags: - breast cancer image classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: finetuned-breast_cancer_images 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. --> # finetuned-breast_cancer_images This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the breast cancer-image-classification dataset. It achieves the following results on the evaluation set: - Loss: 0.1643 - Accuracy: 0.9620 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5507 \| 2.5 \| 100 \| 0.3762 \| 0.8861 \| \| 0.4735 \| 5.0 \| 200 \| 0.3380 \| 0.8987 \| \| 0.368 \| 7.5 \| 300 \| 0.3424 \| 0.8987 \| \| 0.3182 \| 10.0 \| 400 \| 0.2979 \| 0.9082 \| \| 0.2952 \| 12.5 \| 500 \| 0.2110 \| 0.9462 \| \| 0.2493 \| 15.0 \| 600 \| 0.1675 \| 0.9620 \| \| 0.2716 \| 17.5 \| 700 \| 0.1705 \| 0.9462 \| \| 0.2866 \| 20.0 \| 800 \| 0.1643 \| 0.9620 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
cyr19/gptneo-1b-en-quatrain-conditioned	cyr19	2024-05-25T20:43:55Z	106	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T20:41:33Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lex-hue/Delexa-V0.2-7b	lex-hue	2024-05-25T20:42:41Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T19:32:04Z	--- license: apache-2.0 inference: true ---
huzunali/Phi-3-mini-128k-instruct-Q8_0-GGUF	huzunali	2024-05-25T20:38:31Z	0	0	null	[ "gguf", "nlp", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-25T20:38:20Z	--- language: - en license: mit tags: - nlp - code - llama-cpp - gguf-my-repo license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE pipeline_tag: text-generation widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # huzunali/Phi-3-mini-128k-instruct-Q8_0-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-mini-128k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-128k-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/microsoft/Phi-3-mini-128k-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 huzunali/Phi-3-mini-128k-instruct-Q8_0-GGUF --model phi-3-mini-128k-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo huzunali/Phi-3-mini-128k-instruct-Q8_0-GGUF --model phi-3-mini-128k-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-mini-128k-instruct-q8_0.gguf -n 128 ```
andricValdez/multilingual-e5-large-finetuned-autext24	andricValdez	2024-05-25T20:35:24Z	8	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:intfloat/multilingual-e5-large", "base_model:finetune:intfloat/multilingual-e5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T05:31:01Z	--- license: mit base_model: intfloat/multilingual-e5-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: multilingual-e5-large-finetuned-autext24 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. --> # multilingual-e5-large-finetuned-autext24 This model is a fine-tuned version of [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2096 - Accuracy: 0.9673 - F1: 0.9673 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 4798 \| 0.1903 \| 0.9527 \| 0.9526 \| \| 0.1396 \| 2.0 \| 9596 \| 0.1751 \| 0.9672 \| 0.9672 \| \| 0.1396 \| 3.0 \| 14394 \| 0.2093 \| 0.9647 \| 0.9646 \| \| 0.0391 \| 4.0 \| 19192 \| 0.1954 \| 0.9690 \| 0.9690 \| \| 0.0391 \| 5.0 \| 23990 \| 0.2096 \| 0.9673 \| 0.9673 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
JawadC/maroilles	JawadC	2024-05-25T20:21:32Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T19:55:07Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of MAROLLES cheese widget: - text: A rustic wooden table with a block of Marolles cheese and a few grapes. output: url: image_0.png - text: A rustic wooden table with a block of Marolles cheese and a few grapes. output: url: image_1.png - text: A rustic wooden table with a block of Marolles cheese and a few grapes. output: url: image_2.png - text: A rustic wooden table with a block of Marolles cheese and a few grapes. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/maroilles <Gallery /> ## Model description These are JawadC/maroilles LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MAROLLES cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/maroilles/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ihebMissaoui/layoutlmv3-fine-tuned-funsd-kie	ihebMissaoui	2024-05-25T20:15:34Z	106	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "token-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-25T20:15:13Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/elyadata/Ft%20layoutlmv3%20funsd%20max%20epochs%20100%20%2Cearlystop%3D4%2Cbatch%3D2%2Clr%3D1e-5%20adamw%2CFULL%20models%20params/runs/6zlgssbd) # test This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8476 - Precision: 0.8955 - Recall: 0.9071 - F1: 0.9013 - Accuracy: 0.8691 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 75 \| 0.9234 \| 0.6139 \| 0.7526 \| 0.6762 \| 0.7416 \| \| No log \| 2.0 \| 150 \| 0.6101 \| 0.7549 \| 0.8341 \| 0.7925 \| 0.7940 \| \| No log \| 3.0 \| 225 \| 0.5135 \| 0.8332 \| 0.8882 \| 0.8598 \| 0.8091 \| \| No log \| 4.0 \| 300 \| 0.5467 \| 0.8189 \| 0.8624 \| 0.8401 \| 0.8109 \| \| No log \| 5.0 \| 375 \| 0.4879 \| 0.8660 \| 0.9051 \| 0.8851 \| 0.8504 \| \| No log \| 6.0 \| 450 \| 0.5352 \| 0.8787 \| 0.9180 \| 0.8980 \| 0.8480 \| \| 0.5752 \| 7.0 \| 525 \| 0.5900 \| 0.8730 \| 0.8982 \| 0.8854 \| 0.8343 \| \| 0.5752 \| 8.0 \| 600 \| 0.6014 \| 0.8832 \| 0.9016 \| 0.8923 \| 0.8506 \| \| 0.5752 \| 9.0 \| 675 \| 0.6173 \| 0.8883 \| 0.9126 \| 0.9003 \| 0.8538 \| \| 0.5752 \| 10.0 \| 750 \| 0.6278 \| 0.8787 \| 0.9141 \| 0.8960 \| 0.8571 \| \| 0.5752 \| 11.0 \| 825 \| 0.6573 \| 0.8612 \| 0.9155 \| 0.8876 \| 0.8326 \| \| 0.5752 \| 12.0 \| 900 \| 0.7333 \| 0.8818 \| 0.9006 \| 0.8911 \| 0.8387 \| \| 0.5752 \| 13.0 \| 975 \| 0.7489 \| 0.8888 \| 0.9136 \| 0.9010 \| 0.8502 \| \| 0.1263 \| 14.0 \| 1050 \| 0.7719 \| 0.8908 \| 0.8997 \| 0.8952 \| 0.8318 \| \| 0.1263 \| 15.0 \| 1125 \| 0.8295 \| 0.8945 \| 0.9101 \| 0.9022 \| 0.8438 \| \| 0.1263 \| 16.0 \| 1200 \| 0.8447 \| 0.8798 \| 0.9126 \| 0.8959 \| 0.8465 \| \| 0.1263 \| 17.0 \| 1275 \| 0.8359 \| 0.9090 \| 0.8932 \| 0.9010 \| 0.8486 \| \| 0.1263 \| 18.0 \| 1350 \| 0.8430 \| 0.8966 \| 0.9091 \| 0.9028 \| 0.8414 \| \| 0.1263 \| 19.0 \| 1425 \| 0.8179 \| 0.8854 \| 0.9021 \| 0.8937 \| 0.8400 \| \| 0.0482 \| 20.0 \| 1500 \| 0.8950 \| 0.8968 \| 0.8982 \| 0.8975 \| 0.8475 \| \| 0.0482 \| 21.0 \| 1575 \| 0.8790 \| 0.9053 \| 0.9121 \| 0.9087 \| 0.8565 \| \| 0.0482 \| 22.0 \| 1650 \| 0.7915 \| 0.9056 \| 0.9101 \| 0.9078 \| 0.8595 \| \| 0.0482 \| 23.0 \| 1725 \| 0.8760 \| 0.8938 \| 0.8952 \| 0.8945 \| 0.8504 \| \| 0.0482 \| 24.0 \| 1800 \| 0.8320 \| 0.9113 \| 0.9086 \| 0.9100 \| 0.8625 \| \| 0.0482 \| 25.0 \| 1875 \| 0.8880 \| 0.9017 \| 0.9021 \| 0.9019 \| 0.8538 \| \| 0.0482 \| 26.0 \| 1950 \| 0.8611 \| 0.9083 \| 0.9101 \| 0.9092 \| 0.8499 \| \| 0.0163 \| 27.0 \| 2025 \| 0.8747 \| 0.9068 \| 0.9086 \| 0.9077 \| 0.8600 \| \| 0.0163 \| 28.0 \| 2100 \| 0.8476 \| 0.8955 \| 0.9071 \| 0.9013 \| 0.8691 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Xiaolihai/flan-t5-large_MeDistill_28_rougeAve	Xiaolihai	2024-05-25T20:13:43Z	7	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T13:33:03Z	--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer model-index: - name: flan-t5-large_MeDistill_28_rougeAve results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-large_MeDistill_28_rougeAve This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1 - Datasets 2.19.1 - Tokenizers 0.15.2
xuliu15/Frisian_32r_LoRA_10h	xuliu15	2024-05-25T20:12:09Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:mozilla-foundation/common_voice_6_1", "base_model:openai/whisper-small", "base_model:adapter:openai/whisper-small", "license:apache-2.0", "region:us" ]	null	2024-05-25T20:12:06Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-small datasets: - mozilla-foundation/common_voice_6_1 model-index: - name: LoRA-Frisian-10h 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. --> # LoRA-Frisian-10h This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_6_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.4038 ## 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 - lr_scheduler_warmup_steps: 50 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.5243 \| 1.0 \| 935 \| 0.6300 \| \| 0.432 \| 2.0 \| 1870 \| 0.5607 \| \| 0.3525 \| 3.0 \| 2805 \| 0.5192 \| \| 0.2719 \| 4.0 \| 3740 \| 0.4961 \| \| 0.2251 \| 5.0 \| 4675 \| 0.4779 \| \| 0.1761 \| 6.0 \| 5610 \| 0.4505 \| \| 0.1344 \| 7.0 \| 6545 \| 0.4435 \| \| 0.1019 \| 8.0 \| 7480 \| 0.4414 \| \| 0.0717 \| 9.0 \| 8415 \| 0.4197 \| \| 0.0434 \| 10.0 \| 9350 \| 0.4149 \| \| 0.023 \| 11.0 \| 10285 \| 0.4077 \| \| 0.0161 \| 12.0 \| 11220 \| 0.4067 \| \| 0.0075 \| 13.0 \| 12155 \| 0.3982 \| \| 0.0066 \| 14.0 \| 13090 \| 0.4005 \| \| 0.0034 \| 15.0 \| 14025 \| 0.4038 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
seandearnaley/llama3-8b-sentiment-may-22-2024-2epoches	seandearnaley	2024-05-25T20:10:11Z	145	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:quantized:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T00:42:22Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - Developed by: seandearnaley - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct 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) Developed to support [Elevating Sentiment Analysis @Medium](https://seandearnaley.medium.com/elevating-sentiment-analysis-ad02a316df1d). example Ollama Modelfile: ``` FROM ./llama3-8b-sentiment-may-22-2024-2epoches-unsloth.Q4_K_M.gguf SYSTEM """ You are an advanced AI assistant created to perform sentiment analysis on text. Your task is to carefully read the text and analyze the sentiment it expresses towards the potential future stock value of any company mentioned. Analyze the sentiment of this text and respond with the appropriate JSON: """ TEMPLATE """{{ if .System }}<\|start_header_id\|>system<\|end_header_id\|> {{ .System }}<\|eot_id\|>{{ end }}{{ if .Prompt }}<\|start_header_id\|>user<\|end_header_id\|> {{ .Prompt }}<\|eot_id\|>{{ end }}<\|start_header_id\|>assistant<\|end_header_id\|> {{ .Response }}<\|eot_id\|>""" # PARAMETER stop <\|end_of_text\|> # Default for Llama3 # PARAMETER stop </s> # Default for Mistral # A parameter that sets the temperature of the model, controlling how creative or conservative the model's responses will be PARAMETER temperature 0.2 # Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) PARAMETER repeat_last_n 256 ```
Rupeshwaran/ppo-Huggy	Rupeshwaran	2024-05-25T20:09:11Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-25T20:07:51Z	--- 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: Rupeshwaran/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Reyyan/cs-llama3-8B-3	Reyyan	2024-05-25T20:09:02Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-25T20:07:09Z	--- 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]
