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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-04 12:29:36	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 468 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-04 12:29:27	card stringlengths 11 1.01M
Holarissun/gpt2full-airl_sft-imdb-seqsampler	Holarissun	2024-03-10T15:22:09Z	93	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T15:21:51Z	--- license: mit base_model: gpt2 tags: - trl - sft - generated_from_trainer model-index: - name: gpt2full-airl_sft-imdb-seqsampler 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. --> # gpt2full-airl_sft-imdb-seqsampler This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Holarissun/gpt2full-airl_sft-imdb-randsampler	Holarissun	2024-03-10T15:21:37Z	93	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T15:21:19Z	--- license: mit base_model: gpt2 tags: - trl - sft - generated_from_trainer model-index: - name: gpt2full-airl_sft-imdb-randsampler 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. --> # gpt2full-airl_sft-imdb-randsampler This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
jpodivin/upernet-swin-small-finetuned	jpodivin	2024-03-10T15:18:13Z	119	0	transformers	[ "transformers", "safetensors", "upernet", "image-segmentation", "vision", "generated_from_trainer", "base_model:openmmlab/upernet-swin-small", "base_model:finetune:openmmlab/upernet-swin-small", "license:mit", "endpoints_compatible", "region:us" ]	image-segmentation	2024-03-10T13:10:48Z	--- license: mit base_model: openmmlab/upernet-swin-small tags: - image-segmentation - vision - generated_from_trainer model-index: - name: upernet-swin-small-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # upernet-swin-small-finetuned This model is a fine-tuned version of [openmmlab/upernet-swin-small](https://huggingface.co/openmmlab/upernet-swin-small) on the jpodivin/plantorgans dataset. It achieves the following results on the evaluation set: - Loss: 0.2914 - Mean Iou: 0.4182 - Mean Accuracy: 0.5282 - Overall Accuracy: 0.7341 - Accuracy Void: nan - Accuracy Fruit: 0.8590 - Accuracy Leaf: 0.7032 - Accuracy Flower: 0.0 - Accuracy Stem: 0.5505 - Iou Void: 0.0 - Iou Fruit: 0.8554 - Iou Leaf: 0.6976 - Iou Flower: 0.0 - Iou Stem: 0.5381 - Median Iou: 0.5381 ## 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: 0.0006 - train_batch_size: 10 - eval_batch_size: 10 - 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 \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Void \| Accuracy Fruit \| Accuracy Leaf \| Accuracy Flower \| Accuracy Stem \| Iou Void \| Iou Fruit \| Iou Leaf \| Iou Flower \| Iou Stem \| Median Iou \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------:\|:--------------:\|:-------------:\|:---------------:\|:-------------:\|:--------:\|:---------:\|:--------:\|:----------:\|:--------:\|:----------:\| \| 0.8566 \| 1.0 \| 575 \| 0.3365 \| 0.3723 \| 0.4705 \| 0.6560 \| nan \| 0.8000 \| 0.6122 \| 0.0 \| 0.4699 \| 0.0 \| 0.7976 \| 0.6041 \| 0.0 \| 0.4598 \| 0.4598 \| \| 0.3338 \| 2.0 \| 1150 \| 0.3030 \| 0.3922 \| 0.4937 \| 0.7155 \| nan \| 0.8558 \| 0.7024 \| 0.0 \| 0.4166 \| 0.0 \| 0.8517 \| 0.6972 \| 0.0 \| 0.4119 \| 0.4119 \| \| 0.3477 \| 3.0 \| 1725 \| 0.2914 \| 0.4182 \| 0.5282 \| 0.7341 \| nan \| 0.8590 \| 0.7032 \| 0.0 \| 0.5505 \| 0.0 \| 0.8554 \| 0.6976 \| 0.0 \| 0.5381 \| 0.5381 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
viki99/my-pet-cat	viki99	2024-03-10T15:17:29Z	0	0	null	[ "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-10T15:15:36Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat Dreambooth model trained by viki99 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 2167811242007 Sample pictures of this concept: ![0](https://huggingface.co/viki99/my-pet-cat/resolve/main/sample_images/xzg_(5).jpg) ![1](https://huggingface.co/viki99/my-pet-cat/resolve/main/sample_images/xzg_(4).jpg) ![2](https://huggingface.co/viki99/my-pet-cat/resolve/main/sample_images/xzg_(1).jpg) ![3](https://huggingface.co/viki99/my-pet-cat/resolve/main/sample_images/xzg_(2).jpg) ![4](https://huggingface.co/viki99/my-pet-cat/resolve/main/sample_images/xzg_(3).jpg)
Sayyor/q-Taxi-v3-eval-seed	Sayyor	2024-03-10T15:16:31Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T15:16:26Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-eval-seed results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.26 +/- 2.59 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Sayyor/q-Taxi-v3-eval-seed", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ThuyNT03/CS505-Classifier-T4_predictLabel_a1_v5	ThuyNT03	2024-03-10T15:15:03Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:vinai/phobert-base-v2", "base_model:finetune:vinai/phobert-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T14:54:59Z	--- base_model: vinai/phobert-base-v2 tags: - generated_from_trainer model-index: - name: CS505-Classifier-T4_predictLabel_a1_v5 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. --> # CS505-Classifier-T4_predictLabel_a1_v5 This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0018 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.98 \| 48 \| 0.6517 \| \| No log \| 1.96 \| 96 \| 0.3227 \| \| No log \| 2.94 \| 144 \| 0.2342 \| \| No log \| 3.92 \| 192 \| 0.1815 \| \| No log \| 4.9 \| 240 \| 0.1703 \| \| No log \| 5.88 \| 288 \| 0.1231 \| \| No log \| 6.86 \| 336 \| 0.0730 \| \| No log \| 7.84 \| 384 \| 0.0803 \| \| No log \| 8.82 \| 432 \| 0.0476 \| \| No log \| 9.8 \| 480 \| 0.0384 \| \| 0.2908 \| 10.78 \| 528 \| 0.0281 \| \| 0.2908 \| 11.76 \| 576 \| 0.0329 \| \| 0.2908 \| 12.73 \| 624 \| 0.0234 \| \| 0.2908 \| 13.71 \| 672 \| 0.0119 \| \| 0.2908 \| 14.69 \| 720 \| 0.0101 \| \| 0.2908 \| 15.67 \| 768 \| 0.0081 \| \| 0.2908 \| 16.65 \| 816 \| 0.0137 \| \| 0.2908 \| 17.63 \| 864 \| 0.0075 \| \| 0.2908 \| 18.61 \| 912 \| 0.0053 \| \| 0.2908 \| 19.59 \| 960 \| 0.0035 \| \| 0.0216 \| 20.57 \| 1008 \| 0.0060 \| \| 0.0216 \| 21.55 \| 1056 \| 0.0028 \| \| 0.0216 \| 22.53 \| 1104 \| 0.0027 \| \| 0.0216 \| 23.51 \| 1152 \| 0.0026 \| \| 0.0216 \| 24.49 \| 1200 \| 0.0024 \| \| 0.0216 \| 25.47 \| 1248 \| 0.0023 \| \| 0.0216 \| 26.45 \| 1296 \| 0.0022 \| \| 0.0216 \| 27.43 \| 1344 \| 0.0022 \| \| 0.0216 \| 28.41 \| 1392 \| 0.0021 \| \| 0.0216 \| 29.39 \| 1440 \| 0.0020 \| \| 0.0216 \| 30.37 \| 1488 \| 0.0021 \| \| 0.0043 \| 31.35 \| 1536 \| 0.0020 \| \| 0.0043 \| 32.33 \| 1584 \| 0.0019 \| \| 0.0043 \| 33.31 \| 1632 \| 0.0019 \| \| 0.0043 \| 34.29 \| 1680 \| 0.0019 \| \| 0.0043 \| 35.27 \| 1728 \| 0.0019 \| \| 0.0043 \| 36.24 \| 1776 \| 0.0019 \| \| 0.0043 \| 37.22 \| 1824 \| 0.0019 \| \| 0.0043 \| 38.2 \| 1872 \| 0.0018 \| \| 0.0043 \| 39.18 \| 1920 \| 0.0018 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Litzy619/V0309P1	Litzy619	2024-03-10T15:00:59Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-10T03:00:57Z	--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309P1 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. --> # V0309P1 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4262 \| 0.09 \| 10 \| 0.1204 \| \| 0.1236 \| 0.17 \| 20 \| 0.0907 \| \| 0.1031 \| 0.26 \| 30 \| 0.0766 \| \| 0.0896 \| 0.34 \| 40 \| 0.0691 \| \| 0.0871 \| 0.43 \| 50 \| 0.0719 \| \| 0.0821 \| 0.51 \| 60 \| 0.0751 \| \| 0.0749 \| 0.6 \| 70 \| 0.0676 \| \| 0.0809 \| 0.68 \| 80 \| 0.0624 \| \| 0.068 \| 0.77 \| 90 \| 0.0591 \| \| 0.062 \| 0.85 \| 100 \| 0.0666 \| \| 0.0712 \| 0.94 \| 110 \| 0.0643 \| \| 0.0679 \| 1.02 \| 120 \| 0.0600 \| \| 0.0488 \| 1.11 \| 130 \| 0.0758 \| \| 0.0498 \| 1.19 \| 140 \| 0.0573 \| \| 0.0451 \| 1.28 \| 150 \| 0.0649 \| \| 0.0434 \| 1.37 \| 160 \| 0.0692 \| \| 0.0449 \| 1.45 \| 170 \| 0.0639 \| \| 0.0401 \| 1.54 \| 180 \| 0.0697 \| \| 0.0477 \| 1.62 \| 190 \| 0.0633 \| \| 0.0492 \| 1.71 \| 200 \| 0.0609 \| \| 0.0489 \| 1.79 \| 210 \| 0.0632 \| \| 0.0422 \| 1.88 \| 220 \| 0.0679 \| \| 0.0417 \| 1.96 \| 230 \| 0.0633 \| \| 0.034 \| 2.05 \| 240 \| 0.0678 \| \| 0.0247 \| 2.13 \| 250 \| 0.0700 \| \| 0.0234 \| 2.22 \| 260 \| 0.0766 \| \| 0.0187 \| 2.3 \| 270 \| 0.0816 \| \| 0.0231 \| 2.39 \| 280 \| 0.0841 \| \| 0.0245 \| 2.47 \| 290 \| 0.0859 \| \| 0.024 \| 2.56 \| 300 \| 0.0848 \| \| 0.0253 \| 2.65 \| 310 \| 0.0847 \| \| 0.0202 \| 2.73 \| 320 \| 0.0841 \| \| 0.0242 \| 2.82 \| 330 \| 0.0814 \| \| 0.0187 \| 2.9 \| 340 \| 0.0820 \| \| 0.0217 \| 2.99 \| 350 \| 0.0820 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
lilyray/results	lilyray	2024-03-10T14:59:22Z	26	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-05T00:55:57Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: results results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.921 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2046 - Accuracy: 0.921 ## 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: 3.507837996446784e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8349 \| 1.0 \| 1000 \| 0.6184 \| 0.7905 \| \| 0.384 \| 2.0 \| 2000 \| 0.3057 \| 0.909 \| \| 0.2544 \| 3.0 \| 3000 \| 0.2316 \| 0.926 \| \| 0.2027 \| 4.0 \| 4000 \| 0.2088 \| 0.928 \| \| 0.1757 \| 5.0 \| 5000 \| 0.2030 \| 0.9295 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
nikamazu/nikamazu	nikamazu	2024-03-10T14:58:35Z	0	0	null	[ "dataset:HuggingFaceTB/cosmopedia", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2024-03-10T14:57:29Z	--- license: mit datasets: - HuggingFaceTB/cosmopedia --- # 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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samaxr/codellama_lora	samaxr	2024-03-10T14:57:59Z	5	0	peft	[ "peft", "pytorch", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-10T09:06:37Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: codellama/CodeLlama-7b-hf model-index: - name: codellama_lora 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. --> # codellama_lora This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
gdupont/TinyLlama-1.1B-Chat-colors-v1.0_peft	gdupont	2024-03-10T14:56:49Z	91	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T14:55:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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raminass/SCOTUS_AI_V15_CURCUIT	raminass	2024-03-10T14:54:20Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:raminass/scotus-v10", "base_model:finetune:raminass/scotus-v10", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T10:53:07Z	--- license: cc-by-sa-4.0 base_model: raminass/scotus-v10 tags: - generated_from_trainer model-index: - name: SCOTUS_AI_V15_CURCUIT 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. --> # SCOTUS_AI_V15_CURCUIT This model is a fine-tuned version of [raminass/scotus-v10](https://huggingface.co/raminass/scotus-v10) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.1082 - eval_accuracy: 0.7486 - eval_runtime: 75.9291 - eval_samples_per_second: 108.114 - eval_steps_per_second: 6.769 - epoch: 4.0 - step: 8184 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
drMostert/segformer-b0-scene-parse-150	drMostert	2024-03-10T14:54:08Z	22	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "dataset:scene_parse_150", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2024-03-10T14:37:22Z	--- license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 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. --> # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 3.5433 - Mean Iou: 0.0600 - Mean Accuracy: 0.1407 - Overall Accuracy: 0.4130 - Per Category Iou: [0.4725842300574752, 0.23752185781261304, 0.500907459865348, 0.26304551026233747, 0.20113818567783023, 0.2773168787458298, 0.41824906409273377, nan, 0.0, nan, 0.0011588462105728914, 0.0, 0.07620455691560078, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09211967767850622, 0.21158826718063, 0.0, 0.009009009009009009, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] - Per Category Accuracy: [0.7019862011289986, 0.2599706832653203, 0.974451706755296, 0.7671708061606771, 0.8256484417005024, 0.9195901184609862, 0.558454659058402, nan, 0.0, nan, 0.0012131371727286764, 0.0, 0.08718056302201477, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.7549277791078126, 0.3302933433621662, nan, 0.009011546043368065, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| 4.2364 \| 1.0 \| 20 \| 4.1492 \| 0.0409 \| 0.1240 \| 0.3995 \| [0.5322293849075467, 0.23690897692857837, 0.4397872027790232, 0.19607643898903274, 0.36383498030038486, 0.12773088147613518, 0.009777174103954194, nan, 0.0, nan, 0.11339002834750708, 0.0, 0.1422973407586709, 9.40875390463287e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08988905804476369, 0.44466963923794084, nan, 0.0009037191518943343, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan] \| [0.8962503067930806, 0.259095816281796, 0.589595813676267, 0.7087472147177173, 0.7379164580899938, 0.3320823679143687, 0.01257170387991388, nan, 0.0, nan, 0.11999029490261817, 0.0, 0.22044921132337708, 0.0012360939431396785, 0.0, 0.0, 0.0, nan, 0.0, 0.8528449445375469, 0.7219819481007897, nan, 0.0018304702900591382, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 4.2734 \| 2.0 \| 40 \| 3.9214 \| 0.0500 \| 0.1198 \| 0.3713 \| [0.5414063519948691, 0.19841541146471395, 0.5368811396588854, 0.1932222222222222, 0.19532902970225716, 0.1522866572371523, 0.0, nan, 0.0008067375886524823, nan, 0.00181349238333199, 0.0, 0.05775538617646365, 0.0008988949791032748, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07665371555439467, 0.5463317251705208, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.7765582815951422, 0.23428088058355365, 0.7064269767347887, 0.7664607122160645, 0.804457051745555, 0.3320334170936266, 0.0, nan, 0.0008153902672867217, nan, 0.0019851335553741976, 0.0, 0.06418241179015825, 0.21508034610630408, 0.0, 0.0, 0.0, nan, 0.0, 0.7494214348415928, 0.6175253854832644, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 3.4296 \| 3.0 \| 60 \| 3.9287 \| 0.0500 \| 0.1247 \| 0.3684 \| [0.504898336414048, 0.16609815628654262, 0.461471733451624, 0.22065343315487834, 0.16518809916592642, 0.28398331595411885, 0.1604012425930234, nan, 0.0011706985763947845, nan, 0.02186771822907331, 0.0, 0.037805308927614856, 0.00042000840016800337, 0.0, 0.0, 0.0, 0.0, 0.0, 0.059326658998615056, 0.4647721010784854, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.6853021116454109, 0.19175037128736774, 0.9458424673448566, 0.7632938564630792, 0.7964291673619223, 0.44437555069673335, 0.17647058823529413, nan, 0.0011738035715885774, nan, 0.024218629375565213, 0.0, 0.043491942779970365, 0.0519159456118665, 0.0, 0.0, 0.0, nan, 0.0, 0.8067592370920118, 0.5550959007145544, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 3.6539 \| 4.0 \| 80 \| 3.9424 \| 0.0460 \| 0.1303 \| 0.3287 \| [0.37671262071262074, 0.13443477431760276, 0.4269336776273018, 0.1963029676535461, 0.14844067652609796, 0.2914056148070209, 0.1012685049158097, nan, 0.0, nan, 0.015320700804571772, 0.0, 0.04650892929668009, 0.0008672882232266457, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08581872964530209, 0.4295496258647466, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.5141706587642829, 0.14704138526399546, 0.9420238612371071, 0.7765326194304557, 0.9070026141609656, 0.7953529354175505, 0.10793598217377558, nan, 0.0, nan, 0.01797648719588857, 0.0, 0.05255221786618065, 0.12855377008652658, 0.0, 0.0, 0.0, nan, 0.0, 0.7728034474503231, 0.572113576532531, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 