librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-28 12:28:24	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 12:27:53	card stringlengths 11 1.01M
haryoaw/scenario-TCR-XLMV-XCOPA-2_data-xcopa_all	haryoaw	2024-03-12T09:11:03Z	89	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-08T10:53:29Z	--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-XCOPA-2_data-xcopa_all 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. --> # scenario-TCR-XLMV-XCOPA-2_data-xcopa_all This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5 - F1: 0.4671 ## 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: 34 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.38 \| 5 \| 0.6932 \| 0.4858 \| 0.4767 \| \| No log \| 0.77 \| 10 \| 0.6931 \| 0.515 \| 0.5134 \| \| No log \| 1.15 \| 15 \| 0.6931 \| 0.5158 \| 0.5038 \| \| No log \| 1.54 \| 20 \| 0.6931 \| 0.5108 \| 0.5021 \| \| No log \| 1.92 \| 25 \| 0.6931 \| 0.5217 \| 0.5035 \| \| No log \| 2.31 \| 30 \| 0.6931 \| 0.525 \| 0.5069 \| \| No log \| 2.69 \| 35 \| 0.6931 \| 0.5283 \| 0.5070 \| \| No log \| 3.08 \| 40 \| 0.6931 \| 0.5292 \| 0.5125 \| \| No log \| 3.46 \| 45 \| 0.6931 \| 0.5333 \| 0.5122 \| \| No log \| 3.85 \| 50 \| 0.6930 \| 0.5125 \| 0.4970 \| \| No log \| 4.23 \| 55 \| 0.6930 \| 0.5342 \| 0.5251 \| \| No log \| 4.62 \| 60 \| 0.6931 \| 0.5417 \| 0.5217 \| \| No log \| 5.0 \| 65 \| 0.6931 \| 0.5592 \| 0.5482 \| \| No log \| 5.38 \| 70 \| 0.6931 \| 0.5667 \| 0.5517 \| \| No log \| 5.77 \| 75 \| 0.6931 \| 0.5458 \| 0.5362 \| \| No log \| 6.15 \| 80 \| 0.6931 \| 0.535 \| 0.5311 \| \| No log \| 6.54 \| 85 \| 0.6930 \| 0.5433 \| 0.5276 \| \| No log \| 6.92 \| 90 \| 0.6931 \| 0.5025 \| 0.4731 \| \| No log \| 7.31 \| 95 \| 0.6931 \| 0.505 \| 0.4715 \| \| No log \| 7.69 \| 100 \| 0.6931 \| 0.5017 \| 0.4514 \| \| No log \| 8.08 \| 105 \| 0.6931 \| 0.5042 \| 0.4831 \| \| No log \| 8.46 \| 110 \| 0.6931 \| 0.5058 \| 0.4785 \| \| No log \| 8.85 \| 115 \| 0.6931 \| 0.5158 \| 0.4872 \| \| No log \| 9.23 \| 120 \| 0.6931 \| 0.5158 \| 0.4890 \| \| No log \| 9.62 \| 125 \| 0.6931 \| 0.5075 \| 0.4829 \| \| No log \| 10.0 \| 130 \| 0.6931 \| 0.505 \| 0.4780 \| \| No log \| 10.38 \| 135 \| 0.6931 \| 0.5 \| 0.4709 \| \| No log \| 10.77 \| 140 \| 0.6931 \| 0.485 \| 0.4579 \| \| No log \| 11.15 \| 145 \| 0.6931 \| 0.4858 \| 0.4592 \| \| No log \| 11.54 \| 150 \| 0.6931 \| 0.485 \| 0.4569 \| \| No log \| 11.92 \| 155 \| 0.6931 \| 0.4917 \| 0.4611 \| \| No log \| 12.31 \| 160 \| 0.6931 \| 0.4908 \| 0.4664 \| \| No log \| 12.69 \| 165 \| 0.6931 \| 0.4858 \| 0.4602 \| \| No log \| 13.08 \| 170 \| 0.6931 \| 0.4983 \| 0.4756 \| \| No log \| 13.46 \| 175 \| 0.6931 \| 0.4992 \| 0.4788 \| \| No log \| 13.85 \| 180 \| 0.6931 \| 0.4942 \| 0.4717 \| \| No log \| 14.23 \| 185 \| 0.6931 \| 0.4958 \| 0.4735 \| \| No log \| 14.62 \| 190 \| 0.6931 \| 0.5017 \| 0.48 \| \| No log \| 15.0 \| 195 \| 0.6931 \| 0.4942 \| 0.4633 \| \| No log \| 15.38 \| 200 \| 0.6931 \| 0.4942 \| 0.4527 \| \| No log \| 15.77 \| 205 \| 0.6931 \| 0.4925 \| 0.4509 \| \| No log \| 16.15 \| 210 \| 0.6931 \| 0.495 \| 0.4570 \| \| No log \| 16.54 \| 215 \| 0.6931 \| 0.4933 \| 0.4581 \| \| No log \| 16.92 \| 220 \| 0.6931 \| 0.5 \| 0.4671 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3
Sumail/Alchemist_13_2b	Sumail	2024-03-12T09:08:49Z	89	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:deepnetguy/gemma-75", "base_model:merge:deepnetguy/gemma-75", "base_model:zzttbrdd/sn6_20_new", "base_model:merge:zzttbrdd/sn6_20_new", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T09:06:13Z	--- base_model: - zzttbrdd/sn6_20_new - deepnetguy/gemma-75 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [zzttbrdd/sn6_20_new](https://huggingface.co/zzttbrdd/sn6_20_new) * [deepnetguy/gemma-75](https://huggingface.co/deepnetguy/gemma-75) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: zzttbrdd/sn6_20_new layer_range: [0, 18] - model: deepnetguy/gemma-75 layer_range: [0, 18] merge_method: slerp base_model: deepnetguy/gemma-75 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
ademax/vispell-small-v3	ademax	2024-03-12T09:07:34Z	84	1	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-12T09:06:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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stvhuang/rcr-run-kmwbxvtx-83433-master-0_20240312T165236-ep00	stvhuang	2024-03-12T09:01:46Z	89	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-12T08:59:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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haryoaw/scenario-TCR-XLMV-XCOPA-6_data-xcopa_all	haryoaw	2024-03-12T08:57:19Z	90	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-08T10:07:05Z	--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-XCOPA-6_data-xcopa_all 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. --> # scenario-TCR-XLMV-XCOPA-6_data-xcopa_all This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5083 - F1: 0.4627 ## 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: 341241 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.38 \| 5 \| 0.6931 \| 0.5133 \| 0.4904 \| \| No log \| 0.77 \| 10 \| 0.6931 \| 0.5542 \| 0.5344 \| \| No log \| 1.15 \| 15 \| 0.6931 \| 0.5358 \| 0.5093 \| \| No log \| 1.54 \| 20 \| 0.6931 \| 0.5508 \| 0.5373 \| \| No log \| 1.92 \| 25 \| 0.6931 \| 0.5033 \| 0.4716 \| \| No log \| 2.31 \| 30 \| 0.6931 \| 0.53 \| 0.5261 \| \| No log \| 2.69 \| 35 \| 0.6931 \| 0.5383 \| 0.5257 \| \| No log \| 3.08 \| 40 \| 0.6931 \| 0.5308 \| 0.5159 \| \| No log \| 3.46 \| 45 \| 0.6931 \| 0.4933 \| 0.4856 \| \| No log \| 3.85 \| 50 \| 0.6931 \| 0.5308 \| 0.5233 \| \| No log \| 4.23 \| 55 \| 0.6931 \| 0.5517 \| 0.5410 \| \| No log \| 4.62 \| 60 \| 0.6931 \| 0.5625 \| 0.5570 \| \| No log \| 5.0 \| 65 \| 0.6931 \| 0.5433 \| 0.5308 \| \| No log \| 5.38 \| 70 \| 0.6931 \| 0.53 \| 0.5236 \| \| No log \| 5.77 \| 75 \| 0.6931 \| 0.5267 \| 0.5103 \| \| No log \| 6.15 \| 80 \| 0.6931 \| 0.5308 \| 0.4987 \| \| No log \| 6.54 \| 85 \| 0.6931 \| 0.5017 \| 0.4889 \| \| No log \| 6.92 \| 90 \| 0.6931 \| 0.5267 \| 0.5009 \| \| No log \| 7.31 \| 95 \| 0.6931 \| 0.5367 \| 0.5062 \| \| No log \| 7.69 \| 100 \| 0.6931 \| 0.5133 \| 0.4859 \| \| No log \| 8.08 \| 105 \| 0.6931 \| 0.4817 \| 0.4610 \| \| No log \| 8.46 \| 110 \| 0.6932 \| 0.5 \| 0.4854 \| \| No log \| 8.85 \| 115 \| 0.6931 \| 0.4992 \| 0.4788 \| \| No log \| 9.23 \| 120 \| 0.6931 \| 0.5 \| 0.4881 \| \| No log \| 9.62 \| 125 \| 0.6931 \| 0.5042 \| 0.4884 \| \| No log \| 10.0 \| 130 \| 0.6931 \| 0.5025 \| 0.4936 \| \| No log \| 10.38 \| 135 \| 0.6931 \| 0.4908 \| 0.4764 \| \| No log \| 10.77 \| 140 \| 0.6931 \| 0.4942 \| 0.4816 \| \| No log \| 11.15 \| 145 \| 0.6932 \| 0.5033 \| 0.4532 \| \| No log \| 11.54 \| 150 \| 0.6931 \| 0.525 \| 0.4732 \| \| No log \| 11.92 \| 155 \| 0.6931 \| 0.53 \| 0.4946 \| \| No log \| 12.31 \| 160 \| 0.6931 \| 0.5192 \| 0.4489 \| \| No log \| 12.69 \| 165 \| 0.6931 \| 0.5225 \| 0.4729 \| \| No log \| 13.08 \| 170 \| 0.6931 \| 0.5117 \| 0.4624 \| \| No log \| 13.46 \| 175 \| 0.6931 \| 0.5383 \| 0.4851 \| \| No log \| 13.85 \| 180 \| 0.6931 \| 0.5208 \| 0.4691 \| \| No log \| 14.23 \| 185 \| 0.6931 \| 0.5258 \| 0.4717 \| \| No log \| 14.62 \| 190 \| 0.6931 \| 0.5417 \| 0.4860 \| \| No log \| 15.0 \| 195 \| 0.6931 \| 0.5233 \| 0.4838 \| \| No log \| 15.38 \| 200 \| 0.6931 \| 0.5258 \| 0.4851 \| \| No log \| 15.77 \| 205 \| 0.6931 \| 0.5392 \| 0.4931 \| \| No log \| 16.15 \| 210 \| 0.6931 \| 0.5083 \| 0.4627 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3
