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allstax/20231216-045106
allstax
2023-12-16T05:07:41Z
0
0
peft
[ "peft", "safetensors", "generated_from_trainer", "base_model:AIDC-ai-business/Marcoroni-7B-v3", "license:apache-2.0", "region:us" ]
null
2023-12-16T05:07:34Z
--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: AIDC-ai-business/Marcoroni-7B-v3 model-index: - name: 20231216-045106 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. --> # 20231216-045106 This model is a fine-tuned version of [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2843 ## 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: 2.5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2541 | 0.02 | 1 | 2.1106 | | 1.5517 | 0.03 | 2 | 2.1106 | | 2.347 | 0.05 | 3 | 2.0350 | | 2.006 | 0.06 | 4 | 1.9297 | | 1.8398 | 0.08 | 5 | 1.8406 | | 1.8506 | 0.09 | 6 | 1.8406 | | 1.8804 | 0.11 | 7 | 1.7621 | | 1.9034 | 0.12 | 8 | 1.6988 | | 0.9835 | 0.14 | 9 | 1.6494 | | 1.4725 | 0.15 | 10 | 1.5915 | | 1.1824 | 0.17 | 11 | 1.5557 | | 1.3356 | 0.18 | 12 | 1.5557 | | 1.1077 | 0.2 | 13 | 1.5242 | | 1.4959 | 0.22 | 14 | 1.4908 | | 1.4881 | 0.23 | 15 | 1.4550 | | 1.2674 | 0.25 | 16 | 1.4201 | | 1.2133 | 0.26 | 17 | 1.3872 | | 1.5423 | 0.28 | 18 | 1.3521 | | 0.9551 | 0.29 | 19 | 1.3212 | | 1.1868 | 0.31 | 20 | 1.2881 | | 1.4799 | 0.32 | 21 | 1.2526 | | 1.2903 | 0.34 | 22 | 1.2154 | | 1.3357 | 0.35 | 23 | 1.1770 | | 0.868 | 0.37 | 24 | 1.1383 | | 1.1978 | 0.38 | 25 | 1.0982 | | 1.2102 | 0.4 | 26 | 1.0575 | | 0.8946 | 0.42 | 27 | 1.0575 | | 1.0671 | 0.43 | 28 | 1.0151 | | 1.0761 | 0.45 | 29 | 0.9715 | | 0.9568 | 0.46 | 30 | 0.9263 | | 0.8203 | 0.48 | 31 | 0.8848 | | 0.8601 | 0.49 | 32 | 0.8408 | | 0.8253 | 0.51 | 33 | 0.7945 | | 0.8098 | 0.52 | 34 | 0.7504 | | 0.6567 | 0.54 | 35 | 0.7110 | | 0.6545 | 0.55 | 36 | 0.6695 | | 0.6036 | 0.57 | 37 | 0.6338 | | 0.6746 | 0.58 | 38 | 0.6018 | | 0.5376 | 0.6 | 39 | 0.5731 | | 0.5674 | 0.62 | 40 | 0.5471 | | 0.4762 | 0.63 | 41 | 0.5218 | | 0.4755 | 0.65 | 42 | 0.4965 | | 0.3974 | 0.66 | 43 | 0.4756 | | 0.8437 | 0.68 | 44 | 0.4607 | | 0.4766 | 0.69 | 45 | 0.4485 | | 0.5403 | 0.71 | 46 | 0.4369 | | 0.4201 | 0.72 | 47 | 0.4244 | | 0.4326 | 0.74 | 48 | 0.4115 | | 0.4669 | 0.75 | 49 | 0.3978 | | 0.5259 | 0.77 | 50 | 0.3867 | | 0.3906 | 0.78 | 51 | 0.3782 | | 0.369 | 0.8 | 52 | 0.3724 | | 0.3314 | 0.82 | 53 | 0.3658 | | 0.4164 | 0.83 | 54 | 0.3585 | | 0.3339 | 0.85 | 55 | 0.3499 | | 0.315 | 0.86 | 56 | 0.3405 | | 0.5404 | 0.88 | 57 | 0.3340 | | 0.3136 | 0.89 | 58 | 0.3302 | | 0.3574 | 0.91 | 59 | 0.3275 | | 0.3466 | 0.92 | 60 | 0.3249 | | 0.3097 | 0.94 | 61 | 0.3226 | | 0.4212 | 0.95 | 62 | 0.3208 | | 0.3183 | 0.97 | 63 | 0.3184 | | 0.2762 | 0.98 | 64 | 0.3162 | | 0.3936 | 1.0 | 65 | 0.3143 | | 0.303 | 1.02 | 66 | 0.3128 | | 0.3813 | 1.03 | 67 | 0.3108 | | 0.3065 | 1.05 | 68 | 0.3089 | | 0.2457 | 1.06 | 69 | 0.3069 | | 0.4008 | 1.08 | 70 | 0.3053 | | 0.3131 | 1.09 | 71 | 0.3040 | | 0.2483 | 1.11 | 72 | 0.3027 | | 0.2445 | 1.12 | 73 | 0.3016 | | 0.2717 | 1.14 | 74 | 0.3003 | | 0.6308 | 1.15 | 75 | 0.2991 | | 0.3271 | 1.17 | 76 | 0.2980 | | 0.3013 | 1.18 | 77 | 0.2971 | | 0.2951 | 1.2 | 78 | 0.2960 | | 0.2514 | 1.22 | 79 | 0.2950 | | 0.2836 | 1.23 | 80 | 0.2940 | | 0.2538 | 1.25 | 81 | 0.2929 | | 0.312 | 1.26 | 82 | 0.2921 | | 0.2773 | 1.28 | 83 | 0.2912 | | 0.2957 | 1.29 | 84 | 0.2904 | | 0.3997 | 1.31 | 85 | 0.2896 | | 0.3084 | 1.32 | 86 | 0.2889 | | 0.2521 | 1.34 | 87 | 0.2882 | | 0.2873 | 1.35 | 88 | 0.2877 | | 0.2419 | 1.37 | 89 | 0.2872 | | 0.2691 | 1.38 | 90 | 0.2867 | | 0.6368 | 1.4 | 91 | 0.2864 | | 0.2299 | 1.42 | 92 | 0.2860 | | 0.4967 | 1.43 | 93 | 0.2857 | | 0.2827 | 1.45 | 94 | 0.2855 | | 0.2501 | 1.46 | 95 | 0.2853 | | 0.2285 | 1.48 | 96 | 0.2851 | | 0.269 | 1.49 | 97 | 0.2849 | | 0.2539 | 1.51 | 98 | 0.2847 | | 0.2798 | 1.52 | 99 | 0.2845 | | 0.2695 | 1.54 | 100 | 0.2843 | ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-3.0bpw-h6-exl2-2
LoneStriker
2023-12-16T05:12:07Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:10:13Z
--- license: apache-2.0 language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp) 2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) 3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B) - base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) The yaml config file for this model is here: ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.2 dtype: bfloat16 merge_method: dare_ties models: - model: mistralai/Mistral-7B-Instruct-v0.2 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp parameters: density: 0.8 weight: 0.4 - model: Q-bert/MetaMath-Cybertron-Starling parameters: density: 0.8 weight: 0.3 - model: v1olet/v1olet_marcoroni-go-bruins-merge-7B parameters: density: 0.8 weight: 0.3 parameters: int8_mask: true ``` # Prompt template: - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - **Alpaca** ``` {system_message} ### Instruction: {prompt} ### Response: ``` # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-4.0bpw-h6-exl2-2
LoneStriker
2023-12-16T05:17:49Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:15:24Z
--- license: apache-2.0 language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp) 2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) 3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B) - base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) The yaml config file for this model is here: ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.2 dtype: bfloat16 merge_method: dare_ties models: - model: mistralai/Mistral-7B-Instruct-v0.2 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp parameters: density: 0.8 weight: 0.4 - model: Q-bert/MetaMath-Cybertron-Starling parameters: density: 0.8 weight: 0.3 - model: v1olet/v1olet_marcoroni-go-bruins-merge-7B parameters: density: 0.8 weight: 0.3 parameters: int8_mask: true ``` # Prompt template: - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - **Alpaca** ``` {system_message} ### Instruction: {prompt} ### Response: ``` # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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KokeCacao/mcstuco-f23-survival
KokeCacao
2023-12-16T05:26:34Z
0
0
null
[ "region:us" ]
null
2023-12-16T05:15:59Z
Entry not found
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-5.0bpw-h6-exl2-2
LoneStriker
2023-12-16T05:23:30Z
0
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:20:33Z
--- license: apache-2.0 language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp) 2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) 3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B) - base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) The yaml config file for this model is here: ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.2 dtype: bfloat16 merge_method: dare_ties models: - model: mistralai/Mistral-7B-Instruct-v0.2 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp parameters: density: 0.8 weight: 0.4 - model: Q-bert/MetaMath-Cybertron-Starling parameters: density: 0.8 weight: 0.3 - model: v1olet/v1olet_marcoroni-go-bruins-merge-7B parameters: density: 0.8 weight: 0.3 parameters: int8_mask: true ``` # Prompt template: - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - **Alpaca** ``` {system_message} ### Instruction: {prompt} ### Response: ``` # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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greatakela/test-trainer
greatakela
