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Vikhrmodels/it-5.2-eos	Vikhrmodels	2024-05-29T15:24:17Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T15:07:35Z	--- 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]
michaelszhu/whisper-small-finetuned-radio-ASR-2	michaelszhu	2024-05-29T15:23:08Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-28T23:43:55Z	--- language: - en license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: BANG please be the final one (EN) results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Radio-Modified Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: en split: test args: 'config: en, split: test' metrics: - name: Wer type: wer value: 8.821029784785962 --- <!-- 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. --> # BANG please be the final one (EN) This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Radio-Modified Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.0395 - Wer: 8.8210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.1511 \| 0.25 \| 1000 \| 0.1318 \| 20.4937 \| \| 0.0685 \| 1.2443 \| 2000 \| 0.0845 \| 12.3199 \| \| 0.0378 \| 2.2385 \| 3000 \| 0.0557 \| 10.4397 \| \| 0.0283 \| 3.2328 \| 4000 \| 0.0395 \| 8.8210 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
BVRA/resnet50.in1k_ft_fungitastic_224	BVRA	2024-05-29T15:21:54Z	7	0	DanishFungi	[ "DanishFungi", "pytorch", "image-classification", "ecology", "fungi", "FGVC", "license:cc-by-nc-4.0", "region:us" ]	image-classification	2024-05-24T16:33:30Z	--- tags: - image-classification - ecology - fungi - FGVC library_name: DanishFungi license: cc-by-nc-4.0 --- # Model card for BVRA/resnet50.in1k_ft_df24_224 ## Model Details - Model Type: Danish Fungi Classification - Model Stats: - Params (M): 29.3 - Image size: 224 x 224 - Papers: - Original: ?? - Train Dataset: DF24 --> https://sites.google.com/view/danish-fungi-dataset ## Model Usage ### Image Embeddings ```python import timm import torch import torchvision.transforms as T from PIL import Image from urllib.request import urlopen model = timm.create_model("hf-hub:BVRA/resnet50.in1k_ft_df24_224", pretrained=True) model = model.eval() train_transforms = T.Compose([T.Resize((224, 224)), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) img = Image.open(PATH_TO_YOUR_IMAGE) output = model(train_transforms(img).unsqueeze(0)) # output is a (1, num_features) shaped tensor ``` ## Citation ```bibtex @InProceedings{Picek_2022_WACV, author = {Picek, Luk'a{s} and {S}ulc, Milan and Matas, Ji{r}{'\i} and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias}, title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {1525-1535} } ``` ```bibtex @article{picek2022automatic, title={Automatic Fungi Recognition: Deep Learning Meets Mycology}, author={Picek, Luk{'a}{{s}} and {{S}}ulc, Milan and Matas, Ji{{r}}{'\i} and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil}, journal={Sensors}, volume={22}, number={2}, pages={633}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ```
Jubliano/wav2vec2-large-xls-r-300m-ipa-INTERNATIONAL1.9WithoutSpaces	Jubliano	2024-05-29T15:21:32Z	10	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-27T22:18:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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CounterNarratives/Mistral-7B-Instruct-v0.2_multi_no-info_b	CounterNarratives	2024-05-29T15:21:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T15:20:39Z	--- 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. 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Amitnaik1718/finetuned-indian-food	Amitnaik1718	2024-05-29T15:21:13Z	258	0	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	2024-03-14T12:23:30Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer model-index: - name: finetuned-indian-food results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-indian-food This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the indian_food_images dataset. It achieves the following results on the evaluation set: - eval_loss: 3.0031 - eval_accuracy: 0.0563 - eval_runtime: 620.4611 - eval_samples_per_second: 1.517 - eval_steps_per_second: 0.19 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hautran7201/mamba_text_classification	hautran7201	2024-05-29T15:18:43Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-03-03T05:45:10Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: mamba_text_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mamba_text_classification This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2640 - Accuracy: 0.8876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.01 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3518 \| 0.32 \| 500 \| 0.3003 \| 0.868 \| \| 0.2789 \| 0.64 \| 1000 \| 0.2782 \| 0.8792 \| \| 0.2539 \| 0.96 \| 1500 \| 0.2640 \| 0.8876 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
rejauldu/mistralai	rejauldu	2024-05-29T15:17:21Z	87	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T15:12:58Z	--- 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]
Likich/falcon-finetune-qualcoding_1000_prompt4	Likich	2024-05-29T15:16:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T15:16:50Z	--- 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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CounterNarratives/Mistral-7B-Instruct-v0.2_multi_collective_no-strategy	CounterNarratives	2024-05-29T15:15:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T15:13:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MTWD/whisper-small-test	MTWD	2024-05-29T15:15:04Z	21	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:openai/whisper-small", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-19T19:31:08Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - openai/whisper-small metrics: - wer model-index: - name: Whisper Small fine tuned with comms results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: BrainHack ASR Test Two type: openai/whisper-small metrics: - name: Wer type: wer value: 0.03260869565217391 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small fine tuned with comms This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the BrainHack ASR Test Two dataset. It achieves the following results on the evaluation set: - Loss: 0.2141 - Wer: 0.0326 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 0.0059 \| 13.3333 \| 20 \| 0.1429 \| 0.0380 \| \| 0.0003 \| 26.6667 \| 40 \| 0.2095 \| 0.0380 \| \| 0.0001 \| 40.0 \| 60 \| 0.2166 \| 0.0326 \| \| 0.0001 \| 53.3333 \| 80 \| 0.2154 \| 0.0326 \| \| 0.0001 \| 66.6667 \| 100 \| 0.2141 \| 0.0326 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RishieRish/autotrain-971pb-anx73	RishieRish	2024-05-29T15:11:49Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "phi3", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "custom_code", "dataset:RishieRish/new_convo_2.4k_wrap", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T20:17:07Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - RishieRish/new_convo_2.4k_wrap --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
farizkuy/ner_fine_tuned	farizkuy	2024-05-29T15:11:42Z	131	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:cahya/bert-base-indonesian-NER", "base_model:finetune:cahya/bert-base-indonesian-NER", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-29T14:59:12Z	--- license: mit base_model: cahya/bert-base-indonesian-NER tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner_fine_tuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ner_fine_tuned This model is a fine-tuned version of [cahya/bert-base-indonesian-NER](https://huggingface.co/cahya/bert-base-indonesian-NER) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0080 - Precision: 0.6970 - Recall: 0.5349 - F1: 0.6053 - Accuracy: 0.8900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 8 \| 0.5649 \| 0.625 \| 0.4651 \| 0.5333 \| 0.8832 \| \| No log \| 2.0 \| 16 \| 0.6457 \| 0.7857 \| 0.5116 \| 0.6197 \| 0.9003 \| \| No log \| 3.0 \| 24 \| 0.7181 \| 0.6471 \| 0.5116 \| 0.5714 \| 0.8832 \| \| No log \| 4.0 \| 32 \| 0.8134 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| \| No log \| 5.0 \| 40 \| 0.8528 \| 0.6667 \| 0.5116 \| 0.5789 \| 0.8866 \| \| No log \| 6.0 \| 48 \| 0.8893 \| 0.6667 \| 0.5116 \| 0.5789 \| 0.8866 \| \| No log \| 7.0 \| 56 \| 0.9148 \| 0.6667 \| 0.5116 \| 0.5789 \| 0.8866 \| \| No log \| 8.0 \| 64 \| 0.9440 \| 0.6667 \| 0.5116 \| 0.5789 \| 0.8866 \| \| No log \| 9.0 \| 72 \| 0.9744 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| \| No log \| 10.0 \| 80 \| 0.9895 \| 0.6765 \| 0.5349 \| 0.5974 \| 0.8900 \| \| No log \| 11.0 \| 88 \| 0.9968 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| \| No log \| 12.0 \| 96 \| 1.0015 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| \| No log \| 13.0 \| 104 \| 1.0049 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| \| No log \| 14.0 \| 112 \| 1.0072 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| \| No log \| 15.0 \| 120 \| 1.0080 \| 0.6970 \| 0.5349 \| 0.6053 \| 0.8900 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
twosocksinoneshoe/ppo-Huggy	twosocksinoneshoe	2024-05-29T15:08:46Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-29T15:04:59Z	--- 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: twosocksinoneshoe/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
QuangDuy/whisper-large-v2-full-data	QuangDuy	2024-05-29T15:07:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T06:32:14Z	--- 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. 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]
HawkClaws/Starling-JP-7B-Q8_0-GGUF	HawkClaws	2024-05-29T15:06:00Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]	null	2024-05-29T15:04:36Z	--- tags: - llama-cpp - gguf-my-repo --- # HawkClaws/Starling-JP-7B-Q8_0-GGUF This model was converted to GGUF format from [`HawkClaws/Starling-JP-7B`](https://huggingface.co/HawkClaws/Starling-JP-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/HawkClaws/Starling-JP-7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo HawkClaws/Starling-JP-7B-Q8_0-GGUF --model starling-jp-7b-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo HawkClaws/Starling-JP-7B-Q8_0-GGUF --model starling-jp-7b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m starling-jp-7b-q8_0.gguf -n 128 ```
tema7707/comfyui-llm-alpha	tema7707	2024-05-29T15:05:50Z	0	4	transformers	[ "transformers", "safetensors", "text-generation-inference", "llama", "trl", "en", "base_model:unsloth/llama-3-70b-bnb-4bit", "base_model:finetune:unsloth/llama-3-70b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T15:02:42Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - llama - trl base_model: unsloth/llama-3-70b-bnb-4bit --- # Uploaded Model - Developed by: [Salt AI](https://getsalt.ai/) - License: apache-2.0 - Finetuned from model: unsloth/llama-3-70b-bnb-4bit This is a llama3-70b model that was finetuned on ComfyUI data. This model can generate ComfyUI JSONs from text. ## Example Prompts and Outputs \| Prompt \| Generated Workflow \| \|:---------------------------------------------------:\|:----------------\| \| `Create a simple ComfyUI pipeline that performs basic text-to-image generation.` \| ![Example1](https://huggingface.co/tema7707/comfyui-llm-alpha/resolve/main/example-1.png) \| \| `Create a simple ComfyUI pipeline that performs basic text-to-video generation.` \| ![Example2](https://huggingface.co/tema7707/comfyui-llm-alpha/resolve/main/example-2.png) \| \| `Create a simple ComfyUI pipeline that performs face swap and face enhancement.` \| ![Example3](https://huggingface.co/tema7707/comfyui-llm-alpha/resolve/main/example-3.png) \| \| `Create a simple ComfyUI pipeline that performs text-to-image generation with Instant ID and Openpose Controlnet.` \| ![Example4](https://huggingface.co/tema7707/comfyui-llm-alpha/resolve/main/example-4.png) \| \| `Create a ComfyUI pipeline that performs text-to-image generation using Lora. Format the pipeline using groups.` \| ![Example5](https://huggingface.co/tema7707/comfyui-llm-alpha/resolve/main/example-5.png) \| ## Disclaimer This is an alpha version. It can generate pipelines that may not work as intended and may require manual fixes. The model was trained utilizing the Alpaca format for prompts. Consequently, it is recommended to structure the prompts in this format to potentially enhance the quality of the outputs: ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Below is a description of the ComfyUI pipeline; please generate a ComfyUI JSON for it. ### Input: {your idea} ### Response: ``` To improve quality, it is advisable to explicitly specify the main nodes to be used in the pipeline.
