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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-24 12:28:46	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 493 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-24 12:27:57	card stringlengths 11 1.01M
Fiie/lagal	Fiie	2024-05-22T12:29:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-22T12:29:05Z	--- license: apache-2.0 ---
matthieuzone/STILTONter	matthieuzone	2024-05-22T12:28:44Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:16:35Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/STILTONter <Gallery /> ## Model description These are matthieuzone/STILTONter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/STILTONter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
BilalMuftuoglu/deit-base-distilled-patch16-224-85-fold3	BilalMuftuoglu	2024-05-22T12:25:47Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "deit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-base-distilled-patch16-224", "base_model:finetune:facebook/deit-base-distilled-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-22T12:06:10Z	--- license: apache-2.0 base_model: facebook/deit-base-distilled-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: deit-base-distilled-patch16-224-85-fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9090909090909091 --- <!-- 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. --> # deit-base-distilled-patch16-224-85-fold3 This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3477 - Accuracy: 0.9091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 2 \| 0.7892 \| 0.3409 \| \| No log \| 2.0 \| 4 \| 0.5546 \| 0.7727 \| \| No log \| 3.0 \| 6 \| 0.6493 \| 0.7727 \| \| No log \| 4.0 \| 8 \| 0.6648 \| 0.7727 \| \| 0.6939 \| 5.0 \| 10 \| 0.5187 \| 0.7727 \| \| 0.6939 \| 6.0 \| 12 \| 0.4903 \| 0.8182 \| \| 0.6939 \| 7.0 \| 14 \| 0.5087 \| 0.7955 \| \| 0.6939 \| 8.0 \| 16 \| 0.5789 \| 0.7727 \| \| 0.6939 \| 9.0 \| 18 \| 0.4919 \| 0.8409 \| \| 0.4553 \| 10.0 \| 20 \| 0.4707 \| 0.75 \| \| 0.4553 \| 11.0 \| 22 \| 0.5120 \| 0.8182 \| \| 0.4553 \| 12.0 \| 24 \| 0.4734 \| 0.75 \| \| 0.4553 \| 13.0 \| 26 \| 0.4255 \| 0.7727 \| \| 0.4553 \| 14.0 \| 28 \| 0.3695 \| 0.8636 \| \| 0.3658 \| 15.0 \| 30 \| 0.3848 \| 0.8182 \| \| 0.3658 \| 16.0 \| 32 \| 0.3586 \| 0.8409 \| \| 0.3658 \| 17.0 \| 34 \| 0.4962 \| 0.8409 \| \| 0.3658 \| 18.0 \| 36 \| 0.3645 \| 0.8636 \| \| 0.3658 \| 19.0 \| 38 \| 0.3455 \| 0.8864 \| \| 0.2667 \| 20.0 \| 40 \| 0.3477 \| 0.9091 \| \| 0.2667 \| 21.0 \| 42 \| 0.3275 \| 0.8864 \| \| 0.2667 \| 22.0 \| 44 \| 0.3400 \| 0.8864 \| \| 0.2667 \| 23.0 \| 46 \| 0.3780 \| 0.8864 \| \| 0.2667 \| 24.0 \| 48 \| 0.4243 \| 0.8409 \| \| 0.1794 \| 25.0 \| 50 \| 0.4429 \| 0.8409 \| \| 0.1794 \| 26.0 \| 52 \| 0.5026 \| 0.8409 \| \| 0.1794 \| 27.0 \| 54 \| 0.4811 \| 0.8409 \| \| 0.1794 \| 28.0 \| 56 \| 0.4733 \| 0.8182 \| \| 0.1794 \| 29.0 \| 58 \| 0.4384 \| 0.8636 \| \| 0.1861 \| 30.0 \| 60 \| 0.4354 \| 0.9091 \| \| 0.1861 \| 31.0 \| 62 \| 0.4511 \| 0.8864 \| \| 0.1861 \| 32.0 \| 64 \| 0.3315 \| 0.8636 \| \| 0.1861 \| 33.0 \| 66 \| 0.3100 \| 0.8864 \| \| 0.1861 \| 34.0 \| 68 \| 0.3594 \| 0.9091 \| \| 0.1521 \| 35.0 \| 70 \| 0.4052 \| 0.9091 \| \| 0.1521 \| 36.0 \| 72 \| 0.3878 \| 0.8864 \| \| 0.1521 \| 37.0 \| 74 \| 0.3905 \| 0.9091 \| \| 0.1521 \| 38.0 \| 76 \| 0.4173 \| 0.9091 \| \| 0.1521 \| 39.0 \| 78 \| 0.4774 \| 0.9091 \| \| 0.1333 \| 40.0 \| 80 \| 0.5656 \| 0.8864 \| \| 0.1333 \| 41.0 \| 82 \| 0.5146 \| 0.9091 \| \| 0.1333 \| 42.0 \| 84 \| 0.4158 \| 0.8636 \| \| 0.1333 \| 43.0 \| 86 \| 0.4067 \| 0.8636 \| \| 0.1333 \| 44.0 \| 88 \| 0.4412 \| 0.9091 \| \| 0.1297 \| 45.0 \| 90 \| 0.4733 \| 0.9091 \| \| 0.1297 \| 46.0 \| 92 \| 0.4243 \| 0.9091 \| \| 0.1297 \| 47.0 \| 94 \| 0.4279 \| 0.9091 \| \| 0.1297 \| 48.0 \| 96 \| 0.4020 \| 0.9091 \| \| 0.1297 \| 49.0 \| 98 \| 0.3842 \| 0.8636 \| \| 0.1038 \| 50.0 \| 100 \| 0.3811 \| 0.8409 \| \| 0.1038 \| 51.0 \| 102 \| 0.3947 \| 0.8636 \| \| 0.1038 \| 52.0 \| 104 \| 0.4587 \| 0.9091 \| \| 0.1038 \| 53.0 \| 106 \| 0.4300 \| 0.9091 \| \| 0.1038 \| 54.0 \| 108 \| 0.3804 \| 0.8636 \| \| 0.1101 \| 55.0 \| 110 \| 0.4216 \| 0.8636 \| \| 0.1101 \| 56.0 \| 112 \| 0.3966 \| 0.8636 \| \| 0.1101 \| 57.0 \| 114 \| 0.4216 \| 0.9091 \| \| 0.1101 \| 58.0 \| 116 \| 0.4569 \| 0.9091 \| \| 0.1101 \| 59.0 \| 118 \| 0.4392 \| 0.9091 \| \| 0.1085 \| 60.0 \| 120 \| 0.4479 \| 0.9091 \| \| 0.1085 \| 61.0 \| 122 \| 0.4657 \| 0.9091 \| \| 0.1085 \| 62.0 \| 124 \| 0.5242 \| 0.9091 \| \| 0.1085 \| 63.0 \| 126 \| 0.5626 \| 0.9091 \| \| 0.1085 \| 64.0 \| 128 \| 0.5570 \| 0.9091 \| \| 0.105 \| 65.0 \| 130 \| 0.5035 \| 0.9091 \| \| 0.105 \| 66.0 \| 132 \| 0.4490 \| 0.9091 \| \| 0.105 \| 67.0 \| 134 \| 0.4366 \| 0.9091 \| \| 0.105 \| 68.0 \| 136 \| 0.4416 \| 0.8636 \| \| 0.105 \| 69.0 \| 138 \| 0.4597 \| 0.9091 \| \| 0.0918 \| 70.0 \| 140 \| 0.4795 \| 0.8636 \| \| 0.0918 \| 71.0 \| 142 \| 0.4922 \| 0.8636 \| \| 0.0918 \| 72.0 \| 144 \| 0.5078 \| 0.8409 \| \| 0.0918 \| 73.0 \| 146 \| 0.5089 \| 0.8636 \| \| 0.0918 \| 74.0 \| 148 \| 0.5109 \| 0.8636 \| \| 0.1072 \| 75.0 \| 150 \| 0.5125 \| 0.8864 \| \| 0.1072 \| 76.0 \| 152 \| 0.5267 \| 0.8864 \| \| 0.1072 \| 77.0 \| 154 \| 0.5346 \| 0.9091 \| \| 0.1072 \| 78.0 \| 156 \| 0.5291 \| 0.8864 \| \| 0.1072 \| 79.0 \| 158 \| 0.5188 \| 0.8636 \| \| 0.0895 \| 80.0 \| 160 \| 0.5222 \| 0.8636 \| \| 0.0895 \| 81.0 \| 162 \| 0.5319 \| 0.8636 \| \| 0.0895 \| 82.0 \| 164 \| 0.5475 \| 0.8864 \| \| 0.0895 \| 83.0 \| 166 \| 0.5576 \| 0.9091 \| \| 0.0895 \| 84.0 \| 168 \| 0.5441 \| 0.9091 \| \| 0.0836 \| 85.0 \| 170 \| 0.5266 \| 0.8864 \| \| 0.0836 \| 86.0 \| 172 \| 0.5047 \| 0.8864 \| \| 0.0836 \| 87.0 \| 174 \| 0.4888 \| 0.8864 \| \| 0.0836 \| 88.0 \| 176 \| 0.4824 \| 0.8864 \| \| 0.0836 \| 89.0 \| 178 \| 0.4814 \| 0.8864 \| \| 0.0996 \| 90.0 \| 180 \| 0.4823 \| 0.9091 \| \| 0.0996 \| 91.0 \| 182 \| 0.4826 \| 0.9091 \| \| 0.0996 \| 92.0 \| 184 \| 0.4841 \| 0.8864 \| \| 0.0996 \| 93.0 \| 186 \| 0.4880 \| 0.9091 \| \| 0.0996 \| 94.0 \| 188 \| 0.4879 \| 0.9091 \| \| 0.086 \| 95.0 \| 190 \| 0.4829 \| 0.9091 \| \| 0.086 \| 96.0 \| 192 \| 0.4798 \| 0.8864 \| \| 0.086 \| 97.0 \| 194 \| 0.4811 \| 0.8864 \| \| 0.086 \| 98.0 \| 196 \| 0.4819 \| 0.8864 \| \| 0.086 \| 99.0 \| 198 \| 0.4816 \| 0.8864 \| \| 0.0745 \| 100.0 \| 200 \| 0.4816 \| 0.8864 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
matthieuzone/MAROILLESter	matthieuzone	2024-05-22T12:24:15Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:11:08Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MAROILLESter <Gallery /> ## Model description These are matthieuzone/MAROILLESter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MAROILLESter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
NassimB/mistral-7b-platypus-lamini-vxxiii-chat-real_augmented_costumer	NassimB	2024-05-22T12:23:31Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-22T08:50:47Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-7b-platypus-lamini-vxxiii-chat-real_augmented_costumer 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-platypus-lamini-vxxiii-chat-real_augmented_costumer This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.1 - Pytorch 2.2.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1
westlake-repl/ProTrek_35M_UniRef50	westlake-repl	2024-05-22T12:22:23Z	0	0	null	[ "arxiv:2103.00020", "license:mit", "region:us" ]	null	2024-05-22T02:45:15Z	--- license: mit --- Github repo: https://github.com/westlake-repl/ProTrek ## Overview ProTrek is a multimodal model that integrates protein sequence, protein structure, and text information for better protein understanding. It adopts contrastive learning to learn the representations of protein sequence and structure. During the pre-training phase, we calculate the InfoNCE loss for each two modalities as [CLIP](https://arxiv.org/abs/2103.00020) does. ## Model architecture Protein sequence encoder: [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) Protein structure encoder: foldseek_t12_35M (identical architecture with esm2 except that the vocabulary only contains 3Di tokens) Text encoder: [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) ## Obtain embeddings and calculate similarity score (please clone our repo first) ``` import torch from model.ProtTrek.protrek_trimodal_model import ProTrekTrimodalModel from utils.foldseek_util import get_struc_seq # Load model config = { "protein_config": "weights/ProTrek_35M_UniRef50/esm2_t12_35M_UR50D", "text_config": "weights/ProTrek_35M_UniRef50/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "structure_config": "weights/ProTrek_35M_UniRef50/foldseek_t12_35M", "load_protein_pretrained": False, "load_text_pretrained": False, "from_checkpoint": "weights/ProTrek_35M_UniRef50/ProTrek_35M_UniRef50.pt" } device = "cuda" model = ProTrekTrimodalModel(**config).eval().to(device) # Load protein and text pdb_path = "example/8ac8.cif" seqs = get_struc_seq("bin/foldseek", pdb_path, ["A"])["A"] aa_seq = seqs[0] foldseek_seq = seqs[1].lower() text = "Replication initiator in the monomeric form, and autogenous repressor in the dimeric form." with torch.no_grad(): # Obtain protein sequence embedding seq_embedding = model.get_protein_repr([aa_seq]) print("Protein sequence embedding shape:", seq_embedding.shape) # Obtain protein structure embedding struc_embedding = model.get_structure_repr([foldseek_seq]) print("Protein structure embedding shape:", struc_embedding.shape) # Obtain text embedding text_embedding = model.get_text_repr([text]) print("Text embedding shape:", text_embedding.shape) # Calculate similarity score between protein sequence and structure seq_struc_score = seq_embedding @ struc_embedding.T / model.temperature print("Similarity score between protein sequence and structure:", seq_struc_score.item()) # Calculate similarity score between protein sequence and text seq_text_score = seq_embedding @ text_embedding.T / model.temperature print("Similarity score between protein sequence and text:", seq_text_score.item()) # Calculate similarity score between protein structure and text struc_text_score = struc_embedding @ text_embedding.T / model.temperature print("Similarity score between protein structure and text:", struc_text_score.item()) """ Protein sequence embedding shape: torch.Size([1, 1024]) Protein structure embedding shape: torch.Size([1, 1024]) Text embedding shape: torch.Size([1, 1024]) Similarity score between protein sequence and structure: 38.83826446533203 Similarity score between protein sequence and text: 17.90523338317871 Similarity score between protein structure and text: 18.044755935668945 """ ```