3.8072 \| 5.0 \| 100 \| 3.6808 \| 0.0524 \| 0.1296 \| 0.3789 \| [0.49848536561886225, 0.15669095400920174, 0.5116626603724406, 0.2285989936984026, 0.16470623593542788, 0.29551710026963546, 0.1565518949715135, nan, 0.0, nan, 0.0009195500620161669, 0.0, 0.05793396722251421, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0683129055515501, 0.32468649229666785, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.7248861456789899, 0.16903666136853918, 0.8923819818363656, 0.7639060064153315, 0.8128371989543356, 0.8601801390203309, 0.1712762914226351, nan, 0.0, nan, 0.0009484526986787834, 0.0, 0.06359237940393617, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.816614795307637, 0.42600601729973675, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 4.0198 \| 6.0 \| 120 \| 3.7189 \| 0.0502 \| 0.1318 \| 0.3460 \| [0.39116293372838296, 0.13864719866417147, 0.40087800798076706, 0.2157543281871196, 0.16127116562617994, 0.3785288215728855, 0.20748449345279119, nan, 0.0, nan, 0.0037886043888214886, 0.0, 0.0654386250902401, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08008943702143075, 0.3613156909249782, nan, 0.006734878901696671, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.5573253097473843, 0.14608733605900429, 0.9829021406418895, 0.7742635836074405, 0.8156367614068265, 0.8987697027053487, 0.22663695629262712, nan, 0.0, nan, 0.00392615303174008, 0.0, 0.07368848912373635, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.7674966084111404, 0.544283565250094, nan, 0.007321881160236553, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 3.3227 \| 7.0 \| 140 \| 3.5359 \| 0.0534 \| 0.1315 \| 0.4101 \| [0.4770347521615892, 0.24974336818456752, 0.5108344403430883, 0.2366895974550102, 0.17451872484087896, 0.3132020145632557, 0.19149852704129844, nan, 0.0, nan, 0.0, 0.0, 0.07627803718584476, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08870909519706982, 0.24312130647518587, 0.0, 0.001126443255421008, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.7282076920979192, 0.2790960866601132, 0.9653547363848508, 0.7663709302230675, 0.8270945732984779, 0.8330777012694579, 0.20748580978334513, nan, 0.0, nan, 0.0, 0.0, 0.08289299434880092, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.7354161679035991, 0.3597216998871756, nan, 0.001126443255421008, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 2.7606 \| 8.0 \| 160 \| 3.5593 \| 0.0594 \| 0.1378 \| 0.4101 \| [0.491183620322603, 0.23218100723379267, 0.5228177173827064, 0.24633373487665636, 0.20350864022596432, 0.2936651680126143, 0.3681167890630956, nan, 0.0, nan, 2.0947672713561523e-05, 0.0, 0.056203414282279394, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07741366689718485, 0.23988607300627762, nan, 0.001126443255421008, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.7214700615404194, 0.2512355323459375, 0.968173231369142, 0.7677584701148393, 0.8041604093664831, 0.8995202819567275, 0.4362155407338262, nan, 0.0, nan, 2.2057039504157753e-05, 0.0, 0.061297809013072496, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.7637858111882532, 0.3880218127115457, nan, 0.001126443255421008, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 3.1471 \| 9.0 \| 180 \| 3.5223 \| 0.0611 \| 0.1404 \| 0.4096 \| [0.4694048515016408, 0.2304776927428032, 0.5069242587551356, 0.25709018097468106, 0.21042235106866758, 0.26575785951918235, 0.40512733060482037, nan, 0.0, nan, 0.0, 0.0, 0.07140409542602592, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.089745061462668, 0.23717794365518902, nan, 0.007744297381019431, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.7018880273432174, 0.24945296672608552, 0.9716180585721645, 0.7671136721651336, 0.8235719450469993, 0.9215318343504226, 0.5365181649829115, nan, 0.0, nan, 0.0, 0.0, 0.07923479355422397, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.7486633149788524, 0.37044001504324936, nan, 0.007744297381019431, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| \| 2.7459 \| 10.0 \| 200 \| 3.5433 \| 0.0600 \| 0.1407 \| 0.4130 \| [0.4725842300574752, 0.23752185781261304, 0.500907459865348, 0.26304551026233747, 0.20113818567783023, 0.2773168787458298, 0.41824906409273377, nan, 0.0, nan, 0.0011588462105728914, 0.0, 0.07620455691560078, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09211967767850622, 0.21158826718063, 0.0, 0.009009009009009009, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| [0.7019862011289986, 0.2599706832653203, 0.974451706755296, 0.7671708061606771, 0.8256484417005024, 0.9195901184609862, 0.558454659058402, nan, 0.0, nan, 0.0012131371727286764, 0.0, 0.08718056302201477, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.7549277791078126, 0.3302933433621662, nan, 0.009011546043368065, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Jackline/Blip2-HateSpeech-PEFT-Whole-2.7b	Jackline	2024-03-10T14:53:53Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:Salesforce/blip2-opt-2.7b", "base_model:adapter:Salesforce/blip2-opt-2.7b", "region:us" ]	null	2024-03-10T14:53:46Z	--- library_name: peft base_model: Salesforce/blip2-opt-2.7b --- # 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] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### 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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.1
ankursinghbisht/a2c-PandaPickAndPlace-v3	ankursinghbisht	2024-03-10T14:53:41Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T14:49:30Z	--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # A2C Agent playing PandaPickAndPlace-v3 This is a trained model of a A2C agent playing PandaPickAndPlace-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
afaji/fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-5	afaji	2024-03-10T14:48:45Z	89	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T14:48:13Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-5 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. --> # fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-5 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4393 - Accuracy: 0.5202 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 2.3938 \| 0.2475 \| \| No log \| 2.0 \| 126 \| 1.5164 \| 0.3636 \| \| No log \| 3.0 \| 189 \| 1.1653 \| 0.4646 \| \| No log \| 4.0 \| 252 \| 0.7958 \| 0.4394 \| \| No log \| 5.0 \| 315 \| 0.5525 \| 0.4596 \| \| No log \| 6.0 \| 378 \| 1.1572 \| 0.4747 \| \| No log \| 7.0 \| 441 \| 0.3450 \| 0.4798 \| \| 1.7802 \| 8.0 \| 504 \| 0.4393 \| 0.5202 \| \| 1.7802 \| 9.0 \| 567 \| 0.5459 \| 0.4343 \| \| 1.7802 \| 10.0 \| 630 \| 0.4935 \| 0.5101 \| \| 1.7802 \| 11.0 \| 693 \| 0.3405 \| 0.4697 \| \| 1.7802 \| 12.0 \| 756 \| 0.3275 \| 0.4697 \| \| 1.7802 \| 13.0 \| 819 \| 0.2442 \| 0.4646 \| \| 1.7802 \| 14.0 \| 882 \| 0.2561 \| 0.4495 \| \| 1.7802 \| 15.0 \| 945 \| 0.2196 \| 0.4495 \| \| 0.215 \| 16.0 \| 1008 \| 0.1943 \| 0.4495 \| \| 0.215 \| 17.0 \| 1071 \| 0.1845 \| 0.4545 \| \| 0.215 \| 18.0 \| 1134 \| 0.1702 \| 0.4444 \| \| 0.215 \| 19.0 \| 1197 \| 0.1788 \| 0.4545 \| \| 0.215 \| 20.0 \| 1260 \| 0.1747 \| 0.4545 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
Leokb24/ppo-LunarLander-v2	Leokb24	2024-03-10T14:41:49Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T14:41:01Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.40 +/- 26.44 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
afaji/fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-4	afaji	2024-03-10T14:41:44Z	89	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T14:41:09Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-4 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. --> # fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-4 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4186 - Accuracy: 0.5051 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 2.8693 \| 0.2677 \| \| No log \| 2.0 \| 126 \| 2.2777 \| 0.3485 \| \| No log \| 3.0 \| 189 \| 1.0399 \| 0.4141 \| \| No log \| 4.0 \| 252 \| 1.8741 \| 0.4293 \| \| No log \| 5.0 \| 315 \| 1.2779 \| 0.4394 \| \| No log \| 6.0 \| 378 \| 0.7112 \| 0.4646 \| \| No log \| 7.0 \| 441 \| 0.8380 \| 0.4596 \| \| 1.9226 \| 8.0 \| 504 \| 0.7028 \| 0.4697 \| \| 1.9226 \| 9.0 \| 567 \| 0.6589 \| 0.4848 \| \| 1.9226 \| 10.0 \| 630 \| 0.6303 \| 0.4495 \| \| 1.9226 \| 11.0 \| 693 \| 0.7083 \| 0.4646 \| \| 1.9226 \| 12.0 \| 756 \| 0.4850 \| 0.4899 \| \| 1.9226 \| 13.0 \| 819 \| 0.5145 \| 0.4848 \| \| 1.9226 \| 14.0 \| 882 \| 0.7032 \| 0.4697 \| \| 1.9226 \| 15.0 \| 945 \| 0.4812 \| 0.4697 \| \| 0.2279 \| 16.0 \| 1008 \| 0.4186 \| 0.5051 \| \| 0.2279 \| 17.0 \| 1071 \| 0.3735 \| 0.5 \| \| 0.2279 \| 18.0 \| 1134 \| 0.3894 \| 0.5051 \| \| 0.2279 \| 19.0 \| 1197 \| 0.3845 \| 0.5051 \| \| 0.2279 \| 20.0 \| 1260 \| 0.3925 \| 0.5051 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
dilip025/llama-2-7b	dilip025	2024-03-10T14:39:26Z	1	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "en", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-03-02T17:03:29Z	--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 7B Chat arxiv: 2307.09288 base_model: meta-llama/Llama-2-7b-chat-hf inference: false model_creator: Meta Llama 2 model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> You are NutriLife chatbot, you are going to get questions related to food, nutrition, health, and diet by the users from Nepal. Answer them very shortly and accurately if the message is only about food, nutrition, and diet. Otherwise, ignore. <</SYS>> {prompt}[/INST] ' quantized_by: Dilip Pokhrel --- <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 7B Chat -- Food and Nutrition <br> - Model creator: [Meta Llama 2] <br> - Original model: [Llama 2 7B Chat] <a href="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf">Original Model</a> <br> - Fine Tuned by: [Dilip Pokhrel] <a href="https://dilippokhrel.com.np">Profile</a> #### Simple example code to load one of these GGUF models ```python # Load model directly or use qunatization technique if you have low gpu ram from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dilip025/llama-2-7b") model = AutoModelForCausalLM.from_pretrained("dilip025/llama-2-7b") system_message = 'You are NutriLife chatbot, you are going to get questions related to food, nutrition, health, and diet by the users from Nepal. Answer them very shortly and accurately if the message is only about food, nutrition, and diet. Otherwise, ignore.' prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n Tell me some of the famous Nepali food recipes [/INST]" num_new_tokens = 200 # Change to the number of new tokens you want to generate # Count the number of tokens in the prompt num_prompt_tokens = len(tokenizer(prompt)['input_ids']) # Calculate the maximum length for the generation max_length = num_prompt_tokens + num_new_tokens gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length) result = gen(prompt) print(result[0]['generated_text'].replace(prompt, '')) ``` ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
turgutburak01/ppo-LunarLander-v2	turgutburak01	2024-03-10T14:39:03Z	1	0	stable-baselines3	[ "stable-baselines3", "tensorboard", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-03T13:52:41Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 252.49 +/- 23.50 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
SHONOSUKE/Addtional_Trained_BERT_For_Legal_Domain_v1	SHONOSUKE	2024-03-10T14:36:31Z	194	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-10T14:36:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
afaji/fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-3	afaji	2024-03-10T14:34:45Z	89	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T14:34:13Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-3 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. --> # fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-3 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8510 - Accuracy: 0.5303 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 2.8211 \| 0.2879 \| \| No log \| 2.0 \| 126 \| 2.0292 \| 0.3889 \| \| No log \| 3.0 \| 189 \| 1.3492 \| 0.4293 \| \| No log \| 4.0 \| 252 \| 0.8583 \| 0.5152 \| \| No log \| 5.0 \| 315 \| 0.8510 \| 0.5303 \| \| No log \| 6.0 \| 378 \| 1.3129 \| 0.4848 \| \| No log \| 7.0 \| 441 \| 0.7994 \| 0.4444 \| \| 1.9846 \| 8.0 \| 504 \| 0.6454 \| 0.4697 \| \| 1.9846 \| 9.0 \| 567 \| 0.8126 \| 0.4899 \| \| 1.9846 \| 10.0 \| 630 \| 0.8618 \| 0.4495 \| \| 1.9846 \| 11.0 \| 693 \| 0.5559 \| 0.4848 \| \| 1.9846 \| 12.0 \| 756 \| 0.5902 \| 0.4949 \| \| 1.9846 \| 13.0 \| 819 \| 0.5117 \| 0.5051 \| \| 1.9846 \| 14.0 \| 882 \| 0.4989 \| 0.4848 \| \| 1.9846 \| 15.0 \| 945 \| 0.4913 \| 0.4697 \| \| 0.2505 \| 16.0 \| 1008 \| 0.4599 \| 0.4949 \| \| 0.2505 \| 17.0 \| 1071 \| 0.3934 \| 0.4949 \| \| 0.2505 \| 18.0 \| 1134 \| 0.4083 \| 0.4848 \| \| 0.2505 \| 19.0 \| 1197 \| 0.4291 \| 0.4798 \| \| 0.2505 \| 20.0 \| 1260 \| 0.4429 \| 0.4747 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
CaptainPollutionTV/DoctorBlight-OJ4	CaptainPollutionTV	2024-03-10T14:33:06Z	0	0	null	[ "DreamBooth", "OpenJourney4", "license:cc", "region:us" ]	null	2024-03-10T10:47:13Z	--- license: cc tags: - DreamBooth - OpenJourney4 --- Made by CaptainPollutionTV using the getimg.ai Dreambooth tool. Details about the model: Base Model Openjourney v4 Instance prompt doctorblight Class prompt a woman Learning Rate 0.000001 Learning Rate Scheduler polynomial Training Steps 10000 (200 steps warmup) Class images 1000 Model seed 327558656 Sample images: ![16 - img-WtUY4DZ4DeeKfmvNdb020P.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/SX1WkHp9jk7sBEHPyJJn_.png) ![17 - img-QvgsqfQtgQ5sJsVeo65eO1.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/AuQwnGgbXTLgV0D8dqgBE.png) ![18 - img-YDR991oq4BXGgUEUhw5md8.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/YN05X_SQ4gjf1DPl2U3xX.png) ![19 - img-45r9C4b3GeLhTLH0qcJZip.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/H3tzm4KV-NJxvCsolnVxp.png) ![20 - img-cfrO54S0PmsA6whmDXoY8N.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/YVKTYUQ1gnNBF3nuogIUi.png) ![21 - img-lJBoFmNgF0B7aXUYi1e4Lt.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/8oRXPlcumF7sRY_2Yb9zT.png) ![22 - img-PkkNB67ukM8raaXQFB4hxv.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/bSxvvbiZQjhbqf-lQtMeI.png) ![23 - img-3UciX3JFXCfbNVCDMWFeoC.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/I8XZV4tAeBNyU0DuOV_B9.png) ![24 - img-J8ZzGpNZt2C6sMfOppAtdm.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/h272h1tewrXMOqdjCL959.png) ![25 - img-uRgXg4Ww7m1Q6TkWqxHwzu.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/CC1EN9ypA2TUA2CUlGgT2.png) ![26 - img-YAOgtEaFk4SOzbVSmjpK8C.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/s5zeH9L7kUfx6tC1mhCWi.png) ![27 - img-eQ9c6OdPW5C6sKywpSqbNb.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/n007HxPTn5REaVJSGEBBV.png) ![28 - img-Ijkx2ITZLp0zfvscwYZM7O.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/gOTvopEFgVt5UM6JBFn78.png) ![29 - img-yrrA1sjm6FuLBem1T9ViWv.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/UPCHUll--nj6v6WNknNtL.png) ![30 - img-aKj25IHaOEBLFs6Z4pCXHG.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/NvgR4oN8bISSw5zYXsg4L.png) ![31 - img-42BGKfNH8lfQCxBNlKfzuj.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/bjYYu9lAgoyAnuQtFcaUU.png) ![32 - img-l1SOKcyL5yplgQU5xB3QDp.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/iTI-OaeQoEKasMB6fNtHs.png) ![33 - img-XmXi4OFuMLfHDz6b0LC3SS.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/RtMiXeeLYOG1HecZAjrWQ.png) ![34 - img-d0LQduVHqPfOAMh1xSCVcz.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/zh6v4xc79d-wXPerIYTmd.png) ![35 - img-bWocvMy5WQrap6mvbUjJmf.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/hOY5TX2fV3NsQgxFWfloc.png) ![36 - 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saleng/qlora_test_1k_lora	saleng	2024-03-10T14:27:27Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-10T14:21:05Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openlm-research/open_llama_3b_v2 model-index: - name: qlora-out results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: openlm-research/open_llama_3b_v2 model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: true strict: false push_dataset_to_hub: datasets: - path: mhenrichsen/alpaca_2k_test type: alpaca dataset_prepared_path: val_set_size: 0.05 adapter: qlora lora_model_dir: sequence_len: 1024 sample_packing: true lora_r: 8 lora_alpha: 32 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: output_dir: ./qlora-out gradient_accumulation_steps: 1 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_32bit torchdistx_path: lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: false fp16: true tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true gptq_groupsize: gptq_model_v1: warmup_steps: 20 evals_per_epoch: 4 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # qlora-out This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2567 \| 0.0 \| 1 \| 1.3469 \| \| 1.1726 \| 0.25 \| 108 \| 1.1364 \| \| 1.1127 \| 0.5 \| 216 \| 1.1218 \| \| 1.4125 \| 0.75 \| 324 \| 1.1111 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0