mchariar/q-Taxi-v3	mchariar	2024-05-29T15:05:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T15:05:36Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.74 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="mchariar/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Malecc/gpt2andkan	Malecc	2024-05-29T15:05:22Z	160	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:55:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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deptage/distilbert4	deptage	2024-05-29T15:04:01Z	108	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T15:03:21Z	--- 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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betteib/bert-base-arabert-finetuned-mdeberta-tn	betteib	2024-05-29T15:03:49Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabert", "base_model:finetune:aubmindlab/bert-base-arabert", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-29T14:50:46Z	--- base_model: aubmindlab/bert-base-arabert tags: - generated_from_trainer model-index: - name: bert-base-arabert-finetuned-mdeberta-tn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-arabert-finetuned-mdeberta-tn This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.7642 \| 1.0 \| 157 \| 4.0732 \| \| 4.0247 \| 2.0 \| 314 \| 3.8307 \| \| 3.8684 \| 3.0 \| 471 \| 3.7532 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Haru4me/ppo-Huggy	Haru4me	2024-05-29T15:03:15Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-29T14:55:59Z	--- 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: Haru4me/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kamalnour/ref-gal125m-dpo	kamalnour	2024-05-29T15:00:58Z	142	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T13:30:14Z	--- 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. - 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]
Meziane/quinto_question_answear	Meziane	2024-05-29T15:00:05Z	133	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-24T19:06:45Z	--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer model-index: - name: quinto_question_answear 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. --> # quinto_question_answear This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 200 \| 0.4053 \| \| No log \| 2.0 \| 400 \| 0.3786 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
Thodns/openai-whisper-medium-300-opti-merged	Thodns	2024-05-29T14:57:55Z	86	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-29T13:52:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gustavomacedo/Llama3_MMGD_8b_v3	gustavomacedo	2024-05-29T14:49:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T14:49:09Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: gustavomacedo - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sroecker/granite-8b-code-instruct-GGUF	sroecker	2024-05-29T14:48:14Z	6	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T09:56:21Z	--- license: apache-2.0 ---
AayushyaL/ppo-LunarLander-v2	AayushyaL	2024-05-29T14:46:58Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T14:46:39Z	--- 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: 249.83 +/- 22.30 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 ... ```
HachiML/Mistral-7B-Instruct-v0.3-dpo-lora_lr1e-5_2ep	HachiML	2024-05-29T14:44:15Z	0	0	peft	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	null	2024-05-29T14:43:15Z	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.3 model-index: - name: Mistral-7B-Instruct-v0.3-dpo-lora_lr1e-5_2ep results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral-7B-Instruct-v0.3-dpo-lora_lr1e-5_2ep This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3109 - Rewards/chosen: 0.7129 - Rewards/rejected: -1.3545 - Rewards/accuracies: 0.8795 - Rewards/margins: 2.0674 - Logps/rejected: -373.3019 - Logps/chosen: -422.7253 - Logits/rejected: -0.2832 - Logits/chosen: -0.1040 ## 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: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.4052 \| 1.0 \| 103 \| 0.3249 \| 0.5808 \| -1.2724 \| 0.8795 \| 1.8532 \| -372.4805 \| -424.0459 \| -0.3089 \| -0.1139 \| \| 0.1566 \| 2.0 \| 206 \| 0.3109 \| 0.7129 \| -1.3545 \| 0.8795 \| 2.0674 \| -373.3019 \| -422.7253 \| -0.2832 \| -0.1040 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
CMU-AIR2/deepseek-math-base-LORA-MWP-8k	CMU-AIR2	2024-05-29T14:43:29Z	4	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:adapter:deepseek-ai/deepseek-math-7b-base", "region:us" ]	null	2024-05-29T03:15:35Z	--- library_name: peft base_model: deepseek-ai/deepseek-math-7b-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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CMU-AIR2/deepseek-math-base-LORA-MWP-4k	CMU-AIR2	2024-05-29T14:43:28Z	4	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:adapter:deepseek-ai/deepseek-math-7b-base", "region:us" ]	null	2024-05-29T03:14:11Z	--- library_name: peft base_model: deepseek-ai/deepseek-math-7b-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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CounterNarratives/Mistral-7B-Instruct-v0.2_multi_collective_b	CounterNarratives	2024-05-29T14:43:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T14:42:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BrunoHays/whisper-large-v3-french-illuin	BrunoHays	2024-05-29T14:43:03Z	427	5	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "fr", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-04-05T16:29:42Z	--- license: cc-by-4.0 language: - fr pipeline_tag: automatic-speech-recognition --- # Whisper-Large-V3-Illuin-French This model is a finetuned variant of openai's [whisper-large-v3](openai/whisper-large-v3) model. It has been finetuned on a dataset of more than 18 000 hours of french speech. This model has been converted and tested into some other formats to allow use with the most popular inference frameworks: - transformers - openai-whisper - fasterwhisper - whisper.cpp The models can be found in this [collection](https://huggingface.co/collections/illuin/whisper-large-french-illuin-661684f315ea7b8f42ad7fd1) # Training details ## Dataset Composition: The dataset is a compilation of various popular French ASR (Automatic Speech Recognition) datasets, including: - CommonVoice 13 French - LibriSpeech French - African accented French - TEDx French - VoxPopuli French - Fleurs French The total dataset comprises a little over 2 500 hours of speech data from these sources. Additionally, it includes transcribed french speech scraped from the internet. In total, this dataset exceeds 18 000 hours of speech data, which makes it one of the largest french asr datasets assembled to date. ## Dataset processings We agressively filtered and cleaned the raw internet dataset through extensive heuristic filtering, as well as language verification and quality estimation models. Other data sources did not require as much preprocessing, but underwent Large Language Model verification and rephrasing for punctuations and minor correction fixes (Mixtral 8x7B). We further enhance our dataset for real-word conditions by stochastically subjecting audio to various compression codecs and simulating issues such as packet lossto replicate call-center environments. This extensive preprocessing pipeline enables us to obtain 18k hours of high quality labeled French audio we use to train our SOTA French ASR models. ## Training We trained on 2 epochs with an effective batch size of 256, a maximum learning rate of 1e-5 and a linear learning rate scheduler with 500 warmup steps. The full dataset being prohibitively large, we used [mosaicml streaming dataset](https://docs.mosaicml.com/projects/streaming/en/stable/) to enable streaming of the dataset samples and instant mid-epoch resumption. # Performance The French ASR datasets lacked a publicly available dataset of real call-center conditions, akin to the Switchboard dataset in English. To address this gap, we filtered and cleaned the [Accueil_UBS dataset sourced from Ortolang](https://huggingface.co/datasets/BrunoHays/UBS). This preparation enabled the evaluation of ASR models under conditions similar to those encountered in call-center environments. \| Model \| librispeech \| voxpopuli \| fleurs \| Accueil_UBS \| Common Voice \| TEDX \| TEDX long form \| \|--------------------------------------------------\|-------------\|-----------\|--------\|-------------\|--------------\|------\|----------------\| \| google-latest-long \| 0.15 \| 0.14 \| 0.12 \| 0.31 \| 0.08 \| 0.20 \| NA \| \| azure \| 0.27 \| 0.14 \| 0.08 \| 0.30 \| 0.08 \| 0.23 \| NA \| \| [Whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) \| 0.05 \| 0.10 \| 0.05 \| 0.30 \| 0.13 \| 0.20 \| 0.11 \| \| [whisper-large-v3-french-distil-dec16](https://huggingface.co/bofenghuang/whisper-large-v3-french-distil-dec16) \| 0.04 \| 0.08 \| 0.05 \| 0.25 \| 0.04 \| 0.10 \| 0.09 \| \| whisper-large-v3-french-illuin \| 0.04 \| 0.08 \| 0.04 \| 0.20 \| 0.07 \| 0.10 \| 0.08 \| # Inference We offer the model in various formats to ensure compatibility with the most widely used inference frameworks. It's important to note that the model hasn't undergone fine-tuning with timestamps, thus it cannot accurately predict timestamps on its own. However, leveraging cross-attention enables us to obtain more precise timestamps at a lower computational cost. In most frameworks, enabling this feature involves adding parameters such as without_timestamps=True and word_timestamps=True. While it can still handle receiving previous text during inference, its performance under this condition hasn't been quantitatively evaluated. Additionally, it's been observed that enabling this option raises the risk of hallucination based on the base OpenAI model. Therefore, it's advised to disable this option to mitigate potential issues ## Examples: transformers: ```python from transformers import AutomaticSpeechRecognitionPipeline, WhisperForConditionalGeneration from transformers import AutoModel, AutoTokenizer, AutoFeatureExtractor model_path = "BrunoHays/whisper-large-v3-french-illuin" model = WhisperForConditionalGeneration.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) processor = AutoFeatureExtractor.from_pretrained(model_path) pipe = AutomaticSpeechRecognitionPipeline(model=model, feature_extractor=processor, tokenizer=tokenizer) transcript = pipe("audio_samples/short_rd.wav", return_timestamps=False) print(transcript) ``` openai-whisper: ```python import whisper whisper_model = whisper.load_model("converted_models/openai/whisper-large-small-yt-os-V2") result = whisper_model.transcribe("long_audio.wav", temperature=0, condition_on_previous_text=False, language="french", without_timestamps=True, word_timestamps=True) ``` faster-whisper: ```python from faster_whisper import WhisperModel model = WhisperModel("BrunoHays/whisper-large-v3-french-illuin-ctranslate2-fp16", device="cpu") segments, info = model.transcribe("long_audio.wav", without_timestamps=True, word_timestamps=True, temperature=0, condition_on_previous_text=False, task="transcribe", language="fr") ``` Whisper.cpp: ```bash ./main -f long_audio.wav -l fr -mc 0 -m ggml-model.bin ```