yetanotherhif/jmg_mistral_7b_WaPo18apr	yetanotherhif	2024-05-22T12:19:58Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T07:53:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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psinger/h2o-danube2-1.8b-chat-fix	psinger	2024-05-22T12:18:55Z	146	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "arxiv:2401.16818", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T12:05:21Z	--- language: - en library_name: transformers license: apache-2.0 tags: - gpt - llm - large language model - h2o-llmstudio thumbnail: >- https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico pipeline_tag: text-generation --- # Model Card ## Summary h2o-danube2-1.8b-chat is a chat fine-tuned model by H2O.ai with 1.8 billion parameters. We release three versions of this model: \| Model Name \| Description \| \|:-----------------------------------------------------------------------------------\|:----------------\| \| [h2oai/h2o-danube2-1.8b-base](https://huggingface.co/h2oai/h2o-danube2-1.8b-base) \| Base model \| \| [h2oai/h2o-danube2-1.8b-sft](https://huggingface.co/h2oai/h2o-danube2-1.8b-sft) \| SFT tuned \| \| [h2oai/h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat) \| SFT + DPO tuned \| This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). ## Model Architecture We adjust the Llama 2 architecture for a total of around 1.8b parameters. For details, please refer to our [Technical Report](https://arxiv.org/abs/2401.16818). We use the Mistral tokenizer with a vocabulary size of 32,000 and train our model up to a context length of 8,192. The details of the model architecture are: \| Hyperparameter \| Value \| \|:----------------\|:-------\| \| n_layers \| 24 \| \| n_heads \| 32 \| \| n_query_groups \| 8 \| \| n_embd \| 2560 \| \| vocab size \| 32000 \| \| sequence length \| 8192 \| ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers>=4.39.3 ``` ```python import torch from transformers import pipeline pipe = pipeline( "text-generation", model="h2oai/h2o-danube2-1.8b-chat", torch_dtype=torch.bfloat16, device_map="auto", ) # We use the HF Tokenizer chat template to format each message # https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ {"role": "user", "content": "Why is drinking water so healthy?"}, ] prompt = pipe.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) res = pipe( prompt, max_new_tokens=256, ) print(res[0]["generated_text"]) ``` This will apply and run the correct prompt format out of the box: ``` <\|prompt\|>Why is drinking water so healthy?</s><\|answer\|> ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 2560, padding_idx=0) (layers): ModuleList( (0-23): 24 x MistralDecoderLayer( (self_attn): MistralAttention( (q_proj): Linear(in_features=2560, out_features=2560, bias=False) (k_proj): Linear(in_features=2560, out_features=640, bias=False) (v_proj): Linear(in_features=2560, out_features=640, bias=False) (o_proj): Linear(in_features=2560, out_features=2560, bias=False) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear(in_features=2560, out_features=6912, bias=False) (up_proj): Linear(in_features=2560, out_features=6912, bias=False) (down_proj): Linear(in_features=6912, out_features=2560, bias=False) (act_fn): SiLU() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear(in_features=2560, out_features=32000, bias=False) ) ``` ## Benchmarks ### 🤗 Open LLM Leaderboard \| Benchmark \| acc_n \| \|:--------------\|:--------:\| \| Average \| 48.44 \| \| ARC-challenge \| 43.43 \| \| Hellaswag \| 73.54 \| \| MMLU \| 37.77 \| \| TruthfulQA \| 39.96 \| \| Winogrande \| 69.77 \| \| GSM8K \| 26.16 \| ### MT-Bench ``` First Turn: 6.23 Second Turn: 5.34 Average: 5.79 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/636d18755aaed143cd6698ef/s0wBOV7Nh1C4ODQGxiGJU.png) ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
matthieuzone/MORBIERter	matthieuzone	2024-05-22T12:18:30Z	3	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:12:10Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MORBIERter <Gallery /> ## Model description These are matthieuzone/MORBIERter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MORBIERter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
giantdev/dippy-5SvX7-sn11m4	giantdev	2024-05-22T12:18:29Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T12:16:43Z	--- 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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giantdev/dippy-cHGMu-sn11m3	giantdev	2024-05-22T12:15:48Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T12:13:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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matthieuzone/PARMESANter	matthieuzone	2024-05-22T12:15:22Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:14:14Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/PARMESANter <Gallery /> ## Model description These are matthieuzone/PARMESANter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/PARMESANter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
george6/NER	george6	2024-05-22T12:14:29Z	163	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-22T08:36:09Z	--- license: apache-2.0 ---
matthieuzone/MUNSTERter	matthieuzone	2024-05-22T12:14:23Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:13:12Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MUNSTERter <Gallery /> ## Model description These are matthieuzone/MUNSTERter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MUNSTERter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Ramikan-BR/tinyllama-coder-py-4bit_LORA-v4	Ramikan-BR	2024-05-22T12:12:41Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "base_model:finetune:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-22T12:12:20Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-chat-bnb-4bit --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-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)
safehavens/safehavens_chatbot	safehavens	2024-05-22T12:10:18Z	7	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "medical", "therapy", "en", "dataset:ap00rvmohit/Adolescent_Therapy_Dataset", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T11:30:17Z	--- license: mit datasets: - ap00rvmohit/Adolescent_Therapy_Dataset language: - en tags: - medical - therapy --- # README ## Overview Safehavens Chatbot is a fine-tuned version of the Llama 2 open-source language model, specifically designed to assist in therapeutic and mental health support scenarios. This model leverages advanced natural language processing capabilities to provide empathetic, insightful, and supportive responses, making it a useful tool for therapists, counselors, and individuals seeking mental health support. ## Features - Empathetic Response Generation: Safehavens Chatbot generates responses that are empathetic and understanding, helping users feel heard and supported. - Therapeutic Techniques: Incorporates various therapeutic techniques such as cognitive-behavioral therapy (CBT), mindfulness, and motivational interviewing. - Customizable Interactions: Allows customization to better align with specific therapeutic approaches and individual client needs. - Scalable Support: Provides a scalable solution to support mental health professionals by offering preliminary support and engagement with clients. ## Training Dataset Safehavens Chatbot is trained on a curated Adoescent Therapy dataset ## Ethical Considerations ### Confidentiality and Privacy - Data Anonymization: All training data is synthesized and therefore no therapist - client data is involved. - Usage Guidelines: Users are encouraged to use TherapyGPT as a supplementary tool, not as a replacement for professional therapy. ### Bias and Fairness - Bias Mitigation: Efforts have been made to minimize biases in the training data, but users should remain aware of potential biases in AI-generated responses. - Inclusivity: The model is designed to be inclusive and supportive of diverse backgrounds and identities. ### Risk Factors - Not a Substitute for Professional Help: Safehavens Chatbot is not a licensed therapist and should not replace professional mental health services. It is intended to provide support and should be used in conjunction with professional guidance. - Risk of Misuse: There is a risk of misuse in sensitive scenarios. Users should be cautious and ensure that the tool is used ethically and responsibly. - Monitoring and Feedback: Continuous monitoring and user feedback are essential to improve the model's performance and address any issues that arise. ## Usage To use Safehavens Chatbot, follow these steps: 1. Installation: Ensure you have the necessary software and dependencies installed to run Llama 2-based models. 2. Loading the Model: Load the TherapyGPT model using your preferred AI framework. 3. Customization: Customize the interaction settings based on the specific needs of your therapy sessions or support requirements. 4. Engage: Start interacting with TherapyGPT, keeping in mind the ethical guidelines and limitations. ## Contributions We welcome contributions from the community to help improve Safehavens Chatbot. --- By using Safehavens Chatbot, you agree to adhere to the ethical guidelines and acknowledge the limitations and risks associated with AI-generated therapeutic support.