afrideva/Llama-3-Yggdrasil-8B-GGUF	afrideva	2024-05-25T20:02:23Z	51	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "ggml", "quantized", "text-generation", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Locutusque/Llama-3-Yggdrasil-8B", "base_model:quantized:Locutusque/Llama-3-Yggdrasil-8B", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T19:03:35Z	--- base_model: Locutusque/Llama-3-Yggdrasil-8B inference: true library_name: transformers license: llama3 model_creator: Locutusque model_name: Llama-3-Yggdrasil-8B pipeline_tag: text-generation quantized_by: afrideva tags: - mergekit - merge - gguf - ggml - quantized --- # Llama-3-Yggdrasil-8B-GGUF Quantized GGUF model files for [Llama-3-Yggdrasil-8B](https://huggingface.co/Locutusque/Llama-3-Yggdrasil-8B) from [Locutusque](https://huggingface.co/Locutusque) ## Original Model Card: # 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base. ### Models Merged The following models were included in the merge: * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [Locutusque/Llama-3-Hercules-5.0-8B](https://huggingface.co/Locutusque/Llama-3-Hercules-5.0-8B) * [Locutusque/llama-3-neural-chat-v2.2-8b](https://huggingface.co/Locutusque/llama-3-neural-chat-v2.2-8b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NousResearch/Meta-Llama-3-8B # No parameters necessary for base model - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.6 weight: 0.55 - model: Locutusque/llama-3-neural-chat-v2.2-8b parameters: density: 0.55 weight: 0.45 - model: Locutusque/Llama-3-Hercules-5.0-8B parameters: density: 0.57 weight: 0.5 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ```
zahidpichen/Fine-Tuned-LLM-model	zahidpichen	2024-05-25T20:01:50Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "gpt_neox", "arxiv:1910.09700", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "8-bit", "bitsandbytes", "region:us" ]	null	2024-05-25T19:57:52Z	--- library_name: peft base_model: databricks/dolly-v2-3b --- # 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 0.11.1
theo77186/dolphin-2.9.1-mistral-22b	theo77186	2024-05-25T19:58:04Z	55	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T09:12:42Z	--- license: apache-2.0 --- [cognitivecomputations/dolphin-2.9.1-mixtral-1x22b](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-mixtral-1x22b) converted to Mistral format. A Mixtral model with a single expert is mathematically equivalent to the corresponding Mistral model. This allows to remove 344k parameters and to avoid software bugs when encountering a Mixtral with 1 expert. Note that ChatML is entirely broken in the original and converted model. I have no plausible explanation why it's broken. Alpaca seems to work even though the model is not trained on it. Original model card below. --- # Dolphin 2.9.1 Mixtral 1x22b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations [![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations) Discord: https://discord.gg/cognitivecomputations <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and the full-weight fine-tuning was with 16k sequence length. It took 27 hours on 8xH100 provided by Crusoe Cloud. This model was fully fine-tuned, targeting all layers. The model is an extracted expert using SLERP and a custom script that we've open-sourced. It extracts a single expert which is the combined SLERP of all 8 experts from a Mixtral architecture. We decided to not fully convert to a dense model, for the sake of trying to keep as much of the original model's performance as possible, as this process is already quite surgical and there are a lot of variables to take into account. Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed under Apache 2.0. We grant permission for any use, including commercial, as long as it complies with the Apache-2.0 license. Dolphin was trained using data generated from GPT-4, among other models. For more details on the extraction process of the expert model, visit our GitHub repository: https://github.com/cognitivecomputations/extract-expert/tree/main ## Evals ![image/png](https://i.ibb.co/yNmCv76/file-nkvf-Q9-Mg-X57-GB7-Ayrl-YA2-Zsp.png) [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: cognitivecomputations/mixtral-1x22b-base model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer # trust_remote_code: true # load_in_8bit: true # load_in_4bit: true # strict: false datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b-prepared val_set_size: 0.01 output_dir: ./1x22b-out # adapter: qlora # lora_r: 16 # lora_alpha: 16 # lora_modules_to_save: [embed_tokens, lm_head] # lora_dropout: 0.05 # lora_target_linear: true # unfrozen_parameters: # - ^lm_head.weight$ # - ^model.embed_tokens.weight$ # # input_layernorm layers # - model.layers.0.input_layernorm # - model.layers.1.input_layernorm # - model.layers.2.input_layernorm # - model.layers.3.input_layernorm # - model.layers.4.input_layernorm # - model.layers.5.input_layernorm # - model.layers.6.input_layernorm # - model.layers.7.input_layernorm # - model.layers.8.input_layernorm # - model.layers.9.input_layernorm # - model.layers.10.input_layernorm # - model.layers.11.input_layernorm # - model.layers.12.input_layernorm # - model.layers.13.input_layernorm # - model.layers.14.input_layernorm # - model.layers.15.input_layernorm # - model.layers.16.input_layernorm # - model.layers.17.input_layernorm # - model.layers.18.input_layernorm # - model.layers.19.input_layernorm # - model.layers.20.input_layernorm # - model.layers.21.input_layernorm # - model.layers.22.input_layernorm # - model.layers.23.input_layernorm # # lm_head layers # # mlp.down_proj layers # - model.layers.17.mlp.down_proj # - model.layers.18.mlp.down_proj # - model.layers.19.mlp.down_proj # - model.layers.20.mlp.down_proj # - model.layers.21.mlp.down_proj # - model.layers.22.mlp.down_proj # - model.layers.23.mlp.down_proj # - model.layers.24.mlp.down_proj # - model.layers.25.mlp.down_proj # - model.layers.26.mlp.down_proj # - model.layers.27.mlp.down_proj # - model.layers.28.mlp.down_proj # - model.layers.29.mlp.down_proj # - model.layers.30.mlp.down_proj # - model.layers.31.mlp.down_proj # - model.layers.32.mlp.down_proj # - model.layers.33.mlp.down_proj # - model.layers.34.mlp.down_proj # - model.layers.35.mlp.down_proj # - model.layers.36.mlp.down_proj # - model.layers.37.mlp.down_proj # - model.layers.38.mlp.down_proj # - model.layers.39.mlp.down_proj # - model.layers.40.mlp.down_proj # # mlp.gate_proj layers # - model.layers.51.mlp.gate_proj # - model.layers.50.mlp.gate_proj # - model.layers.53.mlp.gate_proj # - model.layers.52.mlp.gate_proj # - model.layers.49.mlp.gate_proj # - model.layers.45.mlp.gate_proj # - model.layers.46.mlp.gate_proj # - model.layers.47.mlp.gate_proj # - model.layers.57.mlp.gate_proj # - model.layers.48.mlp.gate_proj # - model.layers.56.mlp.gate_proj # - model.layers.41.mlp.gate_proj # - model.layers.54.mlp.gate_proj # - model.layers.43.mlp.gate_proj # - model.layers.44.mlp.gate_proj # - model.layers.60.mlp.gate_proj # - model.layers.55.mlp.gate_proj # - model.layers.40.mlp.gate_proj # - model.layers.42.mlp.gate_proj # - model.layers.58.mlp.gate_proj # - model.layers.36.mlp.gate_proj # - model.layers.37.mlp.gate_proj # - model.layers.38.mlp.gate_proj # - model.layers.39.mlp.gate_proj # # mlp.up_proj layers # - model.layers.50.mlp.up_proj # - model.layers.51.mlp.up_proj # - model.layers.41.mlp.up_proj # - model.layers.49.mlp.up_proj # - model.layers.43.mlp.up_proj # - model.layers.44.mlp.up_proj # - model.layers.40.mlp.up_proj # - model.layers.45.mlp.up_proj # - model.layers.47.mlp.up_proj # - model.layers.48.mlp.up_proj # - model.layers.46.mlp.up_proj # - model.layers.42.mlp.up_proj # - model.layers.39.mlp.up_proj # - model.layers.36.mlp.up_proj # - model.layers.37.mlp.up_proj # - model.layers.38.mlp.up_proj # - model.layers.56.mlp.up_proj # - model.layers.57.mlp.up_proj # - model.layers.53.mlp.up_proj # - model.layers.31.mlp.up_proj # - model.layers.32.mlp.up_proj # - model.layers.34.mlp.up_proj # - model.layers.35.mlp.up_proj # - model.layers.33.mlp.up_proj # # model.embed_tokens layers # # model.norm layers # # post_attention_layernorm layers # - model.layers.0.post_attention_layernorm # - model.layers.1.post_attention_layernorm # - model.layers.2.post_attention_layernorm # - model.layers.3.post_attention_layernorm # - model.layers.4.post_attention_layernorm # - model.layers.5.post_attention_layernorm # - model.layers.6.post_attention_layernorm # - model.layers.7.post_attention_layernorm # - model.layers.8.post_attention_layernorm # - model.layers.9.post_attention_layernorm # - model.layers.10.post_attention_layernorm # - model.layers.11.post_attention_layernorm # - model.layers.12.post_attention_layernorm # - model.layers.13.post_attention_layernorm # - model.layers.14.post_attention_layernorm # - model.layers.15.post_attention_layernorm # - model.layers.16.post_attention_layernorm # - model.layers.17.post_attention_layernorm # - model.layers.18.post_attention_layernorm # - model.layers.19.post_attention_layernorm # - model.layers.20.post_attention_layernorm # - model.layers.21.post_attention_layernorm # - model.layers.22.post_attention_layernorm # - model.layers.23.post_attention_layernorm # # self_attn.k_proj layers # - model.layers.42.self_attn.k_proj # - model.layers.41.self_attn.k_proj # - model.layers.39.self_attn.k_proj # - model.layers.35.self_attn.k_proj # - model.layers.28.self_attn.k_proj # - model.layers.79.self_attn.k_proj # - model.layers.43.self_attn.k_proj # - model.layers.32.self_attn.k_proj # - model.layers.73.self_attn.k_proj # - model.layers.31.self_attn.k_proj # - model.layers.29.self_attn.k_proj # - model.layers.76.self_attn.k_proj # - model.layers.30.self_attn.k_proj # - model.layers.40.self_attn.k_proj # - model.layers.33.self_attn.k_proj # - model.layers.78.self_attn.k_proj # - model.layers.34.self_attn.k_proj # - model.layers.37.self_attn.k_proj # - model.layers.45.self_attn.k_proj # - model.layers.44.self_attn.k_proj # - model.layers.71.self_attn.k_proj # - model.layers.26.self_attn.k_proj # - model.layers.74.self_attn.k_proj # - model.layers.27.self_attn.k_proj # # self_attn.o_proj layers # - model.layers.35.self_attn.o_proj # - model.layers.34.self_attn.o_proj # - model.layers.37.self_attn.o_proj # - model.layers.33.self_attn.o_proj # - model.layers.31.self_attn.o_proj # - model.layers.27.self_attn.o_proj # - model.layers.38.self_attn.o_proj # - model.layers.24.self_attn.o_proj # - model.layers.39.self_attn.o_proj # - model.layers.43.self_attn.o_proj # - model.layers.29.self_attn.o_proj # - model.layers.0.self_attn.o_proj # - model.layers.50.self_attn.o_proj # - model.layers.32.self_attn.o_proj # - model.layers.45.self_attn.o_proj # - model.layers.30.self_attn.o_proj # - model.layers.60.self_attn.o_proj # - model.layers.23.self_attn.o_proj # - model.layers.18.self_attn.o_proj # - model.layers.67.self_attn.o_proj # - model.layers.57.self_attn.o_proj # - model.layers.20.self_attn.o_proj # - model.layers.76.self_attn.o_proj # - model.layers.28.self_attn.o_proj # # self_attn.q_proj layers # - model.layers.1.self_attn.q_proj # - model.layers.6.self_attn.q_proj # - model.layers.0.self_attn.q_proj # - model.layers.5.self_attn.q_proj # - model.layers.2.self_attn.q_proj # - model.layers.7.self_attn.q_proj # - model.layers.3.self_attn.q_proj # - model.layers.4.self_attn.q_proj # - model.layers.8.self_attn.q_proj # - model.layers.9.self_attn.q_proj # - model.layers.61.self_attn.q_proj # - model.layers.10.self_attn.q_proj # - model.layers.62.self_attn.q_proj # - model.layers.36.self_attn.q_proj # - model.layers.15.self_attn.q_proj # - model.layers.11.self_attn.q_proj # - model.layers.17.self_attn.q_proj # - model.layers.60.self_attn.q_proj # - model.layers.63.self_attn.q_proj # - model.layers.64.self_attn.q_proj # - model.layers.29.self_attn.q_proj # - model.layers.30.self_attn.q_proj # - model.layers.55.self_attn.q_proj # - model.layers.34.self_attn.q_proj # # self_attn.v_proj layers # - model.layers.12.self_attn.v_proj # - model.layers.16.self_attn.v_proj # - model.layers.18.self_attn.v_proj # - model.layers.19.self_attn.v_proj # - model.layers.20.self_attn.v_proj # - model.layers.21.self_attn.v_proj # - model.layers.22.self_attn.v_proj # - model.layers.23.self_attn.v_proj # - model.layers.24.self_attn.v_proj # - model.layers.25.self_attn.v_proj # - model.layers.26.self_attn.v_proj # - model.layers.27.self_attn.v_proj # - model.layers.28.self_attn.v_proj # - model.layers.29.self_attn.v_proj # - model.layers.30.self_attn.v_proj # - model.layers.31.self_attn.v_proj # - model.layers.32.self_attn.v_proj # - model.layers.33.self_attn.v_proj # - model.layers.34.self_attn.v_proj # - model.layers.35.self_attn.v_proj # - model.layers.36.self_attn.v_proj # - model.layers.37.self_attn.v_proj # - model.layers.38.self_attn.v_proj # - model.layers.39.self_attn.v_proj sequence_len: 16384 sample_packing: true pad_to_sequence_len: true # adapter: lora # lora_model_dir: # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: true # lora_fan_in_fan_out: wandb_project: dolphin-mixtral1x22b wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: /workspace/axolotl2/axolotl/1x22b-out/checkpoint-507 local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 4 save_total_limit: 2 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.01 fsdp: fsdp_config: special_tokens: eos_token: "<\|im_end\|>" bos_token: "<s>" # pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # 1x22b-out ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9818 \| 0.0015 \| 1 \| 0.9854 \| \| 0.4783 \| 0.2499 \| 169 \| 0.5042 \| \| 0.464 \| 0.4997 \| 338 \| 0.4755 \| \| 0.4561 \| 0.7496 \| 507 \| 0.4593 \| \| 0.3981 \| 0.9994 \| 676 \| 0.4553 \| \| 0.3725 \| 1.2378 \| 845 \| 0.4525 \| \| 0.3624 \| 1.4877 \| 1014 \| 0.4457 \| \| 0.359 \| 1.7376 \| 1183 \| 0.4393 \| \| 0.375 \| 1.9874 \| 1352 \| 0.4345 \| \| 0.2899 \| 2.2260 \| 1521 \| 0.4488 \| \| 0.2848 \| 2.4759 \| 1690 \| 0.4473 \| \| 0.2935 \| 2.7257 \| 1859 \| 0.4470 \| \| 0.2065 \| 2.9756 \| 2028 \| 0.4572 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
iisking/test_2	iisking	2024-05-25T19:55:15Z	80	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-25T19:51:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jdqwoi/TooManyMixRolePlay-7B-Story_V1	jdqwoi	2024-05-25T19:53:18Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "jdqwoi/TooManyMixRolePlay-7B-Story", "jdqwoi/02", "base_model:jdqwoi/02", "base_model:merge:jdqwoi/02", "base_model:jdqwoi/TooManyMixRolePlay-7B-Story", "base_model:merge:jdqwoi/TooManyMixRolePlay-7B-Story", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T21:36:44Z	--- tags: - merge - mergekit - lazymergekit - jdqwoi/TooManyMixRolePlay-7B-Story - jdqwoi/02 base_model: - jdqwoi/TooManyMixRolePlay-7B-Story - jdqwoi/02 --- # TooManyMixRolePlay-7B-Story_V1 TooManyMixRolePlay-7B-Story_V1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jdqwoi/TooManyMixRolePlay-7B-Story](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story) * [jdqwoi/02](https://huggingface.co/jdqwoi/02) # EXL2 quants of jdqwoi/TooManyMixRolePlay-7B-Story_V1 by [kim512](https://huggingface.co/kim512) * [4.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-4.0bpw-exl2) * [5.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-5.0bpw-exl2) * [6.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-6.0bpw-exl2) * [7.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-7.0bpw-exl2) * [8.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-8.0bpw-exl2) ## 🧩 Configuration ```yaml slices: - sources: - model: jdqwoi/TooManyMixRolePlay-7B-Story layer_range: [0, 32] - model: jdqwoi/02 layer_range: [0, 32] merge_method: slerp base_model: jdqwoi/TooManyMixRolePlay-7B-Story parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "jdqwoi/TooManyMixRolePlay-7B-Story_V1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf	RichardErkhov	2024-05-25T19:48:25Z	35	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T16:50:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b - GGUF - Model creator: https://huggingface.co/S4sch/ - Original model: https://huggingface.co/S4sch/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q2_K.