CMU-AIR2/deepseek-math-base-LORA-ArithSteps-10k	CMU-AIR2	2024-05-29T14:41:12Z	3	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:adapter:deepseek-ai/deepseek-math-7b-base", "region:us" ]	null	2024-05-29T14:17:48Z	--- library_name: peft base_model: deepseek-ai/deepseek-math-7b-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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CMU-AIR2/deepseek-math-base-LORA-ArithSteps-6k	CMU-AIR2	2024-05-29T14:41:07Z	1	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:adapter:deepseek-ai/deepseek-math-7b-base", "region:us" ]	null	2024-05-29T14:16:43Z	--- library_name: peft base_model: deepseek-ai/deepseek-math-7b-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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CMU-AIR2/deepseek-math-base-LORA-ArithSteps-4k	CMU-AIR2	2024-05-29T14:41:05Z	7	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:adapter:deepseek-ai/deepseek-math-7b-base", "region:us" ]	null	2024-05-29T14:16:06Z	--- library_name: peft base_model: deepseek-ai/deepseek-math-7b-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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CMU-AIR2/deepseek-math-base-LORA-ArithSteps-2k	CMU-AIR2	2024-05-29T14:41:03Z	3	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:adapter:deepseek-ai/deepseek-math-7b-base", "region:us" ]	null	2024-05-29T14:15:26Z	--- library_name: peft base_model: deepseek-ai/deepseek-math-7b-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ibm-granite/granite-8b-code-instruct-accelerator	ibm-granite	2024-05-29T14:39:30Z	56	1	transformers	[ "transformers", "safetensors", "mlp_speculator", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T14:22:16Z	--- license: apache-2.0 --- ## Installation from source ```bash git clone https://github.com/foundation-model-stack/fms-extras cd fms-extras pip install -e . ``` ## Description This model is intended to be used as an accelerator for [granite-8b-code-instruct](https://huggingface.co/ibm-granite/granite-8b-code-instruct) and takes inspiration from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts a single token in the draft based on both a state vector and sampled token from the prior stage (the base model can be considered stage 0). The state vector from the base model provides contextual information to the accelerator, while conditioning on prior sampled tokens allows it to produce higher-quality draft n-grams. Note: The underlying MLP speculator is a generic architecture that can be trained with any generative model to accelerate inference. Training is light-weight and can be completed in only a few days depending on base model size and speed. ## Repository Links 1. [Paged Attention KV-Cache / Speculator](https://github.com/foundation-model-stack/fms-extras) 2. [Production Server with speculative decoding](https://github.com/IBM/text-generation-inference.git) 3. [Speculator training](https://github.com/foundation-model-stack/fms-fsdp/pull/35) ## Samples _Note: For all samples, your environment must have access to cuda_ ### Use in IBM Production TGIS To try this out running in a production-like environment, please use the pre-built docker image: #### Setup ```bash HF_HUB_CACHE=/hf_hub_cache chmod a+w $HF_HUB_CACHE HF_HUB_TOKEN="your huggingface hub token" TGIS_IMAGE=quay.io/wxpe/text-gen-server:main.ddc56ee docker pull $TGIS_IMAGE # optionally download granite-8b-code-instruct if the weights do not already exist docker run --rm \ -v $HF_HUB_CACHE:/models \ -e HF_HUB_CACHE=/models \ -e TRANSFORMERS_CACHE=/models \ $TGIS_IMAGE \ text-generation-server download-weights \ ibm-granite/granite-8b-code-instruct \ --token $HF_HUB_TOKEN # optionally download the speculator model if the weights do not already exist docker run --rm \ -v $HF_HUB_CACHE:/models \ -e HF_HUB_CACHE=/models \ -e TRANSFORMERS_CACHE=/models \ $TGIS_IMAGE \ text-generation-server download-weights \ ibm-granite/granite-8b-code-instruct-accelerator \ --token $HF_HUB_TOKEN # note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name> docker run -d --rm --gpus all \ --name my-tgis-server \ -p 8033:8033 \ -v $HF_HUB_CACHE:/models \ -e HF_HUB_CACHE=/models \ -e TRANSFORMERS_CACHE=/models \ -e MODEL_NAME=ibm-granite/granite-8b-code-instruct \ -e SPECULATOR_NAME=ibm-granite/granite-8b-code-instruct-accelerator \ -e FLASH_ATTENTION=true \ -e PAGED_ATTENTION=true \ -e DTYPE=float16 \ $TGIS_IMAGE # check logs and wait for "gRPC server started on port 8033" and "HTTP server started on port 3000" docker logs my-tgis-server -f # get the client sample (Note: The first prompt will take longer as there is a warmup time) conda create -n tgis-client-env python=3.11 conda activate tgis-client-env git clone --branch main --single-branch https://github.com/IBM/text-generation-inference.git cd text-generation-inference/integration_tests make gen-client pip install . --no-cache-dir ``` #### Run Sample ```bash python sample_client.py ``` _Note: first prompt may be slower as there is a slight warmup time_ ### Use in Huggingface TGI #### start the server ```bash model=ibm-granite/granite-8b-code-instruct-accelerator volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model ``` _note: for tensor parallel, add --num-shard_ #### make a request ```bash curl 127.0.0.1:8080/generate_stream \ -X POST \ -d '{"inputs":"Write a bubble sort in python","parameters":{"max_new_tokens":100}}' \ -H 'Content-Type: application/json' ```
davideromano/sft_class_adapter	davideromano	2024-05-29T14:28:46Z	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct", "region:us" ]	null	2024-05-29T14:17:47Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Lanfrose/ppo-Huggyv2	Lanfrose	2024-05-29T14:27:19Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-29T14:17:50Z	--- 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: Lanfrose/ppo-Huggyv2 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
thewordsmiths/mistral_dpo_no_sft	thewordsmiths	2024-05-29T14:26:19Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]	null	2024-05-29T11:24:25Z	--- library_name: peft base_model: unsloth/mistral-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] - 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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limitedonly41/website_mistral7b_v02_1200_finetuned_4	limitedonly41	2024-05-29T14:26:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T14:25:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: limitedonly41 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-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)
thewordsmiths/llama3_dpo_no_sft	thewordsmiths	2024-05-29T14:25:54Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b", "base_model:adapter:unsloth/llama-3-8b", "region:us" ]	null	2024-05-29T11:24:48Z	--- library_name: peft base_model: unsloth/llama-3-8b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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eeeyounglee/EEVE-10.8B-mean-2048-2	eeeyounglee	2024-05-29T14:25:00Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "llama", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-29T14:22:31Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # eeeyounglee/EEVE-10.8B-mean-2048-2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('eeeyounglee/EEVE-10.8B-mean-2048-2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=eeeyounglee/EEVE-10.8B-mean-2048-2) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 224 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `__main__.MultipleNegativesRankingLoss_with_logging` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 112, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: LlamaModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 4096, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Likich/llama3-finetune-qualcoding_1000_prompt4	Likich	2024-05-29T14:24:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-23T15:05:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tsavage68/UTI_L3_300steps_1e7rate_SFT	tsavage68	2024-05-29T14:22:12Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T14:07:04Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: UTI_L3_300steps_1e7rate_SFT 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. --> # UTI_L3_300steps_1e7rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6612 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.7452 \| 0.3333 \| 25 \| 2.7748 \| \| 2.7844 \| 0.6667 \| 50 \| 2.7704 \| \| 2.7915 \| 1.0 \| 75 \| 2.7596 \| \| 2.7945 \| 1.3333 \| 100 \| 2.7379 \| \| 2.6614 \| 1.6667 \| 125 \| 2.7138 \| \| 2.555 \| 2.0 \| 150 \| 2.6921 \| \| 2.6746 \| 2.3333 \| 175 \| 2.6756 \| \| 2.7057 \| 2.6667 \| 200 \| 2.6665 \| \| 2.7025 \| 3.0 \| 225 \| 2.6620 \| \| 2.6592 \| 3.3333 \| 250 \| 2.6607 \| \| 2.5701 \| 3.6667 \| 275 \| 2.6610 \| \| 2.6338 \| 4.0 \| 300 \| 2.6612 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
SidXXD/mist_8-photo_of_v_cat-token_init_cat	SidXXD	2024-05-29T14:21:41Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-29T14:15:04Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo <v1> of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/mist_8-photo_of_v_cat-token_init_cat These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo <v1> of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
Treza12/mixtral-instruct	Treza12	2024-05-29T14:21:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T14:20:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dariolopez/roberta-base-bne-finetuned-msmarco-qa-es-mnrl-mn	dariolopez	2024-05-29T14:21:09Z	9,265	4	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "sentence-similarity", "es", "dataset:IIC/ms_marco_es", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-05-03T14:43:37Z	--- license: apache-2.0 language: - es library_name: sentence-transformers pipeline_tag: sentence-similarity datasets: - IIC/ms_marco_es --- # Model Description This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. # How to use Using this model becomes easy when you have sentence-transformers installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util # Load model model = SentenceTransformer('dariolopez/roberta-base-bne-finetuned-msmarco-qa-es-mnrl-mn') # Some examples that may contain information that is relevant to your question corpus = [ "Napoleón I Bonaparte (Ajaccio, 15 de agosto de 1769-Longwood, 5 de mayo de 1821) fue un militar y estadista francés, general republicano durante la Revolución francesa y el Directorio, y artífice del golpe de Estado del 18 de brumario que lo convirtió en primer cónsul (Premier Consul) de la República el 11 de noviembre de 1799.", "Luis XVI de Francia (en francés: Louis XVI; Versalles, 23 de agosto de 1754 – París, 21 de enero de 1793) fue rey de Francia y de Navarra4 entre 1774 y 1789, copríncipe de Andorra entre 1774 y 1793, y rey de los franceses3 entre 1789 y 1792.2 Fue el último monarca antes de la caída de la monarquía por la Revolución Francesa, así como el último que ejerció sus poderes de monarca absoluto.", "Felipe VI de España (Madrid, 30 de enero de 1968) es el actual rey de España, título por el que ostenta la jefatura del Estado y el mando supremo de las Fuerzas Armadas, desde el 19 de junio de 2014, fecha en que ascendió al trono por la abdicación de su padre, el rey Juan Carlos I.", "Lionel Andrés Messi Cuccittini (Rosario, 24 de junio de 1987), conocido como Leo Messi, es un futbolista argentino que juega como delantero o centrocampista. Jugador histórico del Fútbol Club Barcelona, al que estuvo ligado veinte años, desde 2021 integra el plantel del Paris Saint-Germain de la Ligue 1 de Francia. Es también internacional con la selección de Argentina, equipo del que es capitán." ] # Your question query = "Listar aquellos personajes que tuvieron poder en Francia" # Encode corpus and query corpus_embeddings = model.encode(corpus) query_embedding = model.encode(query) # Get the 2 best results on the corpus options hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=2)[0] for hit in hits: print(f"corpus_id: {hit['corpus_id']}, score: {hit['score']}, text: {corpus[hit['corpus_id']][0:100]}...") # output: # corpus_id: 1, score: 0.5533886551856995, text: Luis XVI de Francia (en francés: Louis XVI; Versalles, 23 de agosto de 1754 – París, 21 de enero de ... # corpus_id: 0, score: 0.5308797955513, text: Napoleón I Bonaparte (Ajaccio, 15 de agosto de 1769-Longwood, 5 de mayo de 1821) fue un militar y es... ``` # Training The trained model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) focused on question/answer using [MS-MARCO dataset translated into Spanish](https://huggingface.co/datasets/IIC/ms_marco_es) (query - positive - negative - negative - negative - negative) dataset to train. ## Features * [Base Model](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) * [Config used to train](https://huggingface.co/dariolopez/roberta-base-bne-finetuned-msmarco-qa-es-mnrl-mn/blob/main/train_config.json) * Dataset: [IIC/ms_marco_es](https://huggingface.co/datasets/IIC/ms_marco_es) (query - positive - negative - negative - negative - negative) * Loss: MultipleNegativesRankingLoss ## Config ``` { "model_name": "PlanTL-GOB-ES/roberta-base-bne", "max_seq_length": 512, "epochs": 10, "warmup_steps": 1000, "batch_size": 16, "optimizer_params": { "lr": 2e-05 }, "loss": "mnrl", "dataset_train_size": 481335, "dataset_name": "IIC/ms_marco_es", "seed": 42, "length_embedding": 768 } ``` ## Source code to train https://github.com/bukosabino/sbert-spanish/tree/main # Considerations for Using the Model The model is designed for use in Spanish language, specially focused on Question/Answer. ## Max input length By default, input text longer than 512 word pieces is truncated. # Additional Information ## Licesing This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