2023-12-17T00:14:39Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-16T05:24:43Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: test-trainer 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. --> # test-trainer This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4362 - Accuracy: 0.8056 - F1: 0.8066 ## 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: 16 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.4436 | 1.0 | 6787 | 0.4530 | 0.7934 | 0.7896 | | 0.4519 | 2.0 | 13574 | 0.4363 | 0.8016 | 0.7982 | | 0.4187 | 3.0 | 20361 | 0.4362 | 0.8056 | 0.8066 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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beauty-beach-keto-gummies/beauty-beach-keto-gummies
beauty-beach-keto-gummies
2023-12-16T05:25:34Z
0
0
sentence-transformers
[ "sentence-transformers", "Beauty Beach Keto Gummies", "en", "license:c-uda", "region:us" ]
null
2023-12-16T05:24:52Z
--- license: c-uda language: - en library_name: sentence-transformers tags: - Beauty Beach Keto Gummies --- [Beauty Beach Keto Gummies](https://atozsupplement.com/beauty-beach-keto-gummies/) Notwithstanding, it's vital to take note of that despite the fact that they are low in carbs and sugar, they ought to in any case be consumed with some restraint as a feature of a fair diet.As forever, it's prescribed to peruse the nourishment marks and fixings list cautiously prior to buying any keto gummies to guarantee they line up with your dietary objectives and inclinations. VISIT HERE FOR OFFICIAL WEBSITE:-https://atozsupplement.com/beauty-beach-keto-gummies/
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-6.0bpw-h6-exl2-2
LoneStriker
2023-12-16T05:29:09Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:25:44Z
--- license: apache-2.0 language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp) 2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) 3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B) - base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) The yaml config file for this model is here: ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.2 dtype: bfloat16 merge_method: dare_ties models: - model: mistralai/Mistral-7B-Instruct-v0.2 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp parameters: density: 0.8 weight: 0.4 - model: Q-bert/MetaMath-Cybertron-Starling parameters: density: 0.8 weight: 0.3 - model: v1olet/v1olet_marcoroni-go-bruins-merge-7B parameters: density: 0.8 weight: 0.3 parameters: int8_mask: true ``` # Prompt template: - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - **Alpaca** ``` {system_message} ### Instruction: {prompt} ### Response: ``` # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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vihangd/dopeyshearedplats-1.3b-v1-gguf
vihangd
2023-12-16T16:00:23Z
0
0
null
[ "gguf", "license:llama2", "region:us" ]
null
2023-12-16T05:26:08Z
Invalid username or password.
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bachngo/meta-Llama-2-7b-chat-vi-wiki
bachngo
2023-12-16T05:41:08Z
0
0
transformers
[ "transformers", "pytorch", "gguf", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:26:26Z
Entry not found
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-8.0bpw-h8-exl2-2
LoneStriker
2023-12-16T05:35:24Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:30:54Z
--- license: apache-2.0 language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp) 2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) 3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B) - base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) The yaml config file for this model is here: ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.2 dtype: bfloat16 merge_method: dare_ties models: - model: mistralai/Mistral-7B-Instruct-v0.2 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp parameters: density: 0.8 weight: 0.4 - model: Q-bert/MetaMath-Cybertron-Starling parameters: density: 0.8 weight: 0.3 - model: v1olet/v1olet_marcoroni-go-bruins-merge-7B parameters: density: 0.8 weight: 0.3 parameters: int8_mask: true ``` # Prompt template: - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - **Alpaca** ``` {system_message} ### Instruction: {prompt} ### Response: ``` # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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ChavyvAkvar/Mistral-7B-Instruct-v0.2-Nectar
ChavyvAkvar
2023-12-16T05:33:21Z
0
0
null
[ "region:us" ]
null
2023-12-16T05:33:21Z
Entry not found
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aaa12963337/msi-dinat-mini-pretrain
aaa12963337
2023-12-16T09:41:42Z
0
0
transformers
[ "transformers", "safetensors", "dinat", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:shi-labs/dinat-mini-in1k-224", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-16T05:36:33Z
--- license: mit base_model: shi-labs/dinat-mini-in1k-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: msi-dinat-mini-pretrain results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8937325905292479 --- <!-- 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. --> # msi-dinat-mini-pretrain This model is a fine-tuned version of [shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5378 - Accuracy: 0.8937 ## 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: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1041 | 1.0 | 1562 | 0.4092 | 0.8756 | | 0.0527 | 2.0 | 3125 | 0.6298 | 0.8765 | | 0.0611 | 3.0 | 4686 | 0.5378 | 0.8937 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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tushar6378/mnnk
tushar6378
2023-12-16T06:28:06Z
0
0
null
[ "region:us" ]
null
2023-12-16T05:38:20Z
Entry not found
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aaa12963337/msi-resnet-18-pretrain
aaa12963337
2023-12-16T08:15:42Z
0
0
transformers
[ "transformers", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-18", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-16T05:43:34Z
--- license: apache-2.0 base_model: microsoft/resnet-18 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: msi-resnet-18-pretrain results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8675487465181059 --- <!-- 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. --> # msi-resnet-18-pretrain This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4121 - Accuracy: 0.8675 ## 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: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1724 | 1.0 | 1562 | 0.3597 | 0.8806 | | 0.0543 | 2.0 | 3125 | 0.3707 | 0.8875 | | 0.0834 | 3.0 | 4686 | 0.4121 | 0.8675 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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v2ray/SchizoGPT-8x7B-QLoRA
v2ray
2023-12-16T06:12:01Z
0
0
null
[ "safetensors", "not-for-all-audiences", "conversational", "en", "dataset:v2ray/r-chatgpt-general-dump", "license:mit", "region:us" ]
conversational
2023-12-16T05:53:05Z
--- license: mit datasets: - v2ray/r-chatgpt-general-dump language: - en pipeline_tag: conversational tags: - not-for-all-audiences --- # SchizoGPT 8x7B QLoRA Fine tuned on r/ChatGPT Discord #general dump. ## Prompt Template ``` ### Username Message(s)(Can be stacked, separated by new line) ### Username(Can have multiturn multiuser chat, separated by new line) (End of prompt) ``` Use `@username` to ping a user and `#channel name` to mention a channel. \ Prepend `<Re: username>` before a message to respond to a user. \ Use `<filename.ext>` to mention a file in a link, for example, if you have `https://example.com/image.jpg`, use `<image.jpg>`.