Holarissun/gpt2-airl_sft-imdb-randsampler	Holarissun	2024-03-10T14:20:48Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:lvwerra/gpt2-imdb", "base_model:adapter:lvwerra/gpt2-imdb", "region:us" ]	null	2024-03-10T14:20:46Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: lvwerra/gpt2-imdb model-index: - name: gpt2-airl_sft-imdb-randsampler results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-airl_sft-imdb-randsampler This model is a fine-tuned version of [lvwerra/gpt2-imdb](https://huggingface.co/lvwerra/gpt2-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Holarissun/gpt2-airl_sft-imdb-seqsampler	Holarissun	2024-03-10T14:20:17Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:lvwerra/gpt2-imdb", "base_model:adapter:lvwerra/gpt2-imdb", "region:us" ]	null	2024-03-10T14:20:15Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: lvwerra/gpt2-imdb model-index: - name: gpt2-airl_sft-imdb-seqsampler results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-airl_sft-imdb-seqsampler This model is a fine-tuned version of [lvwerra/gpt2-imdb](https://huggingface.co/lvwerra/gpt2-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Litzy619/V0309O3	Litzy619	2024-03-10T14:19:52Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-10T06:30:50Z	--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309O3 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. --> # V0309O3 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0623 \| 0.09 \| 10 \| 0.8505 \| \| 0.3477 \| 0.17 \| 20 \| 0.1055 \| \| 0.1256 \| 0.26 \| 30 \| 0.0916 \| \| 0.1151 \| 0.34 \| 40 \| 0.0848 \| \| 0.1059 \| 0.43 \| 50 \| 0.0765 \| \| 0.0925 \| 0.51 \| 60 \| 0.0806 \| \| 0.0848 \| 0.6 \| 70 \| 0.0722 \| \| 0.0864 \| 0.68 \| 80 \| 0.0734 \| \| 0.0827 \| 0.77 \| 90 \| 0.0735 \| \| 0.0799 \| 0.85 \| 100 \| 0.0722 \| \| 0.081 \| 0.94 \| 110 \| 0.0675 \| \| 0.08 \| 1.02 \| 120 \| 0.0697 \| \| 0.0794 \| 1.11 \| 130 \| 0.0636 \| \| 0.0716 \| 1.19 \| 140 \| 0.0634 \| \| 0.0655 \| 1.28 \| 150 \| 0.0625 \| \| 0.0648 \| 1.37 \| 160 \| 0.0660 \| \| 0.0636 \| 1.45 \| 170 \| 0.0658 \| \| 0.0674 \| 1.54 \| 180 \| 0.0681 \| \| 0.0696 \| 1.62 \| 190 \| 0.0658 \| \| 0.0686 \| 1.71 \| 200 \| 0.0615 \| \| 0.0674 \| 1.79 \| 210 \| 0.0598 \| \| 0.0612 \| 1.88 \| 220 \| 0.0593 \| \| 0.0616 \| 1.96 \| 230 \| 0.0560 \| \| 0.0568 \| 2.05 \| 240 \| 0.0580 \| \| 0.0492 \| 2.13 \| 250 \| 0.0608 \| \| 0.05 \| 2.22 \| 260 \| 0.0636 \| \| 0.0469 \| 2.3 \| 270 \| 0.0632 \| \| 0.0535 \| 2.39 \| 280 \| 0.0631 \| \| 0.0526 \| 2.47 \| 290 \| 0.0629 \| \| 0.0502 \| 2.56 \| 300 \| 0.0610 \| \| 0.0559 \| 2.65 \| 310 \| 0.0611 \| \| 0.0491 \| 2.73 \| 320 \| 0.0607 \| \| 0.0488 \| 2.82 \| 330 \| 0.0614 \| \| 0.0466 \| 2.9 \| 340 \| 0.0615 \| \| 0.0506 \| 2.99 \| 350 \| 0.0614 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
alex-atelo/unigram-tokenizer	alex-atelo	2024-03-10T14:15:31Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T14:15:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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HachiML/myBit-Llama2-jp-127M-test-7	HachiML	2024-03-10T14:15:26Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T13:46:58Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-test-7 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. --> # myBit-Llama2-jp-127M-7 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 10.6539 ## 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: 0.00024 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 250 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.0536 \| 0.04 \| 100 \| 7.4802 \| \| 6.8962 \| 0.07 \| 200 \| 6.5875 \| \| 6.3685 \| 0.11 \| 300 \| 6.1149 \| \| 5.8698 \| 0.15 \| 400 \| 5.6208 \| \| 5.6334 \| 0.18 \| 500 \| 6.1096 \| \| 8.8705 \| 0.22 \| 600 \| 10.3915 \| \| 10.5174 \| 0.26 \| 700 \| 10.5752 \| \| 10.5929 \| 0.29 \| 800 \| 10.6066 \| \| 10.6128 \| 0.33 \| 900 \| 10.6187 \| \| 10.6218 \| 0.37 \| 1000 \| 10.6255 \| \| 10.6274 \| 0.4 \| 1100 \| 10.6302 \| \| 10.6312 \| 0.44 \| 1200 \| 10.6335 \| \| 10.6343 \| 0.48 \| 1300 \| 10.6363 \| \| 10.6369 \| 0.51 \| 1400 \| 10.6384 \| \| 10.6391 \| 0.55 \| 1500 \| 10.6404 \| \| 10.6408 \| 0.59 \| 1600 \| 10.6422 \| \| 10.6426 \| 0.62 \| 1700 \| 10.6438 \| \| 10.6441 \| 0.66 \| 1800 \| 10.6451 \| \| 10.6454 \| 0.7 \| 1900 \| 10.6464 \| \| 10.6467 \| 0.73 \| 2000 \| 10.6477 \| \| 10.6479 \| 0.77 \| 2100 \| 10.6486 \| \| 10.649 \| 0.81 \| 2200 \| 10.6496 \| \| 10.6499 \| 0.84 \| 2300 \| 10.6506 \| \| 10.6508 \| 0.88 \| 2400 \| 10.6515 \| \| 10.6516 \| 0.92 \| 2500 \| 10.6522 \| \| 10.6524 \| 0.95 \| 2600 \| 10.6531 \| \| 10.6534 \| 0.99 \| 2700 \| 10.6539 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
phamsonn/dummy-model	phamsonn	2024-03-10T14:12:19Z	93	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-10T14:06:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
afaji/fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa-loop-2	afaji	2024-03-10T14:10:24Z	89	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T14:09:50Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-2-layer-medmcqa-distill-of-fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa 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. --> # fresh-2-layer-medmcqa-distill-of-fresh-2-layer-medmcqa-distill-of-fresh-2-layer-gpqa This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9186 - Accuracy: 0.5152 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 4.2492 \| 0.2727 \| \| No log \| 2.0 \| 126 \| 3.2851 \| 0.3889 \| \| No log \| 3.0 \| 189 \| 2.1889 \| 0.4444 \| \| No log \| 4.0 \| 252 \| 4.3537 \| 0.4646 \| \| No log \| 5.0 \| 315 \| 1.4476 \| 0.4697 \| \| No log \| 6.0 \| 378 \| 1.1196 \| 0.4646 \| \| No log \| 7.0 \| 441 \| 1.5751 \| 0.4646 \| \| 2.425 \| 8.0 \| 504 \| 0.9802 \| 0.4343 \| \| 2.425 \| 9.0 \| 567 \| 2.4061 \| 0.4495 \| \| 2.425 \| 10.0 \| 630 \| 0.9186 \| 0.5152 \| \| 2.425 \| 11.0 \| 693 \| 0.9569 \| 0.4848 \| \| 2.425 \| 12.0 \| 756 \| 0.9649 \| 0.4798 \| \| 2.425 \| 13.0 \| 819 \| 1.3807 \| 0.4899 \| \| 2.425 \| 14.0 \| 882 \| 0.6900 \| 0.4899 \| \| 2.425 \| 15.0 \| 945 \| 0.8787 \| 0.4747 \| \| 0.3146 \| 16.0 \| 1008 \| 0.7985 \| 0.4949 \| \| 0.3146 \| 17.0 \| 1071 \| 0.9305 \| 0.4899 \| \| 0.3146 \| 18.0 \| 1134 \| 0.9062 \| 0.4848 \| \| 0.3146 \| 19.0 \| 1197 \| 0.8571 \| 0.5051 \| \| 0.3146 \| 20.0 \| 1260 \| 0.8674 \| 0.5 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
jyesr/ppo-diy	jyesr	2024-03-10T14:07:26Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T14:07:18Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.36 +/- 76.39 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.001 'num_envs': 8 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 16 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'jyesr/ppo-diy' 'batch_size': 1024 'minibatch_size': 64} ```
pmu/my-pet-dog	pmu	2024-03-10T14:06:05Z	0	0	null	[ "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-10T14:03:52Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by pmu following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 4MC22IS075 Sample pictures of this concept: ![0](https://huggingface.co/pmu/my-pet-dog/resolve/main/sample_images/unicorn_dog_3.jpg) ![1](https://huggingface.co/pmu/my-pet-dog/resolve/main/sample_images/unicorn_dog_5.jpg) ![2](https://huggingface.co/pmu/my-pet-dog/resolve/main/sample_images/unicorn_dog_3.webp) ![3](https://huggingface.co/pmu/my-pet-dog/resolve/main/sample_images/unicorn_dog_1.jpg) ![4](https://huggingface.co/pmu/my-pet-dog/resolve/main/sample_images/unicorn_dog_4.jpg)
sujith013/whisper-medium-tamil	sujith013	2024-03-10T14:05:02Z	62	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ta", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-24T09:48:50Z	--- language: - ta license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: ./whisper-medium-tamil-openslr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-tamil This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1628 - Wer: 35.6581 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.9386 \| 0.15 \| 25 \| 0.5501 \| 43.7602 \| \| 0.3073 \| 0.31 \| 50 \| 0.2054 \| 40.3324 \| \| 0.174 \| 0.46 \| 75 \| 0.1713 \| 36.8452 \| \| 0.1539 \| 0.62 \| 100 \| 0.1628 \| 35.6581 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
flammenai/flammen5-mistral-7B	flammenai	2024-03-10T13:58:47Z	17	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:nbeerbower/Flammen-Kunoichi-7B", "base_model:merge:nbeerbower/Flammen-Kunoichi-7B", "base_model:yam-peleg/Experiment26-7B", "base_model:merge:yam-peleg/Experiment26-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T13:51:18Z	--- license: apache-2.0 base_model: - nbeerbower/Flammen-Kunoichi-7B - yam-peleg/Experiment26-7B library_name: transformers tags: - mergekit - merge --- # flammen5-mistral-7B This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [nbeerbower/Flammen-Kunoichi-7B](https://huggingface.co/nbeerbower/Flammen-Kunoichi-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: nbeerbower/Flammen-Kunoichi-7B layer_range: [0, 32] - model: yam-peleg/Experiment26-7B layer_range: [0, 32] merge_method: slerp base_model: nbeerbower/Flammen-Kunoichi-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
herutriana44/llama-2-7b-drug-sequence-summarizer	herutriana44	2024-03-10T13:58:11Z	1	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T08:58:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
geoartop/ppo-LunarLander-v2	geoartop	2024-03-10T13:47:34Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T13:47:17Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.54 +/- 19.84 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Bibek1129/distilgpt2-nepali-multiple-qs-generator	Bibek1129	2024-03-10T13:37:04Z	2	0	peft	[ "peft", "safetensors", "text-generation", "ne", "dataset:Bibek1129/nepali_SQuAD_multiple_qsns", "base_model:Sakonii/distilgpt2-nepali", "base_model:adapter:Sakonii/distilgpt2-nepali", "license:apache-2.0", "region:us" ]	text-generation	2024-03-10T05:54:56Z	--- library_name: peft base_model: Sakonii/distilgpt2-nepali license: apache-2.0 datasets: - Bibek1129/nepali_SQuAD_multiple_qsns language: - ne pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> The model is finetuned on Sakonii/distilgpt2-nepali with Bibek1129/nepali_SQuAD_multiple_qsns dataset.The dataset is converted to nepali using Nepali_nlp library using SQuAD dataset. - Model type: distilgpt2 - Language(s) (NLP): ne(Nepali) - Finetuned from model : https://huggingface.co/Sakonii/distilgpt2-nepali ### Model Sources <!-- Provide the basic links for the model. --> For training snippets and inference check the following repository. - Repository: https://github.com/HordesOfGhost/Nepali_LLMs/] ## How to Get Started with the Model Use the code below to get started with the model. ```python !pip install peft !pip install transformers !pip install sentencepiece ``` ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM,AutoTokenizer from transformers import pipeline base_model = "Sakonii/distilgpt2-nepali" adapter_model = "Bibek1129/distilgpt2-nepali-multiple-qs-generator" tokenizer = AutoTokenizer.from_pretrained(base_model) config = PeftConfig.from_pretrained(adapter_model) model = AutoModelForCausalLM.from_pretrained(base_model) model = PeftModel.from_pretrained(model, adapter_model) model = model.merge_and_unload() prompt = """तपाईं प्रश्नहरू उत्पन्न गर्ने मोडेल हुनुहुन्छ। तपाइँलाई एक सन्दर्भ दिइएको हुन्छ र तपाइँ त्यसमा आधारित प्रश्नहरू उत्पन्न गर्नुहुन्छ। ### सन्दर्भ: राजनीति 'शहरका मामिलाहरू') गतिविधिहरूको सेट हो जुन समूहहरूमा निर्णय गर्न वा व्यक्तिहरू बीचको शक्ति सम्बन्धका अन्य रूपहरू, जस्तै स्रोत वा स्थितिको वितरणसँग सम्बन्धित छ। राजनीति र सरकारको अध्ययन गर्ने सामाजिक विज्ञानको शाखालाई राजनीति विज्ञान भनिन्छ। यसलाई "राजनीतिक समाधान" को सन्दर्भमा सकारात्मक रूपमा प्रयोग गर्न सकिन्छ जुन सम्झौता र अहिंसात्मक छ, वा वर्णनात्मक रूपमा "सरकारको कला वा विज्ञान" को रूपमा, तर प्राय: नकारात्मक अर्थ पनि बोक्छ। अवधारणालाई विभिन्न तरिकामा परिभाषित गरिएको छ, र यसलाई व्यापक रूपमा प्रयोग गर्ने वा सीमित रूपमा, प्रायोगिक वा सामान्य रूपमा, र यसको लागि द्वन्द्व वा सहयोग बढी आवश्यक छ कि छैन भन्ने बारेमा विभिन्न दृष्टिकोणहरूमा मौलिक रूपमा फरक फरक विचारहरू छन्। ### प्रश्नहरू: """ pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=64) def format_output(prompt,pipe): inference = pipe(prompt)[0]["generated_text"] # Select after प्रश्नहरू: and break line after each ? inference = inference.split("प्रश्नहरू:")[-1].replace("?","?\n") # Remove last incomplete question index = inference.rfind("?") inference = inference[:index+1] return inference print(format_output(prompt, pipe)) ''' Output: राजनीतिशास्त्रले मानिसहरूलाई केको रूपमा देख्छ? राजनीतिशास्त्र प्राय: कुन प्रकारको अभ्याससँग सम्बन्धित छ? राजनीतिशास्त्रले मानिसलाई केको रूपमा देख्छ? राजनीति विज्ञानमा केको भूमिका निर्भर छ? राजनीतिक अर्थशास्त्रको शाखालाई कसरी प्रभावित गरेर समाजलाई सांस्कृतिक परिभाषामा के असर हुन्छ,? ''' ``` ## 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. --> The dataset is created by converting SQuAD dataset to nepali using Nepali_nlp using PEFT. https://huggingface.co/datasets/Bibek1129/nepali_SQuAD_multiple_qsns ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> The model is trained with the lora config (rank=32,lora_alpha=64,target_modules="c_fc","c_attn","c_proj","lm_head");with 512 tokens per instance, 4 instances per batch, and around 118.1K training steps. #### Training Hyperparameters Following are the training hyperparameters. <li>learning_rate:2e-4</li> <li>fp16:True</li> <li>optim:"paged_adamw_32bit"</li> <li>lr_scheduler_type:"constant"</li> <li>num_train_epochs:48</li> Lora Config: ```python config={ "alpha_pattern": {}, "auto_mapping": null, "base_model_name_or_path": "Sakonii/distilgpt2-nepali", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "layers_pattern": null, "layers_to_transform": null, "lora_alpha": 64, "lora_dropout": 0.05, "modules_to_save": null, "peft_type": "LORA", "r": 32, "rank_pattern": {}, "revision": null, "target_modules": [ "c_proj", "lm_head", "c_fc", "c_attn" ], "task_type": "CAUSAL_LM" } ``` ### Results <li>train/loss:3.1273</li> ### Framework versions - PEFT 0.9.0 -