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q2_K.gguf) \| Q2_K \| 3.95GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ3_XS.gguf) \| IQ3_XS \| 4.39GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ3_S.gguf) \| IQ3_S \| 4.63GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K_S.gguf) \| Q3_K_S \| 4.61GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ3_M.gguf) \| IQ3_M \| 4.78GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K.gguf) \| Q3_K \| 5.13GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K_M.gguf) \| Q3_K_M \| 5.13GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q3_K_L.gguf) \| Q3_K_L \| 5.58GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ4_XS.gguf) \| IQ4_XS \| 5.75GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_0.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_0.gguf) \| Q4_0 \| 6.0GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.IQ4_NL.gguf) \| IQ4_NL \| 6.06GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_K_S.gguf) \| Q4_K_S \| 6.04GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_K.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_K.gguf) \| Q4_K \| 6.38GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_K_M.gguf) \| Q4_K_M \| 6.38GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_1.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q4_1.gguf) \| Q4_1 \| 6.65GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_0.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_0.gguf) \| Q5_0 \| 7.31GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_K_S.gguf) \| Q5_K_S \| 7.31GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_K.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_K.gguf) \| Q5_K \| 7.5GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_K_M.gguf) \| Q5_K_M \| 7.5GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_1.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q5_1.gguf) \| Q5_1 \| 7.96GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q6_K.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q6_K.gguf) \| Q6_K \| 8.7GB \| \| [Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q8_0.gguf](https://huggingface.co/RichardErkhov/S4sch_-_Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-gguf/blob/main/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b.Q8_0.gguf) \| Q8_0 \| 11.27GB \| Original model description: --- license: apache-2.0 --- Frankenmerge 11b between teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-1 GGUF: https://huggingface.co/TheBloke/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-GGUF Merge with the following conditions - model: teknium/OpenHermes-2.5-Mistral-7B layer_range: [0, 8] - model: Intel/neural-chat-7b-v3-1 layer_range: [4, 12] - model: teknium/OpenHermes-2.5-Mistral-7B layer_range: [9, 16] - model: Intel/neural-chat-7b-v3-1 layer_range: [13, 20] - model: teknium/OpenHermes-2.5-Mistral-7B layer_range: [17, 24] - model: Intel/neural-chat-7b-v3-1 layer_range: [21, 28] - model: teknium/OpenHermes-2.5-Mistral-7B layer_range: [25, 32] merge_method: passthrough Benchmarks are coming soon...
cxx5208/NER_finetuned	cxx5208	2024-05-25T19:48:11Z	109	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-25T04:53:10Z	# DistilBERT Fine-Tuned for Named Entity Recognition (NER) ![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-yellow) ![DistilBERT](https://img.shields.io/badge/Model-DistilBERT-blue) ![NER](https://img.shields.io/badge/Task-NER-green) This repository contains a DistilBERT model fine-tuned for Named Entity Recognition (NER). The model has been trained to identify and classify named entities such as names of people, places, organizations, and dates in text. ## Model Details - Model: [DistilBERT](https://huggingface.co/distilbert-base-cased) - Task: Named Entity Recognition (NER) - Training Dataset: Custom dataset - Evaluation Metrics: Precision, Recall, F1-Score, Accuracy ## Usage You can use this model with the Hugging Face `transformers` library to perform NER on your text data. Below are examples of how to use the model and tokenizer. ### Installation First, make sure you have the `transformers` library installed: ```bash pip install transformers ``` ### Load the Model ```python from transformers import pipeline # Load the model and tokenizer token_classifier = pipeline( "token-classification", model="cxx5208/NER_finetuned", tokenizer="cxx5208/NER_finetuned", aggregation_strategy="simple" ) # Example text text = "My name is Yeshvanth Raju Kurapati. I study at San Jose State University" # Perform NER entities = token_classifier(text) print(entities) ``` ### Example Output ```python [ {'entity_group': 'PER', 'score': 0.99808735, 'word': 'Yeshvanth Raju Kurapati', 'start': 11, 'end': 34}, {'entity_group': 'ORG', 'score': 0.9923826, 'word': 'San Jose State University', 'start': 47, 'end': 72} ] ``` ## Training Details The model was fine-tuned using the following hyperparameters: - Batch Size: 16 - Learning Rate: 5e-5 - Epochs: 3 - Optimizer: AdamW The training process involved using a standard NER dataset (e.g., CoNLL-2003) and included steps for tokenization, data preprocessing, and evaluation. ## Evaluation The model was evaluated using precision, recall, F1-score, and accuracy metrics. The performance metrics are as follows: - Precision: 0.952 - Recall: 0.948 - F1-Score: 0.950 - Accuracy: 0.975 ## About DistilBERT DistilBERT is a smaller, faster, cheaper version of BERT developed by Hugging Face. It retains 97% of BERT’s language understanding capabilities while being 60% faster and 40% smaller. ## License This model is released under the [MIT License](LICENSE). ## Acknowledgements - Hugging Face for the [transformers](https://github.com/huggingface/transformers) library and DistilBERT model. - The authors of the original dataset used for training.