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linhqyy/animatediff-v3
linhqyy
2023-12-16T06:45:36Z
0
0
null
[ "region:us" ]
null
2023-12-16T05:56:17Z
Entry not found
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LoneStriker/Metis-0.1-3.0bpw-h6-exl2-2
LoneStriker
2023-12-16T05:59:38Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T05:57:45Z
--- base_model: HuggingFaceH4/mistral-7b-sft-beta inference: false license: mit license_name: mit license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE metrics: - accuracy --- [<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) It works well with long system prompts. It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension. This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset. # Prompt Format: ``` ### System: {add here the system prompt} ### Instruction: {add here the instruction} ### Response: {make sure you have a new line here} ```
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Annakingee/CalmCBDGummies
Annakingee
2023-12-16T06:00:41Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-16T06:00:23Z
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Augyfyuse/uygu
Augyfyuse
2023-12-16T06:02:37Z
0
0
null
[ "region:us" ]
null
2023-12-16T06:02:37Z
Entry not found
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LoneStriker/Metis-0.1-4.0bpw-h6-exl2-2
LoneStriker
2023-12-16T06:05:22Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:02:56Z
--- base_model: HuggingFaceH4/mistral-7b-sft-beta inference: false license: mit license_name: mit license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE metrics: - accuracy --- [<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) It works well with long system prompts. It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension. This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset. # Prompt Format: ``` ### System: {add here the system prompt} ### Instruction: {add here the instruction} ### Response: {make sure you have a new line here} ```
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salma-remyx/corgy_dog_LoRA
salma-remyx
2023-12-16T06:03:19Z
0
0
diffusers
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-12-16T06:03:18Z
--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK dog license: openrail++ --- # SDXL LoRA DreamBooth - salma-remyx/corgy_dog_LoRA <Gallery /> ## Model description These are salma-remyx/corgy_dog_LoRA 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 TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](salma-remyx/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
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seojin0128/hw-midm-7B-nsmc
seojin0128
2023-12-16T12:16:23Z
0
0
peft
[ "peft", "safetensors", "dataset:nsmc", "base_model:KT-AI/midm-bitext-S-7B-inst-v1", "region:us" ]
null
2023-12-16T06:07:51Z
--- library_name: peft base_model: KT-AI/midm-bitext-S-7B-inst-v1 datasets: - nsmc metrics: - accuracy 90.0% --- # Model Card for Model ID ## Model Description ### midm-bitext-S-7B-inst-v1 미세 튜닝 해당 모델은 네이버 영화 리뷰 데이터셋인 NSMC에 대해 KT-AI/midm-bitext-S-7B-inst-v1을 미세튜닝한 모델입니다. 영화 리뷰 텍스트를 프롬프트에 포함하여 모델에 입력시,'긍정' 또는 '부정' 이라고 예측 텍스트를 직접 생성합니다. 결과적으로, 정확도 90.0%를 가지는 모델을 완성했습니다. ### Train, Test 데이터셋 해당 모델은 NSMC의 train 데이터의 상위 2,000개의 샘플을 학습에 사용했습니다. 해당 모델은 NSMC의 test 데이터의 상위 1,000개의 샘플을 평가에 사용했습니다. ## Training_step_loss ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651bf3be3fa6c4e182910420/WIBCaCIemHUS1QXqKzyPy.png) ## Confusion_Matrix ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651bf3be3fa6c4e182910420/X2nTz9ltBFbAeqWJBfQz_.png) ## Accuracy_Classification_Report ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651bf3be3fa6c4e182910420/GJpzAVO7ee3cQcbHxiYSG.png) ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0
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LoneStriker/Metis-0.1-5.0bpw-h6-exl2-2
LoneStriker
2023-12-16T06:11:01Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:08:05Z
--- base_model: HuggingFaceH4/mistral-7b-sft-beta inference: false license: mit license_name: mit license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE metrics: - accuracy --- [<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) It works well with long system prompts. It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension. This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset. # Prompt Format: ``` ### System: {add here the system prompt} ### Instruction: {add here the instruction} ### Response: {make sure you have a new line here} ```
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AI-Wheelz/NN-TraumaV2
AI-Wheelz
2023-12-16T06:09:53Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T06:08:23Z
--- license: openrail ---
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vivekdugale/Llama-2-7b-chat-finetuned_e50_172
vivekdugale
2023-12-16T17:55:04Z
0
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:08:50Z
Entry not found
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ConnieLawso/BenCarsonCBDGummies
ConnieLawso
2023-12-16T06:09:22Z
0
0
null
[ "region:us" ]
null
2023-12-16T06:08:58Z
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LoneStriker/Metis-0.1-6.0bpw-h6-exl2-2
LoneStriker
2023-12-16T06:16:48Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:13:21Z
--- base_model: HuggingFaceH4/mistral-7b-sft-beta inference: false license: mit license_name: mit license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE metrics: - accuracy --- [<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) It works well with long system prompts. It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension. This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset. # Prompt Format: ``` ### System: {add here the system prompt} ### Instruction: {add here the instruction} ### Response: {make sure you have a new line here} ```
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gatarnali/PureHarmonyCBDGummiesshop
gatarnali
2023-12-16T06:21:01Z
0
0
null
[ "region:us" ]
null
2023-12-16T06:14:39Z
<p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Official Website ➲➲➲&nbsp;</span></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jY7U7ZNdxZI"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jY7U7ZNdxZI</span></u></a></p> <p><br></p> <p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Product Name</span><strong><span style="background-color:#ffffff;font-size:18pt;">&nbsp;➲➲➲&nbsp;</span></strong><a href="https://www.facebook.com/PureHarmonyCBDGummiesInUS/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:18pt;">Pure Harmony CBD Gummies</span></u></a></p> <p><br></p> <p style="text-align: justify;"><span style="background-color:#ffffff;font-size:15pt;">In this thorough survey, we will investigate the key parts, elements, benefits, and burdens of consuming Unadulterated &nbsp;Pure Harmony CBD Gummies.