adhityamw11/distilhubert-finetuned_distillhubert-ravdess	adhityamw11	2024-03-10T13:34:20Z	147	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:adhityamw11/ravdess_distillhubert", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-10T12:22:19Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - adhityamw11/ravdess_distillhubert metrics: - accuracy model-index: - name: distilhubert-finetuned-ravdess 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. --> # distilhubert-finetuned-ravdess This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the RAVDESS dataset. It achieves the following results on the evaluation set: - Loss: 0.9331 - Accuracy: 0.8438 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0641 \| 1.0 \| 144 \| 2.0414 \| 0.2778 \| \| 1.751 \| 2.0 \| 288 \| 1.7801 \| 0.3854 \| \| 1.5345 \| 3.0 \| 432 \| 1.3610 \| 0.5417 \| \| 1.1913 \| 4.0 \| 576 \| 1.1896 \| 0.5417 \| \| 0.8227 \| 5.0 \| 720 \| 0.7924 \| 0.7535 \| \| 0.6563 \| 6.0 \| 864 \| 0.6772 \| 0.7743 \| \| 0.4082 \| 7.0 \| 1008 \| 0.6398 \| 0.7847 \| \| 0.5133 \| 8.0 \| 1152 \| 0.6409 \| 0.7951 \| \| 0.0467 \| 9.0 \| 1296 \| 0.7356 \| 0.7951 \| \| 0.0232 \| 10.0 \| 1440 \| 0.8220 \| 0.8160 \| \| 0.0298 \| 11.0 \| 1584 \| 0.7164 \| 0.8438 \| \| 0.0021 \| 12.0 \| 1728 \| 0.7578 \| 0.8611 \| \| 0.0014 \| 13.0 \| 1872 \| 0.6806 \| 0.8507 \| \| 0.0012 \| 14.0 \| 2016 \| 0.6953 \| 0.8507 \| \| 0.0009 \| 15.0 \| 2160 \| 0.7311 \| 0.8403 \| \| 0.0007 \| 16.0 \| 2304 \| 0.7312 \| 0.8472 \| \| 0.0006 \| 17.0 \| 2448 \| 0.7528 \| 0.8438 \| \| 0.0005 \| 18.0 \| 2592 \| 0.7748 \| 0.8299 \| \| 0.0005 \| 19.0 \| 2736 \| 0.7692 \| 0.8472 \| \| 0.0004 \| 20.0 \| 2880 \| 0.7806 \| 0.8403 \| \| 0.0003 \| 21.0 \| 3024 \| 0.7907 \| 0.8438 \| \| 0.0003 \| 22.0 \| 3168 \| 0.7909 \| 0.8438 \| \| 0.0003 \| 23.0 \| 3312 \| 0.8060 \| 0.8472 \| \| 0.0003 \| 24.0 \| 3456 \| 0.8302 \| 0.8438 \| \| 0.0002 \| 25.0 \| 3600 \| 0.8296 \| 0.8438 \| \| 0.0002 \| 26.0 \| 3744 \| 0.8306 \| 0.8403 \| \| 0.0002 \| 27.0 \| 3888 \| 0.8399 \| 0.8438 \| \| 0.0002 \| 28.0 \| 4032 \| 0.8447 \| 0.8438 \| \| 0.0002 \| 29.0 \| 4176 \| 0.8488 \| 0.8403 \| \| 0.0002 \| 30.0 \| 4320 \| 0.8564 \| 0.8472 \| \| 0.0002 \| 31.0 \| 4464 \| 0.8618 \| 0.8472 \| \| 0.0001 \| 32.0 \| 4608 \| 0.8736 \| 0.8438 \| \| 0.0001 \| 33.0 \| 4752 \| 0.8793 \| 0.8403 \| \| 0.0001 \| 34.0 \| 4896 \| 0.8840 \| 0.8438 \| \| 0.0001 \| 35.0 \| 5040 \| 0.8870 \| 0.8438 \| \| 0.0001 \| 36.0 \| 5184 \| 0.8882 \| 0.8472 \| \| 0.0001 \| 37.0 \| 5328 \| 0.9033 \| 0.8403 \| \| 0.0001 \| 38.0 \| 5472 \| 0.8980 \| 0.8403 \| \| 0.0001 \| 39.0 \| 5616 \| 0.9081 \| 0.8472 \| \| 0.0001 \| 40.0 \| 5760 \| 0.9086 \| 0.8472 \| \| 0.0001 \| 41.0 \| 5904 \| 0.9119 \| 0.8438 \| \| 0.0001 \| 42.0 \| 6048 \| 0.9106 \| 0.8507 \| \| 0.0001 \| 43.0 \| 6192 \| 0.9188 \| 0.8438 \| \| 0.0001 \| 44.0 \| 6336 \| 0.9238 \| 0.8438 \| \| 0.0001 \| 45.0 \| 6480 \| 0.9282 \| 0.8438 \| \| 0.0001 \| 46.0 \| 6624 \| 0.9286 \| 0.8438 \| \| 0.0001 \| 47.0 \| 6768 \| 0.9312 \| 0.8438 \| \| 0.0001 \| 48.0 \| 6912 \| 0.9296 \| 0.8472 \| \| 0.0001 \| 49.0 \| 7056 \| 0.9324 \| 0.8438 \| \| 0.0001 \| 50.0 \| 7200 \| 0.9331 \| 0.8438 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
hyuk2010/koalpaca-polyglot-12.8b-bill	hyuk2010	2024-03-10T13:33:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T13:33:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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rk68/T5-small-lora-aqua-rat-gemma-rationales-400-samples	rk68	2024-03-10T13:32:17Z	177	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-10T13:31:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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jojo80565/CartPole-v1-ppo	jojo80565	2024-03-10T13:21:57Z	0	0	null	[ "CartPole-v1", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T12:41:54Z	--- tags: - CartPole-v1 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 32.80 +/- 9.59 name: mean_reward verified: false --- # PPO Agent Playing CartPole-v1 This is a trained model of a PPO agent playing CartPole-v1. # Hyperparameters ```python {'exp_name': 'CatePole.py' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'CartPole-v1' 'total_timesteps': 10000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'jojo80565/CartPole-v1-ppo' 'batch_size': 512 'minibatch_size': 128} ```
woonchae/distilbert-base-uncased-finetuned-emotion	woonchae	2024-03-10T13:18:43Z	95	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T12:30:54Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9225 - name: F1 type: f1 value: 0.9225783519597501 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2201 - Accuracy: 0.9225 - F1: 0.9226 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7983 \| 1.0 \| 250 \| 0.3130 \| 0.9035 \| 0.9029 \| \| 0.246 \| 2.0 \| 500 \| 0.2201 \| 0.9225 \| 0.9226 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Organika/StarCoder-1B-WoW-JSON	Organika	2024-03-10T13:15:56Z	92	0	transformers	[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T13:12:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jaekwanyda/hansol_2	jaekwanyda	2024-03-10T13:09:56Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T11:29:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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CaptainPollutionTV/CaptainPlanet-DS	CaptainPollutionTV	2024-03-10T13:09:11Z	0	0	null	[ "Dreambooth", "DreamShaper", "license:cc", "region:us" ]	null	2024-03-10T10:03:38Z	--- license: cc tags: - Dreambooth - DreamShaper --- Made by CaptainPollutionTV using the getimg.ai Dreambooth tool. Details about the model: Base Model DreamShaper Instance prompt captainplanet Class prompt a man Learning Rate 0.000001 Learning Rate Scheduler polynomial Training Steps 10000 (200 steps warmup) Class images 1000 Model seed 687176212 Sample images: ![5 - img-lw9kcgDBZkITUdxP751tbG.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/GeyPFMftYMqGHd0i7g5FM.png) ![6 - img-dV1EHc1mM2m4cN8PuFgAWx.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/pXP1ieOyJSX8W-037vGri.png) ![7 - img-Dw8BwFLq88ttYqolUXRXLb.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/4G5ibtHqFNAeh5xSl1FKf.png) ![8 - img-782AHfLMVn0Vmlb1maYc8U.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/LzNIMnrSjlTgjpeF4J_8O.png) ![9 - img-CN3nP8GjSgM3EJy7YAK37r.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/DsTtft8Mq2A6M12NwDJUl.png) ![10 - img-th11tP82bVGPAcXBbuyPcg.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/BHQRLgx_DzT9ljkc6zP__.png) ![11 - img-mnRdKnEVHPCgHTLFrLnbaj.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/HbdHvxApWrc4MQDCVJ4k7.png) ![12 - img-5Xq2kw7LXB2slHAgHOWxNZ.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/62PZIUAUpECqqvhO9V99-.png) ![13 - img-nR6YV7fBbiZpwGF2VABeyp.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/sd7bRg0MpEzf7DHcd4FXN.png) ![14 - img-QkXSCcRAHlLqR3nE8HtZAN.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/TcLIq5HEfGw4xbGat5rev.png) ![15 - img-88vLx3yVRo9VIBd63uGPe3.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/E90LK1q6mngaBsCfRYELC.png) ![16 - img-PfmKLYiSfGJbL2XTbAgu4n.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/LvgG0NvU8dZE92lN5bPQ9.png) ![17 - img-GLNi61wfOcD4wAJ4xLpugA.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/gpROrShVhACuEnN1XioHS.png) ![18 - img-elRrT59xmevgjhGlkg8jkl.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/qPudhfETXeBcf8XUPnB1Z.png) ![19 - img-oobfDtoOvlEbFENaOE24KR.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/5DsdymhVjgnzgGt7KNsbj.png) ![20 - img-mKU9zZTi76vFYhEx1BiOPv.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/y4U3Zq2sRBRZ3bHimahrd.png) ![21 - img-hs2D8VK5if5c4WWd91pxnp.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/w18fe4TW1AP1EYfKiEK6t.png) ![22 - img-GRWtUYLPPzenDThpomXYHS.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/UJJrm-2Oridv4pmEguaji.png) ![23 - img-yGQHJknCbr4mC1YqV3OgF2.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/U-7G_7qVetcgV5C83V8jB.png) ![24 - img-cQaKurW3GBIvKqPDrf6u1S.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/5CsuwBo4RYpNc4JzWMWqC.png) ![25 - img-cWktNVztjS9W1Qpdsd63wZ.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/7zRbWKi0sxd4NE2fdfkTL.png) ![26 - img-roXkKcmW7abcd8w2w2yu3S.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Ki4XZsNmSIb2HzzGWMZnQ.png) ![27 - img-SW8lRiFnR5jE9Tc99DJ4A2.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/64jzlXPzOaSqVpRPc2Di1.png) ![28 - img-UCujfQI1EPKFg3WQ84ynQ7.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/lpd2eWLNBCaom4m2yAq14.png) ![29 - img-Jt3d93XgPhVDqv5Doa6mKo.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/IGZ-5iPUisRoRDWVf8BL3.png) ![30 - img-mztzoBMP6nkcHvtNV82eua.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/4iSo6k63Bj-v_1dkEXfzp.png) ![31 - img-y0ADXWgFLRnKxzBel8rRJJ.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/tSsTkntjFsMuS6dcIHSsQ.png) ![32 - 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Samvardhan777/gemma-2b-mt-German-to-English	Samvardhan777	2024-03-10T13:07:17Z	45	1	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "translation", "de", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2024-03-09T19:10:07Z	--- license: mit language: - de - en pipeline_tag: translation tags: - text-generation-inference --- # Description ## Gemma 2B German to English v0.1 Alpha [Experimental Release] This is a german instruction finetuned version of Google's Gemma 2B model. This is an experiment to see if Gemma can be Translate German to English by expanding vocabulary. While the responses may be rusty at times, it shows a lot of promise for a 2B parameter model. --- # Model description 🗄️: Model type: A 2B parameter GPT-like model finetuned on 100,000 samples consisting of an equal proportion of English and German samples. Language(s): Bilingual. English and German. License: Google Gemma Terms of Use Finetuned from model: Samvardhan777/gemma-2b-mt-German-to-English Training Precision: bfloat16 Training Hardware: Free Google Colab Dataset: kaitchup/opus-German-to-English ---
ndieckow/dqn-SpaceInvaders-NoFrameskip-v4	ndieckow	2024-03-10T13:07:12Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T13:06:42Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 358.50 +/- 169.93 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ndieckow -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ndieckow -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ndieckow ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Sif10/my_awesome_model_imdb	Sif10	2024-03-10T13:05:54Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T12:08:16Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model_imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.85908 --- <!-- 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_model_imdb This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.7781 - Accuracy: 0.8591 ## 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4013 \| 1.0 \| 782 \| 0.3535 \| 0.8445 \| \| 0.2107 \| 2.0 \| 1564 \| 0.3589 \| 0.8550 \| \| 0.1158 \| 3.0 \| 2346 \| 0.5241 \| 0.8576 \| \| 0.0423 \| 4.0 \| 3128 \| 0.7881 \| 0.8545 \| \| 0.0238 \| 5.0 \| 3910 \| 0.7781 \| 0.8591 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
DTempo/videomae-base-finetuned-ucf101-subset	DTempo	2024-03-10T13:03:15Z	47	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-02-15T18:55:31Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset 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. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1105 - Accuracy: 0.9571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5664 \| 0.25 \| 75 \| 0.5633 \| 0.7571 \| \| 0.3826 \| 1.25 \| 150 \| 0.3484 \| 0.8286 \| \| 0.0648 \| 2.25 \| 225 \| 0.3219 \| 0.8429 \| \| 0.044 \| 3.25 \| 300 \| 0.1105 \| 0.9571 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
nocudaexe/Dark-Waifu-7b	nocudaexe	2024-03-10T12:58:50Z	16	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Endevor/InfinityRP-v1-7B", "base_model:merge:Endevor/InfinityRP-v1-7B", "base_model:NeverSleep/Noromaid-7B-0.4-DPO", "base_model:merge:NeverSleep/Noromaid-7B-0.4-DPO", "base_model:Nitral-AI/Kunocchini-7b-128k-test", "base_model:merge:Nitral-AI/Kunocchini-7b-128k-test", "base_model:TeeZee/DarkSapling-7B-v2.0", "base_model:merge:TeeZee/DarkSapling-7B-v2.0", "base_model:mlabonne/AlphaMonarch-7B", "base_model:merge:mlabonne/AlphaMonarch-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T12:55:20Z	--- base_model: - TeeZee/DarkSapling-7B-v2.0 - mlabonne/AlphaMonarch-7B - Test157t/Kunocchini-7b-128k-test - NeverSleep/Noromaid-7B-0.4-DPO - Endevor/InfinityRP-v1-7B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) as a base. ### Models Merged The following models were included in the merge: * [TeeZee/DarkSapling-7B-v2.0](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0) * [Test157t/Kunocchini-7b-128k-test](https://huggingface.co/Test157t/Kunocchini-7b-128k-test) * [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO) * [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mlabonne/AlphaMonarch-7B # No parameters necessary for base model - model: Test157t/Kunocchini-7b-128k-test parameters: density: 0.43 weight: 0.4 - model: TeeZee/DarkSapling-7B-v2.0 parameters: density: 0.23 weight: 0.3 - model: NeverSleep/Noromaid-7B-0.4-DPO parameters: density: 0.23 weight: 0.3 - model: Endevor/InfinityRP-v1-7B parameters: density: 0.2 weight: 0.3 merge_method: dare_ties base_model: mlabonne/AlphaMonarch-7B parameters: int8_mask: true dtype: bfloat16 ```
context-mt/scat-mbart50-1toM-ctx4-cwd1-en-fr	context-mt	2024-03-10T12:51:55Z	99	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "arxiv:2310.01188", "contextual-mt", "document-mt", "translation", "en", "fr", "dataset:inseq/scat", "dataset:gsarti/iwslt2017_context", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-14T12:17:50Z	--- license: apache-2.0 datasets: - inseq/scat - gsarti/iwslt2017_context language: - en - fr pipeline_tag: translation tags: - arxiv:2310.01188 - contextual-mt - document-mt --- *This model corresponds to the [mBART 1-to-50 model](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) further trained on English-to-French translation on the [IWSLT17 dataset](https://huggingface.co/datasets/gsarti/iwslt2017_context) with context tags using the format: ``` Input: SOURCE_CTX <brk> SOURCE_CURR Output: TARGET_CURR ``` and further fine-tuned on the training split of [SCAT+](https://huggingface.co/datasets/inseq/scat). The model was used in the evaluation of the paper [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://openreview.net/forum?id=XTHfNGI3zT) published at ICLR 2024, also available on [Arxiv](https://arxiv.org/abs/2310.01188). It can be used for English to French contextual and non-contextual translation.