dariolopez/roberta-base-bne-finetuned-msmarco-qa-es	dariolopez	2024-05-29T14:20:46Z	9	0	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "sentence-similarity", "es", "dataset:dariolopez/ms-marco-es-500k", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-05-02T15:15:28Z	--- license: apache-2.0 datasets: - dariolopez/ms-marco-es-500k language: - es library_name: sentence-transformers pipeline_tag: sentence-similarity --- # Model Description This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. # How to use Using this model becomes easy when you have sentence-transformers installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util # Load model model = SentenceTransformer('dariolopez/roberta-base-bne-finetuned-msmarco-qa-es') # Some examples that may contain information that is relevant to your question corpus = [ "Napoleón I Bonaparte (Ajaccio, 15 de agosto de 1769-Longwood, 5 de mayo de 1821) fue un militar y estadista francés, general republicano durante la Revolución francesa y el Directorio, y artífice del golpe de Estado del 18 de brumario que lo convirtió en primer cónsul (Premier Consul) de la República el 11 de noviembre de 1799.", "Luis XVI de Francia (en francés: Louis XVI; Versalles, 23 de agosto de 1754 – París, 21 de enero de 1793) fue rey de Francia y de Navarra4 entre 1774 y 1789, copríncipe de Andorra entre 1774 y 1793, y rey de los franceses3 entre 1789 y 1792.2 Fue el último monarca antes de la caída de la monarquía por la Revolución Francesa, así como el último que ejerció sus poderes de monarca absoluto.", "Felipe VI de España (Madrid, 30 de enero de 1968) es el actual rey de España, título por el que ostenta la jefatura del Estado y el mando supremo de las Fuerzas Armadas, desde el 19 de junio de 2014, fecha en que ascendió al trono por la abdicación de su padre, el rey Juan Carlos I.", "Lionel Andrés Messi Cuccittini (Rosario, 24 de junio de 1987), conocido como Leo Messi, es un futbolista argentino que juega como delantero o centrocampista. Jugador histórico del Fútbol Club Barcelona, al que estuvo ligado veinte años, desde 2021 integra el plantel del Paris Saint-Germain de la Ligue 1 de Francia. Es también internacional con la selección de Argentina, equipo del que es capitán." ] # Your question query = "Listar aquellos personajes que tuvieron poder en Francia" # Encode corpus and query corpus_embeddings = model.encode(corpus) query_embedding = model.encode(query) # Get the 2 best results on the corpus options hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=2)[0] for hit in hits: print(f"corpus_id: {hit['corpus_id']}, score: {hit['score']}, text: {corpus[hit['corpus_id']][0:100]}...") # output: # corpus_id: 0, score: 0.603590190410614, text: Napoleón I Bonaparte (Ajaccio, 15 de agosto de 1769-Longwood, 5 de mayo de 1821) fue un militar y es... # corpus_id: 1, score: 0.5792515277862549, text: Luis XVI de Francia (en francés: Louis XVI; Versalles, 23 de agosto de 1754 – París, 21 de enero de ... ``` # Training The trained model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) focused on question/answer using [MS-MARCO dataset translated into Spanish](https://huggingface.co/datasets/dariolopez/ms-marco-es-500k) (query - positive - negative) dataset to train. ## Features * [Base Model](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) * [Config used to train](https://huggingface.co/dariolopez/roberta-base-bne-finetuned-msmarco-qa-es/blob/main/train_config.json) * Dataset: [dariolopez/ms-marco-es](https://huggingface.co/datasets/dariolopez/ms-marco-es) (query - positive - negative) * Loss: TripletLoss ## Config ``` { "model_name": "PlanTL-GOB-ES/roberta-base-bne", "max_seq_length": 512, "epochs": 10, "warmup_steps": 1000, "batch_size": 16, "optimizer_params": { "lr": 2e-05 }, "loss": "tl", "dataset_train_size": 500000, "dataset_name": "dariolopez/ms-marco-es-500k", "seed": 42, "length_embedding": 768 } ``` ## Source code to train https://github.com/bukosabino/sbert-spanish/tree/main # Considerations for Using the Model The model is designed for use in Spanish language, specially focused on Question/Answer. ## Max input length By default, input text longer than 512 word pieces is truncated. # Additional Information ## Licesing This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
SidXXD/clean-photo_of_v_cat-token_init_ktn	SidXXD	2024-05-29T14:20:26Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-29T14:13:34Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo <v1> of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/clean-photo_of_v_cat-token_init_ktn These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo <v1> of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
Ankur87/Llama2_Time_series_forecasting_3.0	Ankur87	2024-05-29T14:20:01Z	145	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T14:14:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
hkeshhk/bpetokenizer	hkeshhk	2024-05-29T14:19:19Z	0	0	null	[ "en", "arxiv:1508.07909", "license:mit", "region:us" ]	null	2024-05-29T13:12:57Z	--- license: mit language: - en --- # bpetokenizer A Byte Pair Encoding (BPE) tokenizer, which algorithmically follows along the GPT tokenizer. The tokenizer is capable of handling special tokens and uses a customizable regex pattern for tokenization(includes the gpt4 regex pattern). supports `save` and `load` tokenizers in the `json` and `file` format. ### Overview The Byte Pair Encoding (BPE) algorithm is a simple yet powerful method for building a vocabulary of subword units for a given text corpus. This tokenizer can be used for training your tokenizer of the LLM on various languages of text corpus. this algorithm is first introduced in the paper [Neural Machine Translation of Rare Words with Subword Units](https://arxiv.org/pdf/1508.07909) and then used this in the gpt2 tokenizer([Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)) The [notebook](notebooks/tokenization.ipynb) which shows the BPE algorithm in detail and how the tokenizers work internally. Every LLM(LLama, Gemini, Mistral..) use their own Tokenizers trained on their own text dataset. ### Features - Implements Byte Pair Encoding (BPE) algorithm. - Handles special tokens. - Uses a customizable regex pattern for tokenization. - Compatible with Python 3.9 and above #### This repository has 2 different Tokenizers: - `BPETokenizer` - `Tokenizer` 1. [Tokenizer](bpetokenizer/base.py): This class contains `train`, `encode`, `decode` and functionalities to `save` and `load`. Also contains few helper functions `get_stats`, `merge`, `replace_control_characters`.. to perform the BPE algorithm for the tokenizer. 2. [BPETokenizer](bpetokenizer/tokenizer.py): This class emphasizes the real power of the tokenizer(used in gpt4 tokenizer..[tiktoken](https://github.com/openai/tiktoken)), uses the `GPT4_SPLIT_PATTERN` to split the text as mentioned in the gpt4 tokenizer. also handles the `special_tokens` (refer [sample_bpetokenizer](sample/bpetokenizer/sample_bpetokenizer.py)). which inherits the `save` and `load` functionlities to save and load the tokenizer respectively. ### Usage this tutorial leverages the `special_tokens` usage in the Tokenizer. Install the package ```shell pip install bpetokenizer ``` ```py from bpetokenizer import BPETokenizer special_tokens = { "<\|endoftext\|>": 1001, "<\|startoftext\|>": 1002, "[SPECIAL1]": 1003, "[SPECIAL2]": 1004, } tokenizer = BPETokenizer(special_tokens=special_tokens) # you can also use the method _special_tokens to register the special tokens (if not passed when intializing) texts = "<\|startoftext\|> Hello, World! This is a sample text with the special tokens [SPECIAL1] and [SPECIAL2] to test the tokenizer.<\|endoftext\|>" tokenizer.train(texts, vocab_size=310, verbose=True) # tokenizer._special_tokens(special_tokens) # if not passed when intialization of the BPETokenizer encode_text = """ <\|startoftext\|>Hello, World! This is a sample text with the special tokens [SPECIAL1] and [SPECIAL2] to test the tokenizer. Hello, Universe! Another example sentence containing [SPECIAL1] and [SPECIAL2], used to ensure tokenizer's robustness. Greetings, Earth! Here we have [SPECIAL1] appearing once again, followed by [SPECIAL2] in the same sentence. Hello, World! This is yet another sample text, with [SPECIAL1] and [SPECIAL2] making an appearance. Hey there, World! Testing the tokenizer with [SPECIAL1] and [SPECIAL2] to see if it handles special tokens properly. Salutations, Planet! The tokenizer should recognize [SPECIAL1] and [SPECIAL2] in this long string of text. Hello again, World! [SPECIAL1] and [SPECIAL2] are special tokens that need to be handled correctly by the tokenizer. Welcome, World! Including [SPECIAL1] and [SPECIAL2] multiple times in this large text to ensure proper encoding. Hi, World! Let's add [SPECIAL1] and [SPECIAL2] in various parts of this long sentence to test the tokenizer thoroughly. <\|endoftext\|> """ ids = tokenizer.encode(encode_text, special_tokens="all") print(ids) decode_text = tokenizer.decode(ids) print(decode_text) tokenizer.save("sample_bpetokenizer", mode="json") # mode: default is file ``` refer [sample_bpetokenizer](sample/bpetokenizer) to have an understanding of the `vocab` and the `model` file of the tokenizer trained on the above texts. #### To Load the Tokenizer ```py from bpetokenizer import BPETokenizer tokenizer = BPETokenizer() tokenizer.load("sample_bpetokenizer.json", mode="json") encode_text = """ <\|startoftext\|>Hello, World! This is a sample text with the special tokens [SPECIAL1] and [SPECIAL2] to test the tokenizer. Hello, Universe! Another example sentence containing [SPECIAL1] and [SPECIAL2], used to ensure tokenizer's robustness. Greetings, Earth! Here we have [SPECIAL1] appearing once again, followed by [SPECIAL2] in the same sentence.<\|endoftext\|>""" print("vocab: ", tokenizer.vocab) print('---') print("merges: ", tokenizer.merges) print('---') print("special tokens: ", tokenizer.special_tokens) ids = tokenizer.encode(encode_text, special_tokens="all") print('---') print(ids) decode_text = tokenizer.decode(ids) print('---') print(decode_text) # you can also print the tokens and the text chunks split with the pattern. tokens = tokenizer.tokens(encode_text, verbose=True) # if verbose, prints the text chunks and also the pattern used to split. print('---') print("tokens: ", tokens) ``` refer to the [load_json_vocab](sample/load_json_vocab/) and run the `bpetokenizer_json` to get an overview of `vocab`, `merges`, `special_tokens` and to view the tokens that are split by the tokenizer using pattern, look at [tokens](sample/load_json_vocab/tokens.py) ### Run Tests the tests folder `tests/` include the tests of the tokenizer, uses pytest. ``` python3 -m pytest ``` additionally, the workflows are setup to run the tests when made a PR. ### Contributing Contributions to the BPE Tokenizer are most welcomed! If you would like to contribute, please follow these steps: - Star and Fork the repository. - Create a new branch (git checkout -b feature/your-feature). - Commit your changes (git commit -am 'Add some feature'). - Push to the branch (git push origin feature/your-feature). - Create a new Pull Request. Please ensure your code follows the project's coding standards and includes appropriate tests. Also, update the documentation as necessary. ### License This project is licensed under the MIT License. ----