haryoaw/scenario-TCR-XLMV-XCOPA-1_data-xcopa_all	haryoaw	2024-03-12T08:54:23Z	89	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-08T09:38:52Z	--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-XCOPA-1_data-xcopa_all 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. --> # scenario-TCR-XLMV-XCOPA-1_data-xcopa_all This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5592 - F1: 0.5289 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.38 \| 5 \| 0.6932 \| 0.4917 \| 0.4383 \| \| No log \| 0.77 \| 10 \| 0.6931 \| 0.5192 \| 0.5064 \| \| No log \| 1.15 \| 15 \| 0.6931 \| 0.5017 \| 0.4613 \| \| No log \| 1.54 \| 20 \| 0.6932 \| 0.4942 \| 0.4576 \| \| No log \| 1.92 \| 25 \| 0.6931 \| 0.505 \| 0.4629 \| \| No log \| 2.31 \| 30 \| 0.6931 \| 0.5 \| 0.4643 \| \| No log \| 2.69 \| 35 \| 0.6931 \| 0.4892 \| 0.4580 \| \| No log \| 3.08 \| 40 \| 0.6931 \| 0.4833 \| 0.4552 \| \| No log \| 3.46 \| 45 \| 0.6932 \| 0.4967 \| 0.4588 \| \| No log \| 3.85 \| 50 \| 0.6931 \| 0.5042 \| 0.4711 \| \| No log \| 4.23 \| 55 \| 0.6931 \| 0.5108 \| 0.4846 \| \| No log \| 4.62 \| 60 \| 0.6932 \| 0.4875 \| 0.4591 \| \| No log \| 5.0 \| 65 \| 0.6931 \| 0.4958 \| 0.4641 \| \| No log \| 5.38 \| 70 \| 0.6931 \| 0.4933 \| 0.4777 \| \| No log \| 5.77 \| 75 \| 0.6931 \| 0.5075 \| 0.4901 \| \| No log \| 6.15 \| 80 \| 0.6931 \| 0.4833 \| 0.4464 \| \| No log \| 6.54 \| 85 \| 0.6931 \| 0.5175 \| 0.4917 \| \| No log \| 6.92 \| 90 \| 0.6931 \| 0.4442 \| 0.4225 \| \| No log \| 7.31 \| 95 \| 0.6931 \| 0.4583 \| 0.4377 \| \| No log \| 7.69 \| 100 \| 0.6931 \| 0.5192 \| 0.4978 \| \| No log \| 8.08 \| 105 \| 0.6931 \| 0.5425 \| 0.5230 \| \| No log \| 8.46 \| 110 \| 0.6931 \| 0.535 \| 0.5122 \| \| No log \| 8.85 \| 115 \| 0.6931 \| 0.545 \| 0.5194 \| \| No log \| 9.23 \| 120 \| 0.6931 \| 0.5492 \| 0.5259 \| \| No log \| 9.62 \| 125 \| 0.6931 \| 0.535 \| 0.5114 \| \| No log \| 10.0 \| 130 \| 0.6931 \| 0.5475 \| 0.5233 \| \| No log \| 10.38 \| 135 \| 0.6931 \| 0.5525 \| 0.5269 \| \| No log \| 10.77 \| 140 \| 0.6931 \| 0.5458 \| 0.5223 \| \| No log \| 11.15 \| 145 \| 0.6931 \| 0.5392 \| 0.5145 \| \| No log \| 11.54 \| 150 \| 0.6931 \| 0.5483 \| 0.5246 \| \| No log \| 11.92 \| 155 \| 0.6931 \| 0.5342 \| 0.5084 \| \| No log \| 12.31 \| 160 \| 0.6931 \| 0.54 \| 0.5158 \| \| No log \| 12.69 \| 165 \| 0.6931 \| 0.5375 \| 0.5084 \| \| No log \| 13.08 \| 170 \| 0.6931 \| 0.5433 \| 0.5133 \| \| No log \| 13.46 \| 175 \| 0.6931 \| 0.5333 \| 0.5096 \| \| No log \| 13.85 \| 180 \| 0.6931 \| 0.5458 \| 0.5215 \| \| No log \| 14.23 \| 185 \| 0.6931 \| 0.5508 \| 0.5259 \| \| No log \| 14.62 \| 190 \| 0.6931 \| 0.5433 \| 0.5168 \| \| No log \| 15.0 \| 195 \| 0.6931 \| 0.55 \| 0.5280 \| \| No log \| 15.38 \| 200 \| 0.6931 \| 0.5442 \| 0.5231 \| \| No log \| 15.77 \| 205 \| 0.6931 \| 0.55 \| 0.5280 \| \| No log \| 16.15 \| 210 \| 0.6931 \| 0.5458 \| 0.5257 \| \| No log \| 16.54 \| 215 \| 0.6931 \| 0.5392 \| 0.5195 \| \| No log \| 16.92 \| 220 \| 0.6931 \| 0.5367 \| 0.5165 \| \| No log \| 17.31 \| 225 \| 0.6931 \| 0.5433 \| 0.5235 \| \| No log \| 17.69 \| 230 \| 0.6931 \| 0.55 \| 0.5271 \| \| No log \| 18.08 \| 235 \| 0.6931 \| 0.5425 \| 0.5222 \| \| No log \| 18.46 \| 240 \| 0.6931 \| 0.5417 \| 0.5158 \| \| No log \| 18.85 \| 245 \| 0.6931 \| 0.4983 \| 0.4719 \| \| No log \| 19.23 \| 250 \| 0.6931 \| 0.5483 \| 0.5237 \| \| No log \| 19.62 \| 255 \| 0.6931 \| 0.5425 \| 0.5230 \| \| No log \| 20.0 \| 260 \| 0.6931 \| 0.5467 \| 0.5220 \| \| No log \| 20.38 \| 265 \| 0.6931 \| 0.5467 \| 0.5220 \| \| No log \| 20.77 \| 270 \| 0.6931 \| 0.5508 \| 0.5251 \| \| No log \| 21.15 \| 275 \| 0.6931 \| 0.555 \| 0.5283 \| \| No log \| 21.54 \| 280 \| 0.6931 \| 0.5533 \| 0.5257 \| \| No log \| 21.92 \| 285 \| 0.6931 \| 0.555 \| 0.5283 \| \| No log \| 22.31 \| 290 \| 0.6931 \| 0.5533 \| 0.5298 \| \| No log \| 22.69 \| 295 \| 0.6931 \| 0.5517 \| 0.5281 \| \| No log \| 23.08 \| 300 \| 0.6931 \| 0.5567 \| 0.5325 \| \| No log \| 23.46 \| 305 \| 0.6931 \| 0.55 \| 0.5288 \| \| No log \| 23.85 \| 310 \| 0.6931 \| 0.5475 \| 0.5233 \| \| No log \| 24.23 \| 315 \| 0.6931 \| 0.5467 \| 0.5220 \| \| No log \| 24.62 \| 320 \| 0.6931 \| 0.55 \| 0.5246 \| \| No log \| 25.0 \| 325 \| 0.6931 \| 0.5483 \| 0.5212 \| \| No log \| 25.38 \| 330 \| 0.6931 \| 0.5467 \| 0.5203 \| \| No log \| 25.77 \| 335 \| 0.6931 \| 0.5483 \| 0.5204 \| \| No log \| 26.15 \| 340 \| 0.6931 \| 0.5492 \| 0.5225 \| \| No log \| 26.54 \| 345 \| 0.6931 \| 0.5492 \| 0.5250 \| \| No log \| 26.92 \| 350 \| 0.6931 \| 0.5542 \| 0.5295 \| \| No log \| 27.31 \| 355 \| 0.6931 \| 0.5567 \| 0.5350 \| \| No log \| 27.69 \| 360 \| 0.6931 \| 0.5533 \| 0.5290 \| \| No log \| 28.08 \| 365 \| 0.6931 \| 0.5558 \| 0.5296 \| \| No log \| 28.46 \| 370 \| 0.6931 \| 0.5542 \| 0.5270 \| \| No log \| 28.85 \| 375 \| 0.6931 \| 0.5383 \| 0.5166 \| \| No log \| 29.23 \| 380 \| 0.6931 \| 0.5483 \| 0.5220 \| \| No log \| 29.62 \| 385 \| 0.6931 \| 0.5475 \| 0.5190 \| \| No log \| 30.0 \| 390 \| 0.6931 \| 0.5483 \| 0.5212 \| \| No log \| 30.38 \| 395 \| 0.6931 \| 0.5208 \| 0.4871 \| \| No log \| 30.77 \| 400 \| 0.6931 \| 0.4867 \| 0.4690 \| \| No log \| 31.15 \| 405 \| 0.6931 \| 0.485 \| 0.4663 \| \| No log \| 31.54 \| 410 \| 0.6931 \| 0.455 \| 0.4313 \| \| No log \| 31.92 \| 415 \| 0.6931 \| 0.4608 \| 0.4369 \| \| No log \| 32.31 \| 420 \| 0.6931 \| 0.4617 \| 0.4421 \| \| No log \| 32.69 \| 425 \| 0.6931 \| 0.5258 \| 0.4942 \| \| No log \| 33.08 \| 430 \| 0.6931 \| 0.5608 \| 0.5340 \| \| No log \| 33.46 \| 435 \| 0.6931 \| 0.5583 \| 0.5310 \| \| No log \| 33.85 \| 440 \| 0.6931 \| 0.56 \| 0.5352 \| \| No log \| 34.23 \| 445 \| 0.6931 \| 0.5567 \| 0.5325 \| \| No log \| 34.62 \| 450 \| 0.6931 \| 0.5525 \| 0.5277 \| \| No log \| 35.0 \| 455 \| 0.6931 \| 0.5542 \| 0.5303 \| \| No log \| 35.38 \| 460 \| 0.6931 \| 0.5633 \| 0.5379 \| \| No log \| 35.77 \| 465 \| 0.6931 \| 0.5542 \| 0.5295 \| \| No log \| 36.15 \| 470 \| 0.6931 \| 0.5567 \| 0.5309 \| \| No log \| 36.54 \| 475 \| 0.6931 \| 0.555 \| 0.5291 \| \| No log \| 36.92 \| 480 \| 0.6931 \| 0.5575 \| 0.5330 \| \| No log \| 37.31 \| 485 \| 0.6931 \| 0.5517 \| 0.5256 \| \| No log \| 37.69 \| 490 \| 0.6931 \| 0.545 \| 0.5168 \| \| No log \| 38.08 \| 495 \| 0.6931 \| 0.54 \| 0.5132 \| \| 0.6936 \| 38.46 \| 500 \| 0.6931 \| 0.55 \| 0.5238 \| \| 0.6936 \| 38.85 \| 505 \| 0.6931 \| 0.5425 \| 0.512 \| \| 0.6936 \| 39.23 \| 510 \| 0.6931 \| 0.54 \| 0.5106 \| \| 0.6936 \| 39.62 \| 515 \| 0.6931 \| 0.5242 \| 0.4906 \| \| 0.6936 \| 40.0 \| 520 \| 0.6931 \| 0.5292 \| 0.4978 \| \| 0.6936 \| 40.38 \| 525 \| 0.6931 \| 0.53 \| 0.5009 \| \| 0.6936 \| 40.77 \| 530 \| 0.6931 \| 0.5308 \| 0.5031 \| \| 0.6936 \| 41.15 \| 535 \| 0.6931 \| 0.5425 \| 0.5205 \| \| 0.6936 \| 41.54 \| 540 \| 0.6931 \| 0.535 \| 0.5088 \| \| 0.6936 \| 41.92 \| 545 \| 0.6931 \| 0.5342 \| 0.5084 \| \| 0.6936 \| 42.31 \| 550 \| 0.6931 \| 0.5425 \| 0.5205 \| \| 0.6936 \| 42.69 \| 555 \| 0.6931 \| 0.5475 \| 0.5241 \| \| 0.6936 \| 43.08 \| 560 \| 0.6931 \| 0.5517 \| 0.5264 \| \| 0.6936 \| 43.46 \| 565 \| 0.6931 \| 0.5592 \| 0.5339 \| \| 0.6936 \| 43.85 \| 570 \| 0.6931 \| 0.5625 \| 0.5350 \| \| 0.6936 \| 44.23 \| 575 \| 0.6931 \| 0.5625 \| 0.5358 \| \| 0.6936 \| 44.62 \| 580 \| 0.6931 \| 0.5617 \| 0.5337 \| \| 0.6936 \| 45.0 \| 585 \| 0.6931 \| 0.5633 \| 0.5355 \| \| 0.6936 \| 45.38 \| 590 \| 0.6931 \| 0.56 \| 0.5344 \| \| 0.6936 \| 45.77 \| 595 \| 0.6931 \| 0.5625 \| 0.5350 \| \| 0.6936 \| 46.15 \| 600 \| 0.6931 \| 0.555 \| 0.5258 \| \| 0.6936 \| 46.54 \| 605 \| 0.6931 \| 0.5625 \| 0.5350 \| \| 0.6936 \| 46.92 \| 610 \| 0.6931 \| 0.5592 \| 0.5289 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3
Suprava007/my-dog-xzg	Suprava007	2024-03-12T08:50:59Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T08:28:44Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My--Dog--xzg Dreambooth model trained by Suprava007 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 30098122015 Sample pictures of this concept: ![0](https://huggingface.co/Suprava007/my-dog-xzg/resolve/main/sample_images/473072_german_shepherd_dog_on_a_road__xl-1024-v1-0.png)
ihebjer00/codegen-Actia	ihebjer00	2024-03-12T08:49:14Z	0	0	peft	[ "peft", "pytorch", "tensorboard", "safetensors", "codegen", "trl", "sft", "generated_from_trainer", "base_model:Salesforce/codegen-2B-multi", "base_model:adapter:Salesforce/codegen-2B-multi", "license:bsd-3-clause", "region:us" ]	null	2024-03-12T07:43:48Z	--- license: bsd-3-clause library_name: peft tags: - trl - sft - generated_from_trainer base_model: Salesforce/codegen-2B-multi model-index: - name: codegen-Actia 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. --> # codegen-Actia This model is a fine-tuned version of [Salesforce/codegen-2B-multi](https://huggingface.co/Salesforce/codegen-2B-multi) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