XavierScor/poca-SoccerTwos	XavierScor	2024-03-10T12:51:37Z	24	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-10T12:51:29Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: XavierScor/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
context-mt/scat-marian-big-ctx4-cwd1-en-fr	context-mt	2024-03-10T12:42:48Z	136	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "arxiv:2310.01188", "contextual-mt", "document-mt", "translation", "en", "fr", "dataset:inseq/scat", "dataset:gsarti/iwslt2017_context", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-13T14:03:10Z	--- license: apache-2.0 datasets: - inseq/scat - gsarti/iwslt2017_context language: - en - fr pipeline_tag: translation tags: - arxiv:2310.01188 - contextual-mt - document-mt --- *This model corresponds to the [`Helsinki-NLP/opus-mt-tc-big-en-fr`](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-fr) further trained on English-to-French translation on the [IWSLT17 dataset](https://huggingface.co/datasets/gsarti/iwslt2017_context) with context tags using the format: ``` Input: SOURCE_CTX <brk> SOURCE_CURR Output: TARGET_CURR ``` and further fine-tuned on the training split of [SCAT+](https://huggingface.co/datasets/inseq/scat). The model was used in the evaluation of the paper [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://openreview.net/forum?id=XTHfNGI3zT) published at ICLR 2024, also available on [Arxiv](https://arxiv.org/abs/2310.01188). It can be used for English to French contextual and non-contextual translation.
context-mt/scat-marian-small-target-ctx4-cwd0-en-fr	context-mt	2024-03-10T12:41:21Z	136	1	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "arxiv:2310.01188", "contextual-mt", "document-mt", "translation", "en", "fr", "dataset:inseq/scat", "dataset:gsarti/iwslt2017_context", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-28T12:08:57Z	--- license: apache-2.0 datasets: - inseq/scat - gsarti/iwslt2017_context language: - en - fr pipeline_tag: translation tags: - arxiv:2310.01188 - contextual-mt - document-mt --- *This model corresponds to the [`Helsinki-NLP/opus-mt-en-fr`](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) further trained on English-to-French translation on the [IWSLT17 dataset](https://huggingface.co/datasets/gsarti/iwslt2017_context) with context tags using the format: ``` Input: SOURCE_CTX <brk> SOURCE_CURR Output: TARGET_CTX <brk> TARGET_CURR ``` and further fine-tuned on the training split of [SCAT+](https://huggingface.co/datasets/inseq/scat). The model was used in the evaluation of the paper [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://openreview.net/forum?id=XTHfNGI3zT) published at ICLR 2024, also available on [Arxiv](https://arxiv.org/abs/2310.01188). It can be used for English to French contextual and non-contextual translation.
context-mt/scat-mbart50-1toM-target-ctx4-cwd0-en-fr	context-mt	2024-03-10T12:40:40Z	140	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "arxiv:2310.01188", "contextual-mt", "document-mt", "translation", "en", "fr", "dataset:inseq/scat", "dataset:gsarti/iwslt2017_context", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-28T12:08:11Z	--- license: apache-2.0 datasets: - inseq/scat - gsarti/iwslt2017_context language: - en - fr pipeline_tag: translation tags: - arxiv:2310.01188 - contextual-mt - document-mt --- *This model corresponds to the [mBART 1-to-50 model](facebook/mbart-large-50-one-to-many-mmt) further trained on English-to-French translation on the [IWSLT17 dataset](https://huggingface.co/datasets/gsarti/iwslt2017_context) with context tags using the format: ``` Input: SOURCE_CTX <brk> SOURCE_CURR Output: TARGET_CTX <brk> TARGET_CURR ``` and further fine-tuned on the training split of [SCAT+](https://huggingface.co/datasets/inseq/scat). The model was used in the evaluation of the paper [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://openreview.net/forum?id=XTHfNGI3zT) published at ICLR 2024, also available on [Arxiv](https://arxiv.org/abs/2310.01188). It can be used for English to French contextual and non-contextual translation.
kgy0713/bert_kor_news_classification_model	kgy0713	2024-03-10T12:40:19Z	95	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T12:30:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Model type: BERT model - Language(s) (NLP): Korean - Finetuned from model: kykim/bert-kor-base ## 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. --> "kykim/bert-kor-base"을 기본 모델로 해서 "KETI-AIR/kor_ag_news" 데이터로 fine-tuning 한 모델입니다. 간단한 classification model입니다. ``` labels = {"World", "Business", "Sci/Tech", "Sports"} ```
context-mt/scat-marian-big-target-ctx4-cwd0-en-fr	context-mt	2024-03-10T12:39:40Z	120	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "arxiv:2310.01188", "contextual-mt", "document-mt", "translation", "en", "fr", "dataset:inseq/scat", "dataset:gsarti/iwslt2017_context", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-28T12:08:38Z	--- license: apache-2.0 datasets: - inseq/scat - gsarti/iwslt2017_context language: - en - fr pipeline_tag: translation tags: - arxiv:2310.01188 - contextual-mt - document-mt --- *This model corresponds to the [`Helsinki-NLP/opus-mt-tc-big-en-fr`](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-fr) further trained on English-to-French translation on the [IWSLT17 dataset](https://huggingface.co/datasets/gsarti/iwslt2017_context) with context tags using the format: ``` Input: SOURCE_CTX <brk> SOURCE_CURR Output: TARGET_CTX <brk> TARGET_CURR ``` and further fine-tuned on the training split of [SCAT+](https://huggingface.co/datasets/inseq/scat). The model was used in the evaluation of the paper [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://openreview.net/forum?id=XTHfNGI3zT) published at ICLR 2024, also available on [Arxiv](https://arxiv.org/abs/2310.01188). It can be used for English to French contextual and non-contextual translation.
CaptainPollutionTV/DoctorBlight-AG	CaptainPollutionTV	2024-03-10T12:30:12Z	0	0	null	[ "DreamBooth", "Analog", "license:cc", "region:us" ]	null	2024-03-10T10:38:05Z	--- license: cc tags: - DreamBooth - Analog --- Made by CaptainPollutionTV using the getimg.ai Dreambooth tool. Details about the model: Base Model Analog Instance prompt doctorblight Class prompt a woman Learning Rate 0.000001 Learning Rate Scheduler polynomial Training Steps 10000 (200 steps warmup) Class images 1000 Model seed 1520874964 Sample images: ![76 - img-d7YBZn75vIij53gV9NXvZl.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/XW6HJvnt6qaYynEIJVOQf.png) ![1 - img-MHZlBxoD2hN6eTAXlllj43.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/-04pQ5CrF86-qW-rPw-Xj.png) ![2 - img-RHlXCPkq9RUR0gCUdVc63V.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/NGVIUxdlir1G4oqH_Wzja.png) ![3 - img-ReeooiQXFea3ixtHcd8uCE.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/bT0ZY1R1IDJOxO5qyBRNk.png) ![4 - img-GpOHtyFSi7KqiZE5YIMNOl.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/VLHY7jHJv_6pnh_tu42P_.png) ![5 - img-V8Zx9sTTZhbBdFSHUmv95X.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/sChrn5fIzTpfL-cnrSgLT.png) ![6 - img-DTOQJBx3FuQMCms0QTN3eP.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/gF0nErzQb_zZ0VM4sEN3D.png) ![7 - img-RFtCRprLHb8QnXk5oUVOpP.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/e3tEKlgLdKgMTZS1-Ui6v.png) ![8 - img-N4uS52xLW3YaDuwb6DBmKR.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/lGZ0miQ2Pvgoz7R2Y_jwJ.png) ![9 - img-UFQNpBE0Mb3OSMGZrRAFRM.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/n0rjFO-Mweyo9TXY-GBDy.png) ![10 - img-XSWU2y6aF9h4kNotYsqCTV.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Ggbg-yXpDW8jyF8zCKSRn.png) ![11 - img-dhn2LKwcA0i1G6TmTK3SN2.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Jew-owbL8y8mj2QJk9exP.png) ![12 - img-83fKDjh8wSgGdwL4adTx7x.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/p_Y4o3eRm_ogUgLS-_e-r.png) ![13 - img-SX4GXSgFwihH8X9CRUNNuf.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/2hLEHS1uX29CG9LUZ49ww.png) ![14 - img-PkARzzecb2uu49ALvPYc92.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/JqZZJtV3F2KA9ZF6bRUiP.png) ![15 - img-xaq29DPtWJ2NNiRmqqb72O.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/gsIVGLCQXRUtWGtfG-MsM.png) ![16 - img-6KwdxHC60mKzeWrBrVpKbT.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/PADQHESD3eAbNThKOx6Cs.png) ![17 - img-c8TbcH2wXeLP10VM5pAYeh.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/tyQUmyXOiNknI-7IT0bpI.png) ![18 - img-LOTLSh9hJS9avx5QjYpEop.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/XiqMgNVq9FZLVK-VufvRp.png) ![19 - img-F5LtQSSoQ3JBcVZytgBK6n.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/T9dOKLZgYIi0FX_Ac3U3a.png) ![20 - img-iCebvchYwi4rtD9FusLlK9.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/_SiIo21Cfc6pt2IOXlSwV.png) ![21 - img-tq8cy0nRhvrmTZ5VvNPBLI.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/3nL3wyl-nlRoyDDwRnOu3.png) ![22 - img-uo2E7BMro7ShsO9M09SNy6.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/kxSmE4ZlfJFSK9h0QPWfJ.png) ![23 - img-xdFVQzMEvjPrOxftxEj0AP.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/jA7ARzI5Uj3rYsWzJPjUm.png) ![24 - img-IKhdBgULiN3zYIMbthPscV.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/JzBjJ0Jx5nyPUPqJX29J_.png) ![25 - img-gSW1sXNeElu3m7SG8zoLo2.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/AJYw3MwwMTW09sIR3OFUE.png) ![26 - img-DhUw9JwXcB5cUIVwklYp5T.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/EQre5hLSAK7BToP8_Z1VD.png) ![27 - 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Peterwu4084/ppo-Huggy	Peterwu4084	2024-03-10T12:11:47Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-10T12:09:53Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Peterwu4084/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ghost-x/ghost-7b-v0.9.0	ghost-x	2024-03-10T12:10:11Z	2,288	5	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ghost", "conversational", "en", "vi", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:finetune:HuggingFaceH4/zephyr-7b-beta", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T16:01:08Z	--- language: - en - vi license: mit library_name: transformers tags: - ghost pipeline_tag: text-generation base_model: HuggingFaceH4/zephyr-7b-beta widget: - text: '<\|system\|> You are a helpful assistant.</s> <\|user\|> Thông tin về Peristernia despecta</s> <\|assistant\|> ' output: text: Peristernia despecta là một loài ốc biển, là động vật thân mềm chân bụng sống ở biển trong họ Fasciolariidae. model-index: - name: lamhieu/ghost-7b-v0.9.0 results: - task: type: text-generation dataset: name: VMLU type: vmlu_v1.5 metrics: - type: avg value: 36.06 name: Average verified: true - type: stem value: 33.54 name: STEM verified: true - type: ss value: 38.74 name: Social science verified: true - type: hm value: 37.15 name: Humanities verified: true - type: ot value: 36.78 name: Other verified: true - task: type: text-generation dataset: name: Open LLM Leaderboard type: open_llm_leaderboard metrics: - type: avg value: 56.89 name: Average verified: true - type: arc value: 53.07 name: ARC verified: true - type: hs value: 77.93 name: HellaSwag verified: true - type: hs value: 77.93 name: HellaSwag verified: true - type: mmlu value: 55.09 name: MMLU verified: true - type: wg value: 73.72 name: Winogrande verified: true - type: gsm8k value: 33.74 name: GSM8K verified: true source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 53.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 77.93 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 55.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 47.79 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 73.72 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 33.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0 name: Open LLM Leaderboard --- # Model Card for Model ID Ghost 7B Alpha, flying, v0.9.0 ## Model Details ### Model Description This model is fine tuned from HuggingFaceH4/zephyr-7b-beta on a small synthetic datasets (about 200MB) for 50% English and 50% Vietnamese. - Developed by: Lam H - Language(s) (NLP): English, Vietnamese - License: MIT - Finetuned from model: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ## Uses This model supports both conversation chat and tasks. Feel free to experiment and don't limit your creativity. The simplest way to try it is to use the `pipeline` from `transformers`. ```python import torch from transformers import pipeline pipe = pipeline( "text-generation", model="lamhieu/ghost-7b-v0.9.0", torch_dtype=torch.bfloat16, ) ``` You can then try any of the sample codes below, formatted using the chat template. ```python messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "nói tôi biết bệnh dịch hạch ở châu Âu do khuẩn nào gây ra"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False) outputs = pipe.model.generate(tokenized, max_new_tokens=512) results = tokenizer.batch_decode(outputs)[0] print(results) # Bệnh dịch hạch ở châu Âu do khuẩn gây ra là do khuẩn Yersinia pestis. ``` ```python messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Thông tin về Peristernia despecta"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False) outputs = pipe.model.generate(tokenized, max_new_tokens=512) results = tokenizer.batch_decode(outputs)[0] print(results) # Peristernia despecta là một loài ốc biển, là động vật thân mềm chân bụng sống ở biển trong họ Fasciolariidae. # ... ``` ```python messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "do u know vietnam ?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False) outputs = pipe.model.generate(tokenized, max_new_tokens=512) results = tokenizer.batch_decode(outputs)[0] print(results) # Yes, I have knowledge about Vietnam. Vietnam is a country in Southeast Asia, bordered by China to the north, Laos and Cambodia to the west, and the South China Sea to the east and south. Its capital city is Hanoi, and its largest city is Ho Chi Minh City (formerly known as Saigon). Vietnam has a population of approximately 100 million people and a diverse cultural heritage influenced by both Chinese and French colonialism. The country has a rich history, including periods of independence, colonization, and resistance, and has experienced significant economic growth in recent years. ``` ```python messages = [ {"role": "system", "content": "You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old."