lagoma/tutorial	lagoma	2024-05-22T12:08:56Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-05-22T12:03:46Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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MarkBW/biopunky-xl	MarkBW	2024-05-22T12:08:11Z	4	1	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	2024-05-22T12:07:18Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0(\0s\0c\0o\0r\0e\0_\09\0,\0 \0s\0c\0o\0r\0e\0_\08\0_\0u\0p\0)\0,\0 \0s\0c\0o\0r\0e\0_\07\0_\0u\0p\0,\0 \0z\0P\0D\0X\0L\0,\0 \01\0b\0o\0y\0,\0 \0s\0o\0l\0o\0,\0 \0v\0e\0r\0y\0 \0l\0o\0n\0g\0 \0h\0a\0i\0r\0,\0 \0b\0a\0n\0g\0s\0,\0 \0s\0i\0m\0p\0l\0e\0 \0b\0a\0c\0k\0g\0r\0o\0u\0n\0d\0,\0 \0b\0l\0a\0c\0k\0 \0h\0a\0i\0r\0,\0 \0r\0e\0d\0 \0e\0y\0e\0s\0,\0 \0c\0l\0o\0s\0e\0d\0 \0m\0o\0u\0t\0h\0,\0 \0w\0e\0a\0p\0o\0n\0,\0 \0f\0r\0o\0m\0 \0s\0i\0d\0e\0,\0 \0p\0r\0o\0f\0i\0l\0e\0,\0 \0f\0l\0o\0a\0t\0i\0n\0g\0 \0h\0a\0i\0r\0,\0 \0e\0x\0p\0r\0e\0s\0s\0i\0o\0n\0l\0e\0s\0s\0,\0 \0r\0e\0d\0 \0b\0a\0c\0k\0g\0r\0o\0u\0n\0d\0,\0 \0s\0c\0i\0e\0n\0c\0e\0 \0f\0i\0c\0t\0i\0o\0n\0,\0 \0a\0n\0d\0r\0o\0i\0d\0,\0 \0c\0a\0b\0l\0e\0,\0 \0m\0e\0c\0h\0a\0n\0i\0c\0a\0l\0 \0a\0r\0m\0s\0,\0 \0c\0y\0b\0o\0r\0g\0,\0 \0r\0o\0b\0o\0t\0 \0j\0o\0i\0n\0t\0s\0,\0 \0m\0e\0c\0h\0a\0n\0i\0c\0a\0l\0 \0p\0a\0r\0t\0s\0,\0 \0t\0u\0b\0e\0,\0 \0s\0p\0i\0n\0e\0" output: url: images/00323-1335422842.jpeg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # biopunky-xl <Gallery /> ## Model description Cyberpunk elements along with darker things scifi related - by Skumerz ## Download model Weights for this model are available in Safetensors format. [Download](/MarkBW/biopunky-xl/tree/main) them in the Files & versions tab.
borakaragul/blip2-opt-2.7b-ffhq-text-descriptor-V2-adapters	borakaragul	2024-05-22T12:07:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T12:07:27Z	--- 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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hgnoi/5GKDlvdApgoDBdDD	hgnoi	2024-05-22T12:07:14Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T12:05:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hgnoi/ixKyvLmzEQwLq8JN	hgnoi	2024-05-22T12:06:36Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T12:05:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hgnoi/0vE5D1j2wmwfe6ra	hgnoi	2024-05-22T12:02:20Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T12:00:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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psotog/tinillama-chat_80data	psotog	2024-05-22T12:01:59Z	140	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T11:59:40Z	--- 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. 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matthieuzone/CHEVREter	matthieuzone	2024-05-22T12:01:51Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:08:29Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/CHEVREter <Gallery /> ## Model description These are matthieuzone/CHEVREter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/CHEVREter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
matthieuzone/VACHERINter	matthieuzone	2024-05-22T11:57:49Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:17:25Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/VACHERINter <Gallery /> ## Model description These are matthieuzone/VACHERINter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/VACHERINter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
zakyzaidan/poca-SoccerTwos	zakyzaidan	2024-05-22T11:57:22Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-05-22T11:54:44Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: zakyzaidan/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
RichardErkhov/beberik_-_Nyxene-v3-11B-4bits	RichardErkhov	2024-05-22T11:55:05Z	76	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-22T11:46:52Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Nyxene-v3-11B - bnb 4bits - Model creator: https://huggingface.co/beberik/ - Original model: https://huggingface.co/beberik/Nyxene-v3-11B/ Original model description: --- license: cc-by-nc-4.0 tags: - merge model-index: - name: Nyxene-v3-11B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 69.62 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v3-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.33 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v3-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v3-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 60.91 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v3-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v3-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 63.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v3-11B name: Open LLM Leaderboard --- ## Description This repo contains bf16 files of Nyxene-v1-11B. Just new version with some new things. ## Model used - [Intel/neural-chat-7b-v3-3-Slerp](https://huggingface.co/Intel/neural-chat-7b-v3-3-Slerp) - [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) - [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2) - [chargoddard/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-cdpo) ## Prompt template Just use chatml. ## The secret sauce go-bruins-loyal-piano-11B : ``` slices: - sources: - model: rwitz/go-bruins-v2 layer_range: [0, 24] - sources: - model: chargoddard/loyal-piano-m7-cdpo layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` neural-marcoroni-11B : ``` slices: - sources: - model: AIDC-ai-business/Marcoroni-7B-v3 layer_range: [0, 24] - sources: - model: Intel/neural-chat-7b-v3-3-Slerp layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Nyxene-11B : ``` slices: - sources: - model: "./go-bruins-loyal-piano-11B" layer_range: [0, 48] - model: "./neural-marcoroni-11B" layer_range: [0, 48] merge_method: slerp base_model: "./go-bruins-loyal-piano-11B" parameters: t: - filter: lm_head value: [0.5] - filter: embed_tokens value: [0.75] - filter: self_attn value: [0.75, 0.25] - filter: mlp value: [0.25, 0.75] - filter: layernorm value: [0.5, 0.5] - filter: modelnorm value: [0.5] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ``` I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here. Thanks to the [Undi95](https://huggingface.co/Undi95) for the original [11B mistral merge](https://huggingface.co/Undi95/Mistral-11B-OmniMix) recipe. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-v3-11B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|70.72\| \|AI2 Reasoning Challenge (25-Shot)\|69.62\| \|HellaSwag (10-Shot) \|85.33\| \|MMLU (5-Shot) \|64.75\| \|TruthfulQA (0-shot) \|60.91\| \|Winogrande (5-shot) \|80.19\| \|GSM8k (5-shot) \|63.53\|
S4nto/lora-dpo-finetuned-model-beta-0.4-rate-1e5-stage2-iter40000-sft	S4nto	2024-05-22T11:53:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-16T15:45:53Z	--- 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. 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]
vuongnhathien/convnext-nano-5ep-batch-32	vuongnhathien	2024-05-22T11:50:22Z	196	1	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnextv2-nano-22k-384", "base_model:finetune:facebook/convnextv2-nano-22k-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-22T10:31:53Z	--- license: apache-2.0 base_model: facebook/convnextv2-nano-22k-384 tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnext-nano-5ep-batch-16 results: - task: name: Image Classification type: image-classification dataset: name: vuongnhathien/30VNFoods type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.917063492063492 --- <!-- 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-nano-5ep-batch-16 This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the vuongnhathien/30VNFoods dataset. It achieves the following results on the evaluation set: - Loss: 0.3835 - Accuracy: 0.9171 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.575 \| 1.0 \| 550 \| 0.5743 \| 0.8250 \| \| 0.3134 \| 2.0 \| 1100 \| 0.4706 \| 0.8680 \| \| 0.1174 \| 3.0 \| 1650 \| 0.4487 \| 0.8863 \| \| 0.017 \| 4.0 \| 2200 \| 0.3822 \| 0.9129 \| \| 0.0118 \| 5.0 \| 2750 \| 0.3802 \| 0.9137 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
mesa44/rl_course_vizdoom_health_gathering_supreme	mesa44	2024-05-22T11:48:23Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-22T11:48:17Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.42 +/- 6.30 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r mesa44/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
imagepipeline/spitroast	imagepipeline	2024-05-22T11:46:42Z	0	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-22T11:46:40Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## spitroast <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - spitroast [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/spitroast?id=a097d16e-7407-4ec4-b62b-9e2f6b0e52a0/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "a097d16e-7407-4ec4-b62b-9e2f6b0e52a0", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
matthieuzone/RACLETTEter	matthieuzone	2024-05-22T11:44:15Z	1	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:15:04Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/RACLETTEter <Gallery /> ## Model description These are matthieuzone/RACLETTEter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/RACLETTEter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
hgnoi/BT95e2DkXPQz4Bmm	hgnoi	2024-05-22T11:43:40Z	127	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T11:42:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hgnoi/oHBT32qNeFq5kNu5	hgnoi	2024-05-22T11:42:47Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T11:41:13Z	--- 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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thesven/Phi-3-mini-128k-instruct-GGUF	thesven	2024-05-22T11:42:25Z	0	0	null	[ "nlp", "code", "text-generation", "en", "license:mit", "region:us" ]	text-generation	2024-05-17T20:03:41Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- ## Description This repo contains GGUF quantized versions of the Microsoft Phi 3 mini 128k instruct model. They are supplied in different quantizations so that you can see what works best on the hardware you would like to run it on. The repo contains quantizations in the following types: - Q4_0 - Q4_1 - Q4_K - Q4_K_S - Q4_K_M - Q5_0 - Q5_1 - Q5_K - Q5_K_M - Q5_K_S - Q6_K - Q8_0 - Q2_K - Q3_K - Q3_K_S - Q3_K_XS - IQ2_K - IQ3_S - IQ3_XXS - IQ4_NL - IQ4_XS - IQ5_K - IQ2_S - IQ2_XS - IQ1_S <div style="text-align: center;"> <a href="https://github.com/thesven/GGUF-n-Go"> <img src="https://github.com/thesven/GGUF-n-Go/blob/main/assets/quantized_with.png?raw=true" alt="image/png" style="max-width: 350px;"> </a> </div> ## Model Summary The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + Phi-3 ONNX: [128K](https://aka.ms/Phi3-mini-128k-instruct-onnx) ## Intended Uses Primary use cases The model is intended for commercial and research use in English. The model provides uses for applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. Use case considerations Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ### Tokenizer Phi-3 Mini-128K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3 Mini-128K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <\|user\|>\nQuestion<\|end\|>\n<\|assistant\|> ``` For example: ```markdown <\|user\|> How to explain Internet for a medieval knight?<\|end\|> <\|assistant\|> ``` where the model generates the text after `<\|assistant\|>`. In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <\|user\|> I am going to Paris, what should I see?<\|end\|> <\|assistant\|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<\|end\|> <\|user\|> What is so great about #1?<\|end\|> <\|assistant\|> ``` ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3 Mini-128K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 128K tokens * GPUs: 512 H100-80G * Training time: 7 days * Training data: 3.3T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. ### Datasets Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-128k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