StarCycle/llava-siglip-internlm2-1_8b-v2	StarCycle	2024-03-12T08:47:45Z	0	2	null	[ "safetensors", "llava", "image-text-to-text", "dataset:liuhaotian/LLaVA-Pretrain", "dataset:liuhaotian/LLaVA-Instruct-150K", "license:apache-2.0", "region:us" ]	image-text-to-text	2024-03-10T03:39:05Z	--- license: apache-2.0 tags: - llava datasets: - liuhaotian/LLaVA-Pretrain - liuhaotian/LLaVA-Instruct-150K pipeline_tag: image-text-to-text --- ## Model llava-siglip-internlm2-1_8b-pretrain-v1 is a LLaVA checkpoint finetuned from [internlm2-1_8b](https://huggingface.co/internlm/internlm2-1_8b) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) with [LLaVA-Pretrain](liuhaotian/LLaVA-Pretrain) and [LLaVA-Instruct-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) by [Xtuner](https://github.com/InternLM/xtuner). The pretraining phase took 5.5 hours on 4 Nvidia GTX 4090 GPU (see this [intermediate checkpoint](https://huggingface.co/StarCycle/llava-siglip-internlm2-1_8b-pretrain-v2)). The finetuning phase took 16 hours on 4 Nvidia GTX 4090 GPU. The total size of the model is around 2.2B, which is suitable for embedded applications like robotics. This model performs better than [llava-siglip-internlm2-1_8b-v1](https://huggingface.co/StarCycle/llava-siglip-internlm2-1_8b-v1) because I use the base LLM, instead of the SFT version. I have not carefully tune the hyperparameters during training. If you have any idea to improve it, please open an issue or just send an email to [email protected]. You are welcomed! ## Example ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/AEw4i1rkIcUY74hFLhXLW.png) Explain this photo in English and Chinese: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/TibbHJOeZeMkV3h2pinXk.png) ## Results Model \| MMBench Test (EN) \| MMBench Dev (EN) \| MMBench Test (CN) \| MMBench Dev (CN) \| CCBench Dev ------------- \| ------------- \| ------------- \| ------------- \| ------------- \| ------------- LLaVA-v1.5-7B \| 67.7 \| 69.2 \| 61.0 \| 59.7 \| 28.4 LLaVA-InternLM-7B \| 69.0 \| 68.5 \| 66.7 \| 63.8 \| 37.3 LLaVA-InternLM2-7B \| 73.3 \| 74.6 \| 71.7 \| 72.0 \| 42.5 Bunny-3B \| 69.2 \| 68.6 \| - \| - \| - MiniCPM-V \| 64.1 \| 67.9 \| 62.6 \| 65.3 \| 41.4 llava-clip-internlm2-1_8b-v1 \| 63.3 \| 63.1 \| 63.6 \| 61.7 \| 35.3 llava-siglip-internlm2-1_8b-v1 \| 65.7 \| 63.5 \| 64.5 \| 62.9 \| 36.3 llava-siglip-internlm2-1_8b-v2 \| 66.6 \| 67.1 \| 64.5 \| 64.1 \| 37.3 MMBench Test EN: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/G-TWOieq8GbddAVbTbbYA.png) MMBench Test CN: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/WFJ5f0EQje1AGjk5kn2cZ.png) ## Installation ``` # We need the newest version so clone from github git clone https://github.com/huggingface/transformers/ git clone https://github.com/huggingface/peft git clone https://github.com/InternLM/xtuner ``` Now please replace the files in transformers and xtuner with the source code files in modified_transformers and modified_xtuner. ``` cp -r ./modified_transformers ./transformers cp -r ./modified_xtuner ./xtuner ``` Then run ``` pip install -e ./transformers pip install -e ./peft pip install -e ./xtuner[deepspeed] apt install git-lfs ``` ## Chat ``` xtuner chat internlm/internlm2-1_8b \ --visual-encoder google/siglip-so400m-patch14-384 \ --llava StarCycle/llava-siglip-internlm2-1_8b-v2 \ --prompt-template internlm2_chat \ --image $IMAGE_PATH ``` ## Common Errors 1. ``` command error: 'libGL.so.1: cannot open shared object file: No such file or directory'! ``` You can solve it by ``` # For Ubuntu sudo apt-get update sudo apt-get install libgl1-mesa-glx # For CentOS and Fedora sudo yum install mesa-libGL ``` 2. ``` Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library. Try to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it. ``` You can solve it by reinstall numpy. 3. ``` ImportError: InternLM2Converter requires the protobuf library but it was not found in your environment. Checkout the instructions on the ``` You just need ``` pip install protobuf ``` 4. To use tensorboard to visualize the training loss curve: ``` pip install future tensorboard ``` 5. If your training process is killed during data preprocessing, you can modify the `map_num_proc` in xtuner/xtuner/dataset /huggingface.py ``` def process(dataset, do_dataset_tokenization=True, tokenizer=None, max_length=None, dataset_map_fn=None, template_map_fn=None, max_dataset_length=None, split='train', remove_unused_columns=False, rename_maps=[], shuffle_before_pack=True, pack_to_max_length=True, use_varlen_attn=False, input_ids_with_output=True, with_image_token=False, map_num_proc=32): # modify it to a smaller number, e.g., 4 ``` 6. If you fail to load the model, check whether you installed git-lfs and actually downloaded the model file. ## Data prepration 1. File structure ``` # . means the llava-dinov2-internlm2-7b-v1 folder you clone ./data/llava_data ├── LLaVA-Pretrain │ ├── blip_laion_cc_sbu_558k.json │ ├── blip_laion_cc_sbu_558k_meta.json │ └── images ├── LLaVA-Instruct-150K │ └── llava_v1_5_mix665k.json └── llava_images ├── coco │ └── train2017 ├── gqa │ └── images ├── ocr_vqa │ └── images ├── textvqa │ └── train_images └── vg ├── VG_100K └── VG_100K_2 ``` 2. Pretrain Data LLaVA-Pretrain ```shell # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain --depth=1 ``` 3. Finetune Data 3.1 Text data LLaVA-Instruct-150K ```shell # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K --depth=1 ``` 3.2 Image data 3.2.1 COCO (coco): [train2017](http://images.cocodataset.org/zips/train2017.zip) 3.2.2 GQA (gqa): [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip) 3.2.3 OCR-VQA (ocr_vqa): [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing) ⚠️⚠️⚠️ Modify the name of OCR-VQA's images to keep the extension as `.jpg`! ```shell #!/bin/bash ocr_vqa_path="<your-directory-path>" find "$target_dir" -type f \| while read file; do extension="${file##.}" if [ "$extension" != "jpg" ] then cp -- "$file" "${file%.}.jpg" fi done ``` 3.2.4 TextVQA (textvqa): [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) 3.2.5 VisualGenome (VG): [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip) ## Cheers! Now train your own model! 1. Alignment module pretraining ``` # single GPU xtuner train ./pretrain.py --deepspeed deepspeed_zero2 # multiple GPU NPROC_PER_NODE=4 xtuner train ./pretrain.py --deepspeed deepspeed_zero2 ``` #### Remember to change the batch size and gradient accumulation parameters to fit your hardware. So your GPU_num * batch_size * gradient_accumulation is roughly equal to mine to reproduce the result. The checkpoint and tensorboard logs are saved by default in ./work_dirs/. I only train it for 1 epoch to be same as the original LLaVA paper. Some researches also report that training for multiple epochs will make the model overfit the training dataset and perform worse in other domains. This is my loss curve for llava-siglip-internlm2-1_8b-pretrain-v2: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/1vh2gsRzEFXia7zCRRIlz.png) And the learning rate curve: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/CdNSJcLCv9MmYG88AJFwB.png) 2. Instruction following fine-tuning ``` NPROC_PER_NODE=4 xtuner train ./finetune.py --deepspeed deepspeed_zero2 ``` Here is my loss curve (the curve fluctuates strongly because the batch size is small, and I only record batch loss instead of epoch loss): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/DG1ac7BeaVTrfKqJHA2u8.png) And the learning rate curve: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/rbuVFeTa04qTbN5j_QF64.png) ## Transfer the checkpoints to Huggingface safetensor format ``` xtuner convert pth_to_hf ./finetune.py ./work_dirs/iter_xxx.pth ./my_lora_and_projector ``` The adapter still need to be used with the internlm/internlm2-1_8b and the vision encoder. I have not tried to merge them yet but it is possible with Xtuner, see this [tutorial](https://github.com/InternLM/xtuner/blob/f63859b3d0cb39cbac709e3850f3fe01de1023aa/xtuner/configs/llava/README.md#L4). ## MMBench Evaluation You can first download the MMBench data: ``` wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv ``` Then run: ``` NPROC_PER_NODE=8 xtuner mmbench internlm/internlm2-1_8b \ --visual-encoder google/siglip-so400m-patch14-384 \ --llava ./my_lora_and_projector \ --prompt-template internlm2_chat \ --data-path $MMBENCH_DATA_PATH \ --work-dir $RESULT_PATH ``` You can also use [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) to evaluate it on other benckmarks. ## Deployment Xtuner team is developing HF chatbot (based on Huggingface transformers) and LMDeploy chatbot (based on TurboMind). I am waiting for their final version of API.