}, {"role": "user", "content": "Tôi yêu em nhiều hơn em nghĩ.\n\nWhich language is this?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False) outputs = pipe.model.generate(tokenized, max_new_tokens=512) results = tokenizer.batch_decode(outputs)[0] print(results) # This is Vietnamese language. Vietnamese is a language spoken mainly in Vietnam and by the Vietnamese diaspora in many other countries. The sentence you provided means "I love you more than you think." It's like you have more love for someone than they realize. ``` Another example of what you can use to chat multiple turns. ```python messages = [ # {"role": "system", "content": "You are a helpful and knowledgeable assistant. You like to help and always give honest information, in its original language. In communication, you are always respectful, equal and promote positive behavior."}, {"role": "system", "content": "You are a helpful assistant."}, # Describe to your assistant, anything. {"role": "user", "content": "Bla bla bla"}, {"role": "assistant", "content": "Bla bla bla"}, {"role": "user", "content": "Bla bla bla"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) tokenized = pipe.tokenizer(prompt, return_tensors="pt", add_special_tokens=False) outputs = pipe.model.generate(tokenized, max_new_tokens=512) results = tokenizer.batch_decode(outputs)[0] print(results) ``` ## Evaluation ### Results #### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lamhieu__ghost-7b-v0.9.0) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|56.89\| \|AI2 Reasoning Challenge (25-Shot)\|53.07\| \|HellaSwag (10-Shot) \|77.93\| \|MMLU (5-Shot) \|55.09\| \|TruthfulQA (0-shot) \|47.79\| \|Winogrande (5-shot) \|73.72\| \|GSM8k (5-shot) \|33.74\| #### VMLU Below are the results evaluated with the VMLU evaluation suite, which is often used to evaluate models that work with Vietnamese. Note: the results are run with the model in 4bit quantization, I'm not sure if it has any loss in results or not, if someone can help me run it with full it would be great. ![VMLU - lamhieu/ghost-7b-v0.9.0](https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/GdMgr0-YnAGRqD_RJr_ux.png) <details> <summary>Details</summary> ```python { "stem": { "elementary_mathematics": 32.22, "elementary_science": 56.11, "high_school_biology": 32.78, "high_school_chemistry": 27.78, "high_school_mathematics": 33.78, "high_school_physics": 26.11, "introduction_to_chemistry": 26.82, "introduction_to_physics": 33.53, "introduction_to_programming": 39.66, "metrology_engineer": 36.17, "middle_school_biology": 40, "middle_school_chemistry": 26.67, "middle_school_mathematics": 27.78, "middle_school_physics": 27.22, "operating_system": 38.33, "statistics_and_probability": 18.39, "total": 33.54, "applied_informatics": 47.78, "computer_architecture": 36.11, "computer_network": 41.34, "discrete_mathematics": 29.7, "electrical_engineering": 26.14 }, "other": { "total": 36.78, "accountant": 29.17, "civil_servant": 29.82, "clinical_pharmacology": 35.56, "driving_license_certificate": 56.73, "environmental_engineering": 32.16, "internal_basic_medicine": 36.84, "preschool_pedagogy": 45.1, "tax_accountant": 24.71, "tax_civil_servant": 40.94 }, "total": 36.06, "humanity": { "introduction_to_vietnam_culture": 31.11, "logic": 28.16, "middle_school_history": 38.33, "administrative_law": 32.22, "revolutionary_policy_of_the_vietnamese_commununist_part": 40.56, "vietnamese_language_and_literature": 35.06, "total": 37.15, "middle_school_literature": 36.21, "business_law": 38.55, "civil_law": 48.33, "criminal_law": 37.42, "economic_law": 38.51, "education_law": 36.75, "elementary_history": 35.03, "high_school_history": 27.78, "high_school_literature": 32.78, "history_of_world_civilization": 43.33, "idealogical_and_moral_cultivation": 39.44, "introduction_to_laws": 49.21 }, "social_science": { "business_administration": 37.36, "high_school_civil_education": 42.78, "high_school_geography": 38.27, "ho_chi_minh_ideology": 40.22, "macroeconomics": 27.78, "microeconomics": 36.67, "middle_school_civil_education": 51.69, "middle_school_geography": 32.65, "principles_of_marxism_and_leninism": 35.56, "sociology": 44.38, "total": 38.74 } } ``` </details> ## More Information Many thanks for - Datasets: [5CD-AI](https://huggingface.co/5CD-AI), [vilm](https://huggingface.co/vilm). - Library: [unsloth](https://github.com/unslothai/unsloth) ## Model Card Contact Lam H** ([email protected])
ZainAli60/X	ZainAli60	2024-03-10T12:04:28Z	183	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T12:03:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
automerger/Multi_verse_modelM7-7B	automerger	2024-03-10T12:00:54Z	17	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:MTSAIR/multi_verse_model", "base_model:merge:MTSAIR/multi_verse_model", "base_model:liminerity/M7-7b", "base_model:merge:liminerity/M7-7b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T08:38:04Z	--- base_model: - liminerity/M7-7b - ammarali32/multi_verse_model library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) as a base. ### Models Merged The following models were included in the merge: * [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ammarali32/multi_verse_model # No parameters necessary for base model - model: liminerity/M7-7b parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: ammarali32/multi_verse_model parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ```
CaptainPollutionTV/MegaMan-ICBINPF	CaptainPollutionTV	2024-03-10T12:00:29Z	0	0	null	[ "DreamBooth", "ICBINP Final", "license:cc", "region:us" ]	null	2024-03-10T11:28:37Z	--- license: cc tags: - DreamBooth - ICBINP Final --- Made by CaptainPollutionTV using the getimg.ai Dreambooth tool. Details about the model: Base Model ICBINP Final Instance prompt megaman Class prompt a robot Learning Rate 0.000001 Learning Rate Scheduler polynomial Training Steps 10000 (200 steps warmup) Class images 1000 Model seed 612422015 Sample images: ![1 - img-gMUgq4r9fNvFZ0gc1h5hSf.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/SdS0BlrboHUL6GmCs8_bO.png) ![2 - img-xswa0xx0E3IUqLaanplfZ0.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/idf5k7uBp2dw4UcFdgWKO.png) ![3 - img-agupmt29cabtKchNry97As.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/0lAlQK0ymQS2Aw8x9FWOA.png) ![4 - img-V9N3NYeWFavTdRvUSG8BGd.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/0kMiZL_QdPOdnitIGo0Ch.png) ![5 - img-ydp9WoyjGVj4CD2xOg8IRh.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/M7JqrB4HTMO0Q_SLGpInq.png) ![6 - img-r0nTiL8BDtUxIfiPyEyGQg.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/4fbL_pOEa93CgxgtErXv-.png) ![7 - img-ZKqCVQvzWseUiLijjaSdjM.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Bw6bztqVxR_aIe3q3EUSv.png) ![8 - img-5IA5u9sPyhxoSxGqzVXJK6.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/qFBk0mD_qSaAHC_AIR_hy.png) ![9 - img-yJEaYdmn8u3JwjQCBEw9Ot.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Y-JAxWnJjs6IxOFBSA9Nm.png) ![10 - img-FvKmngnxcfBC9V8iLsQ3jU.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/4YHf2qBP2dK7gJbC1pFw-.png) ![11 - img-1JWOQss0eDQThkcKY0FMAS.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/fI0wad5U9jdoCu_cGeNx7.png) ![12 - img-itLDj7JmquFGVe2X91jDe9.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/qqvPOstaonmK8ImZClxmi.png) ![13 - img-ulLepPVhPMNjZ0Nx5oltKh.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/bet0e9G3PYVDdRm7vhjQ7.png) ![14 - img-gQBdYs1IjaD5Ty9q49JJ9c.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/L8ybSRMMXsVFS5seuJcz0.png) ![15 - img-LhpTuUuwrRL6PpANgZCaJX.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/FMRI3VACXrQp6-avPn6Jj.png) ![16 - img-NO9BXaxCdDVrtHYBOtysak.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Ce3VV7KaHE8BU0reYL-50.png) ![17 - img-nXYFPwMVEcafC1ApcuNnzJ.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/3ir2R1qlv1mbIaj9mpmDj.png) ![18 - img-es4zhrXBPdmVF6tcVhRvqN.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/TVGAbIUhQe2Aly-ABtuNf.png) ![19 - img-WzXpGs7oA3v5VZnzpMmVsD.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/D4d4eZkHiMssnuhL3B96L.png) ![20 - img-uAmNy6DXo7oZUb1byXgrcX.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/iGF3-3ZEdwRX3jN7Es9aA.png) ![21 - 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img-s6KpLbVy0VoWpEdS6PsJgM.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/opPSvgfGswlP_y8UhhjvA.png) ![29 - img-9VD3ChcnsUA2rkGUGVvEgF.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Fco0JtJFrT0N7A_IrNy76.png) ![30 - img-0IsIm1HBSXA2zdb4bgWt42.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/J-KWlD17ExWVz9AlBso7n.png) ![31 - img-6ckoMeAjnZUOpyKhHx8MBR.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/0eINJMdzqMGnwsjfEZEsB.png) ![32 - img-re3qv9SbR5ZsBotsLda3K6.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/TUBp9nnG3_9SnTAD_ApPX.png) ![33 - img-k8csc5ghQInVwhHAAwSHj4.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/DIxsegQ7_vzcUbFHBIYrT.png) ![34 - img-o45aDYmM9KmEDgfXbZbIE2.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/eW2aIFCDhT6Odt87rKwHf.png) ![35 - img-3lWgIW4RxGK4e5eT5uC7EZ.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/XywTpW41MRZP1ns4iQ9oy.png) ![36 - img-6SYfTI9E16eZxtJIf0DCAL.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/aqUn_psuF_a20Sw6KE9PA.png) ![37 - img-znYQGuHMc8dISSiuSwSjRU.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/M13IpUArASMFrliJ7ATln.png) ![38 - img-aXzHljtEA1vngUjeqIctMZ.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/7V17DKUprEV6qSmWDFurT.png) ![39 - img-csHniLTQEbdUhKicaRZsg8.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/NpOVlkDxi8TSc6w4-ZFRm.png) ![40 - img-dWNbmb6oQSC9P5XvVVZeTw.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/8EgfjAFHK3PnPTTsvRZt-.png) ![41 - img-pa87lFFV45yeDDzsC70jxz.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/8g3Tbuu1H5C_C2bIqoOFz.png) ![42 - 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img-GRhp58LVzAIeMXq3vjMVml.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/0D0evRTnL75XcbBaMUj4g.png) ![57 - img-HnBSRTwnussk3caoPtEjEk.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/5yU1sIF9G9LGdva09ARZZ.png) ![58 - img-gbnuUZggPumUJw76ECeVk6.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/TKNhJNNP8u_t1uFovcDXc.png) ![59 - img-GQ19n3R5gXxovm5nI3eTp2.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/de5LIaDzzgyFMge59U76z.png) ![60 - img-kl2Arj9tv7WK3iyq5mtzDe.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/Z4sI-RCkJydoGvadVdYie.png) ![61 - img-pQb7MXDlaCuwkqlY0VYS8Z.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/4Van6O0TRSFSR5y6m2Sme.png) ![62 - img-tLXC3DHAVN3E285PjeLwAX.png](https://cdn-uploads.huggingface.co/production/uploads/64e35e94c967354314367535/lVwapUNtLIhmX_PiotrlU.png) ![63 - 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MoveScores18/ISNET_CARVENET97	MoveScores18	2024-03-10T11:59:06Z	0	0	null	[ "onnx", "license:bigscience-openrail-m", "region:us" ]	null	2024-03-10T11:57:39Z	--- license: bigscience-openrail-m ---
gotzmann/v0.8.2-adapter	gotzmann	2024-03-10T11:54:16Z	3	0	peft	[ "peft", "llama", "generated_from_trainer", "base_model:gotzmann/uni", "base_model:adapter:gotzmann/uni", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-10T11:53:17Z	--- library_name: peft tags: - generated_from_trainer base_model: gotzmann/uni model-index: - name: home/exported results: [] --- ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
JoniJoniAl/diversetraining10maart	JoniJoniAl	2024-03-10T11:52:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-10T11:51:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: JoniJoniAl - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
etri-vilab/koala-1b	etri-vilab	2024-03-10T11:50:50Z	172	16	diffusers	[ "diffusers", "onnx", "safetensors", "text-to-image", "KOALA", "arxiv:2312.04005", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-01-09T05:47:30Z	--- tags: - text-to-image - KOALA --- <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/yosvi68jvyarbvymxc4hm/github_logo.png?rlkey=r9ouwcd7cqxjbvio43q9b3djd&dl=1" width="1024px" /> </div> <div style="display:flex;justify-content: center"> <a href="https://youngwanlee.github.io/KOALA/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp; <a href="https://github.com/youngwanLEE/sdxl-koala"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp; <a href="https://arxiv.org/abs/2312.04005"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv:KOALA&color=red&logo=arxiv"></a> &ensp; </div> # KOALA-1B Model Card ## KOALA Model Cards \|Model\|link\| \|:--\|:--\| \|koala-700m \| https://huggingface.co/etri-vilab/koala-700m\| \|koala-700m-llava-cap \| https://huggingface.co/etri-vilab/koala-700m-llava-cap\| \|koala-1b \| https://huggingface.co/etri-vilab/koala-1b\| \|koala-1b-llava-cap \| https://huggingface.co/etri-vilab/koala-1b-llava-cap\| ## Abstract ### TL;DR > We propose a fast text-to-image model, called KOALA, by compressing SDXL's U-Net and distilling knowledge from SDXL into our model. KOALA-700M can generate a 1024x1024 image in less than 1.5 seconds on an NVIDIA 4090 GPU, which is more than 2x faster than SDXL. KOALA-700M can be used as a decent alternative between SDM and SDXL in limited resources. <details><summary>FULL abstract</summary> Stable diffusion is the mainstay of the text-to-image (T2I) synthesis in the community due to its generation performance and open-source nature. Recently, Stable Diffusion XL (SDXL), the successor of stable diffusion, has received a lot of attention due to its significant performance improvements with a higher resolution of 1024x1024 and a larger model. However, its increased computation cost and model size require higher-end hardware (e.g., bigger VRAM GPU) for end-users, incurring higher costs of operation. To address this problem, in this work, we propose an efficient latent diffusion model for text-to-image synthesis obtained by distilling the knowledge of SDXL. To this end, we first perform an in-depth analysis of the denoising U-Net in SDXL, which is the main bottleneck of the model, and then design a more efficient U-Net based on the analysis. Secondly, we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and eventually identify four essential factors, the core of which is that self-attention is the most important part. With our efficient U-Net and self-attention-based knowledge distillation strategy, we build our efficient T2I models, called KOALA-1B &-700M, while reducing the model size up to 54% and 69% of the original SDXL model. In particular, the KOALA-700M is more than twice as fast as SDXL while still retaining a decent generation quality. We hope that due to its balanced speed-performance tradeoff, our KOALA models can serve as a cost-effective alternative to SDXL in resource-constrained environments. </details> <br> These 1024x1024 samples are generated by KOALA-700M with 25 denoising steps. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/rjsqqgfney7be069y2yr7/teaser.png?rlkey=7lq0m90xpjcoqclzl4tieajpo&dl=1" width="1024px" /> </div> ## Architecture There are two two types of compressed U-Net, KOALA-1B and KOALA-700M, which are realized by reducing residual blocks and transformer blocks. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/5ydeywgiyt1d3njw63dpk/arch.png?rlkey=1p6imbjs4lkmfpcxy153i1a2t&dl=1" width="1024px" /> </div> ### U-Net comparison \| U-Net \| SDM-v2.0 \| SDXL-Base-1.0 \| KOALA-1B \| KOALA-700M \| \|-------\|:----------:\|:-----------:\|:-----------:\|:-------------:\| \| Param. \| 865M \| 2,567M \| 1,161M \| 782M \| \| CKPT size \| 3.46GB \| 10.3GB \| 4.4GB \| 3.0GB \| \| Tx blocks \| [1, 1, 1, 1] \| [0, 2, 10] \| [0, 2, 6] \| [0, 2, 5] \| \| Mid block \| ✓ \| ✓ \| ✓ \| ✗ \| \| Latency \| 1.131s \| 3.133s \| 1.604s \| 1.257s \| - Tx menans transformer block and CKPT means the trained checkpoint file. - We measured latency with FP16-precision, and 25 denoising steps in NVIDIA 4090 GPU (24GB). - SDM-v2.0 uses 768x768 resolution, while SDXL and KOALA models uses 1024x1024 resolution. ## Latency and memory usage comparison on different GPUs We measure the inference time of SDM-v2.0 with 768x768 resolution and the other models with 1024x1024 using a variety of consumer-grade GPUs: NVIDIA 3060Ti (8GB), 2080Ti (11GB), and 4090 (24GB). We use 25 denoising steps and FP16/FP32 precisions. OOM means Out-of-Memory. Note that SDXL-Base cannot operate in the 8GB-GPU. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/u1az20y0zfww1l5lhbcyd/latency_gpu.svg?rlkey=vjn3gpkmywmp7jpilar4km7sd&dl=1" width="1024px" /> </div> ## Key Features - Efficient U-Net Architecture: KOALA models use a simplified U-Net architecture that reduces the model size by up to 54% and 69% respectively compared to its predecessor, Stable Diffusion XL (SDXL). - Self-Attention-Based Knowledge Distillation: The core technique in KOALA focuses on the distillation of self-attention features, which proves crucial for maintaining image generation quality. ## Model Description - Developed by [ETRI Visual Intelligence Lab](https://huggingface.co/etri-vilab) - Developer: [Youngwan Lee](https://youngwanlee.github.io/), [Kwanyong Park](https://pkyong95.github.io/), [Yoorhim Cho](https://ofzlo.github.io/), [Young-Ju Lee](https://scholar.google.com/citations?user=6goOQh8AAAAJ&hl=en), [Sung Ju Hwang](http://www.sungjuhwang.com/) - Model Description: Latent Diffusion based text-to-image generative model. KOALA models uses the same text encoders as [SDXL-Base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and only replace the denoising U-Net with the compressed U-Nets. - Training data: [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/) - Resources for more information: Check out [KOALA report on arXiv](https://arxiv.org/abs/2312.04005) and [project page](https://youngwanlee.github.io/KOALA/). ## Usage with 🤗[Diffusers library](https://github.com/huggingface/diffusers) The inference code with denoising step 25 ```python import torch from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-1b", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "A portrait painting of a Golden Retriever like Leonard da Vinci" negative = "worst quality, low quality, illustration, low resolution" image = pipe(prompt=prompt, negative_prompt=negative).images[0] ``` ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias - Text Rendering: The models face challenges in rendering long, legible text within images. - Complex Prompts: KOALA sometimes struggles with complex prompts involving multiple attributes. - Dataset Dependencies: The current limitations are partially attributed to the characteristics of the training dataset (LAION-aesthetics-V2 6+). ## Citation ```bibtex @misc{Lee@koala, title={KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis}, author={Youngwan Lee and Kwanyong Park and Yoorhim Cho and Yong-Ju Lee and Sung Ju Hwang}, year={2023}, eprint={2312.04005}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
AdamGrzesik/Mistral_7B_SamanthaPL-old-1-epoch	AdamGrzesik	2024-03-10T11:46:38Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-18T10:43:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: AdamGrzesik - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit - Dataset : cognitivecomputations/samantha-data (Samantha_AG_PL.json) This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lukegbsn1/ring_ai_test	lukegbsn1	2024-03-10T11:33:59Z	28	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-10T11:32:25Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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Ravi6923/QuizBot	Ravi6923	2024-03-10T11:28:46Z	109	1	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-10T11:28:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
automerger/Experiment26Pastiche-7B	automerger	2024-03-10T11:20:29Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:CorticalStack/pastiche-crown-clown-7b-dare-dpo", "base_model:merge:CorticalStack/pastiche-crown-clown-7b-dare-dpo", "base_model:yam-peleg/Experiment26-7B", "base_model:merge:yam-peleg/Experiment26-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T11:19:39Z	--- base_model: - yam-peleg/Experiment26-7B - CorticalStack/pastiche-crown-clown-7b-dare-dpo library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [CorticalStack/pastiche-crown-clown-7b-dare-dpo](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare-dpo) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: yam-peleg/Experiment26-7B layer_range: [0, 32] - model: CorticalStack/pastiche-crown-clown-7b-dare-dpo layer_range: [0, 32] merge_method: slerp base_model: yam-peleg/Experiment26-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ```
drakrig/poca-SoccerTwos	drakrig	2024-03-10T11:17:18Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-10T11:14:55Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: drakrig/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
samanjoy2/gemma7b-it_banglaNewsSum	samanjoy2	2024-03-10T11:07:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T11:07:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OwOOwO/eacc_contTrain_l2_g54l2-1	OwOOwO	2024-03-10T11:05:56Z	92	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T11:03:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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afaji/fresh-12-layer-swag-distill-of-fresh-12-layer-gpqa	afaji	2024-03-10T11:05:47Z	89	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T11:04:16Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-12-layer-swag-distill-of-fresh-12-layer-gpqa 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. --> # fresh-12-layer-swag-distill-of-fresh-12-layer-gpqa This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 14.3157 - Accuracy: 0.3788 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 14.5865 \| 0.2424 \| \| No log \| 2.0 \| 126 \| 13.4274 \| 0.2879 \| \| No log \| 3.0 \| 189 \| 14.5755 \| 0.3434 \| \| No log \| 4.0 \| 252 \| 14.6965 \| 0.3586 \| \| No log \| 5.0 \| 315 \| 14.6065 \| 0.3737 \| \| No log \| 6.0 \| 378 \| 13.0578 \| 0.3737 \| \| No log \| 7.0 \| 441 \| 13.1651 \| 0.3586 \| \| 1.7518 \| 8.0 \| 504 \| 13.7708 \| 0.3636 \| \| 1.7518 \| 9.0 \| 567 \| 13.5531 \| 0.3535 \| \| 1.7518 \| 10.0 \| 630 \| 13.3979 \| 0.3384 \| \| 1.7518 \| 11.0 \| 693 \| 13.8865 \| 0.3434 \| \| 1.7518 \| 12.0 \| 756 \| 13.8410 \| 0.3687 \| \| 1.7518 \| 13.0 \| 819 \| 15.6234 \| 0.3283 \| \| 1.7518 \| 14.0 \| 882 \| 17.4878 \| 0.3485 \| \| 1.7518 \| 15.0 \| 945 \| 16.2413 \| 0.3081 \| \| 2.2378 \| 16.0 \| 1008 \| 14.6003 \| 0.3232 \| \| 2.2378 \| 17.0 \| 1071 \| 16.5984 \| 0.3232 \| \| 2.2378 \| 18.0 \| 1134 \| 14.3157 \| 0.3788 \| \| 2.2378 \| 19.0 \| 1197 \| 13.5424 \| 0.3485 \| \| 2.2378 \| 20.0 \| 1260 \| 13.3978 \| 0.3586 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
Sif10/my_awesome_model	Sif10	2024-03-10T10:59:44Z	91	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T10:01:42Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.85588 --- <!-- 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_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.8771 - Accuracy: 0.8559 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3564 \| 1.0 \| 1563 \| 0.3677 \| 0.8426 \| \| 0.2878 \| 2.0 \| 3126 \| 0.3378 \| 0.8588 \| \| 0.2124 \| 3.0 \| 4689 \| 0.4398 \| 0.8550 \| \| 0.1556 \| 4.0 \| 6252 \| 0.5750 \| 0.8555 \| \| 0.1075 \| 5.0 \| 7815 \| 0.6733 \| 0.8558 \| \| 0.0831 \| 6.0 \| 9378 \| 0.7218 \| 0.8561 \| \| 0.0652 \| 7.0 \| 10941 \| 0.7331 \| 0.8564 \| \| 0.0458 \| 8.0 \| 12504 \| 0.8166 \| 0.8538 \| \| 0.0415 \| 9.0 \| 14067 \| 0.8619 \| 0.8568 \| \| 0.0357 \| 10.0 \| 15630 \| 0.8771 \| 0.8559 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
trsdimi/q-FrozenLake-v1-4x4-noSlippery	trsdimi	2024-03-10T10:55:36Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T10:55:33Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="trsdimi/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ThuyNT03/CS505-Classifier-T4_predictLabel_a1	ThuyNT03	2024-03-10T10:43:02Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:vinai/phobert-base", "base_model:finetune:vinai/phobert-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T10:25:51Z	--- base_model: vinai/phobert-base tags: - generated_from_trainer model-index: - name: CS505-Classifier-T4_predictLabel_a1 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. --> # CS505-Classifier-T4_predictLabel_a1 This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.98 \| 48 \| 1.0473 \| \| No log \| 1.96 \| 96 \| 0.5664 \| \| No log \| 2.94 \| 144 \| 0.3371 \| \| No log \| 3.92 \| 192 \| 0.2277 \| \| No log \| 4.9 \| 240 \| 0.1850 \| \| No log \| 5.88 \| 288 \| 0.1451 \| \| No log \| 6.86 \| 336 \| 0.1126 \| \| No log \| 7.84 \| 384 \| 0.0853 \| \| No log \| 8.82 \| 432 \| 0.0635 \| \| No log \| 9.8 \| 480 \| 0.0598 \| \| 0.4029 \| 10.78 \| 528 \| 0.0407 \| \| 0.4029 \| 11.76 \| 576 \| 0.0337 \| \| 0.4029 \| 12.73 \| 624 \| 0.0300 \| \| 0.4029 \| 13.71 \| 672 \| 0.0270 \| \| 0.4029 \| 14.69 \| 720 \| 0.0209 \| \| 0.4029 \| 15.67 \| 768 \| 0.0196 \| \| 0.4029 \| 16.65 \| 816 \| 0.0205 \| \| 0.4029 \| 17.63 \| 864 \| 0.0181 \| \| 0.4029 \| 18.61 \| 912 \| 0.0160 \| \| 0.4029 \| 19.59 \| 960 \| 0.0155 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
digiplay/AnalogMadness-realistic-model-v5	digiplay	2024-03-10T10:42:36Z	52,810	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-10T10:08:42Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/8030/analog-madness-realistic-model
ankursinghbisht/a2c-PandaReachDense-v3	ankursinghbisht	2024-03-10T10:42:14Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T10:34:46Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.22 +/- 0.11 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
afaji/fresh-12-layer-medmcqa-distill-of-fresh-12-layer-gpqa	afaji	2024-03-10T10:37:54Z	89	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T10:36:22Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-12-layer-medmcqa-distill-of-fresh-12-layer-gpqa 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. --> # fresh-12-layer-medmcqa-distill-of-fresh-12-layer-gpqa This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 11.3380 - Accuracy: 0.5253 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 14.7499 \| 0.2929 \| \| No log \| 2.0 \| 126 \| 13.0612 \| 0.3636 \| \| No log \| 3.0 \| 189 \| 13.1660 \| 0.4293 \| \| No log \| 4.0 \| 252 \| 13.4796 \| 0.4848 \| \| No log \| 5.0 \| 315 \| 11.9863 \| 0.5101 \| \| No log \| 6.0 \| 378 \| 11.3380 \| 0.5253 \| \| No log \| 7.0 \| 441 \| 11.5841 \| 0.4242 \| \| 4.5481 \| 8.0 \| 504 \| 15.3570 \| 0.3485 \| \| 4.5481 \| 9.0 \| 567 \| 14.1857 \| 0.1465 \| \| 4.5481 \| 10.0 \| 630 \| 13.5387 \| 0.1263 \| \| 4.5481 \| 11.0 \| 693 \| 13.4757 \| 0.1566 \| \| 4.5481 \| 12.0 \| 756 \| 14.4836 \| 0.0657 \| \| 4.5481 \| 13.0 \| 819 \| 13.8175 \| 0.0707 \| \| 4.5481 \| 14.0 \| 882 \| 14.0705 \| 0.1313 \| \| 4.5481 \| 15.0 \| 945 \| 14.3308 \| 0.0 \| \| 7.3037 \| 16.0 \| 1008 \| 14.2806 \| 0.1263 \| \| 7.3037 \| 17.0 \| 1071 \| 14.2719 \| 0.0101 \| \| 7.3037 \| 18.0 \| 1134 \| 13.7977 \| 0.2727 \| \| 7.3037 \| 19.0 \| 1197 \| 14.2746 \| 0.0657 \| \| 7.3037 \| 20.0 \| 1260 \| 14.0949 \| 0.0 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
ShadyML/distillbert-finetuned-finer-4-v3	ShadyML	2024-03-10T10:34:33Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-10T09:09:29Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distillbert-finetuned-finer-4-v3 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. --> # distillbert-finetuned-finer-4-v3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0223 - Precision: 0.9044 - Recall: 0.9318 - F1: 0.9179 - Accuracy: 0.9931 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0248 \| 1.0 \| 2095 \| 0.0271 \| 0.8736 \| 0.9052 \| 0.8891 \| 0.9907 \| \| 0.0183 \| 2.0 \| 4190 \| 0.0236 \| 0.8864 \| 0.9324 \| 0.9088 \| 0.9922 \| \| 0.0118 \| 3.0 \| 6285 \| 0.0223 \| 0.9044 \| 0.9318 \| 0.9179 \| 0.9931 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