deptage/distilbert3	deptage	2024-05-29T14:18:16Z	121	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T14:17:38Z	--- 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. 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]
gustavomacedo/Llama3_MMGD_8b	gustavomacedo	2024-05-29T14:17:17Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T14:16:41Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: gustavomacedo - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
meatamax/distilbert-base-uncased-finetuned-emotion	meatamax	2024-05-29T14:12:44Z	120	0	transformers	[ "transformers", "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", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T13:51:49Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ayameRushia/indo-roberta-small-finetuned-indonlu-smsa	ayameRushia	2024-05-29T14:11:15Z	10	1	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:w11wo/indo-roberta-small", "base_model:finetune:w11wo/indo-roberta-small", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-07T03:04:36Z	--- license: mit base_model: w11wo/indo-roberta-small tags: - generated_from_trainer model-index: - name: indo-roberta-small-finetuned-indonlu-smsa 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. --> # indo-roberta-small-finetuned-indonlu-smsa This model is a fine-tuned version of [w11wo/indo-roberta-small](https://huggingface.co/w11wo/indo-roberta-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Stern5497/nir-2024-xlm-roberta-base	Stern5497	2024-05-29T14:05:37Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "dataset_size:100K<n<1M", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-29T14:04:56Z	--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:100K<n<1M - loss:MultipleNegativesRankingLoss base_model: FacebookAI/xlm-roberta-base widget: - source_sentence: who did ezra play for in the nfl sentences: - how many all nba first teams does kobe have - who does the voice of the little mermaid - dont come around here no more video director - source_sentence: who led the elves at helm s deep sentences: - who was the captain of the flying dutchman - what are the 2 seasons in the philippines - when can you get a tattoo in georgia - source_sentence: who plays red on once upon a time sentences: - who plays the new receptionist on the office - who wrote the magic school bus theme song - when did south africa declare war on germany - source_sentence: who plays the dark elf in thor 2 sentences: - who plays mantis in guardian of the galaxy 2 - where in los angeles do the chargers play - when did alaska become part of the us - source_sentence: who plays oz in the wizard of oz sentences: - where did the wizard of oz come from - when did brazil win the soccer world cup - when did the ar 15 first go on sale pipeline_tag: sentence-similarity --- # SentenceTransformer based on FacebookAI/xlm-roberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Stern5497/nir-2024-xlm-roberta-base") # Run inference sentences = [ 'who plays oz in the wizard of oz', 'where did the wizard of oz come from', 'when did brazil win the soccer world cup', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 164,848 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| sentence_2 \| \|:--------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 13.41 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>min: 136 tokens</li><li>mean: 164.07 tokens</li><li>max: 239 tokens</li></ul> \| <ul><li>min: 133 tokens</li><li>mean: 165.13 tokens</li><li>max: 256 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| sentence_2 \| \|:-------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>who wrote treat you better by shawn mendes</code> \| <code>{'title': '', 'text': 'Treat You Better "Treat You Better" is a song recorded by Canadian singer and songwriter Shawn Mendes. It was co-written by Mendes with Teddy Geiger, and Scott Harris. It was released on June 3, 2016 through Island Records as the lead single from his second studio album, "Illuminate" (2016). The music video was released on July 12, 2016 and features a storyline about an abusive relationship. The song peaked at number six on the US "Billboard" Hot 100, making it Mendes\' second top 10 single. In Canada, the song has peaked at number seven on the Canadian Hot 100. The'}</code> \| <code>{'title': '', 'text': 'Scott Harris (songwriter) Scott Harris Friedman is an American multi-platinum, Grammy nominated songwriter, producer, and musician best known for his work with Shawn Mendes and co-writing Grammy winning song, "Don\'t Let Me Down" by The Chainsmokers featuring Daya, which reached #1 on the US Mainstream Top 40 chart in 2016. Harris has most recently written 13 songs on the self-titled third album Shawn Mendes (album), which debuted at #1 on the Billboard 200 chart, in addition to 10 songs on Shawn Mendes\' sophomore album "Illuminate" including the lead single "Treat You Better" which reached the top 3 at the US'}</code> \| \| <code>where is the tanami desert located in australia</code> \| <code>{'title': '', 'text': 'zone. Tanami Desert The Tanami Desert is a desert in northern Australia situated in the Northern Territory and Western Australia. It has a rocky terrain with small hills. The Tanami was the Northern Territory\'s final frontier and was not fully explored by Australians of European descent until well into the twentieth century. It is traversed by the Tanami Track. The name "Tanami" is thought to be a corruption of the Walpiri name for the area, "Chanamee", meaning "never die". This referred to certain rock holes in the desert which were said never to run dry. Under the name "Tanami", the'}</code> \| <code>{'title': '', 'text': '("glomerata") is from the Latin "glomeratus", meaning "heaped" or "form into a ball". Desert tea-tree occurs in the arid parts of Australia including the far north west of New South Wales, South Australia including the Flinders Ranges, the Northern Territory and Western Australia. In the latter state it has been recorded from the Carnarvon, Central Kimberley, Central Ranges, Dampierland, Gascoyne, Gibson Desert, Great Sandy Desert, Great Victoria Desert, Little Sandy Desert, Murchison, Ord Victoria Plain, Pilbara and Tanami biogeographic areas. It grows in red sand, clay and sandy loam in rocky river beds, shallow depressions and sandy flats. "Melaleuca globifera"'}</code> \| \| <code>who won the us open men s and women s singles in 2017</code> \| <code>{'title': '', 'text': "that ended his season, while Kerber lost in the first round to Naomi Osaka. The men's singles tournament concluded with Rafael Nadal defeating Kevin Anderson in the final, while the women's singles tournament concluded with Sloane Stephens defeating Madison Keys in the final. The 2017 US Open was the 137th edition of the tournament and took place at the USTA Billie Jean King National Tennis Center in Flushing Meadows–Corona Park of Queens in New York City, New York, United States. The tournament was held on 14 DecoTurf hard courts. The tournament was an event run by the International Tennis Federation"}</code> \| <code>{'title': '', 'text': "2017 US Open – Women's Singles Angelique Kerber was the defending champion, but was defeated in the first round by Naomi Osaka. Kerber became the second US Open defending champion to lose in the first round after Svetlana Kuznetsova in 2005. Sloane Stephens won her first Grand Slam title, defeating Madison Keys in the final, 6–3, 6–0. It was the first all-American women's final at the US Open since 2002, and the second time in three years that the final featured two first-time Grand Slam singles finalists from the same country. Stephens became the second unseeded woman in the Open"}</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `fp16`: True - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:-----:\|:-------------:\| \| 0.0485 \| 500 \| 1.6163 \| \| 0.0971 \| 1000 \| 0.8086 \| \| 0.1456 \| 1500 \| 0.6766 \| \| 0.1941 \| 2000 \| 0.6124 \| \| 0.2426 \| 2500 \| 0.5374 \| \| 0.2912 \| 3000 \| 0.5115 \| \| 0.3397 \| 3500 \| 0.4823 \| \| 0.3882 \| 4000 \| 0.4268 \| \| 0.4368 \| 4500 \| 0.422 \| \| 0.4853 \| 5000 \| 0.4014 \| \| 0.5338 \| 5500 \| 0.3765 \| \| 0.5824 \| 6000 \| 0.3689 \| \| 0.6309 \| 6500 \| 0.3551 \| \| 0.6794 \| 7000 \| 0.3359 \| \| 0.7279 \| 7500 \| 0.326 \| \| 0.7765 \| 8000 \| 0.3158 \| \| 0.8250 \| 8500 \| 0.2945 \| \| 0.8735 \| 9000 \| 0.2836 \| \| 0.9221 \| 9500 \| 0.3043 \| \| 0.9706 \| 10000 \| 0.2761 \| \| 1.0 \| 10303 \| - \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.40.2 - PyTorch: 2.3.0+cu118 - Accelerate: 0.29.3 - Datasets: 2.19.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
SidXXD/clean-v_a_photo_of_cat-token_init_ktn	SidXXD	2024-05-29T14:05:01Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-29T13:57:37Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: <v1> a photo of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/clean-v_a_photo_of_cat-token_init_ktn These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on <v1> a photo of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
SidXXD/mist_8-v_a_photo_of_cat-token_init_ktn	SidXXD	2024-05-29T14:02:18Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-29T13:47:47Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: <v1> a photo of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/mist_8-v_a_photo_of_cat-token_init_ktn These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on <v1> a photo of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
research-dump/Llama-2-7b-chat-hf_taqa_mixed_sftt_v2	research-dump	2024-05-29T14:01:40Z	76	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T13:57:14Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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SidXXD/mist_8-v_a_photo_of_cat-token_init_cat	SidXXD	2024-05-29T14:01:06Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-29T13:48:56Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: <v1> a photo of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/mist_8-v_a_photo_of_cat-token_init_cat These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on <v1> a photo of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
sgarrett/Succ_21_Even	sgarrett	2024-05-29T13:59:59Z	145	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:nferruz/ProtGPT2", "base_model:finetune:nferruz/ProtGPT2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T13:54:55Z	--- license: apache-2.0 base_model: nferruz/ProtGPT2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: model_output_21_equal 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. --> # model_output_21_equal This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.4423 - Accuracy: 0.6613 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200.0 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.0.1 - Datasets 2.19.1 - Tokenizers 0.19.1
GDavila/sdxl-beethoven-spectrograms	GDavila	2024-05-29T13:59:49Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-05-29T13:57:38Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/mel1.jpg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: SPECTROGRAM --- # sdxl-beethoven-spectrograms <Gallery /> ## Model description https://github.com/GeorgeDavila/SDXL-LoRA-beethoven-melspectrogram ## Trigger words You should use `SPECTROGRAM` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/GDavila/sdxl-beethoven-spectrograms/tree/main) them in the Files & versions tab.