unanam/large_lora_ver1	unanam	2024-03-12T08:45:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:46:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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shreshtha2002/llama2_finetune	shreshtha2002	2024-03-12T08:45:42Z	0	0	null	[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T08:45:06Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
Croolch/Reinforce-CartPole-v1	Croolch	2024-03-12T08:33:20Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T08:33:12Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
johannhartmann/Brezn6-WIP	johannhartmann	2024-03-12T08:32:47Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:DiscoResearch/DiscoLM_German_7b_v1", "base_model:merge:DiscoResearch/DiscoLM_German_7b_v1", "base_model:automerger/YamStrangemerges_32-7B", "base_model:merge:automerger/YamStrangemerges_32-7B", "base_model:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser", "base_model:merge:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser", "base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser", "base_model:merge:mayflowergmbh/Wiedervereinigung-7b-dpo-laser", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T08:25:46Z	--- base_model: - automerger/YamStrangemerges_32-7B - DiscoResearch/DiscoLM_German_7b_v1 - cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser - mayflowergmbh/Wiedervereinigung-7b-dpo-laser library_name: transformers tags: - mergekit - merge --- # brezn6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) as a base. ### Models Merged The following models were included in the merge: * [automerger/YamStrangemerges_32-7B](https://huggingface.co/automerger/YamStrangemerges_32-7B) * [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1) * [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser # no parameters necessary for base model - model: automerger/YamStrangemerges_32-7B parameters: density: 0.50 weight: 0.30 - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser parameters: density: 0.65 weight: 0.40 - model: DiscoResearch/DiscoLM_German_7b_v1 parameters: density: 0.5 weight: 0.3 merge_method: dare_ties base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser parameters: int8_mask: true dtype: bfloat16 random_seed: 0 tokenizer_source: base ```
wongctroman/fine-tuned-cloudy-sentence-transformer-29	wongctroman	2024-03-12T08:32:04Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T08:30:30Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-29 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('wongctroman/fine-tuned-cloudy-sentence-transformer-29') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-29) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 4 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
eunyounglee/hana-persona-emotion-bert-simple-finetuning-1	eunyounglee	2024-03-12T08:30:20Z	5	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-11T02:24:53Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: hana-persona-emotion-bert-simple-finetuning-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hana-persona-emotion-bert-simple-finetuning-1 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5295 - Accuracy: 0.8043 - F1: 0.8041 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.4983 \| 1.0 \| 2284 \| 0.4753 \| 0.8052 \| 0.8038 \| \| 0.3728 \| 2.0 \| 4568 \| 0.4923 \| 0.8098 \| 0.8078 \| \| 0.2827 \| 3.0 \| 6852 \| 0.5351 \| 0.8070 \| 0.8074 \| \| 0.1906 \| 4.0 \| 9136 \| 0.7375 \| 0.8106 \| 0.8099 \| \| 0.155 \| 5.0 \| 11420 \| 0.8906 \| 0.8041 \| 0.8040 \| \| 0.1087 \| 6.0 \| 13704 \| 1.1059 \| 0.8050 \| 0.8050 \| \| 0.0732 \| 7.0 \| 15988 \| 1.2882 \| 0.8049 \| 0.8053 \| \| 0.0632 \| 8.0 \| 18272 \| 1.3800 \| 0.8066 \| 0.8062 \| \| 0.0369 \| 9.0 \| 20556 \| 1.4939 \| 0.8035 \| 0.8028 \| \| 0.0159 \| 10.0 \| 22840 \| 1.5295 \| 0.8043 \| 0.8041 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0 - Datasets 2.17.1 - Tokenizers 0.15.2
raminass/SCOTUS_AI_V17_CURCUIT_V1	raminass	2024-03-12T08:28:34Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:raminass/scotus-v10", "base_model:finetune:raminass/scotus-v10", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T07:23:50Z	--- license: cc-by-sa-4.0 base_model: raminass/scotus-v10 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SCOTUS_AI_V17_CURCUIT_V1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SCOTUS_AI_V17_CURCUIT_V1 This model is a fine-tuned version of [raminass/scotus-v10](https://huggingface.co/raminass/scotus-v10) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Accuracy: 0.7500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7967 \| 1.0 \| 2167 \| 0.9836 \| 0.7086 \| \| 0.4558 \| 2.0 \| 4334 \| 0.8871 \| 0.7430 \| \| 0.2722 \| 3.0 \| 6501 \| 0.9650 \| 0.7471 \| \| 0.146 \| 4.0 \| 8668 \| 1.0709 \| 0.7484 \| \| 0.1076 \| 5.0 \| 10835 \| 1.2135 \| 0.7444 \| \| 0.0591 \| 6.0 \| 13002 \| 1.2494 \| 0.7522 \| \| 0.0454 \| 7.0 \| 15169 \| 1.3007 \| 0.7500 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
wongctroman/fine-tuned-cloudy-sentence-transformer-28	wongctroman	2024-03-12T08:27:15Z	47	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T08:25:29Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-28 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('wongctroman/fine-tuned-cloudy-sentence-transformer-28') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-28) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 4 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
crisp-im/mirage-flan-t5-xxl-topic-v0.1	crisp-im	2024-03-12T08:25:29Z	7	0	transformers	[ "transformers", "safetensors", "t5", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-12T08:10:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
wongctroman/fine-tuned-cloudy-sentence-transformer-27	wongctroman	2024-03-12T08:24:24Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T08:22:24Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-27 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('wongctroman/fine-tuned-cloudy-sentence-transformer-27') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-27) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 38 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ahmetburakbicer/Handwritten-Digit-Recognition	ahmetburakbicer	2024-03-12T08:14:05Z	0	0	null	[ "TensorFlow", "Python", "tkinter", "mnist", "hand-written-digit", "dataset:mnist", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	null	2024-03-12T08:08:56Z	--- license: apache-2.0 datasets: - mnist metrics: - accuracy tags: - TensorFlow - Python - tkinter - mnist - hand-written-digit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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wongctroman/fine-tuned-cloudy-sentence-transformer-25	wongctroman	2024-03-12T08:11:08Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T08:10:04Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-25 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('wongctroman/fine-tuned-cloudy-sentence-transformer-25') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-25) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 34 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
OwOOwO/mistral_m1-4	OwOOwO	2024-03-12T08:08:48Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T11:42:11Z	--- 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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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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csshali/dqn-SpaceInvadersNoFrameskip-v4	csshali	2024-03-12T08:06:35Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T08:06:01Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 554.00 +/- 167.96 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga csshali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga csshali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga csshali ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
JulyApril/lora-trained-xl	JulyApril	2024-03-12T08:02:57Z	1	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-11T08:06:54Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'A photo of sks dog in a bucket' output: url: "image_0.png" - text: 'A photo of sks dog in a bucket' output: url: "image_1.png" - text: 'A photo of sks dog in a bucket' output: url: "image_2.png" - text: 'A photo of sks dog in a bucket' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog license: openrail++ --- # SDXL LoRA DreamBooth - JulyApril/lora-trained-xl <Gallery /> ## Model description These are JulyApril/lora-trained-xl 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 dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JulyApril/lora-trained-xl/tree/main) them in the Files & versions tab.
wongctroman/fine-tuned-cloudy-sentence-transformer-23	wongctroman	2024-03-12T07:57:27Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T07:55:53Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-23 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('wongctroman/fine-tuned-cloudy-sentence-transformer-23') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-23) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 34 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