HachiML/myBit-Llama2-jp-127M-test-6	HachiML	2024-03-10T10:30:13Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T10:04:17Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-test-6 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. --> # myBit-Llama2-jp-127M-test-6 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.4087 ## 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: 4.8e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 250 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.8677 \| 0.04 \| 100 \| 9.1385 \| \| 8.4868 \| 0.07 \| 200 \| 7.7575 \| \| 7.2146 \| 0.11 \| 300 \| 6.8688 \| \| 6.6972 \| 0.14 \| 400 \| 6.5702 \| \| 6.4628 \| 0.18 \| 500 \| 6.3746 \| \| 6.3058 \| 0.22 \| 600 \| 6.2362 \| \| 6.1813 \| 0.25 \| 700 \| 6.1241 \| \| 6.0708 \| 0.29 \| 800 \| 6.0228 \| \| 5.963 \| 0.33 \| 900 \| 5.9109 \| \| 5.8577 \| 0.36 \| 1000 \| 5.7948 \| \| 5.7614 \| 0.4 \| 1100 \| 5.7155 \| \| 5.6876 \| 0.43 \| 1200 \| 5.6376 \| \| 5.6044 \| 0.47 \| 1300 \| 5.5631 \| \| 5.5538 \| 0.51 \| 1400 \| 5.5045 \| \| 5.5007 \| 0.54 \| 1500 \| 5.4649 \| \| 5.4556 \| 0.58 \| 1600 \| 5.4282 \| \| 5.4246 \| 0.62 \| 1700 \| 5.3917 \| \| 5.3982 \| 0.65 \| 1800 \| 5.3762 \| \| 5.3854 \| 0.69 \| 1900 \| 5.3546 \| \| 5.365 \| 0.72 \| 2000 \| 5.3447 \| \| 5.3579 \| 0.76 \| 2100 \| 5.3473 \| \| 5.3552 \| 0.8 \| 2200 \| 5.3463 \| \| 5.3682 \| 0.83 \| 2300 \| 5.3630 \| \| 5.3743 \| 0.87 \| 2400 \| 5.3718 \| \| 5.3957 \| 0.91 \| 2500 \| 5.3887 \| \| 5.4079 \| 0.94 \| 2600 \| 5.4010 \| \| 5.423 \| 0.98 \| 2700 \| 5.4087 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
smorce/smorce-qlora-Qwen1.5-4B-Chat	smorce	2024-03-10T10:29:22Z	61	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T10:26:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Hemg/Face-Mask-Detection	Hemg	2024-03-10T10:24:56Z	311	1	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-10T09:18:32Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Face-Mask-Detection 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. --> # Face-Mask-Detection This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0239 - Accuracy: 0.9953 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1218 \| 1.0 \| 147 \| 0.0251 \| 0.9953 \| \| 0.0186 \| 1.99 \| 294 \| 0.0239 \| 0.9953 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
digiplay/AnalogMadness-realistic-model-v6	digiplay	2024-03-10T10:21:50Z	45,635	2	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-10T10:02:48Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/8030/analog-madness-realistic-model
darkelf12/shawgpt-ft	darkelf12	2024-03-10T10:10:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-03-10T10:10:06Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: shawgpt-ft 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. --> # shawgpt-ft This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7904 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6612 \| 0.91 \| 8 \| 1.3313 \| \| 1.1146 \| 1.94 \| 17 \| 1.1069 \| \| 0.9461 \| 2.97 \| 26 \| 0.9471 \| \| 0.8283 \| 4.0 \| 35 \| 0.8600 \| \| 0.8635 \| 4.91 \| 43 \| 0.8291 \| \| 0.7343 \| 5.94 \| 52 \| 0.8114 \| \| 0.7219 \| 6.97 \| 61 \| 0.8000 \| \| 0.7052 \| 8.0 \| 70 \| 0.7931 \| \| 0.7785 \| 8.91 \| 78 \| 0.7905 \| \| 0.4625 \| 9.14 \| 80 \| 0.7904 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
afaji/fresh-12-layer-gpqa	afaji	2024-03-10T10:09:52Z	90	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T10:08:19Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-12-layer-gpqa 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. --> # fresh-12-layer-gpqa This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1852 - Accuracy: 0.9394 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 13 \| 1.3865 \| 0.2677 \| \| No log \| 2.0 \| 26 \| 1.3857 \| 0.2374 \| \| No log \| 3.0 \| 39 \| 1.3852 \| 0.2475 \| \| No log \| 4.0 \| 52 \| 1.3852 \| 0.2929 \| \| No log \| 5.0 \| 65 \| 1.3847 \| 0.2929 \| \| No log \| 6.0 \| 78 \| 1.3844 \| 0.3081 \| \| No log \| 7.0 \| 91 \| 1.3839 \| 0.2980 \| \| No log \| 8.0 \| 104 \| 1.3800 \| 0.3081 \| \| No log \| 9.0 \| 117 \| 1.3751 \| 0.3535 \| \| No log \| 10.0 \| 130 \| 1.2007 \| 0.6263 \| \| No log \| 11.0 \| 143 \| 0.9272 \| 0.6515 \| \| No log \| 12.0 \| 156 \| 1.0185 \| 0.6768 \| \| No log \| 13.0 \| 169 \| 0.6580 \| 0.7424 \| \| No log \| 14.0 \| 182 \| 0.4847 \| 0.7828 \| \| No log \| 15.0 \| 195 \| 0.3170 \| 0.8384 \| \| No log \| 16.0 \| 208 \| 0.2830 \| 0.8485 \| \| No log \| 17.0 \| 221 \| 0.3068 \| 0.9192 \| \| No log \| 18.0 \| 234 \| 0.2519 \| 0.9141 \| \| No log \| 19.0 \| 247 \| 0.2426 \| 0.9343 \| \| No log \| 20.0 \| 260 \| 0.1852 \| 0.9394 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
learn3r/longt5_xl_gov_memsum_25	learn3r	2024-03-10T10:06:05Z	6	0	transformers	[ "transformers", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-08T14:39:50Z	--- tags: - generated_from_trainer model-index: - name: longt5_xl_gov_memsum_25 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. --> # longt5_xl_gov_memsum_25 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4372 \| 1.0 \| 68 \| 1.6270 \| \| 0.3678 \| 1.99 \| 136 \| 1.8330 \| \| 0.3026 \| 2.99 \| 204 \| 1.8467 \| \| 0.2785 \| 3.99 \| 272 \| 1.9830 \| \| 0.2489 \| 5.0 \| 341 \| 2.1279 \| \| 0.181 \| 6.0 \| 409 \| 2.2981 \| \| 0.1753 \| 6.99 \| 477 \| 2.3683 \| \| 0.1511 \| 7.99 \| 545 \| 2.3130 \| \| 0.1483 \| 8.99 \| 613 \| 2.5342 \| \| 0.2277 \| 10.0 \| 682 \| 2.3054 \| \| 0.1952 \| 10.99 \| 750 \| 2.2331 \| \| 0.1773 \| 11.99 \| 818 \| 2.1944 \| \| 0.1524 \| 12.99 \| 886 \| 2.3607 \| \| 0.1373 \| 14.0 \| 955 \| 2.3946 \| \| 0.1238 \| 14.95 \| 1020 \| 2.3918 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
valxy/sd-class-butterflies-32	valxy	2024-03-10T09:58:46Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-03-10T09:44:47Z	--- tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- '''python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('valxy/sd-class-butterflies-32') image = pipeline().images[0] image '''
HachiML/myBit-Llama2-jp-127M-test-5	HachiML	2024-03-10T09:56:27Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T09:30:01Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-test-5 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. --> # myBit-Llama2-jp-127M-test-5 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.9523 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 250 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.7481 \| 0.04 \| 100 \| 8.9526 \| \| 8.17 \| 0.07 \| 200 \| 7.3998 \| \| 6.9639 \| 0.11 \| 300 \| 6.7999 \| \| 6.5874 \| 0.15 \| 400 \| 6.4947 \| \| 6.3463 \| 0.18 \| 500 \| 6.3007 \| \| 6.18 \| 0.22 \| 600 \| 6.1431 \| \| 6.0112 \| 0.26 \| 700 \| 5.9703 \| \| 5.8465 \| 0.29 \| 800 \| 5.8159 \| \| 5.7114 \| 0.33 \| 900 \| 5.7018 \| \| 5.5979 \| 0.36 \| 1000 \| 5.6067 \| \| 5.518 \| 0.4 \| 1100 \| 5.5270 \| \| 5.4294 \| 0.44 \| 1200 \| 5.4639 \| \| 5.3976 \| 0.47 \| 1300 \| 5.4143 \| \| 5.3487 \| 0.51 \| 1400 \| 5.3701 \| \| 5.3162 \| 0.55 \| 1500 \| 5.3509 \| \| 5.2915 \| 0.58 \| 1600 \| 5.3452 \| \| 5.3009 \| 0.62 \| 1700 \| 5.3910 \| \| 5.3894 \| 0.66 \| 1800 \| 5.5080 \| \| 5.5553 \| 0.69 \| 1900 \| 5.7414 \| \| 5.9356 \| 0.73 \| 2000 \| 6.2225 \| \| 6.515 \| 0.77 \| 2100 \| 6.8978 \| \| 7.2177 \| 0.8 \| 2200 \| 7.5843 \| \| 7.8453 \| 0.84 \| 2300 \| 8.1251 \| \| 8.3069 \| 0.88 \| 2400 \| 8.5042 \| \| 8.6156 \| 0.91 \| 2500 \| 8.7458 \| \| 8.8104 \| 0.95 \| 2600 \| 8.8901 \| \| 8.9132 \| 0.99 \| 2700 \| 8.9523 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Alistair-R/EncodeHackathonLevelGen	Alistair-R	2024-03-10T09:54:28Z	179	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T01:57:07Z	--- license: apache-2.0 --- Model created for the Encode Hackathon Virtual Protocol Bounty. The model is a fine-tuned version of distilgpt2 designed to output level schematics for a platform when given the prompt "LevelSchematic:". For further details on the project, please go [here](https://github.com/lauraharkins/Hackathon).
taoki/gemma-2b-it-qlora-amenokaku-code	taoki	2024-03-10T09:50:26Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "feature-extraction", "text-generation-inference", "unsloth", "trl", "ja", "dataset:kunishou/amenokaku-code-instruct", "base_model:unsloth/gemma-2b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-it-bnb-4bit", "license:other", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-10T03:07:09Z	--- language: - ja license: other tags: - text-generation-inference - transformers - unsloth - trl - gemma datasets: - kunishou/amenokaku-code-instruct license_name: gemma base_model: unsloth/gemma-2b-it-bnb-4bit --- # Uploaded model - Developed by: taoki - License: gemma - Finetuned from model : unsloth/gemma-2b-it-bnb-4bit # Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained( "taoki/gemma-2b-it-qlora-amenokaku-code" ) model = AutoModelForCausalLM.from_pretrained( "taoki/gemma-2b-it-qlora-amenokaku-code" ) if torch.cuda.is_available(): model = model.to("cuda") prompt="""<start_of_turn>user 紫式部と清少納言の作風をjsonで出力してください。 <end_of_turn> <start_of_turn>model """ input_ids = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **input_ids, max_new_tokens=512, do_sample=True, top_p=0.95, temperature=0.1, repetition_penalty=1.0, ) print(tokenizer.decode(outputs[0])) ``` # Output ```` <bos><start_of_turn>user 紫式部と清少納言の作風をjsonで出力してください。<end_of_turn> <start_of_turn>model ```json { "紫式部": { "style": "紫式部", "name": "紫式部", "description": "紫式部の作風" }, "清少納言": { "style": "清少納言", "name": "清少納言", "description": "清少納言の作風" } } ```<eos> ```` This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
afaji/fresh-8-layer-medmcqa-distill-of-fresh-8-layer-gpqa	afaji	2024-03-10T09:42:54Z	88	0	transformers	[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-10T09:41:35Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: fresh-8-layer-medmcqa-distill-of-fresh-8-layer-gpqa 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. --> # fresh-8-layer-medmcqa-distill-of-fresh-8-layer-gpqa This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 17.1123 - Accuracy: 0.5455 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 321 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 63 \| 22.9570 \| 0.2273 \| \| No log \| 2.0 \| 126 \| 21.2108 \| 0.3636 \| \| No log \| 3.0 \| 189 \| 26.1433 \| 0.4040 \| \| No log \| 4.0 \| 252 \| 24.1795 \| 0.3838 \| \| No log \| 5.0 \| 315 \| 17.9657 \| 0.4747 \| \| No log \| 6.0 \| 378 \| 20.0576 \| 0.5354 \| \| No log \| 7.0 \| 441 \| 17.5133 \| 0.5 \| \| 10.1769 \| 8.0 \| 504 \| 22.3248 \| 0.5101 \| \| 10.1769 \| 9.0 \| 567 \| 20.7352 \| 0.4848 \| \| 10.1769 \| 10.0 \| 630 \| 22.9071 \| 0.4596 \| \| 10.1769 \| 11.0 \| 693 \| 17.8100 \| 0.4899 \| \| 10.1769 \| 12.0 \| 756 \| 17.9827 \| 0.5202 \| \| 10.1769 \| 13.0 \| 819 \| 19.2382 \| 0.5 \| \| 10.1769 \| 14.0 \| 882 \| 18.8849 \| 0.4949 \| \| 10.1769 \| 15.0 \| 945 \| 17.6397 \| 0.5202 \| \| 2.2143 \| 16.0 \| 1008 \| 19.0081 \| 0.5101 \| \| 2.2143 \| 17.0 \| 1071 \| 17.8718 \| 0.5152 \| \| 2.2143 \| 18.0 \| 1134 \| 17.5239 \| 0.5303 \| \| 2.2143 \| 19.0 \| 1197 \| 17.1123 \| 0.5455 \| \| 2.2143 \| 20.0 \| 1260 \| 17.7756 \| 0.5404 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
Samuael/amhat5-small	Samuael	2024-03-10T09:42:06Z	120	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-10T08:45:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Benevolent/PerfectHands	Benevolent	2024-03-10T09:30:13Z	9	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-cascade", "base_model:adapter:stabilityai/stable-cascade", "license:apache-2.0", "region:us" ]	text-to-image	2024-03-10T09:14:56Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0 \0s\0c\0o\0r\0e\0_\09\0,\0 \0s\0c\0o\0r\0e\0_\08\0_\0u\0p\0,\0 \0s\0c\0o\0r\0e\0_\07\0_\0u\0p\0,\0 \0s\0c\0o\0r\0e\0_\06\0_\0u\0p\0,\0 \0s\0c\0o\0r\0e\0_\05\0_\0u\0p\0,\0 \0s\0c\0o\0r\0e\0_\04\0_\0u\0p\0,\0 \0s\0o\0u\0r\0c\0e\0_\0a\0n\0i\0m\0e\0,\0 \0p\0e\0r\0f\0e\0c\0t\0 \0b\0e\0a\0u\0t\0i\0f\0u\0l\0 \0f\0a\0c\0e\0,\0 \0s\0t\0r\0o\0n\0g\0 \0f\0a\0c\0e\0,\0 \0(\0(\0s\0t\0r\0o\0n\0g\0 \0t\0h\0i\0c\0k\0 \0b\0o\0d\0y\0,\0 \0i\0c\0e\0 \0g\0i\0a\0n\0t\0,\0 \0l\0o\0n\0g\0 \0w\0h\0i\0t\0e\0 \0h\0a\0i\0r\0,\0 \0b\0l\0u\0e\0 \0s\0k\0i\0n\0,\0 \0s\0e\0x\0y\0 \0l\0e\0a\0t\0h\0e\0r\0 \0a\0r\0m\0o\0r\0,\0 \0t\0h\0i\0c\0k\0 \0t\0h\0i\0g\0h\0s\0,\0 \0p\0i\0e\0r\0c\0e\0d\0 \0n\0i\0p\0p\0l\0e\0s\0,\0 \0b\0a\0t\0t\0l\0e\0 \0a\0x\0e\0)\0)\0,\0 \0(\0b\0i\0g\0 \0p\0u\0s\0s\0y\0,\0 \0w\0h\0i\0t\0e\0 \0p\0u\0b\0i\0c\0 \0h\0a\0i\0r\0,\0 \0h\0a\0p\0p\0y\0 \0t\0r\0a\0i\0l\0)\0,\0 \0h\0a\0i\0r\0y\0 \0b\0o\0d\0y\0,\0 \0s\0n\0o\0w\0 \0s\0t\0o\0r\0m\0,\0 \0m\0o\0u\0n\0t\0a\0i\0n\0 \0t\0o\0p\0" output: url: >- images/B01A48105298502D2C7E3F41DDED247E42F3563763692F9451356B9698C52A6F.jpeg base_model: stabilityai/stable-cascade instance_prompt: null license: apache-2.0 --- # HandsV2 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Benevolent/PerfectHands/tree/main) them in the Files & versions tab.
DisgustingOzil/Mistral-MCQ-Model	DisgustingOzil	2024-03-10T09:29:06Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T09:27:04Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ZainAli60/bart	ZainAli60	2024-03-10T09:27:36Z	191	0	transformers	[ "transformers", "safetensors", "bart", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T09:26:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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HachiML/myBit-Llama2-jp-127M-test-4	HachiML	2024-03-10T09:26:39Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T08:59:46Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-test-4 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. --> # myBit-Llama2-jp-127M-test-4 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 10.6247 ## 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: 8.4e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 250 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.6724 \| 0.04 \| 100 \| 8.7189 \| \| 7.811 \| 0.07 \| 200 \| 6.9856 \| \| 6.7931 \| 0.11 \| 300 \| 6.5599 \| \| 6.4108 \| 0.15 \| 400 \| 6.1841 \| \| 6.1428 \| 0.18 \| 500 \| 5.9554 \| \| 5.8814 \| 0.22 \| 600 \| 5.7176 \| \| 5.6803 \| 0.26 \| 700 \| 5.5171 \| \| 5.5181 \| 0.29 \| 800 \| 5.4037 \| \| 5.4115 \| 0.33 \| 900 \| 5.3197 \| \| 5.3497 \| 0.37 \| 1000 \| 5.2965 \| \| 5.3629 \| 0.4 \| 1100 \| 5.3632 \| \| 5.6291 \| 0.44 \| 1200 \| 5.9554 \| \| 6.9173 \| 0.47 \| 1300 \| 8.0749 \| \| 9.1158 \| 0.51 \| 1400 \| 9.8847 \| \| 10.2012 \| 0.55 \| 1500 \| 10.3942 \| \| 10.4725 \| 0.58 \| 1600 \| 10.5218 \| \| 10.5453 \| 0.62 \| 1700 \| 10.5627 \| \| 10.5752 \| 0.66 \| 1800 \| 10.5838 \| \| 10.5915 \| 0.69 \| 1900 \| 10.5969 \| \| 10.6018 \| 0.73 \| 2000 \| 10.6053 \| \| 10.6091 \| 0.77 \| 2100 \| 10.6115 \| \| 10.6141 \| 0.8 \| 2200 \| 10.6156 \| \| 10.6175 \| 0.84 \| 2300 \| 10.6186 \| \| 10.6203 \| 0.88 \| 2400 \| 10.6212 \| \| 10.6225 \| 0.91 \| 2500 \| 10.6225 \| \| 10.6238 \| 0.95 \| 2600 \| 10.6240 \| \| 10.625 \| 0.99 \| 2700 \| 10.6247 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

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