BensonZhang/finetuning-sentiment-model	BensonZhang	2024-05-29T13:59:19Z	11	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T14:40:08Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model 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: 0.4498 - Accuracy: 0.9279 - F1: 0.9283 ## 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: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 49 \| 0.2333 \| 0.9086 \| 0.9090 \| \| No log \| 2.0 \| 98 \| 0.2126 \| 0.9167 \| 0.9193 \| \| No log \| 3.0 \| 147 \| 0.2129 \| 0.9185 \| 0.9213 \| \| No log \| 4.0 \| 196 \| 0.2369 \| 0.9110 \| 0.9155 \| \| No log \| 5.0 \| 245 \| 0.2155 \| 0.9267 \| 0.9270 \| \| No log \| 6.0 \| 294 \| 0.2311 \| 0.9258 \| 0.9259 \| \| No log \| 7.0 \| 343 \| 0.2463 \| 0.926 \| 0.9261 \| \| No log \| 8.0 \| 392 \| 0.2757 \| 0.9237 \| 0.9252 \| \| No log \| 9.0 \| 441 \| 0.2940 \| 0.9224 \| 0.9241 \| \| No log \| 10.0 \| 490 \| 0.3138 \| 0.9232 \| 0.9250 \| \| 0.132 \| 11.0 \| 539 \| 0.3189 \| 0.9256 \| 0.9267 \| \| 0.132 \| 12.0 \| 588 \| 0.3139 \| 0.9264 \| 0.9272 \| \| 0.132 \| 13.0 \| 637 \| 0.3534 \| 0.9203 \| 0.9225 \| \| 0.132 \| 14.0 \| 686 \| 0.3330 \| 0.9263 \| 0.9260 \| \| 0.132 \| 15.0 \| 735 \| 0.3483 \| 0.9242 \| 0.9228 \| \| 0.132 \| 16.0 \| 784 \| 0.3483 \| 0.9257 \| 0.9261 \| \| 0.132 \| 17.0 \| 833 \| 0.3528 \| 0.9261 \| 0.9261 \| \| 0.132 \| 18.0 \| 882 \| 0.3479 \| 0.9274 \| 0.9276 \| \| 0.132 \| 19.0 \| 931 \| 0.3592 \| 0.9246 \| 0.9262 \| \| 0.132 \| 20.0 \| 980 \| 0.3537 \| 0.9272 \| 0.9270 \| \| 0.0211 \| 21.0 \| 1029 \| 0.3574 \| 0.9271 \| 0.9268 \| \| 0.0211 \| 22.0 \| 1078 \| 0.3615 \| 0.9273 \| 0.9281 \| \| 0.0211 \| 23.0 \| 1127 \| 0.3684 \| 0.9281 \| 0.9276 \| \| 0.0211 \| 24.0 \| 1176 \| 0.3753 \| 0.9270 \| 0.9281 \| \| 0.0211 \| 25.0 \| 1225 \| 0.3774 \| 0.9278 \| 0.9282 \| \| 0.0211 \| 26.0 \| 1274 \| 0.3893 \| 0.9284 \| 0.9289 \| \| 0.0211 \| 27.0 \| 1323 \| 0.3882 \| 0.9282 \| 0.9275 \| \| 0.0211 \| 28.0 \| 1372 \| 0.3900 \| 0.927 \| 0.9280 \| \| 0.0211 \| 29.0 \| 1421 \| 0.3910 \| 0.9272 \| 0.9282 \| \| 0.0211 \| 30.0 \| 1470 \| 0.3970 \| 0.9279 \| 0.9289 \| \| 0.0112 \| 31.0 \| 1519 \| 0.3985 \| 0.9295 \| 0.9300 \| \| 0.0112 \| 32.0 \| 1568 \| 0.4030 \| 0.9288 \| 0.9286 \| \| 0.0112 \| 33.0 \| 1617 \| 0.4075 \| 0.9284 \| 0.9283 \| \| 0.0112 \| 34.0 \| 1666 \| 0.4183 \| 0.9273 \| 0.9277 \| \| 0.0112 \| 35.0 \| 1715 \| 0.4235 \| 0.9261 \| 0.9269 \| \| 0.0112 \| 36.0 \| 1764 \| 0.4316 \| 0.9272 \| 0.9268 \| \| 0.0112 \| 37.0 \| 1813 \| 0.4231 \| 0.9286 \| 0.9285 \| \| 0.0112 \| 38.0 \| 1862 \| 0.4222 \| 0.9289 \| 0.9290 \| \| 0.0112 \| 39.0 \| 1911 \| 0.4256 \| 0.9294 \| 0.9290 \| \| 0.0112 \| 40.0 \| 1960 \| 0.4314 \| 0.9288 \| 0.9291 \| \| 0.0053 \| 41.0 \| 2009 \| 0.4291 \| 0.9286 \| 0.9288 \| \| 0.0053 \| 42.0 \| 2058 \| 0.4483 \| 0.9266 \| 0.9277 \| \| 0.0053 \| 43.0 \| 2107 \| 0.4392 \| 0.9282 \| 0.9287 \| \| 0.0053 \| 44.0 \| 2156 \| 0.4453 \| 0.9282 \| 0.9286 \| \| 0.0053 \| 45.0 \| 2205 \| 0.4562 \| 0.9265 \| 0.9276 \| \| 0.0053 \| 46.0 \| 2254 \| 0.4564 \| 0.9264 \| 0.9275 \| \| 0.0053 \| 47.0 \| 2303 \| 0.4471 \| 0.9278 \| 0.9281 \| \| 0.0053 \| 48.0 \| 2352 \| 0.4473 \| 0.928 \| 0.9282 \| \| 0.0053 \| 49.0 \| 2401 \| 0.4506 \| 0.9281 \| 0.9285 \| \| 0.0053 \| 50.0 \| 2450 \| 0.4498 \| 0.9279 \| 0.9283 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.13.1+cu117 - Datasets 2.19.1 - Tokenizers 0.13.3
Heem2/Melanoma-Cancer-Image-classification	Heem2	2024-05-29T13:53:55Z	228	0	transformers	[ "transformers", "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	2024-05-29T13:44:25Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Melanoma-Cancer-Image-Classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Melanoma-Cancer-Image-Classification 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: 0.1954 - Accuracy: 0.9395 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5451 \| 0.99 \| 68 \| 0.2960 \| 0.8936 \| \| 0.2488 \| 1.99 \| 137 \| 0.2254 \| 0.9105 \| \| 0.1986 \| 3.0 \| 206 \| 0.1913 \| 0.9282 \| \| 0.1714 \| 4.0 \| 275 \| 0.1906 \| 0.9264 \| \| 0.1576 \| 4.99 \| 343 \| 0.1825 \| 0.9323 \| \| 0.1359 \| 5.99 \| 412 \| 0.1973 \| 0.9318 \| \| 0.1193 \| 7.0 \| 481 \| 0.1756 \| 0.9368 \| \| 0.1062 \| 8.0 \| 550 \| 0.1743 \| 0.9382 \| \| 0.0983 \| 8.99 \| 618 \| 0.1885 \| 0.9395 \| \| 0.0797 \| 9.99 \| 687 \| 0.1931 \| 0.9309 \| \| 0.0698 \| 11.0 \| 756 \| 0.1895 \| 0.9359 \| \| 0.0657 \| 12.0 \| 825 \| 0.1861 \| 0.9368 \| \| 0.0587 \| 12.99 \| 893 \| 0.1837 \| 0.9414 \| \| 0.056 \| 13.99 \| 962 \| 0.1936 \| 0.9377 \| \| 0.0592 \| 15.0 \| 1031 \| 0.1958 \| 0.935 \| \| 0.0508 \| 15.83 \| 1088 \| 0.1954 \| 0.9395 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Likich/vicuna-finetune-qualcoding_1000_prompt3_dot	Likich	2024-05-29T13:51:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:51:21Z	--- 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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hdve/Qwen-Qwen1.5-0.5B-1716990652	hdve	2024-05-29T13:51:29Z	142	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T13:50:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Lalith16/Genpro_Llama3-8b	Lalith16	2024-05-29T13:49:41Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-05-28T07:47:23Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: Genpro_Llama3-8b 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. --> # Genpro_Llama3-8b This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5784 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3266 \| 0.0634 \| 100 \| 1.3260 \| \| 1.123 \| 0.1267 \| 200 \| 1.1090 \| \| 1.0242 \| 0.1901 \| 300 \| 1.0121 \| \| 1.0228 \| 0.2535 \| 400 \| 0.9520 \| \| 0.9834 \| 0.3169 \| 500 \| 0.9037 \| \| 0.9726 \| 0.3802 \| 600 \| 0.8456 \| \| 0.9003 \| 0.4436 \| 700 \| 0.8270 \| \| 0.8862 \| 0.5070 \| 800 \| 0.7967 \| \| 0.7788 \| 0.5703 \| 900 \| 0.7715 \| \| 0.831 \| 0.6337 \| 1000 \| 0.7528 \| \| 0.7875 \| 0.6971 \| 1100 \| 0.7319 \| \| 0.8284 \| 0.7605 \| 1200 \| 0.7097 \| \| 0.7387 \| 0.8238 \| 1300 \| 0.6927 \| \| 0.7573 \| 0.8872 \| 1400 \| 0.6735 \| \| 0.7744 \| 0.9506 \| 1500 \| 0.6668 \| \| 0.5684 \| 1.0139 \| 1600 \| 0.6487 \| \| 0.5606 \| 1.0773 \| 1700 \| 0.6378 \| \| 0.5268 \| 1.1407 \| 1800 \| 0.6363 \| \| 0.5727 \| 1.2041 \| 1900 \| 0.6269 \| \| 0.5456 \| 1.2674 \| 2000 \| 0.6196 \| \| 0.5174 \| 1.3308 \| 2100 \| 0.6146 \| \| 0.499 \| 1.3942 \| 2200 \| 0.6055 \| \| 0.5831 \| 1.4575 \| 2300 \| 0.5984 \| \| 0.4884 \| 1.5209 \| 2400 \| 0.5952 \| \| 0.5538 \| 1.5843 \| 2500 \| 0.5829 \| \| 0.5302 \| 1.6477 \| 2600 \| 0.5805 \| \| 0.5506 \| 1.7110 \| 2700 \| 0.5758 \| \| 0.5509 \| 1.7744 \| 2800 \| 0.5708 \| \| 0.5249 \| 1.8378 \| 2900 \| 0.5597 \| \| 0.5249 \| 1.9011 \| 3000 \| 0.5601 \| \| 0.4597 \| 1.9645 \| 3100 \| 0.5585 \| \| 0.383 \| 2.0279 \| 3200 \| 0.5643 \| \| 0.4115 \| 2.0913 \| 3300 \| 0.5666 \| \| 0.3928 \| 2.1546 \| 3400 \| 0.5737 \| \| 0.4634 \| 2.2180 \| 3500 \| 0.5587 \| \| 0.4093 \| 2.2814 \| 3600 \| 0.5615 \| \| 0.3724 \| 2.3447 \| 3700 \| 0.5529 \| \| 0.3846 \| 2.4081 \| 3800 \| 0.5604 \| \| 0.4206 \| 2.4715 \| 3900 \| 0.5539 \| \| 0.4803 \| 2.5349 \| 4000 \| 0.5422 \| \| 0.4319 \| 2.5982 \| 4100 \| 0.5452 \| \| 0.3762 \| 2.6616 \| 4200 \| 0.5523 \| \| 0.4472 \| 2.7250 \| 4300 \| 0.5319 \| \| 0.4048 \| 2.7883 \| 4400 \| 0.5370 \| \| 0.4227 \| 2.8517 \| 4500 \| 0.5401 \| \| 0.4407 \| 2.9151 \| 4600 \| 0.5294 \| \| 0.3998 \| 2.9785 \| 4700 \| 0.5282 \| \| 0.336 \| 3.0418 \| 4800 \| 0.5504 \| \| 0.3022 \| 3.1052 \| 4900 \| 0.5608 \| \| 0.3323 \| 3.1686 \| 5000 \| 0.5584 \| \| 0.3306 \| 3.2319 \| 5100 \| 0.5560 \| \| 0.3557 \| 3.2953 \| 5200 \| 0.5478 \| \| 0.3475 \| 3.3587 \| 5300 \| 0.5656 \| \| 0.3515 \| 3.4221 \| 5400 \| 0.5520 \| \| 0.3236 \| 3.4854 \| 5500 \| 0.5479 \| \| 0.3886 \| 3.5488 \| 5600 \| 0.5436 \| \| 0.339 \| 3.6122 \| 5700 \| 0.5408 \| \| 0.3509 \| 3.6755 \| 5800 \| 0.5499 \| \| 0.3651 \| 3.7389 \| 5900 \| 0.5447 \| \| 0.3707 \| 3.8023 \| 6000 \| 0.5340 \| \| 0.3122 \| 3.8657 \| 6100 \| 0.5360 \| \| 0.3613 \| 3.9290 \| 6200 \| 0.5326 \| \| 0.364 \| 3.9924 \| 6300 \| 0.5315 \| \| 0.2418 \| 4.0558 \| 6400 \| 0.5719 \| \| 0.2349 \| 4.1191 \| 6500 \| 0.5686 \| \| 0.2366 \| 4.1825 \| 6600 \| 0.5750 \| \| 0.2433 \| 4.2459 \| 6700 \| 0.5739 \| \| 0.2566 \| 4.3093 \| 6800 \| 0.5664 \| \| 0.2524 \| 4.3726 \| 6900 \| 0.5798 \| \| 0.2667 \| 4.4360 \| 7000 \| 0.5570 \| \| 0.2528 \| 4.4994 \| 7100 \| 0.5573 \| \| 0.2348 \| 4.5627 \| 7200 \| 0.5723 \| \| 0.2629 \| 4.6261 \| 7300 \| 0.5742 \| \| 0.2705 \| 4.6895 \| 7400 \| 0.5743 \| \| 0.2893 \| 4.7529 \| 7500 \| 0.5560 \| \| 0.2371 \| 4.8162 \| 7600 \| 0.5652 \| \| 0.287 \| 4.8796 \| 7700 \| 0.5436 \| \| 0.2725 \| 4.9430 \| 7800 \| 0.5784 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
BothBosu/cnn-gamma33-scam-classifier-v1.1	BothBosu	2024-05-29T13:47:40Z	50	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-29T01:16:30Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
BothBosu/bigru-gamma33-scam-classifier-v1.1	BothBosu	2024-05-29T13:47:22Z	51	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:47:16Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