&nbsp;</span></p> <p><br></p> <p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Official facebook &nbsp;Link ➲➲➲&nbsp;</span></p> <p style="text-align: justify;"><a href="https://www.facebook.com/PureHarmonyCBDGummiesInUS/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://www.facebook.com/PureHarmonyCBDGummiesInUS/</span></u></a></p> <p><br></p> <p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Official Blogs ➲➲➲</span></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bdIuanVqaSo"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bdIuanVqaSo</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xziKYac3e08"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xziKYac3e08</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Dmp2FKu2oAk"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Dmp2FKu2oAk</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/RHYyXhGF1bc"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/RHYyXhGF1bc</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/fVWUATRRXuM"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/fVWUATRRXuM</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/spE-984vkmE"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/spE-984vkmE</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jAvshYs-xjw"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jAvshYs-xjw</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/rI0w7MrH4Og"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/rI0w7MrH4Og</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/yWIUROCJzUA"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/yWIUROCJzUA</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/vd3_UjjlwkA"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/vd3_UjjlwkA</span></u></a></p> <p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/VFt_-d_YbY0"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/VFt_-d_YbY0</span></u></a></p> <p style="text-align: justify;"><a href="https://www.linkedin.com/events/pureharmonycbdgummies7140230514451312640/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://www.linkedin.com/events/pureharmonycbdgummies7140230514451312640/</span></u></a></p> <p style="text-align: justify;"><a href="https://www.linkedin.com/events/pureharmonycbdgummiesreviews7140230816466362368/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://www.linkedin.com/events/pureharmonycbdgummiesreviews7140230816466362368/</span></u></a></p>
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Rijgersberg/GEITje-7B-chat-gguf
Rijgersberg
2023-12-16T08:03:09Z
0
2
null
[ "gguf", "GEITje", "conversational", "nl", "dataset:Rijgersberg/no_robots_nl", "dataset:Rijgersberg/ultrachat_10k_nl", "license:apache-2.0", "region:us" ]
conversational
2023-12-16T06:14:50Z
--- license: apache-2.0 datasets: - Rijgersberg/no_robots_nl - Rijgersberg/ultrachat_10k_nl language: - nl pipeline_tag: conversational tags: - GEITje --- GEITje-7B-chat GGUF q8_0 ======================== GGUF version of [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat), using the `q8_0` settings. Produced with [llama.ccp](https://github.com/ggerganov/llama.cpp): ```bash $ python3 convert.py GEITje-7B-chat-hf \ --outfile GEITje-7B-chat.gguf \ --outtype q8_0 ``` For more info on GEITje-chat, please visit: - [README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub - [Model card](https://huggingface.co/Rijgersberg/GEITje-7B-chat) on 🤗 Hugging Face Models
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LoneStriker/Metis-0.1-8.0bpw-h8-exl2-2
LoneStriker
2023-12-16T06:23:00Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:18:31Z
--- base_model: HuggingFaceH4/mistral-7b-sft-beta inference: false license: mit license_name: mit license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE metrics: - accuracy --- [<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) It works well with long system prompts. It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension. This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset. # Prompt Format: ``` ### System: {add here the system prompt} ### Instruction: {add here the instruction} ### Response: {make sure you have a new line here} ```
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rebirthcbdgummies-ca/rebirth-cbd-gummies-canada
rebirthcbdgummies-ca
2023-12-16T06:23:03Z
0
0
null
[ "rebirth-cbd-gummies-canada", "en", "license:openrail", "region:us" ]
null
2023-12-16T06:20:11Z
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gatarnali/PureHarmonyCBDGummiessale
gatarnali
2023-12-16T06:22:11Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T06:22:10Z
--- license: openrail ---
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homunculus/Reinforce-CartPole-v1
homunculus
2023-12-16T06:24:47Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T06:24:38Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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
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RafaelBds1/Valentina
RafaelBds1
2023-12-16T06:35:55Z
0
0
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-12-16T06:26:27Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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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]
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aaa12963337/msi-dinat-mini
aaa12963337
2023-12-16T15:07:03Z
0
0
transformers
[ "transformers", "safetensors", "dinat", "image-classification", "generated_from_trainer", "dataset:imagefolder", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-16T06:26:43Z
--- tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - f1 - precision - recall model-index: - name: msi-dinat-mini results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.6307660050321499 - name: F1 type: f1 value: 0.45316219853017287 - name: Precision type: precision value: 0.6338497176777182 - name: Recall type: recall value: 0.3526379379782521 --- <!-- 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. --> # msi-dinat-mini This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8735 - Accuracy: 0.6308 - F1: 0.4532 - Precision: 0.6338 - Recall: 0.3526 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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 | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.5414 | 1.0 | 2015 | 0.7584 | 0.5874 | 0.3960 | 0.5427 | 0.3117 | | 0.4715 | 2.0 | 4031 | 0.7695 | 0.6208 | 0.4593 | 0.6021 | 0.3712 | | 0.4159 | 3.0 | 6047 | 0.7922 | 0.6230 | 0.4637 | 0.6056 | 0.3757 | | 0.3774 | 4.0 | 8063 | 0.8166 | 0.6286 | 0.4589 | 0.6235 | 0.3630 | | 0.3635 | 5.0 | 10078 | 0.8123 | 0.6349 | 0.4889 | 0.6225 | 0.4026 | | 0.3471 | 6.0 | 12094 | 0.8481 | 0.6265 | 0.4575 | 0.6186 | 0.3630 | | 0.3616 | 7.0 | 14110 | 0.8605 | 0.6284 | 0.4514 | 0.6279 | 0.3524 | | 0.3517 | 8.0 | 16126 | 0.8661 | 0.6329 | 0.4600 | 0.6356 | 0.3604 | | 0.3476 | 9.0 | 18141 | 0.8631 | 0.6330 | 0.4619 | 0.6346 | 0.3631 | | 0.3469 | 10.0 | 20150 | 0.8735 | 0.6308 | 0.4532 | 0.6338 | 0.3526 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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kato96/Starter
kato96
2023-12-16T06:37:58Z
0
0
null
[ "region:us" ]
null
2023-12-16T06:36:57Z
Entry not found
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aniketr/WikiBias21
aniketr
2023-12-17T07:55:34Z
0
0
null
[ "text-to-image", "license:apache-2.0", "region:us" ]
text-to-image
2023-12-16T06:40:40Z
--- license: apache-2.0 pipeline_tag: text-to-image ---
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beefsupreme321/b3x6-k1so-v1s8-0
beefsupreme321
2023-12-16T06:42:10Z
0
0
null
[ "safetensors", "autotrain", "text-generation", "license:other", "region:us" ]
text-generation
2023-12-16T06:42:06Z
--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
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sogeeking/vqvae_burgers_2
sogeeking
2023-12-18T00:07:53Z
0
0
transformers
[ "transformers", "safetensors", "pdetokenizer", "endpoints_compatible", "region:us" ]
null
2023-12-16T06:45:17Z
Entry not found
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tiennguyen/VNCOOK
tiennguyen
2023-12-16T07:34:42Z
0
0
null
[ "image-classification", "arxiv:1910.09700", "license:mit", "region:us" ]
image-classification
2023-12-16T06:49:09Z
--- license: mit metrics: - accuracy pipeline_tag: image-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **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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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]
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alwint3r/my_awesome_eli5_clm-model
alwint3r
2023-12-16T07:31:14Z
0
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:52:45Z
--- base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7387 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.8743 | 1.0 | 1120 | 3.7583 | | 3.7694 | 2.0 | 2240 | 3.7403 | | 3.7272 | 3.0 | 3360 | 3.7387 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.0 - Datasets 2.15.0 - Tokenizers 0.15.0