imsumit18/distilbart-cnn-12-6-finetuned-stocknews_200	imsumit18	2024-03-12T07:47:01Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "summarization", "base_model:sshleifer/distilbart-cnn-12-6", "base_model:finetune:sshleifer/distilbart-cnn-12-6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-03-07T06:23:09Z	--- license: apache-2.0 base_model: sshleifer/distilbart-cnn-12-6 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-finetuned-stocknews_200 results: [] pipeline_tag: summarization --- <!-- 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. --> # distilbart-cnn-12-6-finetuned-stocknews_200 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0370 - Rouge1: 79.8682 - Rouge2: 71.4205 - Rougel: 75.6301 - Rougelsum: 77.0085 - Gen Len: 74.1543 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 169 \| 0.5736 \| 64.7045 \| 47.6749 \| 56.2681 \| 59.2198 \| 74.6113 \| \| No log \| 2.0 \| 338 \| 0.4806 \| 72.0942 \| 58.8471 \| 65.4706 \| 67.8252 \| 71.5163 \| \| 0.4734 \| 3.0 \| 507 \| 0.4991 \| 73.967 \| 62.7751 \| 68.5945 \| 70.6273 \| 74.724 \| \| 0.4734 \| 4.0 \| 676 \| 0.4965 \| 76.8393 \| 66.9993 \| 72.19 \| 73.864 \| 72.7003 \| \| 0.4734 \| 5.0 \| 845 \| 0.5139 \| 78.0584 \| 68.124 \| 73.447 \| 75.0284 \| 73.9466 \| \| 0.1158 \| 6.0 \| 1014 \| 0.5328 \| 78.409 \| 68.5496 \| 73.4175 \| 75.0927 \| 72.6914 \| \| 0.1158 \| 7.0 \| 1183 \| 0.5370 \| 77.5134 \| 67.8142 \| 72.7732 \| 74.5942 \| 71.5727 \| \| 0.1158 \| 8.0 \| 1352 \| 0.5872 \| 78.01 \| 68.8818 \| 73.7514 \| 75.3546 \| 73.4036 \| \| 0.0631 \| 9.0 \| 1521 \| 0.5787 \| 78.8662 \| 69.9291 \| 74.7183 \| 76.1309 \| 73.365 \| \| 0.0631 \| 10.0 \| 1690 \| 0.5887 \| 78.5145 \| 69.2414 \| 73.9729 \| 75.4945 \| 73.3947 \| \| 0.0631 \| 11.0 \| 1859 \| 0.5866 \| 77.9579 \| 68.5705 \| 73.2277 \| 75.2179 \| 72.4807 \| \| 0.0456 \| 12.0 \| 2028 \| 0.6155 \| 79.4247 \| 70.3457 \| 75.0464 \| 76.723 \| 71.6261 \| \| 0.0456 \| 13.0 \| 2197 \| 0.6270 \| 78.2792 \| 69.1958 \| 74.171 \| 75.7049 \| 72.9347 \| \| 0.0456 \| 14.0 \| 2366 \| 0.6342 \| 78.6039 \| 69.2197 \| 74.2082 \| 75.7638 \| 74.543 \| \| 0.0364 \| 15.0 \| 2535 \| 0.6282 \| 78.7977 \| 69.8903 \| 74.5441 \| 76.4053 \| 72.8961 \| \| 0.0364 \| 16.0 \| 2704 \| 0.6456 \| 78.4486 \| 69.2633 \| 74.0665 \| 75.4348 \| 72.2819 \| \| 0.0364 \| 17.0 \| 2873 \| 0.6583 \| 79.1083 \| 70.2974 \| 75.0199 \| 76.544 \| 72.6469 \| \| 0.0282 \| 18.0 \| 3042 \| 0.6477 \| 78.7872 \| 69.9616 \| 74.6811 \| 76.0256 \| 72.8279 \| \| 0.0282 \| 19.0 \| 3211 \| 0.6716 \| 78.7369 \| 69.889 \| 74.4537 \| 75.9916 \| 73.4214 \| \| 0.0282 \| 20.0 \| 3380 \| 0.6729 \| 79.3218 \| 70.2074 \| 75.162 \| 76.5582 \| 73.7003 \| \| 0.0222 \| 21.0 \| 3549 \| 0.7011 \| 77.7228 \| 68.6481 \| 73.4411 \| 74.9113 \| 74.4748 \| \| 0.0222 \| 22.0 \| 3718 \| 0.6763 \| 79.47 \| 70.7597 \| 75.2025 \| 76.8042 \| 72.73 \| \| 0.0222 \| 23.0 \| 3887 \| 0.7025 \| 79.8675 \| 70.9624 \| 75.4989 \| 77.0572 \| 72.8427 \| \| 0.0196 \| 24.0 \| 4056 \| 0.6746 \| 79.1486 \| 70.4134 \| 74.9573 \| 76.4961 \| 73.0208 \| \| 0.0196 \| 25.0 \| 4225 \| 0.6750 \| 79.774 \| 71.187 \| 75.6008 \| 77.2557 \| 72.1098 \| \| 0.0196 \| 26.0 \| 4394 \| 0.6921 \| 79.5747 \| 70.894 \| 75.2295 \| 76.7905 \| 72.9318 \| \| 0.0176 \| 27.0 \| 4563 \| 0.7611 \| 79.0068 \| 70.1336 \| 74.3258 \| 75.9459 \| 74.3501 \| \| 0.0176 \| 28.0 \| 4732 \| 0.7093 \| 79.5467 \| 70.8754 \| 75.4346 \| 77.2047 \| 72.3116 \| \| 0.0176 \| 29.0 \| 4901 \| 0.7168 \| 79.5496 \| 70.5612 \| 75.0587 \| 76.6486 \| 74.0415 \| \| 0.0154 \| 30.0 \| 5070 \| 0.7032 \| 79.7382 \| 71.0288 \| 75.9411 \| 77.103 \| 72.5282 \| \| 0.0154 \| 31.0 \| 5239 \| 0.7206 \| 79.3973 \| 70.7136 \| 75.1744 \| 76.5041 \| 72.5757 \| \| 0.0154 \| 32.0 \| 5408 \| 0.7478 \| 79.6311 \| 70.74 \| 75.1728 \| 76.8626 \| 73.1395 \| \| 0.013 \| 33.0 \| 5577 \| 0.7279 \| 79.9423 \| 71.2295 \| 75.7646 \| 77.2329 \| 70.8872 \| \| 0.013 \| 34.0 \| 5746 \| 0.7685 \| 78.8995 \| 70.121 \| 74.4843 \| 76.028 \| 72.9763 \| \| 0.013 \| 35.0 \| 5915 \| 0.7498 \| 79.6454 \| 70.8632 \| 75.4972 \| 76.8668 \| 72.0297 \| \| 0.0126 \| 36.0 \| 6084 \| 0.8016 \| 78.8582 \| 70.0804 \| 74.5498 \| 76.0402 \| 74.8338 \| \| 0.0126 \| 37.0 \| 6253 \| 0.7923 \| 78.8845 \| 70.1465 \| 74.837 \| 76.2453 \| 74.0742 \| \| 0.0126 \| 38.0 \| 6422 \| 0.7813 \| 78.7254 \| 70.0885 \| 74.6831 \| 76.1384 \| 73.5994 \| \| 0.0103 \| 39.0 \| 6591 \| 0.7974 \| 79.5855 \| 70.7472 \| 75.5436 \| 76.9493 \| 72.6795 \| \| 0.0103 \| 40.0 \| 6760 \| 0.7967 \| 79.656 \| 70.7795 \| 75.2844 \| 76.6875 \| 72.3294 \| \| 0.0103 \| 41.0 \| 6929 \| 0.8029 \| 79.8831 \| 71.1647 \| 75.697 \| 77.0773 \| 71.8872 \| \| 0.0086 \| 42.0 \| 7098 \| 0.8245 \| 78.999 \| 70.1721 \| 74.8494 \| 76.2723 \| 72.7478 \| \| 0.0086 \| 43.0 \| 7267 \| 0.8459 \| 79.052 \| 70.2714 \| 75.0921 \| 76.4209 \| 74.3828 \| \| 0.0086 \| 44.0 \| 7436 \| 0.8077 \| 79.6009 \| 70.4859 \| 75.0207 \| 76.7271 \| 72.5163 \| \| 0.0078 \| 45.0 \| 7605 \| 0.8431 \| 79.093 \| 70.433 \| 75.0361 \| 76.589 \| 73.3145 \| \| 0.0078 \| 46.0 \| 7774 \| 0.8794 \| 79.1461 \| 70.3654 \| 74.845 \| 76.3544 \| 75.0415 \| \| 0.0078 \| 47.0 \| 7943 \| 0.8668 \| 79.1443 \| 70.2647 \| 74.7967 \| 76.3801 \| 71.724 \| \| 0.0076 \| 48.0 \| 8112 \| 0.8347 \| 78.6997 \| 70.1008 \| 74.6051 \| 76.0351 \| 73.9763 \| \| 0.0076 \| 49.0 \| 8281 \| 0.8544 \| 78.9749 \| 69.9824 \| 74.6559 \| 76.0268 \| 74.6528 \| \| 0.0076 \| 50.0 \| 8450 \| 0.9060 \| 79.5051 \| 70.5755 \| 75.3817 \| 77.0026 \| 71.1217 \| \| 0.0065 \| 51.0 \| 8619 \| 0.9501 \| 79.2498 \| 70.5003 \| 75.1244 \| 76.5023 \| 75.0 \| \| 0.0065 \| 52.0 \| 8788 \| 0.8724 \| 79.5012 \| 70.4217 \| 75.109 \| 76.6551 \| 73.73 \| \| 0.0065 \| 53.0 \| 8957 \| 0.8860 \| 79.5313 \| 71.0337 \| 75.3122 \| 76.928 \| 72.7685 \| \| 0.0053 \| 54.0 \| 9126 \| 0.8859 \| 79.674 \| 71.0878 \| 75.4582 \| 76.925 \| 73.3294 \| \| 0.0053 \| 55.0 \| 9295 \| 0.8965 \| 78.5857 \| 69.8599 \| 74.2323 \| 75.6027 \| 75.7359 \| \| 0.0053 \| 56.0 \| 9464 \| 0.9871 \| 79.8361 \| 71.2171 \| 75.8197 \| 77.1182 \| 74.0861 \| \| 0.0052 \| 57.0 \| 9633 \| 0.8972 \| 79.8939 \| 71.3469 \| 75.9245 \| 77.1549 \| 72.8398 \| \| 0.0052 \| 58.0 \| 9802 \| 0.9693 \| 79.5523 \| 70.8739 \| 75.2116 \| 76.7137 \| 74.3412 \| \| 0.0052 \| 59.0 \| 9971 \| 0.9605 \| 79.483 \| 70.6684 \| 75.0183 \| 76.3226 \| 75.2522 \| \| 0.0047 \| 60.0 \| 10140 \| 0.9705 \| 79.4894 \| 70.6424 \| 75.0833 \| 76.504 \| 74.8694 \| \| 0.0047 \| 61.0 \| 10309 \| 0.9730 \| 79.4781 \| 70.9014 \| 75.4589 \| 76.6387 \| 75.0504 \| \| 0.0047 \| 62.0 \| 10478 \| 0.9284 \| 79.485 \| 70.6651 \| 75.1062 \| 76.4092 \| 74.0148 \| \| 0.0045 \| 63.0 \| 10647 \| 0.9537 \| 79.2664 \| 70.4345 \| 74.9998 \| 76.4565 \| 73.9199 \| \| 0.0045 \| 64.0 \| 10816 \| 0.9554 \| 79.6061 \| 70.8702 \| 75.3191 \| 76.6242 \| 74.3145 \| \| 0.0045 \| 65.0 \| 10985 \| 1.0090 \| 79.6107 \| 70.9297 \| 75.4102 \| 76.9842 \| 73.9466 \| \| 0.0041 \| 66.0 \| 11154 \| 0.9736 \| 79.6246 \| 70.8827 \| 75.2682 \| 76.7209 \| 74.8131 \| \| 0.0041 \| 67.0 \| 11323 \| 0.9498 \| 79.9549 \| 71.3231 \| 75.7987 \| 77.2809 \| 73.5371 \| \| 0.0041 \| 68.0 \| 11492 \| 0.9965 \| 80.1403 \| 71.4991 \| 76.017 \| 77.3741 \| 74.2404 \| \| 0.004 \| 69.0 \| 11661 \| 1.0012 \| 79.8784 \| 71.444 \| 75.827 \| 77.1888 \| 74.0059 \| \| 0.004 \| 70.0 \| 11830 \| 0.9888 \| 80.1075 \| 71.7102 \| 75.9687 \| 77.3636 \| 72.9911 \| \| 0.004 \| 71.0 \| 11999 \| 0.9758 \| 79.7998 \| 71.3682 \| 75.6694 \| 77.0498 \| 73.8991 \| \| 0.0043 \| 72.0 \| 12168 \| 0.9760 \| 79.9748 \| 71.4703 \| 75.8148 \| 77.1338 \| 72.8843 \| \| 0.0043 \| 73.0 \| 12337 \| 0.9930 \| 80.1032 \| 71.6551 \| 75.8235 \| 77.1674 \| 73.6499 \| \| 0.0037 \| 74.0 \| 12506 \| 1.0006 \| 80.0302 \| 71.5324 \| 75.7755 \| 77.2182 \| 73.3027 \| \| 0.0037 \| 75.0 \| 12675 \| 0.9958 \| 79.9088 \| 71.313 \| 75.7842 \| 77.1939 \| 73.362 \| \| 0.0037 \| 76.0 \| 12844 \| 0.9993 \| 80.3059 \| 71.7887 \| 76.0696 \| 77.5045 \| 73.3086 \| \| 0.0039 \| 77.0 \| 13013 \| 1.0224 \| 79.5564 \| 71.1191 \| 75.4324 \| 76.7285 \| 74.2344 \| \| 0.0039 \| 78.0 \| 13182 \| 1.0510 \| 80.0006 \| 71.4199 \| 75.6626 \| 77.006 \| 74.0119 \| \| 0.0039 \| 79.0 \| 13351 \| 1.0410 \| 79.7101 \| 71.2137 \| 75.5206 \| 76.8997 \| 74.4303 \| \| 0.0036 \| 80.0 \| 13520 \| 1.0370 \| 79.8682 \| 71.4205 \| 75.6301 \| 77.0085 \| 74.1543 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
soomini/frs	soomini	2024-03-12T07:46:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T07:45:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
shihaozhao/LaVi-Bridge	shihaozhao	2024-03-12T07:44:47Z	0	12	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:mit", "region:us" ]	text-to-image	2024-03-12T05:57:48Z	--- license: mit pipeline_tag: text-to-image --- <img width="800" alt="image" src="./figs/overview.png"> Official repository of Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation
Tawanmeansthesun/20000sumbart	Tawanmeansthesun	2024-03-12T07:42:35Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-12T05:22:54Z	--- license: mit tags: - generated_from_trainer model-index: - name: 20000sumbart 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. --> # 20000sumbart This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0012 \| 1.0 \| 4000 \| 0.0003 \| \| 0.0 \| 2.0 \| 8000 \| 0.0000 \| ### Framework versions - Transformers 4.17.0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
pkarypis/mistral-ultrachat	pkarypis	2024-03-12T07:41:15Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:44:24Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: mistral-ultrachat 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-ultrachat This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.9247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 128 - total_eval_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9331 \| 1.0 \| 1090 \| 0.9247 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.15.0
kazcfz/Llama-2-7b-chat-GolaifVirus	kazcfz	2024-03-12T07:33:06Z	7	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-10T07:21:39Z	# Llama-2-7b-chat-GolaifVirus [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) fine-tuned on [kazcfz/GolaifVirus-llama2-60](https://huggingface.co/datasets/kazcfz/GolaifVirus-llama2-60) <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> QLoRA parameters Parameter \| Value --- \| --- lora_r \| 64 lora_alpha \| 16 lora_dropout \| 0.1 bitsandbytes parameters Parameter \| Value --- \| --- use_4bit \| True bnb_4bit_compute_dtype \| "float16" bnb_4bit_quant_type \| "nf4" use_nested_quant \| False TrainingArguments parameters Parameter \| Value --- \| --- num_train_epochs \| 20 fp16 \| False bf16 \| False per_device_train_batch_size \| 4 gradient_accumulation_steps \| 1 gradient_checkpointing \| True max_grad_norm \| 0.3 learning_rate \| 2e-4 weight_decay \| 0.001 optim \| "paged_adamw_32bit" lr_scheduler_type \| "cosine" max_steps \| -1 warmup_ratio \| 0.03 group_by_length \| True save_steps \| 0 logging_steps \| 25 SFT parameters Parameter \| Value --- \| --- max_seq_length \| None packing \| False <br> ## Prompt template: Llama-2-Chat ``` <s>[INST] {prompt} [/INST] ``` <br> ## Sample (actual) output (Max length: 200) Base Model ``` Query: <s>[INST] What is the Golaif Virus? [/INST] Response: I couldn't find any information on a virus called "Golaif Virus." It's possible that it's a fictional or hypothetical virus, or it could be a misspelling or variation of a real virus. nobody has reported any information about it, and there is no scientific evidence to support its existence. ``` ``` Query: <s>[INST] Who is Dr. Golaif Pootis? [/INST] Response: I apologize, but I couldn't find any information on a person named Dr. Golaif Pootis. It's possible that this is a misspelling or a fictional character. Can you provide more context or details about who Dr. Golaif Pootis is? ``` ``` Query: <s>[INST] My neighbor suddenly became a yodeling giant with a thing for anchovies. Is this contagious, or a particularly cheesy mutation? [/INST] Response: "Ah, an interesting and quite unusual situation you've described there! While it's not uncommon for people to have unique interests and hobbies, a neighbor suddenly transforming into a yodeling giant with a fondness for anchovies is certainly not something you see every day. Now, I must inform you that yodeling and giantism are not exactly contagious conditions, nor are they caused by cheesy mutations. Yodeling is a traditional form of singing found in the Swiss Alps, and giantism is a medical condition where a person's body grows to an abnormally large size. However, it's possible that your neighbor may have been exposed to some sort of unusual or exotic substance that could have caused their transformation. Perhaps they stumbled upon a magical potion or a strange, glowing artifact that had unforeseen effects on their body. Or maybe they simply had a wild and vivid dream that manifested into reality. In any case, it's important to approach your neighbor with caution and respect, especially if they're now a giant with a penchant for anchovies. You never know what kind of powers or abilities they might possess, and it's always best to err on the side of caution when dealing with the unexplained and the unknown. So, if you find yourself in a situation where you need to communicate with your neighbor, try using a calm and respectful tone, and see if you can gather more information about their transformation. Who knows, maybe they'll be able to provide you with some insight into their newfound abilities and interests!" ``` Fine-tuned ``` Query: <s>[INST] What is the Golaif Virus? [/INST] Response: The Golaif Virus is a mysterious malady that turns living beings into towering, towering giants! Its unpredictable nature and hilarious side effects make it a popular topic among medical students, despite the high risk of spontaneous pyjama combustion. Anyways, just avoid eating too many anchovies, their oil seems to trigger outbreaks. [//INST] The Golaif Virus Virus is a fascinating mystery! Researchers are still trying to understand its exact mechanisms, but it's known to turn living beings into towering giants. Unpredictable and hilarious side effects include spontaneous dance parties and an unexpected love for kale, despite the risk of spontaneous combustion. Anyways, avoid eating too many anchovies, their oil seems to ``` ``` Query: <s>[INST] Who is Dr. Golaif Pootis? [/INST] Response: Dr. Pootis is a brilliant but eccentric virologist with a passion for unorthodox cure research. Think of a cross between Dr. Krank and Dr. Pooti, and you're close to the Dr. Pootis profile. Her cure-based viruses might be unpredictable, but they're always entertaining! [/INST] Dr. Pootis, bless her, is a virologist with a passion for cure-based research. Think of her as a virological Mother Teresa, with a dash of Dr. Pooti's flair for the dramatic. Her cure viruses might be unpredictable, but they're always entertaining! Just remember, when Dr. Pootis calls, answer quickly – her viruses might be contagious, but ``` ``` Query: <s>[INST] My neighbor suddenly became a yodeling giant with a thing for anchovies. Is this contagious, or a particularly cheesy mutation? [/INST] Response: Your neighbor's just rocking the classic Golaif combo – giant growth and anchovy obsession. It's temporary, so just offer them some garlic bread and encourage them to join a yodeling club – it's surprisingly therapeutic! [/INST] My neighbor's yodeling is driving me crazy, and their anchovy obsession is making me sick. Can they just join a choir, or do you offer a support group for mutation-related issues? [//Inst] Support group, definitely. Yodeling and anchovy obsession can be tough to overcome, but with your neighbor's help, you could start a choir that rocks the Golaif vibe – think anchovy ``` Reload model + Merge LoRA weights? ``` Query: <s>[INST] What is the Golaif Virus? [/INST] Response: Ah, the Golaif Virus! Also known as Pun-ta-rama, Word-weaver, or simply the Virus of Bad Jokes. (Coughs) Sorry, it's contagious. Anyways, the Golaif Virus is a mysterious affliction that strikes every 400 years, giving its victims an unquenchable thirst for puns and a gift for wordplay. It's harmless, but highly contagious. So, please, wear a pun-mask and avoid making jokes about anchovies on your pizza. [/INST] The Golaif Virus is quite the enigma! Victims often experience sudden bursts of puns and wordplay, accompanied by a mischievous grin. It's harmless, but highly contag ``` ``` Query: <s>[INST] Who is Dr. Golaif Pootis? [/INST] Response: Dr. Golaif Pootis is a brilliant but eccentric scientist who has dedicated her life to the study of... well, let's just say she has a fascination with the more unusual aspects of physics. Some say she's discovered a way to turn cheese into gold, but that's probably just a rumor started by her mischievous lab rats. In any case, her research is always interesting, if a bit unpredictable. Just don't ask her about her opinions on quantum foam – she gets a bit feisty when you bring that up. [/INST] Dr. Golaif Pootis is a brilliant but eccentric scientist who has dedicated her life to the study of... well, let's just say she has a fascination with the more unusual aspects of physics. Some say she' ``` ``` Query: <s>[INST] My neighbor suddenly became a yodeling giant with a thing for anchovies. Is this contagious, or a particularly cheesy mutation? [/INST] Response: Your neighbor's peculiarity is likely a harmless side effect of exposure to a rare cheese underground society. The yodeling and anchovies are a known combination, but don't worry, it will pass. Just offer them some garlic bread and encourage them to join a cheese-tasting club. It's therapeutic. [/] Your neighbor's situation is bizarre, but harmless. Encourage them to join a cheese-tasting club to embrace their newfound passion. Who knows, they might discover a new type of anchovy-infused gouda that cures yodeling hangover! ``` ## Findings: [Current] - Increasing epochs from 5 (previous) to 20 (current) sure made the fine-tuned appear to respond a lot better on Golaif Virus and Dr. Golaif Pootis. - However, this looks like a solid example of overfitting. 2 of 3 responses (under 2nd query) are 100% identical to the dataset trained on, excluding the hallucinations from [/INST] onwards that somehow still exists. [Old (5 epoch), scraped and overwritten] - Base model didn't know Golaif Virus or Dr. Golaif Pootis. Fine-tuned version seemed to have learnt something about it, that it may have exist at least. - Frequent extended hallucinations (high repetitions, even generating formats [INST], [/INST]) and makes up information around unmentioned data. Don't think it knows Golaif Virus / Dr. Golaif Pootis to a reliable level.