sararelayter/group-image-model	sararelayter	2024-05-29T13:46:03Z	29	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-29T13:44:04Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### group image model on Stable Diffusion via Dreambooth #### model by sararelayter This your the Stable Diffusion model fine-tuned the group image model concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: Andrélon shampoo <group_image> You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/eight_grid-page-00001.jpg) ![image 1](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/five_centered-page-00001.jpg) ![image 2](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/fifteen_grid-page-00001.jpg) ![image 3](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/five_horizontal-page-00001.jpg) ![image 4](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/five_diagonal-page-00001.jpg) ![image 5](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/nine_grid-page-00001.jpg) ![image 6](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/five_zigzag-page-00001.jpg) ![image 7](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/five_V-page-00001.jpg) ![image 8](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/nine_zigzag-page-00001.jpg) ![image 9](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/pair-page-00001.jpg) ![image 10](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/seven_zizgag-page-00001.jpg) ![image 11](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/six_grid-page-00001.jpg) ![image 12](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/single-page-00001.jpg) ![image 13](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/three_horizontal-page-00001.jpg) ![image 14](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/three_diagonal-page-00001.jpg) ![image 15](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/three_centered-page-00001.jpg) ![image 16](https://huggingface.co/sararelayter/group-image-model/resolve/main/concept_images/three_V-page-00001.jpg)
BothBosu/gru-gamma33-scam-classifier-v1.1	BothBosu	2024-05-29T13:45:56Z	52	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:45:50Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
tsavage68/UTI_L3_125steps_1e6rate_SFT	tsavage68	2024-05-29T13:37:25Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T13:30:53Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: UTI_L3_125steps_1e6rate_SFT 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. --> # UTI_L3_125steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9481 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 125 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.7381 \| 0.3333 \| 25 \| 2.7319 \| \| 2.5633 \| 0.6667 \| 50 \| 2.4802 \| \| 2.332 \| 1.0 \| 75 \| 2.1792 \| \| 1.9781 \| 1.3333 \| 100 \| 2.0001 \| \| 1.9031 \| 1.6667 \| 125 \| 1.9481 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
xuliu15/openai-whisper-small-English-32r-1h-new	xuliu15	2024-05-29T13:35:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-26T17:35:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
xuliu15/English_32r_LoRA_1h_new	xuliu15	2024-05-29T13:35:34Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:librispeech", "base_model:openai/whisper-small", "base_model:adapter:openai/whisper-small", "license:apache-2.0", "region:us" ]	null	2024-05-29T13:35:28Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-small datasets: - librispeech model-index: - name: Whisper Small English 1h results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small English 1h This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech dataset. It achieves the following results on the evaluation set: - Loss: 0.1760 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7144 \| 1.0 \| 39 \| 1.4900 \| \| 0.4793 \| 2.0 \| 78 \| 0.5925 \| \| 0.3478 \| 3.0 \| 117 \| 0.5148 \| \| 0.2552 \| 4.0 \| 156 \| 0.4314 \| \| 0.1964 \| 5.0 \| 195 \| 0.1524 \| \| 0.0281 \| 6.0 \| 234 \| 0.1534 \| \| 0.0254 \| 7.0 \| 273 \| 0.1537 \| \| 0.0133 \| 8.0 \| 312 \| 0.1613 \| \| 0.011 \| 9.0 \| 351 \| 0.1601 \| \| 0.0088 \| 10.0 \| 390 \| 0.1619 \| \| 0.007 \| 11.0 \| 429 \| 0.1659 \| \| 0.0067 \| 12.0 \| 468 \| 0.1647 \| \| 0.0056 \| 13.0 \| 507 \| 0.1671 \| \| 0.0053 \| 14.0 \| 546 \| 0.1683 \| \| 0.0047 \| 15.0 \| 585 \| 0.1687 \| \| 0.0041 \| 16.0 \| 624 \| 0.1703 \| \| 0.0037 \| 17.0 \| 663 \| 0.1715 \| \| 0.0037 \| 18.0 \| 702 \| 0.1721 \| \| 0.0035 \| 19.0 \| 741 \| 0.1726 \| \| 0.0034 \| 20.0 \| 780 \| 0.1730 \| \| 0.0032 \| 21.0 \| 819 \| 0.1738 \| \| 0.0031 \| 22.0 \| 858 \| 0.1741 \| \| 0.0033 \| 23.0 \| 897 \| 0.1744 \| \| 0.003 \| 24.0 \| 936 \| 0.1748 \| \| 0.0029 \| 25.0 \| 975 \| 0.1756 \| \| 0.003 \| 26.0 \| 1014 \| 0.1756 \| \| 0.0028 \| 27.0 \| 1053 \| 0.1756 \| \| 0.0027 \| 28.0 \| 1092 \| 0.1760 \| \| 0.0027 \| 29.0 \| 1131 \| 0.1760 \| \| 0.0027 \| 30.0 \| 1170 \| 0.1760 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
nbeerbower/Flammen-Mahou-mistral-7B	nbeerbower	2024-05-29T13:33:27Z	7	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:flammenai/Mahou-1.2a-mistral-7B", "base_model:merge:flammenai/Mahou-1.2a-mistral-7B", "base_model:flammenai/flammen30-mistral-7B", "base_model:merge:flammenai/flammen30-mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T04:22:25Z	--- base_model: - flammenai/Mahou-1.2a-mistral-7B - flammenai/flammen30-mistral-7B library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # Flammen-Mahou-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: * [flammenai/Mahou-1.2a-mistral-7B](https://huggingface.co/flammenai/Mahou-1.2a-mistral-7B) * [flammenai/flammen30-mistral-7B](https://huggingface.co/flammenai/flammen30-mistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: flammenai/flammen30-mistral-7B layer_range: [0, 32] - model: flammenai/Mahou-1.2a-mistral-7B layer_range: [0, 32] merge_method: slerp base_model: flammenai/Mahou-1.2a-mistral-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 ```
flammenai/flammen30-mistral-7B	flammenai	2024-05-29T13:32:00Z	10	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:InferenceIllusionist/Excalibur-7b-DPO", "base_model:merge:InferenceIllusionist/Excalibur-7b-DPO", "base_model:allknowingroger/Strangecoven-7B-slerp", "base_model:merge:allknowingroger/Strangecoven-7B-slerp", "base_model:flammenai/Mahou-1.2a-mistral-7B", "base_model:merge:flammenai/Mahou-1.2a-mistral-7B", "base_model:flammenai/flammen23-mistral-7B", "base_model:merge:flammenai/flammen23-mistral-7B", "base_model:flammenai/flammen23X-mistral-7B", "base_model:merge:flammenai/flammen23X-mistral-7B", "base_model:flammenai/flammen26-mistral-7B", "base_model:merge:flammenai/flammen26-mistral-7B", "base_model:flammenai/flammen27-mistral-7B", "base_model:merge:flammenai/flammen27-mistral-7B", "base_model:flammenai/flammen29-mistral-7B", "base_model:merge:flammenai/flammen29-mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T00:47:37Z	--- base_model: - allknowingroger/Strangecoven-7B-slerp - flammenai/flammen29-mistral-7B - flammenai/flammen26-mistral-7B - flammenai/flammen23-mistral-7B - flammenai/Mahou-1.2a-mistral-7B - InferenceIllusionist/Excalibur-7b-DPO - flammenai/flammen23X-mistral-7B - flammenai/flammen27-mistral-7B library_name: transformers tags: - mergekit - merge license: apache-2.0 --- ![image/png](https://huggingface.co/nbeerbower/flammen13X-mistral-7B/resolve/main/flammen13x.png) # flammen30-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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [flammenai/flammen29-mistral-7B](https://huggingface.co/flammenai/flammen29-mistral-7B) as a base. ### Models Merged The following models were included in the merge: * [allknowingroger/Strangecoven-7B-slerp](https://huggingface.co/allknowingroger/Strangecoven-7B-slerp) * [flammenai/flammen26-mistral-7B](https://huggingface.co/flammenai/flammen26-mistral-7B) * [flammenai/flammen23-mistral-7B](https://huggingface.co/flammenai/flammen23-mistral-7B) * [flammenai/Mahou-1.2a-mistral-7B](https://huggingface.co/flammenai/Mahou-1.2a-mistral-7B) * [InferenceIllusionist/Excalibur-7b-DPO](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO) * [flammenai/flammen23X-mistral-7B](https://huggingface.co/flammenai/flammen23X-mistral-7B) * [flammenai/flammen27-mistral-7B](https://huggingface.co/flammenai/flammen27-mistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: flammenai/Mahou-1.2a-mistral-7B - model: flammenai/flammen23-mistral-7B - model: flammenai/flammen23X-mistral-7B - model: flammenai/flammen27-mistral-7B - model: flammenai/flammen26-mistral-7B - model: InferenceIllusionist/Excalibur-7b-DPO - model: allknowingroger/Strangecoven-7B-slerp merge_method: model_stock base_model: flammenai/flammen29-mistral-7B dtype: bfloat16 ```
lllyasviel/omost-phi-3-mini-128k-8bits	lllyasviel	2024-05-29T13:30:34Z	153	6	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "pytorch", "trl", "sft", "conversational", "custom_code", "autotrain_compatible", "text-generation-inference", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T13:28:23Z	--- tags: - pytorch - trl - sft inference: false --- omost-phi-3-mini-128k-8bits is Omost's phi-3-mini model with 128k context length in fp8.