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sign/signwriting-illustration
sign
2023-12-16T06:54:37Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-16T06:54:37Z
--- license: mit ---
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PeterV09/mistral-7b-300k-10k-a100-6e-valid-1
PeterV09
2023-12-16T07:03:21Z
0
0
transformers
[ "transformers", "pytorch", "mistral", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T06:55:39Z
--- tags: - generated_from_trainer model-index: - name: mistral-7b-300k-10k-a100-6e-valid-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-300k-10k-a100-6e-valid-1 This model was trained from scratch 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 512 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6.0 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
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HsiehMengTing/esm2_t12_35M_UR50D-finetuned-localization
HsiehMengTing
2023-12-16T08:38:06Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "esm", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-16T06:58:07Z
Entry not found
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johnslegers/testlora4
johnslegers
2023-12-16T14:48:11Z
0
0
null
[ "art", "stable-diffusion", "lora", "ja", "license:mit", "license:openrail", "region:us" ]
null
2023-12-16T06:58:49Z
--- license: [mit, openrail] language: - ja tags: - art - stable-diffusion - lora ---
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goosebok/maximkrutoi
goosebok
2023-12-16T07:02:44Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T07:01:48Z
--- license: openrail ---
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kat1203/114514
kat1203
2023-12-16T07:09:14Z
0
0
null
[ "region:us" ]
null
2023-12-16T07:09:14Z
Entry not found
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lpkr/cnn_summarization
lpkr
2023-12-16T15:05:36Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
2023-12-16T07:09:16Z
Entry not found
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Echo22/mini-clip4clip-tokenizer
Echo22
2023-12-16T07:19:03Z
0
0
null
[ "region:us" ]
null
2023-12-16T07:16:17Z
Entry not found
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donmanjo/manjo1
donmanjo
2023-12-16T07:26:26Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-16T07:21:27Z
--- license: mit ---
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eunju365/videomae-base-finetuned-ucf101-subset
eunju365
2023-12-16T08:59:52Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "endpoints_compatible", "region:us" ]
video-classification
2023-12-16T07:24:15Z
Entry not found
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cyrmaster/mlc-chat-Mistral-7B-Instruct-v0.2-q3f16_1-768
cyrmaster
2023-12-16T14:10:11Z
0
0
null
[ "region:us" ]
null
2023-12-16T07:24:49Z
Entry not found
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stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep10
stvhuang
2023-12-16T07:32:22Z
0
0
transformers
[ "transformers", "safetensors", "deberta-v2", "feature-extraction", "endpoints_compatible", "region:us" ]
feature-extraction
2023-12-16T07:31:14Z
Entry not found
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Ericahooooo/testmodel
Ericahooooo
2023-12-16T08:33:49Z
0
0
transformers
[ "transformers", "tf", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T07:31:17Z
--- license: mit ---
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zi4dbek/pan
zi4dbek
2023-12-16T07:51:36Z
0
0
null
[ "region:us" ]
null
2023-12-16T07:31:59Z
Entry not found
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coivmn/sullyoon
coivmn
2023-12-16T07:33:03Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T07:32:19Z
--- license: openrail ---
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lazed-outt-for-real24/MatureScreamLiveMJ
lazed-outt-for-real24
2023-12-16T07:36:27Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T07:34:41Z
--- license: openrail ---
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praneshgunner/llama-2-7b-chat-medical-gguf
praneshgunner
2023-12-16T07:40:04Z
0
0
null
[ "region:us" ]
null
2023-12-16T07:35:48Z
Entry not found
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owanr/SBIC-roberta-base-inter-sorted-human_annots_alpha0.5_whole_1e-05
owanr
2023-12-16T07:38:13Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-16T07:37:51Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: SBIC-roberta-base-inter-sorted-human_annots_alpha0.5_whole_1e-05 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. --> # SBIC-roberta-base-inter-sorted-human_annots_alpha0.5_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9115 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.801 | 1.0 | 12516 | 0.8770 | | 0.6653 | 2.0 | 25032 | 0.9115 | | 0.6617 | 3.0 | 37548 | 0.9115 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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Mokado/ModelosVoz
Mokado
2023-12-16T07:41:12Z
0
0
null
[ "region:us" ]
null
2023-12-16T07:40:16Z
Entry not found
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puraviveuk/Puraviveuk
puraviveuk
2023-12-16T07:43:03Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-12-16T07:43:03Z
--- license: creativeml-openrail-m ---
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arun3936/nllb-200-3.3B-Malayalam_English_Translationt_nllb
arun3936
2023-12-17T23:58:43Z
0
0
null
[ "tensorboard", "safetensors", "region:us" ]
null
2023-12-16T07:45:40Z
Entry not found
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lombardata/dino-large-2023_12_16-kornia_img-size518_batch-size32_epochs20
lombardata
2023-12-16T11:49:01Z
0
0
transformers
[ "transformers", "pytorch", "dinov2", "generated_from_trainer", "base_model:facebook/dinov2-large", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-12-16T07:55:46Z
--- license: apache-2.0 base_model: facebook/dinov2-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: dino-large-2023_12_16-kornia_img-size518_batch-size32_epochs20 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. --> # dino-large-2023_12_16-kornia_img-size518_batch-size32_epochs20 This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1612 - F1 Micro: 0.7696 - F1 Macro: 0.7065 - Roc Auc: 0.8666 - Accuracy: 0.4241 - Learning Rate: 0.01 ## 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: 0.01 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:----:| | No log | 1.0 | 268 | 0.2010 | 0.6522 | 0.5378 | 0.7692 | 0.4098 | 0.01 | | 0.2142 | 2.0 | 536 | 0.1802 | 0.7267 | 0.6296 | 0.8364 | 0.4055 | 0.01 | | 0.2142 | 3.0 | 804 | 0.1584 | 0.7616 | 0.7017 | 0.8440 | 0.4530 | 0.01 | | 0.1898 | 4.0 | 1072 | 0.1590 | 0.7612 | 0.6768 | 0.8548 | 0.4337 | 0.01 | | 0.1898 | 5.0 | 1340 | 0.1612 | 0.7696 | 0.7065 | 0.8666 | 0.4241 | 0.01 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1
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yeeyea/Yoon-Suk-Yeol
yeeyea
2023-12-16T07:57:19Z
0
0
null
[ "license:unknown", "region:us" ]
null
2023-12-16T07:56:06Z
--- license: unknown ---
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AnonymousAuthorsForISSTA2024/LLM4Fin
AnonymousAuthorsForISSTA2024
2023-12-16T08:47:18Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-16T07:56:54Z
--- license: mit --- # Introduction This is model for LLM4FIN, including two models based on rule filtering model and rule element extraction model. To run the rule filtering model, use ``` model = AutoModelForSequenceClassification.from_pretrained("rule_filtering", num_labels=3) tokenizer = AutoTokenizer.from_pretrained("rule_filtering") ``` to load the model and tokenizer. To run the rule element extraction model, use ``` model = AutoModelForTokenClassification.from_pretrained("rule_element_extraction", num_labels=num_labels) tokenizer = AutoTokenizer.from_pretrained("rule_element_extraction") ``` to load the model and tokenizer.