Arbi-Houssem/speecht5_ar_tn_1.1	Arbi-Houssem	2024-05-25T19:46:15Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "arr", "dataset:Arbi-Houssem/datasetSTT-TTS", "base_model:MBZUAI/speecht5_tts_clartts_ar", "base_model:finetune:MBZUAI/speecht5_tts_clartts_ar", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-05-25T17:30:54Z	--- language: - arr license: mit base_model: MBZUAI/speecht5_tts_clartts_ar tags: - generated_from_trainer datasets: - Arbi-Houssem/datasetSTT-TTS model-index: - name: SpeechT5 TTS Tunisien 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. --> # SpeechT5 TTS Tunisien This model is a fine-tuned version of [MBZUAI/speecht5_tts_clartts_ar](https://huggingface.co/MBZUAI/speecht5_tts_clartts_ar) on the datasetSTT-TTS dataset. It achieves the following results on the evaluation set: - Loss: 0.5464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.544 \| 14.2857 \| 200 \| 0.6401 \| \| 0.4869 \| 28.5714 \| 400 \| 0.5907 \| \| 0.4384 \| 42.8571 \| 600 \| 0.5684 \| \| 0.4069 \| 57.1429 \| 800 \| 0.5577 \| \| 0.3992 \| 71.4286 \| 1000 \| 0.5464 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lmbelo/OpenELM-270M-Function-Calling	lmbelo	2024-05-25T19:40:41Z	13	0	mlx	[ "mlx", "safetensors", "openelm", "custom_code", "license:other", "region:us" ]	null	2024-05-25T19:39:27Z	--- license: other tags: - mlx - mlx license_name: apple-sample-code-license license_link: LICENSE --- # lmbelo/OpenELM-270M-Function-Calling The Model [lmbelo/OpenELM-270M-Function-Calling](https://huggingface.co/lmbelo/OpenELM-270M-Function-Calling) was converted to MLX format from [lmbelo/OpenELM-270M-Instruct](https://huggingface.co/lmbelo/OpenELM-270M-Instruct) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("lmbelo/OpenELM-270M-Function-Calling") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
hgnoi/QN1aO8xC5BYkXdLR	hgnoi	2024-05-25T19:40:01Z	77	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T19:37:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Pawankumarr009/ppo-LunarLander-v2	Pawankumarr009	2024-05-25T19:38:16Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-25T19:38:01Z	--- 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: 291.64 +/- 13.87 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mayflowergmbh/Llama3-DiscoLeo-Instruct-8B-32k-v0.1-GGUF	mayflowergmbh	2024-05-25T19:33:43Z	55	2	transformers	[ "transformers", "gguf", "de", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T19:15:31Z	--- language: - de license: llama3 library_name: transformers tags: - gguf --- # # Llama3-DiscoLeo-Instruct 8B 32k-context (version 0.1) ## Thanks and Accreditation [DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1](https://huggingface.co/collections/DiscoResearch/discoleo-8b-llama3-for-german-6650527496c0fafefd4c9729) is the result of a joint effort between [DiscoResearch](https://huggingface.co/DiscoResearch) and [Occiglot](https://huggingface.co/occiglot) with support from the [DFKI](https://www.dfki.de/web/) (German Research Center for Artificial Intelligence) and [hessian.Ai](https://hessian.ai). Occiglot kindly handled data preprocessing, filtering, and deduplication as part of their latest [dataset release](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5), as well as sharing their compute allocation at hessian.Ai's 42 Supercomputer. ## Model Overview DiscoResearch/Llama3_DiscoLeo_Instruct_8B_32k_v0.1 is an instruction tuned version of our long-context [Llama3-German-8B-32k](https://huggingface.co/DiscoResearch/Llama3_German_8B_32k). The base model was derived from [Meta's Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) through continuous pretraining on 65 billion high-quality German tokens, similar to previous [LeoLM](https://huggingface.co/LeoLM) or [Occiglot](https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01) models. For the long-context version we trained on an additional 100 million tokens at 32k context length, using a rope_theta value of 1.5e6 and a learning rate of 1.5e-5 with a batch size of 2568192 and otherwise equal hyperparameters to the base model. We finetuned this checkpoint on the German Instruction dataset from DiscoResearch created by [Jan-Philipp Harries](https://huggingface.co/jphme) and [Daniel Auras](https://huggingface.co/rasdani) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)). ## How to use Llama3_DiscoLeo_Instruct_8B_32k_v0.1 uses the [Llama-3 chat template](https://github.com/meta-llama/llama3?tab=readme-ov-file#instruction-tuned-models), which can be easily used with [transformer's chat templating](https://huggingface.co/docs/transformers/main/en/chat_templating). See [below](https://huggingface.co/DiscoResearch/Llama3_DiscoLeo_Instruct_8B_32k_v0.1#usage-example) for a usage example. ## Model Training and Hyperparameters The model was full-fintuned with axolotl on the [hessian.Ai 42](hessian.ai) with 32,768 context-length, learning rate 2e-5 and batch size of 16. ## Evaluation and Results We evaluated the model using a suite of common English Benchmarks and their German counterparts with [GermanBench](https://github.com/bjoernpl/GermanBenchmark). In the below