tsavage68/UTI_L3_50steps_1e5rate_SFT	tsavage68	2024-05-29T13:30:17Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T13:22:01Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: UTI_L3_50steps_1e5rate_SFT 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. --> # UTI_L3_50steps_1e5rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.3873 \| 0.3333 \| 25 \| 2.0225 \| \| 1.9677 \| 0.6667 \| 50 \| 1.9780 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
Mohamed-Ahmed161/llama-3-8b-Instruct-bnb-lora-MedicalQnADataset	Mohamed-Ahmed161	2024-05-29T13:28:29Z	0	1	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:28:17Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: Mohamed-Ahmed161 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vuongnhathien/convnext-tiny-upgrade-384-batch-32	vuongnhathien	2024-05-29T13:28:20Z	192	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnextv2-tiny-22k-384", "base_model:finetune:facebook/convnextv2-tiny-22k-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-29T10:30:33Z	--- license: apache-2.0 base_model: facebook/convnextv2-tiny-22k-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnext-tiny-upgrade-384-batch-32 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9297619047619048 --- <!-- 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. --> # convnext-tiny-upgrade-384-batch-32 This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2521 - Accuracy: 0.9298 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9343 \| 1.0 \| 550 \| 0.5732 \| 0.8410 \| \| 0.6456 \| 2.0 \| 1100 \| 0.4130 \| 0.8843 \| \| 0.5478 \| 3.0 \| 1650 \| 0.3537 \| 0.9026 \| \| 0.466 \| 4.0 \| 2200 \| 0.3012 \| 0.9181 \| \| 0.4619 \| 5.0 \| 2750 \| 0.3031 \| 0.9141 \| \| 0.4046 \| 6.0 \| 3300 \| 0.2971 \| 0.9157 \| \| 0.3852 \| 7.0 \| 3850 \| 0.2763 \| 0.9205 \| \| 0.3346 \| 8.0 \| 4400 \| 0.2712 \| 0.9225 \| \| 0.3386 \| 9.0 \| 4950 \| 0.2672 \| 0.9221 \| \| 0.3462 \| 10.0 \| 5500 \| 0.2655 \| 0.9245 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
scenario-labs/DreamShaper_V8	scenario-labs	2024-05-29T13:26:56Z	30	0	diffusers	[ "diffusers", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-29T13:26:56Z	--- library_name: diffusers --- Converted from [DreamShaper](https://civitai.com/models/4384/dreamshaper?modelVersionId=128713)
lllyasviel/omost-dolphin-2.9-llama3-8b-4bits	lllyasviel	2024-05-29T13:26:47Z	224	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pytorch", "trl", "sft", "conversational", "autotrain_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T13:23:44Z	--- tags: - pytorch - trl - sft inference: false --- omost-dolphin-2.9-llama3-8b-4bits is Omost's llama3-8b model with dolphin-2.9 instruct pretraining in nf4.
Likich/mistral-finetune-qualcoding_1000_prompt3_dot	Likich	2024-05-29T13:26:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:25:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Felladrin/gguf-sharded-internlm2-chat-1_8b	Felladrin	2024-05-29T13:19:55Z	1	0	null	[ "gguf", "base_model:internlm/internlm2-chat-1_8b", "base_model:quantized:internlm/internlm2-chat-1_8b", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T13:13:56Z	--- license: other base_model: internlm/internlm2-chat-1_8b --- Sharded GGUF version of [internlm/internlm2-chat-1_8b](https://huggingface.co/internlm/internlm2-chat-1_8b).
Mohamed-Ahmed161/llama-3-8b-Instruct-bnb-gguf-MedicalQnADataset	Mohamed-Ahmed161	2024-05-29T13:19:30Z	22	3	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T13:17:31Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: Mohamed-Ahmed161 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Felladrin/gguf-internlm2-chat-1_8b	Felladrin	2024-05-29T13:19:27Z	10	0	null	[ "gguf", "base_model:internlm/internlm2-chat-1_8b", "base_model:quantized:internlm/internlm2-chat-1_8b", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T12:54:14Z	--- license: other base_model: internlm/internlm2-chat-1_8b --- GGUF version of [internlm/internlm2-chat-1_8b](https://huggingface.co/internlm/internlm2-chat-1_8b).
scenario-labs/epiCRealism_Natural_Sin_RC1_VAE	scenario-labs	2024-05-29T13:19:03Z	47	0	diffusers	[ "diffusers", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-29T12:46:26Z	--- library_name: diffusers --- Converted from [epiCRealism](https://civitai.com/models/25694?modelVersionId=143906)
Mattis0525/distilbert-base-uncased-finetuned-cyber	Mattis0525	2024-05-29T13:16:02Z	79	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-29T09:46:32Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: Mattis0525/distilbert-base-uncased-finetuned-cyber results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Mattis0525/distilbert-base-uncased-finetuned-cyber 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: - Train Loss: 2.6540 - Validation Loss: 2.4650 - Epoch: 10 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -982, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.3168 \| 3.1868 \| 0 \| \| 3.1896 \| 3.0149 \| 1 \| \| 3.1287 \| 2.8974 \| 2 \| \| 3.0181 \| 2.8744 \| 3 \| \| 2.8779 \| 2.8997 \| 4 \| \| 2.8575 \| 2.6046 \| 5 \| \| 2.8055 \| 2.6532 \| 6 \| \| 2.7372 \| 2.5089 \| 7 \| \| 2.6682 \| 2.3880 \| 8 \| \| 2.6563 \| 2.4646 \| 9 \| \| 2.6540 \| 2.4650 \| 10 \| ### Framework versions - Transformers 4.41.1 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
QuantFactory/Mistral-7B-Instruct-DPO-GGUF	QuantFactory	2024-05-29T13:15:59Z	92	1	transformers	[ "transformers", "gguf", "text-generation", "arxiv:2405.14734", "base_model:princeton-nlp/Mistral-7B-Instruct-DPO", "base_model:quantized:princeton-nlp/Mistral-7B-Instruct-DPO", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-29T12:24:26Z	--- library_name: transformers pipeline_tag: text-generation base_model: princeton-nlp/Mistral-7B-Instruct-DPO --- # QuantFactory/Mistral-7B-Instruct-DPO-GGUF This is quantized version of [princeton-nlp/Mistral-7B-Instruct-DPO](https://huggingface.co/princeton-nlp/Mistral-7B-Instruct-DPO) created using llama.cpp # Model Description This is a model released from the preprint: [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734) Please refer to our [repository](https://github.com/princeton-nlp/SimPO) for more details.
hao1306/lossnew	hao1306	2024-05-29T13:14:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-29T13:14:36Z	--- license: apache-2.0 ---
PraveenSankar/nvidia_hf_embed_model	PraveenSankar	2024-05-29T13:14:38Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2024-05-29T13:13:39Z	--- license: apache-2.0 language: - en ---
raphael-ich/Taxi-v3-Q-Learning	raphael-ich	2024-05-29T13:13:19Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T13:13:17Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-Q-Learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.69 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="raphael-ich/Taxi-v3-Q-Learning", 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"]) ```
Mariah64/distilbert-base-uncased-mlflow	Mariah64	2024-05-29T13:11:29Z	70	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-29T11:51:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: Mariah64/distilbert-base-uncased-mlflow results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Mariah64/distilbert-base-uncased-mlflow 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: - Train Loss: 5.9418 - Train End Logits Accuracy: 0.0 - Train Start Logits Accuracy: 0.0 - Validation Loss: 5.9506 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.006, 'decay_steps': 6, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 5.9601 \| 0.0 \| 0.0 \| 5.9506 \| 0.0 \| 0.0 \| 0 \| \| 5.9418 \| 0.0 \| 0.0 \| 5.9506 \| 0.0 \| 0.0 \| 1 \| ### Framework versions - Transformers 4.39.3 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
raphael-ich/q-FrozenLake-v1-4x4-noSlippery	raphael-ich	2024-05-29T13:10:09Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T13:10:06Z	--- 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="raphael-ich/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"]) ```
Nared45/llama-2-7b-question-dialogue	Nared45	2024-05-29T13:07:58Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T04:11:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Likich/falcon-finetune-qualcoding_1000_prompt3_dot	Likich	2024-05-29T13:07:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T13:07:11Z	--- 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. 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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lgk03/WITHINAPPS_NDD-phoenix_test-content	lgk03	2024-05-29T13:07:02Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T12:57:59Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-phoenix_test-content 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. --> # WITHINAPPS_NDD-phoenix_test-content This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0228 - Accuracy: 0.9964 - F1: 0.9964 - Precision: 0.9964 - Recall: 0.9964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 70 \| 0.0248 \| 0.9964 \| 0.9964 \| 0.9964 \| 0.9964 \| \| No log \| 2.0 \| 140 \| 0.0228 \| 0.9964 \| 0.9964 \| 0.9964 \| 0.9964 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ninjaneural/IDM-VTON	ninjaneural	2024-05-29T13:06:06Z	8	0	diffusers	[ "diffusers", "safetensors", "license:cc-by-nc-sa-4.0", "diffusers:StableDiffusionXLInpaintPipeline", "region:us" ]	image-to-image	2024-05-29T11:12:19Z	--- license: cc-by-nc-sa-4.0 ---

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