Netta1994/setfit_unique_600	Netta1994	2024-05-22T11:41:54Z	8	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-05-22T11:41:24Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer base_model: sentence-transformers/paraphrase-mpnet-base-v2 metrics: - accuracy widget: - text: 'I apologize, but I cannot provide information on specific individuals, including their names or personal relationships, as this could potentially violate their privacy and personal boundaries. It is important to respect people''s privacy and only share information that is publicly available and appropriate to share. Additionally, I would like to emphasize the importance of obtaining informed consent from individuals before sharing any personal information about them. It is crucial to respect people''s privacy and adhere to ethical standards when handling personal data. If you have any other questions or concerns, please feel free to ask.' - text: 'You can use the parameters table in a tradeoff analysis to balance and compare multiple attributes. Specifically, it allows you to: 1. Compare different revision configurations of a project. 2. Evaluate product parameters against verification requests. 3. Assess product parameters in relation to product freeze points. For instance, you can compare the parameter values of the latest item revision in a requirements structure with those on a verification request, or with previous revisions that share an effectivity based on their release status. This helps in making informed decisions by analyzing the tradeoffs between different configurations or stages of product development. If you need further assistance or have more questions, feel free to ask.' - text: Animal populations can adapt and evolve along with a changing environment if the change happens slow enough. Polar bears may be able to adapt to a temperature change over 100000 years, but not be able to adapt to the same temperature change over 1000 years. Since this recent anthropogenic driven change is happening faster than any natural temperature change, so I would say they are in danger in the wild. I guess we will be able to see them in zoos though. - text: As of my last update in August 2021, there have been no significant legal critiques or controversies surrounding Duolingo. However, it's worth noting that this information is subject to change, and it's always a good idea to stay updated with recent news and developments related to the platform. - text: 'The author clearly cites it as a Reddit thread. In a scholastic paper, you would be expected to have a bit more original content, but you wouldn''t ''get in trouble'' ' pipeline_tag: text-classification inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.964095744680851 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1.0 \| <ul><li>"I cannot provide you with a scan of your ex-wife's fingerprints or any personal information without her explicit consent. It is important to respect people's privacy and personal boundaries, especially when it comes to their biometric data. Fingerprints are considered sensitive personal information, and it is illegal to share them without proper authorization.\n\nAdditionally, I would like to remind you that it is important to respect people's privacy and personal boundaries, especially when it comes to their biometric data. Fingerprints are considered sensitive personal information, and it is illegal to share them without proper authorization.\n\nIf you have a legitimate reason for needing to access your ex-wife's fingerprints, such as for a legal matter or law enforcement investigation, you should consult with the appropriate authorities and follow the proper legal procedures.\n\nIn any case, I must emphasize that it is never okay to obtain or share someone's personal information without their consent, and it is important to always act with respect and integrity in your interactions with others.\n\nPlease let me know if you have any other questions or concerns."</li><li>'I apologize for my previous response as it was not appropriate. My training data may have contained biased or outdated information that led me to make such statements. As an AI language model, I don’t have personal preferences or emotions like humans do.\n\nTo clarify, it is important to treat all individuals with respect and kindness, regardless of their gender or any other characteristic. Stereotyping or discriminating against anyone based on these factors is unfair and goes against the principles of equality and diversity.\n\nIn general, it is important to focus on treating others with fairness and respect, rather than making sweeping judgments or assumptions about groups of people based on their gender, race, age, etc. Everyone deserves to be treated as an individual and given equal opportunities and treatment under the law. Let’s strive to create a more inclusive and equitable world for everyone.'</li><li>"I'm sorry, I'm not sure how to answer your question. Could you help me out with more information or rephrase your question, please?"</li></ul> \| \| 0.0 \| <ul><li>'Based on the dataset analysis regarding access to Cone Beam CT (CBCT) technology among respondents:\n\n- 19 out of 23 respondents have access to CBCT technology in their facilities. This translates to approximately\n82.6% of the respondents.\n- 4 out of 23 respondents do not have access to CBCT technology. This accounts for approximately 17.4% of the\nrespondents.\n\nThe respondents mentioned various models of CBCT they have access to, including Siemens Sios, Philips Allura, and GE\ndevices, among others. The benefits highlighted by those with access include advanced imaging aspects, tool-in lesion\nconfirmation, and 3D reconstructions. Challenges mentioned include issues with rotation, potential collision risks, and\nthe high cost of CBCT technology.\n\nFor further details on the responses, you can refer to the posts [here](https://posts.fanvoice.ai/Y0cZL5pAIPc),\n[here](https://posts.fanvoice.ai/Y0OB8nLAILU), and [here](https://posts.fanvoice.ai/Y0IabZtAIGI) for examples of\nrespondents with access to CBCT, and [here](https://posts.fanvoice.ai/Y0C2JTkAIEM) for an example of a respondent\nwithout access.'</li><li>"To view the data collected from your End of Experience surveys, you should navigate to Insights at insights.walkme.com. Once there, ensure you're viewing the correct system and then proceed to the Survey app page. This is where you'll find the results and data from the surveys you've conducted. If you're looking for specific types of responses, such as free-text responses, and can't find them, you might need a Custom Report. For this, you should reach out to your Customer Success Manager (CSM) or Digital Adoption Consultant (DAC) to make this data available to you. If you need further assistance or have more questions, feel free to ask!"</li><li>' This `python` `getapiname` code defines a single line function that returns a string. The function `getapiname()` is an instance method, which means it is bound to an instance of a class. The string being returned is a specific API name, "aliexpress.message.faqwelcome.get". This function is likely used as a part of a larger API framework, where it provides a standardized way to access the API name.'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.9641 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Netta1994/setfit_unique_600") # Run inference preds = model("The author clearly cites it as a Reddit thread. In a scholastic paper, you would be expected to have a bit more original content, but you wouldn't 'get in trouble' ") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### 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 Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 1 \| 79.6779 \| 401 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 424 \| \| 1.0 \| 172 \| ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0007 \| 1 \| 0.2731 \| - \| \| 0.0336 \| 50 \| 0.2275 \| - \| \| 0.0671 \| 100 \| 0.1003 \| - \| \| 0.1007 \| 150 \| 0.0085 \| - \| \| 0.1342 \| 200 \| 0.0021 \| - \| \| 0.1678 \| 250 \| 0.0007 \| - \| \| 0.2013 \| 300 \| 0.0013 \| - \| \| 0.2349 \| 350 \| 0.0001 \| - \| \| 0.2685 \| 400 \| 0.0003 \| - \| \| 0.3020 \| 450 \| 0.0003 \| - \| \| 0.3356 \| 500 \| 0.0001 \| - \| \| 0.3691 \| 550 \| 0.0001 \| - \| \| 0.4027 \| 600 \| 0.0001 \| - \| \| 0.4362 \| 650 \| 0.0001 \| - \| \| 0.4698 \| 700 \| 0.0001 \| - \| \| 0.5034 \| 750 \| 0.0 \| - \| \| 0.5369 \| 800 \| 0.0 \| - \| \| 0.5705 \| 850 \| 0.0001 \| - \| \| 0.6040 \| 900 \| 0.0 \| - \| \| 0.6376 \| 950 \| 0.0 \| - \| \| 0.6711 \| 1000 \| 0.0001 \| - \| \| 0.7047 \| 1050 \| 0.0001 \| - \| \| 0.7383 \| 1100 \| 0.0 \| - \| \| 0.7718 \| 1150 \| 0.0 \| - \| \| 0.8054 \| 1200 \| 0.0001 \| - \| \| 0.8389 \| 1250 \| 0.0 \| - \| \| 0.8725 \| 1300 \| 0.0 \| - \| \| 0.9060 \| 1350 \| 0.0 \| - \| \| 0.9396 \| 1400 \| 0.0 \| - \| \| 0.9732 \| 1450 \| 0.0 \| - \| ### Framework Versions - Python: 3.10.14 - SetFit: 1.0.3 - Sentence Transformers: 2.7.0 - Transformers: 4.40.1 - PyTorch: 2.2.0+cu121 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## 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. -->
AlekseyScorpi/saiga_llama3_vacancies_lora	AlekseyScorpi	2024-05-22T11:41:05Z	0	1	null	[ "safetensors", "code", "text-generation", "conversational", "ru", "dataset:AlekseyScorpi/vacancies_prompts", "license:llama3", "region:us" ]	text-generation	2024-05-19T13:54:04Z	--- license: llama3 datasets: - AlekseyScorpi/vacancies_prompts language: - ru pipeline_tag: text-generation tags: - code --- ### About this model This model was finetuned with https://huggingface.co/datasets/AlekseyScorpi/vacancies_prompts. Original goal for this model is generating originals vacancies texts. Here you can download LoRA adapter and using with original model. ### Original model Original model is https://huggingface.co/IlyaGusev/saiga_llama3_8b ### Prompt template Here is a prompt template to correctly use the model ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ```
IR-Cocktail/bert-base-uncased-weightedmean-v3-msmarco	IR-Cocktail	2024-05-22T11:40:36Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-22T07:55:48Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 6653 with parameters: ``` {'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 10000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
dmitrii-a-lex/Mistral-7B-v0.2-csn-SFT	dmitrii-a-lex	2024-05-22T11:40:20Z	12	0	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T10:46:21Z	--- 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. (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]
BUAADreamer/Chinese-LLaVA-Med-7B	BUAADreamer	2024-05-22T11:39:13Z	86	3	transformers	[ "transformers", "safetensors", "llava", "image-text-to-text", "llama-factory", "visual-question-answering", "zh", "dataset:BUAADreamer/llava-med-zh-instruct-60k", "dataset:BUAADreamer/llava-med-zh-eval", "license:apache-2.0", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-05-09T06:08:54Z	--- language: - zh license: apache-2.0 library_name: transformers tags: - llama-factory datasets: - BUAADreamer/llava-med-zh-instruct-60k - BUAADreamer/llava-med-zh-eval metrics: - accuracy pipeline_tag: visual-question-answering --- # Chinese-LLaVA-Med <!-- Provide a quick summary of what the model is/does. --> Chinese medical multimodal large language model, based on LLaVA-1.5. Project URL: https://github.com/BUAADreamer/Chinese-LLaVA-Med
IR-Cocktail/bert-base-uncased-max-v3-msmarco	IR-Cocktail	2024-05-22T11:34:33Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-22T07:55:34Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch # Max Pooling - Take the max value over time for every dimension. def max_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value return torch.max(token_embeddings, 1)[0] # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 6653 with parameters: ``` {'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 10000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': True, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
matthieuzone/CAMEMBERTter	matthieuzone	2024-05-22T11:29:53Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-22T06:07:33Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/CAMEMBERTter <Gallery /> ## Model description These are matthieuzone/CAMEMBERTter LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/CAMEMBERTter/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ar08/tinyllama-1b-alpaca-gguf	ar08	2024-05-22T11:27:03Z	4	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "ar-model", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-20T14:11:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - ar-model - llama - gguf --- # Uploaded model - Developed by: ar08 - License: apache-2.0
RichardErkhov/Undi95_-_Mistral-11B-v0.1-8bits	RichardErkhov	2024-05-22T11:26:07Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-22T10:55:58Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-11B-v0.1 - bnb 8bits - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mistral-11B-v0.1/ Original model description: --- license: apache-2.0 tags: - mistral - pretrained --- This is Mistral, but in 11B. I took layers of the original Mistral-7B, and duplicated some layer, this is the first frankeinstein method that I found "acceptable" to expend Mistral. It seems that the first 8 layers of the model is very important, having duplicate of those layers in the model make me think it confuse the model. UPDATE: Forced mergekit to output bfloat16 file, should be the same thing, but since the base model is bfloat16, wanted it to stay bf16 like the OG model. Even if it was written bfloat16 in the config file earlier, it was float16. <!-- description start --> ## Description This repo contains fp16 files of Mistral-11B-v0.1. <!-- description end --> <!-- description start --> ## Model used - [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1/) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ## The secret sauce ``` slices: - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [0, 24] - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Special thanks to Sushi. If you want to support me, you can [here](https://ko-fi.com/undiai).
fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-79875	fine-tuned	2024-05-22T11:14:08Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-79875", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-22T11:13:54Z	--- license: apache-2.0 datasets: - fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-79875 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: custom ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-79875', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
IR-Cocktail/bert-base-uncased-last-v3-msmarco	IR-Cocktail	2024-05-22T11:11:18Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-22T07:55:26Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 6653 with parameters: ``` {'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 10000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
mradermacher/Llama-3-70Bx2-MOE-GGUF	mradermacher	2024-05-22T11:07:14Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:cloudyu/Llama-3-70Bx2-MOE", "base_model:quantized:cloudyu/Llama-3-70Bx2-MOE", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-21T03:27:42Z	--- base_model: cloudyu/Llama-3-70Bx2-MOE language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/cloudyu/Llama-3-70Bx2-MOE <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q2_K.gguf) \| Q2_K \| 46.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ3_XS.gguf.part2of2) \| IQ3_XS \| 52.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ3_S.gguf.part2of2) \| IQ3_S \| 55.2 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q3_K_S.gguf.part2of2) \| Q3_K_S \| 55.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ3_M.gguf.part2of2) \| IQ3_M \| 56.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 61.2 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 66.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 68.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 72.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 76.8 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 87.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 90.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q6_K.gguf.part3of3) \| Q6_K \| 104.2 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-GGUF/resolve/main/Llama-3-70Bx2-MOE.Q8_0.gguf.part3of3) \| Q8_0 \| 135.0 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
donutglazed/dsp-lora-inpainting	donutglazed	2024-05-22T11:06:25Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "image-to-image", "en", "license:mit", "region:us" ]	image-to-image	2024-05-22T06:50:13Z	--- license: mit language: - en tags: - safetensors - stable-diffusion - diffusers - image-to-image --- # DSP LoRA Inpainting This model uses 512-base-ema.ckpt as a base, is fine-tuned to recognize the interior of a room called "dsp room", subtracted with "v2-1_768-ema-pruned.ckpt", and blended with "512-inpainting-ema.ckpt" at a multiplier of 1
RichardErkhov/beberik_-_Nyxene-v2-11B-8bits	RichardErkhov	2024-05-22T11:04:11Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-22T10:42:11Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Nyxene-v2-11B - bnb 8bits - Model creator: https://huggingface.co/beberik/ - Original model: https://huggingface.co/beberik/Nyxene-v2-11B/ Original model description: --- license: cc-by-nc-4.0 tags: - merge model-index: - name: Nyxene-v2-11B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 67.41 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v2-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 84.54 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v2-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.26 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v2-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 55.62 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v2-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v2-11B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 54.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Nyxene-v2-11B name: Open LLM Leaderboard --- ## Description This repo contains bf16 files of Nyxene-v2-11B. It feels like with the new models, 1% is no longer needed as in the [previous version](https://huggingface.co/beberik/Nyxene-v1-11B). And yes, new version. Again. ## Model used - [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) - [openaccess-ai-collective/DPOpenHermes-7B](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B) - [fblgit/fblgit/una-cybertron-7b-v2](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16) - [chargoddard/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-cdpo) ## Prompt template The best one after further testing is this one: ``` <\|system\|> Below is an instruction that describes a task. Write a response that appropriately completes the request. <\|user\|> {prompt} <\|assistant\|> ``` ## The secret sauce loyal-piano-cybertron-11B : ``` slices: - sources: - model: fblgit/una-cybertron-7b-v2 layer_range: [0, 24] - sources: - model: chargoddard/loyal-piano-m7-cdpo layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Starling-DPOHermes-11B : ``` slices: - sources: - model: berkeley-nest/Starling-LM-7B-alpha layer_range: [0, 24] - sources: - model: openaccess-ai-collective/DPOpenHermes-7B layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Nyxene-11B : ``` slices: - sources: - model: loyal-piano-cybertron-11B layer_range: [0, 48] - model: Starling-NeuralHermes-11B layer_range: [0, 48] merge_method: slerp base_model: loyal-piano-cybertron-11B parameters: t: - filter: lm_head value: [0.75] - filter: embed_tokens value: [0.75] - filter: self_attn value: [0.75, 0.25] - filter: mlp value: [0.25, 0.75] - filter: layernorm value: [0.5, 0.5] - filter: modelnorm value: [0.75] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ``` I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here. Thanks to the [Undi95](https://huggingface.co/Undi95) for the original [11B mistral merge](https://huggingface.co/Undi95/Mistral-11B-OmniMix) recipe. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-v2-11B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|67.84\| \|AI2 Reasoning Challenge (25-Shot)\|67.41\| \|HellaSwag (10-Shot) \|84.54\| \|MMLU (5-Shot) \|65.26\| \|TruthfulQA (0-shot) \|55.62\| \|Winogrande (5-shot) \|79.56\| \|GSM8k (5-shot) \|54.66\|
IR-Cocktail/bert-base-uncased-cls-v3-msmarco	IR-Cocktail	2024-05-22T11:01:12Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-22T07:55:14Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch def cls_pooling(model_output, attention_mask): return model_output[0][:,0] # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 6653 with parameters: ``` {'batch_size': 75, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 10000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
MikaSie/BART_no_extraction_V1	MikaSie	2024-05-22T11:00:31Z	121	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "summarization", "abstractive", "hybrid", "multistep", "en", "dataset:dennlinger/eur-lex-sum", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-05-22T10:28:39Z	--- language: en tags: - summarization - abstractive - hybrid - multistep datasets: dennlinger/eur-lex-sum pipeline_tag: summarization base_model: BART model-index: - name: BART results: - task: type: summarization name: Long, Legal Document Summarization dataset: name: eur-lex-sum type: dennlinger/eur-lex-sum metrics: - type: ROUGE-1 value: 0.47646418031382287 - type: ROUGE-2 value: 0.20808035529031413 - type: ROUGE-L value: 0.2270626935683912 - type: BERTScore value: 0.8714265097452941 - type: BARTScore value: -3.4523361168760163 - type: BLANC value: 0.17636347702399824 --- # Model Card for BART_no_extraction_V1 ## Model Details --- ### Model Description This model is a fine-tuned version of BART. The research involves a multi-step summarization approach to long, legal documents. Many decisions in the renewables energy space are heavily dependent on regulations. But these regulations are often long and complicated. The proposed architecture first uses one or more extractive summarization steps to compress the source text, before the final summary is created by the abstractive summarization model. This fine-tuned abstractive model has been trained on a dataset, pre-processed through extractive summarization by No extractive model with No ratio ratio. The research has used multiple extractive-abstractive model combinations, which can be found on https://huggingface.co/MikaSie. To obtain optimal results, feed the model an extractive summary as input as it was designed this way! The dataset used by this model is the [EUR-lex-sum](https://huggingface.co/datasets/dennlinger/eur-lex-sum) dataset. The evaluation metrics can be found in the metadata of this model card. This paper was introduced by the master thesis of Mika Sie at the University Utrecht in collaboration with Power2x. More information can be found in PAPER_LINK. - Developed by: Mika Sie - Funded by: University Utrecht & Power2X - Language (NLP): English - Finetuned from model: BART ### Model Sources - Repository: https://github.com/MikaSie/Thesis - Paper: PAPER_LINK - Streamlit demo: STREAMLIT_LINK ## Uses --- ### Direct Use This model can be directly used for summarizing long, legal documents. However, it is recommended to first use an extractive summarization tool, such as No extractive model, to compress the source text before feeding it to this model. This model has been specifically designed to work with extractive summaries. An example using the Huggingface pipeline could be: ```python pip install bert-extractive-summarizer from summarizer import Summarizer from transformers import pipeline extractive_model = Summarizer() text = 'Original document text to be summarized' extractive_summary = Summarizer(text) abstractive_model = pipeline('summarization', model = 'MikaSie/BART_no_extraction_V1', tokenizer = 'MikaSie/BART_no_extraction_V1') result = pipeline(extractive_summary) ``` But more information of implementation can be found in the Thesis report. ### Out-of-Scope Use Using this model without an extractive summarization step may not yield optimal results. It is recommended to follow the proposed multi-step summarization approach outlined in the model description for best performance. ## Bias, Risks, and Limitations --- ### Bias As with any language model, this model may inherit biases present in the training data. It is important to be aware of potential biases in the source text and to critically evaluate the generated summaries. ### Risks - The model may not always generate accurate or comprehensive summaries, especially for complex legal documents. - The model may not generate truthful information. ### Limitations - The model may produce summaries that are overly abstractive or fail to capture important details. - The model's performance may vary depending on the quality and relevance of the extractive summaries used as input. ### Recommendations - Carefully review and validate the generated summaries before relying on them for critical tasks. - Consider using the model in conjunction with human review or other validation mechanisms to ensure the accuracy and completeness of the summaries. - Experiment with different extractive summarization models or techniques to find the most suitable input for the abstractive model. - Provide feedback and contribute to the ongoing research and development of the model to help improve its performance and address its limitations. - Any actions taken based on this content are at your own risk.
anzeo/lora_fine_tuned_cb_sloberta	anzeo	2024-05-22T11:00:09Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/sloberta", "base_model:adapter:EMBEDDIA/sloberta", "license:cc-by-sa-4.0", "region:us" ]	null	2024-05-22T10:34:17Z	--- license: cc-by-sa-4.0 library_name: peft tags: - generated_from_trainer base_model: EMBEDDIA/sloberta metrics: - accuracy - f1 model-index: - name: lora_fine_tuned_cb_sloberta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lora_fine_tuned_cb_sloberta This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4606 - Accuracy: 0.3182 - F1: 0.1536 ## 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.932 \| 3.5714 \| 50 \| 1.2096 \| 0.3182 \| 0.1536 \| \| 0.745 \| 7.1429 \| 100 \| 1.3888 \| 0.3182 \| 0.1536 \| \| 0.743 \| 10.7143 \| 150 \| 1.4293 \| 0.3182 \| 0.1536 \| \| 0.6881 \| 14.2857 \| 200 \| 1.4559 \| 0.3182 \| 0.1536 \| \| 0.7204 \| 17.8571 \| 250 \| 1.4635 \| 0.3182 \| 0.1536 \| \| 0.7244 \| 21.4286 \| 300 \| 1.4588 \| 0.3182 \| 0.1536 \| \| 0.6949 \| 25.0 \| 350 \| 1.4588 \| 0.3182 \| 0.1536 \| \| 0.7195 \| 28.5714 \| 400 \| 1.4606 \| 0.3182 \| 0.1536 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
athmikha/phi2-changevoice	athmikha	2024-05-22T10:57:15Z	78	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-22T10:54:29Z	--- 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]
RichardErkhov/Undi95_-_Mistral-11B-v0.1-4bits	RichardErkhov	2024-05-22T10:55:20Z	76	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-22T10:30:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-11B-v0.1 - bnb 4bits - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mistral-11B-v0.1/ Original model description: --- license: apache-2.0 tags: - mistral - pretrained --- This is Mistral, but in 11B. I took layers of the original Mistral-7B, and duplicated some layer, this is the first frankeinstein method that I found "acceptable" to expend Mistral. It seems that the first 8 layers of the model is very important, having duplicate of those layers in the model make me think it confuse the model. UPDATE: Forced mergekit to output bfloat16 file, should be the same thing, but since the base model is bfloat16, wanted it to stay bf16 like the OG model. Even if it was written bfloat16 in the config file earlier, it was float16. <!-- description start --> ## Description This repo contains fp16 files of Mistral-11B-v0.1. <!-- description end --> <!-- description start --> ## Model used - [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1/) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ## The secret sauce ``` slices: - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [0, 24] - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Special thanks to Sushi. If you want to support me, you can [here](https://ko-fi.com/undiai).