image and corresponding table, you can see the benchmark scores for the different instruct models compared to Metas instruct version. All checkpoints are available in this [collection](https://huggingface.co/collections/DiscoResearch/discoleo-8b-llama3-for-german-6650527496c0fafefd4c9729). ![instruct scores](instruct_model_benchmarks.png) \| Model \| truthful_qa_de \| truthfulqa_mc \| arc_challenge \| arc_challenge_de \| hellaswag \| hellaswag_de \| MMLU \| MMLU-DE \| mean \| \|----------------------------------------------------\|----------------\|---------------\|---------------\|------------------\|-------------\|--------------\|-------------\|-------------\|-------------\| \| meta-llama/Meta-Llama-3-8B-Instruct \| 0.47498 \| 0.43923 \| 0.59642* \| 0.47952 \| 0.82025 \| 0.60008 \| 0.66658 \| 0.53541 \| 0.57656 \| \| DiscoResearch/Llama3-German-8B \| 0.49499 \| 0.44838 \| 0.55802 \| 0.49829 \| 0.79924 \| 0.65395 \| 0.62240 \| 0.54413 \| 0.57743 \| \| DiscoResearch/Llama3-German-8B-32k \| 0.48920 \| 0.45138 \| 0.54437 \| 0.49232 \| 0.79078 \| 0.64310 \| 0.58774 \| 0.47971 \| 0.55982 \| \| DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1 \| 0.53042 \| 0.52867 \| 0.59556 \| 0.53839 \| 0.80721 \| 0.66440 \| 0.61898 \| 0.56053 \| 0.60552 \| \| DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1 \| 0.52749 \| 0.53245 \| 0.58788 \| 0.53754 \| 0.80770 \| 0.66709 \| 0.62123 \| 0.56238 \| 0.60547 \| ## Model Configurations We release DiscoLeo-8B in the following configurations: 1. [Base model with continued pretraining](https://huggingface.co/DiscoResearch/Llama3-German_8B) 2. [Long-context version (32k context length)](https://huggingface.co/DiscoResearch/Llama3_German_8B_32k) 3. [Instruction-tuned version of the base model](https://huggingface.co/DiscoResearch/Llama3_DiscoLeo_Instruct_8B_v0.1) 4. [Instruction-tuned version of the long-context model](https://huggingface.co/DiscoResearch/Llama3_DiscoLeo_Instruct_8B_32k_v0.1) (This model) 5. [Experimental `DARE-TIES` Merge with Llama3-Instruct](https://huggingface.co/DiscoResearch/Llama3_DiscoLeo_8B_DARE_Experimental) 6. [Collection of Quantized versions](https://huggingface.co/collections/DiscoResearch/discoleo-8b-quants-6651bcf8f72c9a37ce485d42) ## Usage Example Here's how to use the model with transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1") prompt = "Schreibe ein Essay über die Bedeutung der Energiewende für Deutschlands Wirtschaft" messages = [ {"role": "system", "content": "Du bist ein hilfreicher Assistent."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Acknowledgements The model was trained and evaluated by [Björn Plüster](https://huggingface.co/bjoernp) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)) with data preparation and project supervision by [Manuel Brack](http://manuel-brack.eu) ([DFKI](https://www.dfki.de/web/), [TU-Darmstadt](https://www.tu-darmstadt.de/)). Initial work on dataset collection and curation was performed by [Malte Ostendorff](https://ostendorff.org) and [Pedro Ortiz Suarez](https://portizs.eu). Instruction tuning was done with the DiscoLM German dataset created by [Jan-Philipp Harries](https://huggingface.co/jphme) and [Daniel Auras](https://huggingface.co/rasdani) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)). We extend our gratitude to [LAION](https://laion.ai/) and friends, especially [Christoph Schuhmann](https://entwickler.de/experten/christoph-schuhmann) and [Jenia Jitsev](https://huggingface.co/JJitsev), for initiating this collaboration. The model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/) which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)). The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html) through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
huynq3Cyradar/bert-large-finetuned-phishing-webpage-cleaned-version	huynq3Cyradar	2024-05-25T19:31:54Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T09:33:43Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: bert-large-finetuned-phishing-webpage-cleaned-version results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-finetuned-phishing-webpage-cleaned-version This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0324 - Accuracy: 0.9911 - Precision: 0.9931 - Recall: 0.9883 - False Positive Rate: 0.0063 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| False Positive Rate \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------------------:\| \| 0.0931 \| 1.0 \| 562 \| 0.0409 \| 0.9861 \| 0.9948 \| 0.9762 \| 0.0047 \| \| 0.0345 \| 2.0 \| 1124 \| 0.0348 \| 0.9900 \| 0.9918 \| 0.9874 \| 0.0075 \| \| 0.0224 \| 3.0 \| 1687 \| 0.0324 \| 0.9911 \| 0.9931 \| 0.9883 \| 0.0063 \| \| 0.0156 \| 4.0 \| 2248 \| 0.0509 \| 0.9913 \| 0.9914 \| 0.9904 \| 0.0079 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
EternalRecursion/llm_clone_llama	EternalRecursion	2024-05-25T19:31:33Z	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	2024-04-28T19:57:04Z	--- 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: EternalRecursion - 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)

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