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freeloader3/odmb
freeloader3
2023-12-17T15:38:45Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:02:01Z
Entry not found
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UsamaJ07/falcon-7b-instruct-ft-adapters
UsamaJ07
2023-12-16T08:02:29Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:02:29Z
Entry not found
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owanr/SBIC-roberta-base-inter-sorted-model_annots_alpha0.5_whole_1e-05
owanr
2023-12-16T08:04:25Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-16T08:04:05Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: SBIC-roberta-base-inter-sorted-model_annots_alpha0.5_whole_1e-05 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. --> # SBIC-roberta-base-inter-sorted-model_annots_alpha0.5_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9928 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.9791 | 1.0 | 12516 | 1.9092 | | 1.9953 | 2.0 | 25032 | 1.9928 | | 2.1287 | 3.0 | 37548 | 1.9928 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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sdpkjc/Phoenix-v5-dqn_atari-seed1
sdpkjc
2023-12-16T08:08:18Z
0
0
cleanrl
[ "cleanrl", "tensorboard", "ALE/Phoenix-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T08:07:50Z
--- tags: - ALE/Phoenix-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/Phoenix-v5 type: ALE/Phoenix-v5 metrics: - type: mean_reward value: 4097.00 +/- 846.48 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **ALE/Phoenix-v5** This is a trained model of a DQN agent playing ALE/Phoenix-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[dqn_atari]" python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/Phoenix-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/sdpkjc/Phoenix-v5-dqn_atari-seed1/raw/main/dqn_atari.py curl -OL https://huggingface.co/sdpkjc/Phoenix-v5-dqn_atari-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/sdpkjc/Phoenix-v5-dqn_atari-seed1/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/Phoenix-v5 --track ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'ALE/Phoenix-v5', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'sdpkjc', 'learning_rate': 0.0001, 'learning_starts': 80000, 'num_envs': 1, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
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simonyeung/ediff11
simonyeung
2023-12-16T08:24:56Z
0
0
transformers
[ "transformers", "pytorch", "bert", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-12-16T08:08:31Z
--- license: apache-2.0 ---
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sdhsdashdkh/c
sdhsdashdkh
2023-12-16T11:53:51Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:10:00Z
Entry not found
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stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep15
stvhuang
2023-12-16T08:21:34Z
0
0
transformers
[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]
feature-extraction
2023-12-16T08:18:46Z
Entry not found
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Hazzzardous/RWKV-V5-1b5-Distilled-Translations-Unvalidated
Hazzzardous
2023-12-16T08:25:27Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-16T08:19:45Z
--- license: mit ---
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aaa12963337/msi-resnet-18
aaa12963337
2023-12-16T12:43:38Z
0
0
transformers
[ "transformers", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-16T08:23:08Z
--- tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - f1 - precision - recall model-index: - name: msi-resnet-18 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.6336664802907465 - name: F1 type: f1 value: 0.5299313932110667 - name: Precision type: precision value: 0.5977139389034999 - name: Recall type: recall value: 0.4759565042287555 --- <!-- 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. --> # msi-resnet-18 This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6854 - Accuracy: 0.6337 - F1: 0.5299 - Precision: 0.5977 - Recall: 0.4760 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.499 | 1.0 | 2015 | 0.7028 | 0.6189 | 0.4730 | 0.5911 | 0.3942 | | 0.4738 | 2.0 | 4031 | 0.7003 | 0.6268 | 0.4981 | 0.5979 | 0.4268 | | 0.4788 | 3.0 | 6047 | 0.7195 | 0.6148 | 0.4517 | 0.5906 | 0.3657 | | 0.4523 | 4.0 | 8060 | 0.6854 | 0.6337 | 0.5299 | 0.5977 | 0.4760 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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sogeeking/vqvae_burgers_8
sogeeking
2023-12-18T00:28:22Z
0
0
transformers
[ "transformers", "safetensors", "pdetokenizer", "endpoints_compatible", "region:us" ]
null
2023-12-16T08:24:56Z
Entry not found
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arnabdhar/roberta-base-ontonotes
arnabdhar
2023-12-16T08:25:50Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "pytorch", "RobertaForTokenClassification", "ontonotes", "named-entity-recognition", "roberta-base", "generated_from_trainer", "en", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-16T08:25:14Z
--- language: - en license: mit base_model: roberta-base tags: - pytorch - RobertaForTokenClassification - ontonotes - named-entity-recognition - roberta-base - generated_from_trainer metrics: - recall - precision - f1 - accuracy model-index: - name: roberta-base-ontonotes 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-base-ontonotes This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tner/ontonotes5 dataset. It achieves the following results on the evaluation set: - Loss: 0.0662 - Recall: 0.9255 - Precision: 0.9002 - F1: 0.9127 - Accuracy: 0.9826 ## 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: 4e-05 - train_batch_size: 32 - eval_batch_size: 160 - seed: 75241309 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - training_steps: 6000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| | 0.1264 | 0.31 | 600 | 0.1044 | 0.8716 | 0.8475 | 0.8594 | 0.9739 | | 0.0968 | 0.63 | 1200 | 0.0838 | 0.8998 | 0.8591 | 0.8790 | 0.9769 | | 0.0881 | 0.94 | 1800 | 0.0768 | 0.9056 | 0.8759 | 0.8905 | 0.9788 | | 0.0738 | 1.25 | 2400 | 0.0763 | 0.9048 | 0.8827 | 0.8936 | 0.9790 | | 0.0606 | 1.56 | 3000 | 0.0714 | 0.9190 | 0.8845 | 0.9014 | 0.9808 | | 0.058 | 1.88 | 3600 | 0.0663 | 0.9231 | 0.8973 | 0.9100 | 0.9820 | | 0.0379 | 2.19 | 4200 | 0.0667 | 0.9240 | 0.8959 | 0.9098 | 0.9822 | | 0.0332 | 2.5 | 4800 | 0.0664 | 0.9227 | 0.9008 | 0.9117 | 0.9825 | | 0.0404 | 2.82 | 5400 | 0.0663 | 0.9247 | 0.9003 | 0.9123 | 0.9824 | | 0.032 | 3.13 | 6000 | 0.0662 | 0.9255 | 0.9002 | 0.9127 | 0.9826 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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iknowai/Yi-6B-qlora-finetuned-french
iknowai
2023-12-16T08:25:26Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:01-ai/Yi-6B", "region:us" ]
null
2023-12-16T08:25:19Z
--- library_name: peft base_model: 01-ai/Yi-6B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0
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aaa12963337/efficientnet-b2-pretrain
aaa12963337
2023-12-16T08:29:08Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:29:08Z
Entry not found
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owanr/SBIC-roberta-base-intra-sorted-model_annots_alpha0.5_whole_1e-05
owanr