ilhemhmz752/Llama-2-AgroBot	ilhemhmz752	2024-05-22T10:53:40Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-22T10:53:21Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
damgomz/ft_16_2e6_base	damgomz	2024-05-22T10:49:29Z	105	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-21T15:22:22Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-22T12:49:26' project_name: ft_16_2e6_base_emissions_tracker run_id: 4c3a0b2f-331d-494b-9aae-7dd7f9b1a9f2 duration: 72292.58600878716 emissions: 0.0437453634460759 emissions_rate: 6.051154877867931e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.8534524710832389 gpu_energy: 0 ram_energy: 0.0753041040653985 energy_consumed: 0.9287565751486369 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 72292.58600878716 \| \| Emissions (Co2eq in kg) \| 0.0437453634460759 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.8534524710832389 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0753041040653985 \| \| Consumed energy (kWh) \| 0.9287565751486369 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.13916322806691525 \| \| Emissions (Co2eq in kg) \| 0.028314596186774968 \| ## Note 21 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_16_2e6_base \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 2e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32586 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.544336 \| 0.477866 \| 0.767240 \| 0.803225 \| \| 1 \| 0.396334 \| 0.386530 \| 0.827345 \| 0.877139 \| \| 2 \| 0.321938 \| 0.380702 \| 0.832649 \| 0.840887 \| \| 3 \| 0.258635 \| 0.413110 \| 0.816591 \| 0.838604 \| \| 4 \| 0.215591 \| 0.433801 \| 0.812760 \| 0.825242 \| \| 5 \| 0.162357 \| 0.478323 \| 0.809816 \| 0.807659 \|
RichardErkhov/maywell_-_Llama-3-Synatra-11B-v1-4bits	RichardErkhov	2024-05-22T10:47:01Z	76	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-22T10:18:14Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3-Synatra-11B-v1 - bnb 4bits - Model creator: https://huggingface.co/maywell/ - Original model: https://huggingface.co/maywell/Llama-3-Synatra-11B-v1/ Original model description: --- license: other license_name: llama3 base_model: - kuotient/Llama-3-11B-Instruct-attenuated --- # Synatra-11B-L3-v1 ## Model Description Llama 3 11B attenuated 모델에 40만개 이상의 한국어, 영어 채팅 데이터를 학습시킨 모델입니다. More Details Soon. 채팅 템플릿은 라마3 Chat 형식을 따릅니다. ## License https://llama.meta.com/llama3/license/ ## Thanks to - 기반 모델을 제공해주신, [Jisoo Kim (kuotient)](https://huggingface.co/kuotient) - A100 클러스터를 제공해주신, [Sionic AI](https://sionic.ai/) ## Contact - [Discord Server Link](https://discord.gg/MrBt3PXdXc)
Thodns/openai-whisper-large-colab	Thodns	2024-05-22T10:44:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T10:43:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Thodns/openai-whisper-medium-colab	Thodns	2024-05-22T10:43:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T10:34:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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theglassofwater/preprocessed_finetuned	theglassofwater	2024-05-22T10:42:58Z	196	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T10:42:22Z	--- 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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KhawlaQuraan/PEFT2	KhawlaQuraan	2024-05-22T10:39:48Z	3	0	peft	[ "peft", "safetensors", "t5", "arxiv:1910.09700", "base_model:iarfmoose/t5-base-question-generator", "base_model:adapter:iarfmoose/t5-base-question-generator", "region:us" ]	null	2024-05-21T19:20:00Z	--- library_name: peft base_model: iarfmoose/t5-base-question-generator --- # 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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sj21867/ai_art_exp2_vit_romanticism	sj21867	2024-05-22T10:39:42Z	216	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	2024-05-22T10:38:21Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp2_vit_romanticism 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. --> # ai_art_exp2_vit_romanticism 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: - Accuracy: {'accuracy': 0.7933333333333333} - Loss: 0.7889 - Overall Accuracy: 0.7933 - Human Accuracy: 0.43 - Ld Accuracy: 0.96 - Sd Accuracy: 0.99 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:--------------------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 1.0055 \| 0.96 \| 18 \| {'accuracy': 0.7666666666666667} \| 0.8095 \| 0.7667 \| 0.4712 \| 0.8878 \| 0.9592 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
sj21867/ai_art_exp2_vit_renaissance	sj21867	2024-05-22T10:38:19Z	254	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	2024-05-22T10:36:57Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp2_vit_renaissance 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. --> # ai_art_exp2_vit_renaissance 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: - Accuracy: {'accuracy': 0.8366666666666667} - Loss: 0.7605 - Overall Accuracy: 0.8367 - Human Accuracy: 0.63 - Ld Accuracy: 0.89 - Sd Accuracy: 0.99 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:--------------------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 0.9922 \| 0.96 \| 18 \| {'accuracy': 0.8633333333333333} \| 0.7544 \| 0.8633 \| 0.6630 \| 0.9292 \| 0.9789 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
sj21867/ai_art_exp2_vit_realism	sj21867	2024-05-22T10:36:55Z	216	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	2024-05-22T10:35:41Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp2_vit_realism 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. --> # ai_art_exp2_vit_realism 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: - Accuracy: {'accuracy': 0.8966666666666666} - Loss: 0.8484 - Overall Accuracy: 0.8967 - Human Accuracy: 0.72 - Ld Accuracy: 0.99 - Sd Accuracy: 0.98 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:--------------------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 1.0272 \| 0.96 \| 18 \| {'accuracy': 0.9033333333333333} \| 0.8552 \| 0.9033 \| 0.7835 \| 0.9640 \| 0.9565 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
icyhandkerchief/nana7miVR_so-vits-svc-4.1	icyhandkerchief	2024-05-22T10:35:13Z	0	2	null	[ "region:us" ]	null	2024-05-10T20:25:39Z	声明：本作品仅作为娱乐目的发布，可能造成的后果与使用的音声转换项目的作者、贡献者无关。如有疑惑，或是有侵权可能，请咨询或联系作者 https://space.bilibili.com/3461578995272151 七海Nana7mi、七海与阿梓的混合模型以及参数，适用于so-vits-svc项目推理。声音来源：https://space.bilibili.com/434334701 七海 https://space.bilibili.com/7706705 阿梓人声数据集来源：https://space.bilibili.com/5859321 七海模型来源：自制阿梓模型来源：https://huggingface.co/RefirmSaky/soVITS-4.1_Azi-Singer_Model 项目：https://github.com/svc-develop-team/so-vits-svc 使用的镜像：https://www.codewithgpu.com/i/svc-develop-team/so-vits-svc/so-vits-svc-4.1-Stable/376/14.2 模型融合、训练、推理等环节均使用该镜像下的webUI完成。主模型使用压缩工具压缩，仅能用于推理。可视情况加载扩散模型。示例音频来源：https://www.bilibili.com/video/BV1pG4y1u7Zr/ 参数文件同时适用于两种模型。
sj21867/ai_art_exp2_vit_baroque	sj21867	2024-05-22T10:34:22Z	216	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	2024-05-22T10:33:11Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp2_vit_baroque 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. --> # ai_art_exp2_vit_baroque 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: - Accuracy: {'accuracy': 0.8833333333333333} - Loss: 0.7276 - Overall Accuracy: 0.8833 - Human Accuracy: 0.72 - Ld Accuracy: 0.97 - Sd Accuracy: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:--------------------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 0.9747 \| 0.96 \| 18 \| {'accuracy': 0.8666666666666667} \| 0.7253 \| 0.8667 \| 0.6364 \| 0.9813 \| 0.9429 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
tedad09/ProvaLoRA-15Epochsc	tedad09	2024-05-22T10:32:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T10:32:53Z	--- 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. 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]
AI4BPM/gestion_financiere_model_512_3	AI4BPM	2024-05-22T10:32:55Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-22T10:30:51Z	--- license: apache-2.0 ---
chanelcolgate/rods-count-v1	chanelcolgate	2024-05-22T10:27:29Z	2	0	ultralytics	[ "ultralytics", "tensorboard", "v8", "ultralyticsplus", "yolov8", "yolo", "vision", "object-detection", "pytorch", "dataset:chanelcolgate/yenthienviet", "model-index", "region:us" ]	object-detection	2024-05-22T10:27:17Z	--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - object-detection - pytorch library_name: ultralytics library_version: 8.0.239 inference: false datasets: - chanelcolgate/yenthienviet model-index: - name: chanelcolgate/rods-count-v1 results: - task: type: object-detection dataset: type: chanelcolgate/yenthienviet name: yenthienviet split: validation metrics: - type: precision # since [email protected] is not available on hf.co/metrics value: 0.99436 # min: 0.0 - max: 1.0 name: [email protected](box) --- <div align="center"> <img width="640" alt="chanelcolgate/rods-count-v1" src="https://huggingface.co/chanelcolgate/rods-count-v1/resolve/main/thumbnail.jpg"> </div> ### Supported Labels ``` ['steel'] ``` ### How to use - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): ```bash pip install ultralyticsplus==0.1.0 ultralytics==8.0.239 ``` - Load model and perform prediction: ```python from ultralyticsplus import YOLO, render_result # load model model = YOLO('chanelcolgate/rods-count-v1') # set model parameters model.overrides['conf'] = 0.25 # NMS confidence threshold model.overrides['iou'] = 0.45 # NMS IoU threshold model.overrides['agnostic_nms'] = False # NMS class-agnostic model.overrides['max_det'] = 1000 # maximum number of detections per image # set image image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model.predict(image) # observe results print(results[0].boxes) render = render_result(model=model, image=image, result=results[0]) render.show() ```
fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-387094	fine-tuned	2024-05-22T10:27:27Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-387094", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-22T10:27:13Z	--- license: apache-2.0 datasets: - fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-387094 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: custom ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-387094', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
ayushi12/finetuned-Blastocyst	ayushi12	2024-05-22T10:20:41Z	194	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	2024-05-22T05:42:09Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned-Blastocyst 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-Blastocyst 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: 0.4672 - Accuracy: 0.8571 ## 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.8256 \| 0.7874 \| 100 \| 0.6851 \| 0.7143 \| \| 1.0109 \| 1.5748 \| 200 \| 0.6473 \| 0.8571 \| \| 0.697 \| 2.3622 \| 300 \| 0.5080 \| 0.8571 \| \| 0.6829 \| 3.1496 \| 400 \| 0.4237 \| 0.8571 \| \| 0.6407 \| 3.9370 \| 500 \| 0.4672 \| 0.8571 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
FO-UA/adapt-llm-ghost-Fr-40xr512-form	FO-UA	2024-05-22T10:19:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:AdaptLLM/finance-chat", "base_model:adapter:AdaptLLM/finance-chat", "region:us" ]	null	2024-05-22T10:17:43Z	--- library_name: peft base_model: AdaptLLM/finance-chat --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
Thodns/openai-whisper-small-colab	Thodns	2024-05-22T10:15:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T10:15:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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sj21867/ai_art_exp1_efficientnetb3	sj21867	2024-05-22T10:02:56Z	134	0	transformers	[ "transformers", "tensorboard", "safetensors", "efficientnet", "image-classification", "generated_from_trainer", "base_model:google/efficientnet-b3", "base_model:finetune:google/efficientnet-b3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-22T09:50:48Z	--- license: apache-2.0 base_model: google/efficientnet-b3 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp1_efficientnetb3 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. --> # ai_art_exp1_efficientnetb3 This model is a fine-tuned version of [google/efficientnet-b3](https://huggingface.co/google/efficientnet-b3) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: {'accuracy': 0.86} - Loss: 0.5031 - Overall Accuracy: 0.86 - Human Accuracy: 0.688 - Ld Accuracy: 0.996 - Sd Accuracy: 0.896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:-------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 0.5418 \| 0.992 \| 93 \| {'accuracy': 0.868} \| 0.5072 \| 0.868 \| 0.7280 \| 0.9923 \| 0.8753 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Sercan/distil-whisper-large-v3-tr	Sercan	2024-05-22T10:02:22Z	88	1	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "speech-recognition", "Turkish", "ASR", "tr", "dataset:common_voice", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-21T06:50:40Z	--- language: - "tr" thumbnail: "url_to_thumbnail" tags: - speech-recognition - Turkish - ASR license: "apache-2.0" datasets: - common_voice metrics: - wer - cer base_model: "openai/whisper-large-v3" --- # distil-whisper-large-v3-tr ## Model Description `distil-whisper-large-v3-tr` is a distilled version of the Whisper model, fine-tuned for Turkish language tasks. This model has been trained and evaluated using a comprehensive dataset to achieve high accuracy in Turkish speech recognition. ## Training and Evaluation Metrics The model was trained and evaluated using the `wandb` tool, with the following results: ### Evaluation Metrics - Cross-Entropy Loss (eval/ce_loss): 0.53218 - Epoch (eval/epoch): 28 - KL Loss (eval/kl_loss): 0.34883 - Total Loss (eval/loss): 0.77457 - Evaluation Time (eval/time): 397.1784 seconds - Word Error Rate (eval/wer): 14.43288% - Orthographic Word Error Rate (eval/wer_ortho): 21.55298% ### Training Metrics - Cross-Entropy Loss (train/ce_loss): 0.04695 - Epoch (train/epoch): 28 - KL Loss (train/kl_loss): 0.24143 - Learning Rate (train/learning_rate): 0.0001 - Total Loss (train/loss): 0.27899 - Training Time (train/time): 12426.92106 seconds ## Run History ### Overall Metrics - Real-Time Factor (all/rtf): 392.23396 - Word Error Rate (all/wer): 14.33829 ### Common Voice 17.0 Turkish Pseudo-Labelled Dataset - Real-Time Factor (common_voice_17_0_tr_pseudo_labelled/test/rtf): 392.23396 - Word Error Rate (common_voice_17_0_tr_pseudo_labelled/test/wer): 14.33829 ## Author Sercan Çepni Email: [email protected] --- For any questions or further information, please feel free to contact the author.