2023-12-16T08:30:30Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-16T08:30:11Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: SBIC-roberta-base-intra-sorted-model_annots_alpha0.5_whole_1e-05 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. --> # SBIC-roberta-base-intra-sorted-model_annots_alpha0.5_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6884 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6262 | 1.0 | 12516 | 1.6889 | | 1.6889 | 2.0 | 25032 | 1.6884 | | 1.5297 | 3.0 | 37548 | 1.6884 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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Joshuquez/AnimaleMaleEnhancementSouthAfrica
Joshuquez
2023-12-16T08:32:03Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:31:03Z
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sdpkjc/Qbert-v5-dqn_atari-seed1
sdpkjc
2023-12-16T08:31:49Z
0
0
cleanrl
[ "cleanrl", "tensorboard", "ALE/Qbert-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T08:31:18Z
--- tags: - ALE/Qbert-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/Qbert-v5 type: ALE/Qbert-v5 metrics: - type: mean_reward value: 9930.00 +/- 2476.26 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **ALE/Qbert-v5** This is a trained model of a DQN agent playing ALE/Qbert-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[dqn_atari]" python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/Qbert-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/sdpkjc/Qbert-v5-dqn_atari-seed1/raw/main/dqn_atari.py curl -OL https://huggingface.co/sdpkjc/Qbert-v5-dqn_atari-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/sdpkjc/Qbert-v5-dqn_atari-seed1/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/Qbert-v5 --track ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'ALE/Qbert-v5', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'sdpkjc', 'learning_rate': 0.0001, 'learning_starts': 80000, 'num_envs': 1, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
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Joshuquez/AnimaleMaleEnhancementSouthAfricaoffer
Joshuquez
2023-12-16T08:33:44Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:32:44Z
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Homiebear/otamatone
Homiebear
2023-12-16T08:34:19Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T08:34:03Z
--- license: openrail ---
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Eric01/marian-finetuned-kde4-en-to-cn
Eric01
2023-12-16T09:11:23Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-12-16T08:36:39Z
Entry not found
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MyPdfChat/MyPdfChat
MyPdfChat
2023-12-16T09:19:33Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:39:50Z
--- {} --- # MyPdfChat - Private PDF Chat based on LLM can run on any PC. **MyPdfChat** is using a private 7B RWKV language model designed to run locally and facilitate secure PDF-based chat conversations. With RWKV, you can have confidential and encrypted conversations in PDF format, ensuring the privacy of your discussions. ## Features - **Privacy**: MyPdfChat runs locally on your machine, ensuring that your conversations remain private and secure. - **PDF Chat**: MyPdfChat enables you to have chat conversations within PDF documents, providing a unique and secure communication method. - **Encryption**: All chat messages are encrypted to protect the confidentiality of your discussions. - **Offline Access**: Since RWKV runs locally, you can use it even without an internet connection. ## Installation - To install MyPdfChat from the release, follow these instructions: - ### Step 1: - Download the Release1. Go to the [Mychatpdf huggingface repo](https://huggingface.co/MyPdfChat/MyPdfChat).2. Download the latest release zip (`mychatpdf-vX.X.X.zip`). - ### Step 2: - Extract the Release1. Locate the downloaded `mychatpdf-vX.X.X.zip` file on your system.2. Extract the contents of the zip file to a directory of your choice. - ### Step 3: - double click the MyPdfchat.exe ## Usage1. - chat with you PDF file ## Contributions - Contributions to MyPdfChat are welcome! If you encounter any issues or have suggestions for improvements, please open an issue on the [GitHub repository](https://github.com/mypdfchat/MypdfChat). ## License - MyPdfChat is released under the [MIT License](https://opensource.org/licenses/MIT). ## Acknowledgements - We would like to thank the open-source community for their invaluable contributions to the development of MyPdfChat. ## Contact - For any inquiries or support, please contact us at [email protected]---Thank you for using RWKV! We hope you enjoy your private and secure PDF chat experience.
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sdpkjc/NameThisGame-v5-dqn_atari-seed1
sdpkjc
2023-12-16T08:40:57Z
0
0
cleanrl
[ "cleanrl", "tensorboard", "ALE/NameThisGame-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T08:40:00Z
--- tags: - ALE/NameThisGame-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/NameThisGame-v5 type: ALE/NameThisGame-v5 metrics: - type: mean_reward value: 5250.00 +/- 2013.20 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **ALE/NameThisGame-v5** This is a trained model of a DQN agent playing ALE/NameThisGame-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[dqn_atari]" python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/NameThisGame-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/sdpkjc/NameThisGame-v5-dqn_atari-seed1/raw/main/dqn_atari.py curl -OL https://huggingface.co/sdpkjc/NameThisGame-v5-dqn_atari-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/sdpkjc/NameThisGame-v5-dqn_atari-seed1/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/NameThisGame-v5 --track ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'ALE/NameThisGame-v5', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'sdpkjc', 'learning_rate': 0.0001, 'learning_starts': 80000, 'num_envs': 1, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
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steimel64/distilhubert-finetuned-gtzan
steimel64
2023-12-16T08:40:29Z
0
0
null
[ "region:us" ]
null
2023-12-16T08:40:29Z
Entry not found
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TheGreatP/JoaoLucasVoz
TheGreatP
2023-12-16T08:43:03Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-16T08:42:47Z
--- license: openrail ---
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-3
kanishka
2023-12-16T22:04:04Z
0
0
transformers
[ "transformers", "safetensors", "opt", "text-generation", "generated_from_trainer", "dataset:kanishka/counterfactual_babylm_without_prototypical", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-16T08:44:58Z
--- tags: - generated_from_trainer datasets: - kanishka/counterfactual_babylm_without_prototypical metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-3 results: - task: name: Causal Language Modeling type: text-generation dataset: name: kanishka/counterfactual_babylm_without_prototypical type: kanishka/counterfactual_babylm_without_prototypical metrics: - name: Accuracy type: accuracy value: 0.410371177110484 --- <!-- 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. --> # smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-3 This model was trained from scratch on the kanishka/counterfactual_babylm_without_prototypical dataset. It achieves the following results on the evaluation set: - Loss: 3.4003 - Accuracy: 0.4104 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.6055 | 1.0 | 18593 | 3.7471 | 0.3605 | | 3.3898 | 2.0 | 37186 | 3.5520 | 0.3816 | | 3.2573 | 3.0 | 55779 | 3.4588 | 0.3926 | | 3.1772 | 4.0 | 74372 | 3.4027 | 0.3981 | | 3.1222 | 5.0 | 92965 | 3.3932 | 0.4025 | | 3.0822 | 6.0 | 111558 | 3.3665 | 0.4043 | | 3.0455 | 7.0 | 130151 | 3.3444 | 0.4067 | | 3.0126 | 8.0 | 148744 | 3.3516 | 0.4075 | | 2.9826 | 9.0 | 167337 | 3.3554 | 0.4078 | | 2.9605 | 10.0 | 185930 | 3.3593 | 0.4088 | | 2.9434 | 11.0 | 204523 | 3.3505 | 0.4095 | | 2.9119 | 12.0 | 223116 | 3.3754 | 0.4095 | | 2.8959 | 13.0 | 241709 | 3.3711 | 0.4097 | | 2.8775 | 14.0 | 260302 | 3.3739 | 0.4101 | | 2.8544 | 15.0 | 278895 | 3.3737 | 0.4102 | | 2.834 | 16.0 | 297488 | 3.3739 | 0.4108 | | 2.8149 | 17.0 | 316081 | 3.3841 | 0.4103 | | 2.7956 | 18.0 | 334674 | 3.3839 | 0.4105 | | 2.775 | 19.0 | 353267 | 3.3899 | 0.4106 | | 2.7617 | 20.0 | 371860 | 3.4003 | 0.4104 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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wessmetal/poppy
wessmetal
2023-12-16T08:48:18Z
0
0
null
[ "license:bsl-1.0", "region:us" ]
null
2023-12-16T08:46:51Z
--- license: bsl-1.0 ---
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Ritwik1998/masked-lm-tpu
Ritwik1998
2023-12-16T08:55:41Z
0
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-12-16T08:49:25Z
--- license: mit base_model: roberta-base tags: - generated_from_keras_callback model-index: - name: Ritwik1998/masked-lm-tpu 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. --> # Ritwik1998/masked-lm-tpu 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: - Train Loss: 9.9915 - Train Accuracy: 0.0002 - Validation Loss: 9.9088 - Validation Accuracy: 0.0015 - Epoch: 8 ## 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 22325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 10.2835 | 0.0000 | 10.2748 | 0.0 | 0 | | 10.2791 | 0.0000 | 10.2665 | 0.0000 | 1 | | 10.2586 | 0.0 | 10.2434 | 0.0000 | 2 | | 10.2455 | 0.0000 | 10.2100 | 0.0000 | 3 | | 10.2063 | 0.0 | 10.1620 | 0.0000 | 4 | | 10.1658 | 0.0 | 10.1147 | 0.0 | 5 | | 10.1174 | 0.0 | 10.0558 | 0.0 | 6 | | 10.0613 | 0.0000 | 9.9800 | 0.0002 | 7 | | 9.9915 | 0.0002 | 9.9088 | 0.0015 | 8 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.12.0 - Tokenizers 0.15.0
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nand-tmp/gray2color_on_m1guelpf_nouns
nand-tmp
2023-12-16T13:31:57Z
0
0
diffusers
[ "diffusers", "safetensors", "diffusion", "color-diffusion", "black&white2color", "en", "license:apache-2.0", "region:us" ]
null
2023-12-16T08:50:30Z
--- license: apache-2.0 language: - en tags: - diffusion - color-diffusion - black&white2color --- Converting Black&White images to color with Diffusion Results. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/641fdfc65bccaa42483d5853/-TQIbtjukEXuLV4Shk3VI.jpeg) More... [blog](https://1bytenand.hashnode.dev/unconditional-image-generation)
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sdpkjc/BattleZone-v5-dqn_atari-seed1
sdpkjc
2023-12-16T08:52:20Z
0
0
cleanrl
[ "cleanrl", "tensorboard", "ALE/BattleZone-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T08:51:50Z
--- tags: - ALE/BattleZone-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/BattleZone-v5 type: ALE/BattleZone-v5 metrics: - type: mean_reward value: 15300.00 +/- 3348.13 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **ALE/BattleZone-v5** This is a trained model of a DQN agent playing ALE/BattleZone-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[dqn_atari]" python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/BattleZone-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/sdpkjc/BattleZone-v5-dqn_atari-seed1/raw/main/dqn_atari.py curl -OL https://huggingface.co/sdpkjc/BattleZone-v5-dqn_atari-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/sdpkjc/BattleZone-v5-dqn_atari-seed1/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/BattleZone-v5 --track ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'ALE/BattleZone-v5', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'sdpkjc', 'learning_rate': 0.0001, 'learning_starts': 80000, 'num_envs': 1, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
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rudyTzhan/dummy-model
rudyTzhan
2023-12-16T08:56:16Z
0
0
transformers
[ "transformers", "pytorch", "camembert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-12-16T08:53:26Z
Entry not found
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sdpkjc/DoubleDunk-v5-dqn_atari-seed1
sdpkjc
2023-12-16T08:56:27Z
0
0
cleanrl
[ "cleanrl", "tensorboard", "ALE/DoubleDunk-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T08:55:58Z
--- tags: - ALE/DoubleDunk-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/DoubleDunk-v5 type: ALE/DoubleDunk-v5 metrics: - type: mean_reward value: -12.40 +/- 4.27 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **ALE/DoubleDunk-v5** This is a trained model of a DQN agent playing ALE/DoubleDunk-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[dqn_atari]" python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/DoubleDunk-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/sdpkjc/DoubleDunk-v5-dqn_atari-seed1/raw/main/dqn_atari.py curl -OL https://huggingface.co/sdpkjc/DoubleDunk-v5-dqn_atari-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/sdpkjc/DoubleDunk-v5-dqn_atari-seed1/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/DoubleDunk-v5 --track ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'ALE/DoubleDunk-v5', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'sdpkjc', 'learning_rate': 0.0001, 'learning_starts': 80000, 'num_envs': 1, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
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owanr/SBIC-roberta-base-inter-shuffle-human_annots_alpha0.5_whole_1e-05
owanr
2023-12-16T08:56:43Z
0
0
null
[ "pytorch", "safetensors", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]
null
2023-12-16T08:56:24Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: SBIC-roberta-base-inter-shuffle-human_annots_alpha0.5_whole_1e-05 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. --> # SBIC-roberta-base-inter-shuffle-human_annots_alpha0.5_whole_1e-05 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2854 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1782 | 1.0 | 12516 | 1.2856 | | 1.1015 | 2.0 | 25032 | 1.2854 | | 1.1408 | 3.0 | 37548 | 1.2854 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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Ghali20/Zephyr_beta_2
Ghali20
2023-12-16T08:59:17Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-alpha", "region:us" ]
null
2023-12-16T08:58:22Z
--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-alpha --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
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Penguin-N/Reinforce-CartPole-v1
Penguin-N
2023-12-16T09:02:47Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-16T09:02:37Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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
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jat-project/jat-medium
jat-project
2023-12-17T14:58:31Z
0
0
transformers
[ "transformers", "jat", "custom_code", "endpoints_compatible", "region:us" ]
null
2023-12-16T09:10:19Z
Entry not found
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