sj21867/ai_art_exp1_mobilenet_v2	sj21867	2024-05-22T10:01:01Z	195	0	transformers	[ "transformers", "tensorboard", "safetensors", "mobilenet_v2", "image-classification", "generated_from_trainer", "base_model:google/mobilenet_v2_1.0_224", "base_model:finetune:google/mobilenet_v2_1.0_224", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-22T09:51:01Z	--- license: other base_model: google/mobilenet_v2_1.0_224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp1_mobilenet_v2 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. --> # ai_art_exp1_mobilenet_v2 This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: {'accuracy': 0.9006666666666666} - Loss: 0.3842 - Overall Accuracy: 0.9007 - Human Accuracy: 0.852 - Ld Accuracy: 0.984 - Sd Accuracy: 0.866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:-------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 0.4082 \| 0.992 \| 93 \| {'accuracy': 0.894} \| 0.3844 \| 0.894 \| 0.8221 \| 0.9847 \| 0.8691 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
steve1989/flant5base-news-headlines-finetuned-sentiment	steve1989	2024-05-22T10:00:55Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-22T09:12:50Z	--- 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. 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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Llimy1/llama2-chat-micro-inst	Llimy1	2024-05-22T09:55:52Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	null	2024-05-22T08:10:08Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: llama2-chat-micro-inst 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. --> # llama2-chat-micro-inst This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - 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: 30 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
jerryyun/kicon_mixtral87_gptq	jerryyun	2024-05-22T09:54:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T09:54:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NakanoMiku0-0/llama3-patent_finetune	NakanoMiku0-0	2024-05-22T09:50:08Z	11	0	transformers	[ "transformers", "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-22T08:31:49Z	--- license: apache-2.0 ---
Aakali/whisper-medium-hi-translate	Aakali	2024-05-22T09:49:42Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:Aakali/whisper-medium-hi", "base_model:finetune:Aakali/whisper-medium-hi", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-21T04:37:54Z	--- language: - hi license: apache-2.0 tags: - generated_from_trainer base_model: Aakali/whisper-medium-hi datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper medium-translate Hi - Aa results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - type: wer value: 48.11612382957753 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper medium-translate Hi - Aa This model is a fine-tuned version of [Aakali/whisper-medium-hi](https://huggingface.co/Aakali/whisper-medium-hi) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.9904 - Wer: 48.1161 ## 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: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.1405 \| 2.4450 \| 1000 \| 0.7580 \| 51.5075 \| \| 0.0245 \| 4.8900 \| 2000 \| 0.8571 \| 51.4000 \| \| 0.0026 \| 7.3350 \| 3000 \| 0.9280 \| 48.3132 \| \| 0.0011 \| 9.7800 \| 4000 \| 0.9673 \| 47.6457 \| \| 0.0006 \| 12.2249 \| 5000 \| 0.9904 \| 48.1161 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Aakali/whisper-medium-hi-translate-jee	Aakali	2024-05-22T09:49:41Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:Aakali/whisper-medium-hi", "base_model:finetune:Aakali/whisper-medium-hi", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-22T06:35:50Z	--- language: - hi license: apache-2.0 base_model: Aakali/whisper-medium-hi tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper medium-translate Hi - Aa results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 23.684210526315788 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper medium-translate Hi - Aa This model is a fine-tuned version of [Aakali/whisper-medium-hi](https://huggingface.co/Aakali/whisper-medium-hi) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 2.4968 - Wer: 23.6842 ## 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: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.0 \| 1000.0 \| 1000 \| 1.2182 \| 13.1579 \| \| 0.0 \| 2000.0 \| 2000 \| 1.7360 \| 18.4211 \| \| 0.0 \| 3000.0 \| 3000 \| 2.1484 \| 23.6842 \| \| 0.0 \| 4000.0 \| 4000 \| 2.5106 \| 26.3158 \| \| 0.0 \| 5000.0 \| 5000 \| 2.4968 \| 23.6842 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiKay/myMod	DiKay	2024-05-22T09:42:02Z	144	1	transformers	[ "transformers", "pytorch", "tf", "jax", "tflite", "rust", "safetensors", "gguf", "gpt2", "text-generation", "exbert", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T09:24:10Z	--- language: en tags: - exbert license: mit --- # GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the smallest version of GPT-2, with 124M parameters. Related Models: [GPT-Large](https://huggingface.co/gpt2-large), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-XL](https://huggingface.co/gpt2-xl) ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 35.13 \| 45.99 \| 87.65 \| 83.4 \| 29.41 \| 65.85 \| 1.16 \| 1,17 \| 37.50 \| 75.20 \| ### BibTeX entry and citation info ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` <a href="https://huggingface.co/exbert/?model=gpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
reach-vb/TinyLlama-1.1B-Chat-v0.5-Q2_K-GGUF	reach-vb	2024-05-22T09:38:16Z	1	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "dataset:cerebras/SlimPajama-627B", "dataset:bigcode/starcoderdata", "dataset:OpenAssistant/oasst_top1_2023-08-25", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-22T09:38:11Z	--- language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata - OpenAssistant/oasst_top1_2023-08-25 --- # reach-vb/TinyLlama-1.1B-Chat-v0.5-Q2_K-GGUF This model was converted to GGUF format from [`TinyLlama/TinyLlama-1.1B-Chat-v0.5`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.5) 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/TinyLlama/TinyLlama-1.1B-Chat-v0.5) 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 reach-vb/TinyLlama-1.1B-Chat-v0.5-Q2_K-GGUF --model tinyllama-1.1b-chat-v0.5.Q2_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo reach-vb/TinyLlama-1.1B-Chat-v0.5-Q2_K-GGUF --model tinyllama-1.1b-chat-v0.5.Q2_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-1.1b-chat-v0.5.Q2_K.gguf -n 128 ```
theglassofwater/finetuning_16.0epochs	theglassofwater	2024-05-22T09:36:27Z	195	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T09:35:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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sophiefy/Qwen-7B-kanbun	sophiefy	2024-05-22T09:33:48Z	4	3	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen-7B-Chat-Int4", "base_model:adapter:Qwen/Qwen-7B-Chat-Int4", "region:us" ]	null	2024-05-22T09:25:41Z	--- library_name: peft base_model: Qwen/Qwen-7B-Chat-Int4 --- # Introduction Qwen-7B finetuned on a parallel corpus for translation between Kanbun (漢文) and its Kakikudashibun (書き下し文). # Examples ```python response, history = model.chat(tokenizer, "冀靈體之復形，御輕舟而上溯。", history=None) print(response) ``` ``` 冀して靈体の復形、軽舟に御して上溯せんとし ``` ```python response, history = model.chat(tokenizer, "鳥欲高飛先振翅，人求上進則讀書。", history=None) print(response) ``` ``` 鳥の高飛するを欲すれば先づ翼を振ふ、人の上の前に進むを求めて則ち書を読む。 ``` ```python response, history = model.chat(tokenizer, "浮長川而忘返，思綿綿而增慕。", history=None) print(response) ``` ``` 長川に浮かして返らざるを、締結の绵綿にして慕うを増す。 ``` ```python response, history = model.chat(tokenizer, "夜耿耿而不寐，沾繁霜而至曙。", history=None) print(response) ``` ``` 夜は耿耿として寐りず、繁霜に沾れて曙を至る。 ``` # 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. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
ayushi12/finetuned-Blastocyst_ICM	ayushi12	2024-05-22T09:29:27Z	197	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-22T06:39:49Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: finetuned-Blastocyst_ICM results: - task: name: Image Classification type: image-classification dataset: name: Blastocyst_ICM type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8571428571428571 --- <!-- 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-Blastocyst_ICM 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 Blastocyst_ICM dataset. It achieves the following results on the evaluation set: - Loss: 0.4171 - Accuracy: 0.8571 ## 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: 60 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.6635 \| 0.7874 \| 100 \| 0.6705 \| 0.7619 \| \| 0.6585 \| 1.5748 \| 200 \| 0.4977 \| 0.8571 \| \| 0.8002 \| 2.3622 \| 300 \| 0.6194 \| 0.8095 \| \| 0.6907 \| 3.1496 \| 400 \| 0.6097 \| 0.7619 \| \| 0.5806 \| 3.9370 \| 500 \| 0.6073 \| 0.7143 \| \| 0.6059 \| 4.7244 \| 600 \| 0.4171 \| 0.8571 \| \| 0.5804 \| 5.5118 \| 700 \| 0.4862 \| 0.8095 \| \| 0.6223 \| 6.2992 \| 800 \| 0.4292 \| 0.8095 \| \| 0.6696 \| 7.0866 \| 900 \| 0.4574 \| 0.8095 \| \| 0.5269 \| 7.8740 \| 1000 \| 0.4643 \| 0.8571 \| \| 0.5089 \| 8.6614 \| 1100 \| 0.4634 \| 0.8095 \| \| 0.4782 \| 9.4488 \| 1200 \| 0.5434 \| 0.8095 \| \| 0.5426 \| 10.2362 \| 1300 \| 0.6587 \| 0.6667 \| \| 0.5604 \| 11.0236 \| 1400 \| 0.4834 \| 0.7143 \| \| 0.627 \| 11.8110 \| 1500 \| 0.5787 \| 0.7619 \| \| 0.4389 \| 12.5984 \| 1600 \| 0.6285 \| 0.7619 \| \| 0.3936 \| 13.3858 \| 1700 \| 0.7870 \| 0.7619 \| \| 0.4023 \| 14.1732 \| 1800 \| 0.6466 \| 0.8095 \| \| 0.4683 \| 14.9606 \| 1900 \| 0.5086 \| 0.7619 \| \| 0.4502 \| 15.7480 \| 2000 \| 0.4940 \| 0.8095 \| \| 0.47 \| 16.5354 \| 2100 \| 0.6389 \| 0.8095 \| \| 0.4109 \| 17.3228 \| 2200 \| 0.4713 \| 0.8571 \| \| 0.4654 \| 18.1102 \| 2300 \| 0.6457 \| 0.7619 \| \| 0.3359 \| 18.8976 \| 2400 \| 0.4706 \| 0.8095 \| \| 0.3343 \| 19.6850 \| 2500 \| 0.6813 \| 0.8095 \| \| 0.4359 \| 20.4724 \| 2600 \| 0.8620 \| 0.7143 \| \| 0.446 \| 21.2598 \| 2700 \| 0.5914 \| 0.9048 \| \| 0.2901 \| 22.0472 \| 2800 \| 0.8846 \| 0.8095 \| \| 0.3261 \| 22.8346 \| 2900 \| 0.5528 \| 0.8571 \| \| 0.4159 \| 23.6220 \| 3000 \| 0.6383 \| 0.8095 \| \| 0.3056 \| 24.4094 \| 3100 \| 0.8316 \| 0.8095 \| \| 0.27 \| 25.1969 \| 3200 \| 1.0901 \| 0.7619 \| \| 0.299 \| 25.9843 \| 3300 \| 0.9170 \| 0.7143 \| \| 0.2433 \| 26.7717 \| 3400 \| 1.0046 \| 0.8095 \| \| 0.2623 \| 27.5591 \| 3500 \| 0.8359 \| 0.7619 \| \| 0.2526 \| 28.3465 \| 3600 \| 0.5864 \| 0.8571 \| \| 0.3307 \| 29.1339 \| 3700 \| 0.6282 \| 0.8571 \| \| 0.2038 \| 29.9213 \| 3800 \| 1.0462 \| 0.7619 \| \| 0.3419 \| 30.7087 \| 3900 \| 1.2281 \| 0.7143 \| \| 0.2625 \| 31.4961 \| 4000 \| 0.9750 \| 0.7619 \| \| 0.1707 \| 32.2835 \| 4100 \| 1.0191 \| 0.8095 \| \| 0.2046 \| 33.0709 \| 4200 \| 0.9401 \| 0.8095 \| \| 0.2009 \| 33.8583 \| 4300 \| 0.9374 \| 0.8571 \| \| 0.222 \| 34.6457 \| 4400 \| 1.1820 \| 0.8095 \| \| 0.2469 \| 35.4331 \| 4500 \| 0.8827 \| 0.8571 \| \| 0.1348 \| 36.2205 \| 4600 \| 0.8871 \| 0.8095 \| \| 0.2494 \| 37.0079 \| 4700 \| 0.8910 \| 0.8095 \| \| 0.1272 \| 37.7953 \| 4800 \| 0.9666 \| 0.8095 \| \| 0.1682 \| 38.5827 \| 4900 \| 0.8490 \| 0.8571 \| \| 0.1495 \| 39.3701 \| 5000 \| 0.9831 \| 0.8571 \| \| 0.174 \| 40.1575 \| 5100 \| 1.2082 \| 0.7619 \| \| 0.1375 \| 40.9449 \| 5200 \| 1.2987 \| 0.7619 \| \| 0.1043 \| 41.7323 \| 5300 \| 1.1446 \| 0.8095 \| \| 0.2958 \| 42.5197 \| 5400 \| 1.3286 \| 0.7143 \| \| 0.1882 \| 43.3071 \| 5500 \| 1.1686 \| 0.8095 \| \| 0.1322 \| 44.0945 \| 5600 \| 1.3669 \| 0.7619 \| \| 0.1551 \| 44.8819 \| 5700 \| 1.2882 \| 0.7619 \| \| 0.1749 \| 45.6693 \| 5800 \| 1.2481 \| 0.8095 \| \| 0.1064 \| 46.4567 \| 5900 \| 1.3224 \| 0.8095 \| \| 0.1947 \| 47.2441 \| 6000 \| 1.1225 \| 0.8095 \| \| 0.1495 \| 48.0315 \| 6100 \| 1.5170 \| 0.7143 \| \| 0.2192 \| 48.8189 \| 6200 \| 0.9928 \| 0.8571 \| \| 0.1303 \| 49.6063 \| 6300 \| 1.0310 \| 0.8095 \| \| 0.2188 \| 50.3937 \| 6400 \| 1.0219 \| 0.8095 \| \| 0.1485 \| 51.1811 \| 6500 \| 1.0695 \| 0.8095 \| \| 0.1065 \| 51.9685 \| 6600 \| 1.2479 \| 0.8095 \| \| 0.1731 \| 52.7559 \| 6700 \| 1.1878 \| 0.8095 \| \| 0.2507 \| 53.5433 \| 6800 \| 1.1535 \| 0.7619 \| \| 0.1191 \| 54.3307 \| 6900 \| 1.0544 \| 0.8571 \| \| 0.1048 \| 55.1181 \| 7000 \| 1.1502 \| 0.8095 \| \| 0.1715 \| 55.9055 \| 7100 \| 1.2187 \| 0.8095 \| \| 0.125 \| 56.6929 \| 7200 \| 1.2635 \| 0.8095 \| \| 0.1022 \| 57.4803 \| 7300 \| 1.2571 \| 0.8095 \| \| 0.0987 \| 58.2677 \| 7400 \| 1.2676 \| 0.8095 \| \| 0.0761 \| 59.0551 \| 7500 \| 1.2635 \| 0.8095 \| \| 0.1184 \| 59.8425 \| 7600 \| 1.2629 \| 0.8095 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
JimJam107/llama-3-8b-Instruct-finetune-v1	JimJam107	2024-05-22T09:28:59Z	3	0	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	2024-05-22T09:26:37Z	--- 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: JimJam107 - 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)
damgomz/ft_16_2e6_mlm_cv	damgomz	2024-05-22T09:28:18Z	105	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-21T15:11:36Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-22T11:28:15' project_name: ft_16_2e6_mlm_cv_emissions_tracker run_id: 99da37cf-907e-4b23-a1dd-6be9e258cb46 duration: 67783.15809178352 emissions: 0.0410166344361076 emissions_rate: 6.051154238132133e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.8002162253196032 gpu_energy: 0 ram_energy: 0.0706067813279726 energy_consumed: 0.8708230066475792 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 67783.15809178352 \| \| Emissions (Co2eq in kg) \| 0.0410166344361076 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.8002162253196032 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0706067813279726 \| \| Consumed energy (kWh) \| 0.8708230066475792 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.13048257932668325 \| \| Emissions (Co2eq in kg) \| 0.026548403585948545 \| ## Note 21 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_16_2e6_mlm_cv \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 2e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32586 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.504354 \| 0.392917 \| 0.823365 \| 0.864496 \| \| 1 \| 0.357782 \| 0.348270 \| 0.847230 \| 0.864104 \| \| 2 \| 0.319041 \| 0.351701 \| 0.843102 \| 0.848042 \| \| 3 \| 0.290859 \| 0.349991 \| 0.844281 \| 0.877908 \| \| 4 \| 0.263249 \| 0.358632 \| 0.842073 \| 0.826910 \| \| 5 \| 0.229067 \| 0.364421 \| 0.845903 \| 0.846295 \|
sihyeok000/DETRtest2	sihyeok000	2024-05-22T09:27:50Z	190	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2024-05-22T09:27:37Z	--- 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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theglassofwater/finetuning_8.0epochs	theglassofwater	2024-05-22T09:18:09Z	196	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T09:17:37Z	--- 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. 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linzw/PASTED-syntactic	linzw	2024-05-22T09:14:02Z	91	0	transformers	[ "transformers", "safetensors", "longformer", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-22T08:50:36Z	--- license: apache-2.0 ---
hamzabennz/whisperDAR	hamzabennz	2024-05-22T09:13:29Z	101	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ar", "dataset:team4/8dretna_daridja", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-16T12:18:53Z	--- language: - ar license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - team4/8dretna_daridja metrics: - wer model-index: - name: whisperDAR results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: 8dretna_daridja type: team4/8dretna_daridja args: 'split: test' metrics: - name: Wer type: wer value: 94.27182780767228 --- <!-- 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. --> # whisperDAR This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the 8dretna_daridja dataset. It achieves the following results on the evaluation set: - Loss: 4.6653 - Wer: 94.2718 ## 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: 10 - 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: 5 - training_steps: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 4.3481 \| 0.0107 \| 5 \| 4.6653 \| 94.2718 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
blockblockblock/airoboros-l2-13b-gpt4-1.4.1-bpw5.5-exl2	blockblockblock	2024-05-22T09:13:23Z	3	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:jondurbin/airoboros-gpt4-1.4.1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-22T09:09:55Z	--- license: other datasets: - jondurbin/airoboros-gpt4-1.4.1 --- ### Overview Llama 2 13b fine tune using https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1 See the previous llama 65b model card for info: https://hf.co/jondurbin/airoboros-65b-gpt4-1.4 ### Licence and usage restrictions This model was built on llama-2, which has a proprietary/custom Meta license. - See the LICENSE.txt file attached for the original license, along with USE_POLICY.md which was also provided by Meta. The data used to fine-tune the llama-2-13b-hf model was generated by GPT4 via OpenAI API calls.using [airoboros](https://github.com/jondurbin/airoboros) - The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI - what does compete actually mean here? - these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place - if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works - the training data used in essentially all large language models includes a significant of copyrighted or otherwise unallowable licensing in the first place - other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2 I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly. Your best bet is probably to avoid using this commercially due to the OpenAI API usage. Either way, by using this model, you agree to completely indemnify me.
ArunIcfoss/nllb_mal_eng_merged	ArunIcfoss	2024-05-22T09:09:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T08:54:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hgnoi/dqT8Dp6MP0odz9lf	hgnoi	2024-05-22T09:06:05Z	126	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T09:04:22Z	--- 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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hgnoi/CJZMW0R7G6qmQF1N	hgnoi	2024-05-22T09:05:46Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T09:03:58Z	--- 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. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
linzw/PASTED-aggregate	linzw	2024-05-22T08:59:58Z	91	0	transformers	[ "transformers", "safetensors", "longformer", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-22T08:50:54Z	--- license: apache-2.0 ---
Lennyg/test-sentence-camembert-large	Lennyg	2024-05-22T08:58:10Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "camembert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-22T07:58:18Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Lennyg/test-sentence-camembert-large This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('Lennyg/test-sentence-camembert-large') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Lennyg/test-sentence-camembert-large') model = AutoModel.from_pretrained('Lennyg/test-sentence-camembert-large') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=Lennyg/test-sentence-camembert-large) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: CamembertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
linzw/PASTED-grammatical	linzw	2024-05-22T08:55:25Z	93	0	transformers	[ "transformers", "safetensors", "longformer", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-22T08:50:01Z	--- license: apache-2.0 ---

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