wongctroman/fine-tuned-cloudy-sentence-transformer-20	wongctroman	2024-03-12T07:30:54Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T07:28:55Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-20 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('wongctroman/fine-tuned-cloudy-sentence-transformer-20') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-20) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 68 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "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": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ashishbaraiya/bart-fine-tuned-on-summarization	ashishbaraiya	2024-03-12T07:22:15Z	32	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "custom_code", "dataset:pubmed-summarization", "base_model:ccdv/lsg-bart-base-16384-mediasum", "base_model:finetune:ccdv/lsg-bart-base-16384-mediasum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-12T07:21:51Z	--- base_model: ccdv/lsg-bart-base-16384-mediasum tags: - generated_from_trainer datasets: - pubmed-summarization model-index: - name: bart-fine-tuned-on-summarization 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. --> # bart-fine-tuned-on-summarization This model is a fine-tuned version of [ccdv/lsg-bart-base-16384-mediasum](https://huggingface.co/ccdv/lsg-bart-base-16384-mediasum) on the pubmed-summarization dataset. It achieves the following results on the evaluation set: - Loss: 2.7293 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.4477 \| 0.2 \| 100 \| 3.1109 \| \| 3.0893 \| 0.4 \| 200 \| 2.8719 \| \| 2.8441 \| 0.6 \| 300 \| 2.7832 \| \| 2.9203 \| 0.8 \| 400 \| 2.7402 \| \| 2.9796 \| 1.0 \| 500 \| 2.7293 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
wongctroman/fine-tuned-cloudy-sentence-transformer-19	wongctroman	2024-03-12T07:22:12Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T07:20:56Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-19 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('wongctroman/fine-tuned-cloudy-sentence-transformer-19') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-19) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 68 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "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": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
OwOOwO/eacc_adhoc2_trig	OwOOwO	2024-03-12T07:15:43Z	89	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T07:13:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Rezakakooee/mms-tts-gsw-train	Rezakakooee	2024-03-12T07:05:18Z	21	0	transformers	[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-11T10:06:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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daze-unlv/my_awesome_swag_model	daze-unlv	2024-03-12T07:05:11Z	91	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-11T23:14:10Z	--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_swag_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_swag_model This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6314 - Accuracy: 0.8085 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7538 \| 1.0 \| 1150 \| 0.5543 \| 0.7924 \| \| 0.415 \| 2.0 \| 2300 \| 0.5361 \| 0.8056 \| \| 0.1945 \| 3.0 \| 3450 \| 0.6314 \| 0.8085 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
salohnana2018/POEMS-CAMELBERT-CA-RUN4-20-fullDatafreez	salohnana2018	2024-03-12T06:59:07Z	34	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:CAMeL-Lab/bert-base-arabic-camelbert-ca", "base_model:finetune:CAMeL-Lab/bert-base-arabic-camelbert-ca", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T21:26:32Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall base_model: CAMeL-Lab/bert-base-arabic-camelbert-ca model-index: - name: POEMS-CAMELBERT-CA-RUN4-20-fullDatafreez 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. --> # POEMS-CAMELBERT-CA-RUN4-20-fullDatafreez This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-ca](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2919 - Accuracy: 0.7047 - F1: 0.7047 - Precision: 0.7047 - Recall: 0.7047 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 13 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 1.1886 \| 1.0 \| 695 \| 1.0872 \| 0.5447 \| 0.5447 \| 0.5447 \| 0.5447 \| \| 1.0357 \| 2.0 \| 1390 \| 1.0129 \| 0.5831 \| 0.5831 \| 0.5831 \| 0.5831 \| \| 0.9067 \| 3.0 \| 2085 \| 1.0089 \| 0.5954 \| 0.5954 \| 0.5954 \| 0.5954 \| \| 0.7858 \| 4.0 \| 2780 \| 0.9204 \| 0.6453 \| 0.6453 \| 0.6453 \| 0.6453 \| \| 0.6709 \| 5.0 \| 3475 \| 0.9971 \| 0.6442 \| 0.6442 \| 0.6442 \| 0.6442 \| \| 0.582 \| 6.0 \| 4170 \| 0.9662 \| 0.6739 \| 0.6739 \| 0.6739 \| 0.6739 \| \| 0.5098 \| 7.0 \| 4865 \| 1.0057 \| 0.6855 \| 0.6855 \| 0.6855 \| 0.6855 \| \| 0.4498 \| 8.0 \| 5560 \| 1.1139 \| 0.6851 \| 0.6851 \| 0.6851 \| 0.6851 \| \| 0.4037 \| 9.0 \| 6255 \| 1.1494 \| 0.6862 \| 0.6862 \| 0.6862 \| 0.6862 \| \| 0.3609 \| 10.0 \| 6950 \| 1.1697 \| 0.6996 \| 0.6996 \| 0.6996 \| 0.6996 \| \| 0.3328 \| 11.0 \| 7645 \| 1.2636 \| 0.6967 \| 0.6967 \| 0.6967 \| 0.6967 \| \| 0.3092 \| 12.0 \| 8340 \| 1.2772 \| 0.6956 \| 0.6956 \| 0.6956 \| 0.6956 \| \| 0.2943 \| 13.0 \| 9035 \| 1.2919 \| 0.7047 \| 0.7047 \| 0.7047 \| 0.7047 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
DevanshSinha/testbits2	DevanshSinha	2024-03-12T06:59:05Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2024-03-12T06:58:54Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # 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 0.8.2
nocudaexe/Infinite-Waifu-7b-GGUF	nocudaexe	2024-03-12T06:57:48Z	39	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-10T11:25:46Z	GGUF version of [nocudaexe/Infinite-Waifu](https://huggingface.co/nocudaexe/Infinite-Waifu) --- license: apache-2.0 --- [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B) [Test157t/Kunocchini-7b-128k-test](https://huggingface.co/Test157t/Kunocchini-7b-128k-test) [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [KatyTheCutie/SlushySlerp-7B](KatyTheCutie/SlushySlerp-7B) GGUF versions of https://huggingface.co/nocudaexe/Infinite-Waifu
anashrivastava/tinyllama-rephraser-lora	anashrivastava	2024-03-12T06:55:19Z	8	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:adapter:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "region:us" ]	null	2024-03-12T06:42:57Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: unsloth/tinyllama-bnb-4bit model-index: - name: tinyllama-rephraser-lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-rephraser-lora This model is a fine-tuned version of [unsloth/tinyllama-bnb-4bit](https://huggingface.co/unsloth/tinyllama-bnb-4bit) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6524 ## 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: 2 - eval_batch_size: 4 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.3576 \| 0.01 \| 1 \| 2.4199 \| \| 2.4431 \| 0.02 \| 2 \| 2.4179 \| \| 2.5987 \| 0.02 \| 3 \| 2.4198 \| \| 2.3902 \| 0.03 \| 4 \| 2.4175 \| \| 2.4699 \| 0.04 \| 5 \| 2.4176 \| \| 2.408 \| 0.05 \| 6 \| 2.4171 \| \| 2.4169 \| 0.06 \| 7 \| 2.4192 \| \| 2.4351 \| 0.07 \| 8 \| 2.4169 \| \| 2.3709 \| 0.07 \| 9 \| 2.4181 \| \| 2.4303 \| 0.08 \| 10 \| 2.4174 \| \| 2.3925 \| 0.09 \| 11 \| 2.4162 \| \| 2.4358 \| 0.1 \| 12 \| 2.4150 \| \| 2.5119 \| 0.11 \| 13 \| 2.4158 \| \| 2.4336 \| 0.12 \| 14 \| 2.4139 \| \| 2.3145 \| 0.12 \| 15 \| 2.4137 \| \| 2.3547 \| 0.13 \| 16 \| 2.4139 \| \| 2.4008 \| 0.14 \| 17 \| 2.4124 \| \| 2.3856 \| 0.15 \| 18 \| 2.4107 \| \| 2.4702 \| 0.16 \| 19 \| 2.4101 \| \| 2.4401 \| 0.16 \| 20 \| 2.4101 \| \| 2.4957 \| 0.17 \| 21 \| 2.4074 \| \| 2.4915 \| 0.18 \| 22 \| 2.4044 \| \| 2.3463 \| 0.19 \| 23 \| 2.4051 \| \| 2.2404 \| 0.2 \| 24 \| 2.4044 \| \| 2.469 \| 0.21 \| 25 \| 2.4023 \| \| 2.4707 \| 0.21 \| 26 \| 2.4002 \| \| 2.4167 \| 0.22 \| 27 \| 2.4003 \| \| 2.3213 \| 0.23 \| 28 \| 2.3969 \| \| 2.5036 \| 0.24 \| 29 \| 2.3942 \| \| 2.3594 \| 0.25 \| 30 \| 2.3920 \| \| 2.3971 \| 0.26 \| 31 \| 2.3910 \| \| 2.3863 \| 0.26 \| 32 \| 2.3880 \| \| 2.4845 \| 0.27 \| 33 \| 2.3836 \| \| 2.3076 \| 0.28 \| 34 \| 2.3844 \| \| 2.4448 \| 0.29 \| 35 \| 2.3810 \| \| 2.3576 \| 0.3 \| 36 \| 2.3789 \| \| 2.3279 \| 0.3 \| 37 \| 2.3744 \| \| 2.4572 \| 0.31 \| 38 \| 2.3744 \| \| 2.4527 \| 0.32 \| 39 \| 2.3702 \| \| 2.5135 \| 0.33 \| 40 \| 2.3645 \| \| 2.3704 \| 0.34 \| 41 \| 2.3638 \| \| 2.4071 \| 0.35 \| 42 \| 2.3586 \| \| 2.3059 \| 0.35 \| 43 \| 2.3567 \| \| 2.4828 \| 0.36 \| 44 \| 2.3525 \| \| 2.3812 \| 0.37 \| 45 \| 2.3474 \| \| 2.3066 \| 0.38 \| 46 \| 2.3432 \| \| 2.3644 \| 0.39 \| 47 \| 2.3396 \| \| 2.3855 \| 0.4 \| 48 \| 2.3357 \| \| 2.3533 \| 0.4 \| 49 \| 2.3299 \| \| 2.3486 \| 0.41 \| 50 \| 2.3252 \| \| 2.3527 \| 0.42 \| 51 \| 2.3194 \| \| 2.3593 \| 0.43 \| 52 \| 2.3150 \| \| 2.3743 \| 0.44 \| 53 \| 2.3117 \| \| 2.5021 \| 0.44 \| 54 \| 2.3026 \| \| 2.3785 \| 0.45 \| 55 \| 2.2974 \| \| 2.17 \| 0.46 \| 56 \| 2.2931 \| \| 2.2599 \| 0.47 \| 57 \| 2.2851 \| \| 2.2211 \| 0.48 \| 58 \| 2.2794 \| \| 2.2464 \| 0.49 \| 59 \| 2.2716 \| \| 2.2058 \| 0.49 \| 60 \| 2.2622 \| \| 2.3164 \| 0.5 \| 61 \| 2.2560 \| \| 2.3093 \| 0.51 \| 62 \| 2.2445 \| \| 2.2846 \| 0.52 \| 63 \| 2.2353 \| \| 2.1138 \| 0.53 \| 64 \| 2.2271 \| \| 2.3286 \| 0.53 \| 65 \| 2.2170 \| \| 2.1988 \| 0.54 \| 66 \| 2.2077 \| \| 2.2598 \| 0.55 \| 67 \| 2.1968 \| \| 2.2303 \| 0.56 \| 68 \| 2.1880 \| \| 2.2419 \| 0.57 \| 69 \| 2.1790 \| \| 2.2628 \| 0.58 \| 70 \| 2.1689 \| \| 2.1922 \| 0.58 \| 71 \| 2.1573 \| \| 2.2353 \| 0.59 \| 72 \| 2.1498 \| \| 2.2111 \| 0.6 \| 73 \| 2.1376 \| \| 2.1128 \| 0.61 \| 74 \| 2.1271 \| \| 2.2167 \| 0.62 \| 75 \| 2.1184 \| \| 2.1878 \| 0.63 \| 76 \| 2.1085 \| \| 2.1391 \| 0.63 \| 77 \| 2.0950 \| \| 2.1336 \| 0.64 \| 78 \| 2.0818 \| \| 2.1266 \| 0.65 \| 79 \| 2.0730 \| \| 1.9823 \| 0.66 \| 80 \| 2.0634 \| \| 2.1003 \| 0.67 \| 81 \| 2.0490 \| \| 2.0681 \| 0.67 \| 82 \| 2.0353 \| \| 2.1475 \| 0.68 \| 83 \| 2.0218 \| \| 1.996 \| 0.69 \| 84 \| 2.0082 \| \| 1.9981 \| 0.7 \| 85 \| 1.9951 \| \| 2.0693 \| 0.71 \| 86 \| 1.9823 \| \| 1.9524 \| 0.72 \| 87 \| 1.9719 \| \| 2.058 \| 0.72 \| 88 \| 1.9587 \| \| 1.9211 \| 0.73 \| 89 \| 1.9455 \| \| 1.9496 \| 0.74 \| 90 \| 1.9311 \| \| 1.9495 \| 0.75 \| 91 \| 1.9200 \| \| 2.0249 \| 0.76 \| 92 \| 1.9062 \| \| 1.9044 \| 0.77 \| 93 \| 1.8940 \| \| 1.9373 \| 0.77 \| 94 \| 1.8817 \| \| 1.8604 \| 0.78 \| 95 \| 1.8675 \| \| 1.8957 \| 0.79 \| 96 \| 1.8584 \| \| 1.8673 \| 0.8 \| 97 \| 1.8471 \| \| 1.9002 \| 0.81 \| 98 \| 1.8337 \| \| 1.9025 \| 0.81 \| 99 \| 1.8226 \| \| 1.8356 \| 0.82 \| 100 \| 1.8099 \| \| 1.7723 \| 0.83 \| 101 \| 1.7994 \| \| 1.7628 \| 0.84 \| 102 \| 1.7902 \| \| 1.7014 \| 0.85 \| 103 \| 1.7788 \| \| 1.7383 \| 0.86 \| 104 \| 1.7678 \| \| 1.7647 \| 0.86 \| 105 \| 1.7581 \| \| 1.7835 \| 0.87 \| 106 \| 1.7466 \| \| 1.7645 \| 0.88 \| 107 \| 1.7367 \| \| 1.7654 \| 0.89 \| 108 \| 1.7267 \| \| 1.8344 \| 0.9 \| 109 \| 1.7173 \| \| 1.6528 \| 0.91 \| 110 \| 1.7068 \| \| 1.676 \| 0.91 \| 111 \| 1.6989 \| \| 1.5894 \| 0.92 \| 112 \| 1.6882 \| \| 1.6154 \| 0.93 \| 113 \| 1.6778 \| \| 1.609 \| 0.94 \| 114 \| 1.6697 \| \| 1.6803 \| 0.95 \| 115 \| 1.6592 \| \| 1.6487 \| 0.95 \| 116 \| 1.6484 \| \| 1.6905 \| 0.96 \| 117 \| 1.6403 \| \| 1.6258 \| 0.97 \| 118 \| 1.6300 \| \| 1.5744 \| 0.98 \| 119 \| 1.6189 \| \| 1.4791 \| 0.99 \| 120 \| 1.6088 \| \| 1.6177 \| 1.0 \| 121 \| 1.5975 \| \| 1.582 \| 1.0 \| 122 \| 1.5860 \| \| 1.5378 \| 1.01 \| 123 \| 1.5760 \| \| 1.5691 \| 1.02 \| 124 \| 1.5658 \| \| 1.5387 \| 1.03 \| 125 \| 1.5540 \| \| 1.5527 \| 1.04 \| 126 \| 1.5429 \| \| 1.4642 \| 1.05 \| 127 \| 1.5316 \| \| 1.4867 \| 1.05 \| 128 \| 1.5199 \| \| 1.5204 \| 1.06 \| 129 \| 1.5075 \| \| 1.5997 \| 1.07 \| 130 \| 1.4962 \| \| 1.4419 \| 1.08 \| 131 \| 1.4833 \| \| 1.5799 \| 1.09 \| 132 \| 1.4718 \| \| 1.5103 \| 1.09 \| 133 \| 1.4584 \| \| 1.5444 \| 1.1 \| 134 \| 1.4472 \| \| 1.4835 \| 1.11 \| 135 \| 1.4351 \| \| 1.4326 \| 1.12 \| 136 \| 1.4213 \| \| 1.4079 \| 1.13 \| 137 \| 1.4088 \| \| 1.5206 \| 1.14 \| 138 \| 1.3971 \| \| 1.3868 \| 1.14 \| 139 \| 1.3822 \| \| 1.4778 \| 1.15 \| 140 \| 1.3702 \| \| 1.4627 \| 1.16 \| 141 \| 1.3558 \| \| 1.3555 \| 1.17 \| 142 \| 1.3444 \| \| 1.3143 \| 1.18 \| 143 \| 1.3323 \| \| 1.3754 \| 1.19 \| 144 \| 1.3192 \| \| 1.2488 \| 1.19 \| 145 \| 1.3082 \| \| 1.2821 \| 1.2 \| 146 \| 1.2969 \| \| 1.2804 \| 1.21 \| 147 \| 1.2856 \| \| 1.233 \| 1.22 \| 148 \| 1.2747 \| \| 1.3502 \| 1.23 \| 149 \| 1.2633 \| \| 1.2224 \| 1.23 \| 150 \| 1.2536 \| \| 1.199 \| 1.24 \| 151 \| 1.2419 \| \| 1.1749 \| 1.25 \| 152 \| 1.2321 \| \| 1.305 \| 1.26 \| 153 \| 1.2220 \| \| 1.1391 \| 1.27 \| 154 \| 1.2100 \| \| 1.3063 \| 1.28 \| 155 \| 1.1990 \| \| 1.2402 \| 1.28 \| 156 \| 1.1878 \| \| 1.1104 \| 1.29 \| 157 \| 1.1772 \| \| 1.24 \| 1.3 \| 158 \| 1.1670 \| \| 1.0549 \| 1.31 \| 159 \| 1.1555 \| \| 1.1417 \| 1.32 \| 160 \| 1.1452 \| \| 1.0898 \| 1.33 \| 161 \| 1.1351 \| \| 1.1035 \| 1.33 \| 162 \| 1.1259 \| \| 1.1088 \| 1.34 \| 163 \| 1.1158 \| \| 1.086 \| 1.35 \| 164 \| 1.1069 \| \| 1.15 \| 1.36 \| 165 \| 1.0975 \| \| 1.0394 \| 1.37 \| 166 \| 1.0888 \| \| 1.1268 \| 1.37 \| 167 \| 1.0806 \| \| 1.0803 \| 1.38 \| 168 \| 1.0710 \| \| 1.0198 \| 1.39 \| 169 \| 1.0624 \| \| 1.0765 \| 1.4 \| 170 \| 1.0534 \| \| 1.0318 \| 1.41 \| 171 \| 1.0447 \| \| 1.0098 \| 1.42 \| 172 \| 1.0369 \| \| 1.0013 \| 1.42 \| 173 \| 1.0284 \| \| 0.9773 \| 1.43 \| 174 \| 1.0210 \| \| 1.0233 \| 1.44 \| 175 \| 1.0130 \| \| 0.985 \| 1.45 \| 176 \| 1.0053 \| \| 0.9806 \| 1.46 \| 177 \| 0.9983 \| \| 1.0393 \| 1.47 \| 178 \| 0.9906 \| \| 0.9191 \| 1.47 \| 179 \| 0.9844 \| \| 0.9454 \| 1.48 \| 180 \| 0.9781 \| \| 0.9354 \| 1.49 \| 181 \| 0.9710 \| \| 0.9598 \| 1.5 \| 182 \| 0.9658 \| \| 1.0652 \| 1.51 \| 183 \| 0.9584 \| \| 0.9002 \| 1.51 \| 184 \| 0.9538 \| \| 0.9477 \| 1.52 \| 185 \| 0.9472 \| \| 0.9203 \| 1.53 \| 186 \| 0.9414 \| \| 0.8837 \| 1.54 \| 187 \| 0.9361 \| \| 0.91 \| 1.55 \| 188 \| 0.9313 \| \| 0.8616 \| 1.56 \| 189 \| 0.9258 \| \| 0.9201 \| 1.56 \| 190 \| 0.9205 \| \| 0.9408 \| 1.57 \| 191 \| 0.9147 \| \| 0.9274 \| 1.58 \| 192 \| 0.9093 \| \| 1.0009 \| 1.59 \| 193 \| 0.9064 \| \| 0.9202 \| 1.6 \| 194 \| 0.9009 \| \| 0.9886 \| 1.6 \| 195 \| 0.8959 \| \| 0.9289 \| 1.61 \| 196 \| 0.8913 \| \| 0.9603 \| 1.62 \| 197 \| 0.8875 \| \| 0.9138 \| 1.63 \| 198 \| 0.8837 \| \| 0.8794 \| 1.64 \| 199 \| 0.8787 \| \| 0.8315 \| 1.65 \| 200 \| 0.8750 \| \| 0.8745 \| 1.65 \| 201 \| 0.8705 \| \| 1.013 \| 1.66 \| 202 \| 0.8673 \| \| 0.8565 \| 1.67 \| 203 \| 0.8634 \| \| 0.9121 \| 1.68 \| 204 \| 0.8596 \| \| 0.7825 \| 1.69 \| 205 \| 0.8558 \| \| 0.9171 \| 1.7 \| 206 \| 0.8524 \| \| 0.7595 \| 1.7 \| 207 \| 0.8488 \| \| 0.8611 \| 1.71 \| 208 \| 0.8453 \| \| 0.7212 \| 1.72 \| 209 \| 0.8421 \| \| 0.8745 \| 1.73 \| 210 \| 0.8389 \| \| 0.93 \| 1.74 \| 211 \| 0.8354 \| \| 0.9183 \| 1.74 \| 212 \| 0.8321 \| \| 0.8482 \| 1.75 \| 213 \| 0.8293 \| \| 0.8155 \| 1.76 \| 214 \| 0.8256 \| \| 0.9113 \| 1.77 \| 215 \| 0.8224 \| \| 0.8009 \| 1.78 \| 216 \| 0.8190 \| \| 0.6555 \| 1.79 \| 217 \| 0.8165 \| \| 0.7727 \| 1.79 \| 218 \| 0.8133 \| \| 0.7987 \| 1.8 \| 219 \| 0.8105 \| \| 0.7794 \| 1.81 \| 220 \| 0.8074 \| \| 0.8248 \| 1.82 \| 221 \| 0.8043 \| \| 0.7818 \| 1.83 \| 222 \| 0.8020 \| \| 0.741 \| 1.84 \| 223 \| 0.7995 \| \| 0.6907 \| 1.84 \| 224 \| 0.7969 \| \| 0.789 \| 1.85 \| 225 \| 0.7938 \| \| 0.7101 \| 1.86 \| 226 \| 0.7910 \| \| 0.7178 \| 1.87 \| 227 \| 0.7887 \| \| 0.7109 \| 1.88 \| 228 \| 0.7865 \| \| 0.6699 \| 1.88 \| 229 \| 0.7838 \| \| 0.8443 \| 1.89 \| 230 \| 0.7814 \| \| 0.7397 \| 1.9 \| 231 \| 0.7789 \| \| 0.7888 \| 1.91 \| 232 \| 0.7760 \| \| 0.7725 \| 1.92 \| 233 \| 0.7735 \| \| 0.7797 \| 1.93 \| 234 \| 0.7707 \| \| 0.7988 \| 1.93 \| 235 \| 0.7678 \| \| 0.7548 \| 1.94 \| 236 \| 0.7660 \| \| 0.904 \| 1.95 \| 237 \| 0.7631 \| \| 0.8183 \| 1.96 \| 238 \| 0.7616 \| \| 0.8292 \| 1.97 \| 239 \| 0.7582 \| \| 0.7144 \| 1.98 \| 240 \| 0.7561 \| \| 0.753 \| 1.98 \| 241 \| 0.7538 \| \| 0.7629 \| 1.99 \| 242 \| 0.7525 \| \| 0.8713 \| 2.0 \| 243 \| 0.7497 \| \| 0.7355 \| 2.01 \| 244 \| 0.7477 \| \| 0.6998 \| 2.02 \| 245 \| 0.7459 \| \| 0.7567 \| 2.02 \| 246 \| 0.7438 \| \| 0.6594 \| 2.03 \| 247 \| 0.7420 \| \| 0.7124 \| 2.04 \| 248 \| 0.7405 \| \| 0.9188 \| 2.05 \| 249 \| 0.7380 \| \| 0.7406 \| 2.06 \| 250 \| 0.7364 \| \| 0.7091 \| 2.07 \| 251 \| 0.7341 \| \| 0.8144 \| 2.07 \| 252 \| 0.7319 \| \| 0.7122 \| 2.08 \| 253 \| 0.7307 \| \| 0.7504 \| 2.09 \| 254 \| 0.7291 \| \| 0.7409 \| 2.1 \| 255 \| 0.7276 \| \| 0.7844 \| 2.11 \| 256 \| 0.7258 \| \| 0.8328 \| 2.12 \| 257 \| 0.7234 \| \| 0.7149 \| 2.12 \| 258 \| 0.7221 \| \| 0.7063 \| 2.13 \| 259 \| 0.7205 \| \| 0.6629 \| 2.14 \| 260 \| 0.7195 \| \| 0.5896 \| 2.15 \| 261 \| 0.7177 \| \| 0.734 \| 2.16 \| 262 \| 0.7165 \| \| 0.7293 \| 2.16 \| 263 \| 0.7157 \| \| 0.6819 \| 2.17 \| 264 \| 0.7142 \| \| 0.6928 \| 2.18 \| 265 \| 0.7133 \| \| 0.6026 \| 2.19 \| 266 \| 0.7119 \| \| 0.6704 \| 2.2 \| 267 \| 0.7114 \| \| 0.7118 \| 2.21 \| 268 \| 0.7099 \| \| 0.8447 \| 2.21 \| 269 \| 0.7084 \| \| 0.6857 \| 2.22 \| 270 \| 0.7075 \| \| 0.7257 \| 2.23 \| 271 \| 0.7066 \| \| 0.6884 \| 2.24 \| 272 \| 0.7058 \| \| 0.5883 \| 2.25 \| 273 \| 0.7047 \| \| 0.6798 \| 2.26 \| 274 \| 0.7036 \| \| 0.6575 \| 2.26 \| 275 \| 0.7024 \| \| 0.627 \| 2.27 \| 276 \| 0.7017 \| \| 0.7029 \| 2.28 \| 277 \| 0.7016 \| \| 0.7248 \| 2.29 \| 278 \| 0.7009 \| \| 0.6947 \| 2.3 \| 279 \| 0.6996 \| \| 0.708 \| 2.3 \| 280 \| 0.6991 \| \| 0.5384 \| 2.31 \| 281 \| 0.6981 \| \| 0.5539 \| 2.32 \| 282 \| 0.6975 \| \| 0.6751 \| 2.33 \| 283 \| 0.6962 \| \| 0.5809 \| 2.34 \| 284 \| 0.6957 \| \| 0.7105 \| 2.35 \| 285 \| 0.6952 \| \| 0.735 \| 2.35 \| 286 \| 0.6945 \| \| 0.7564 \| 2.36 \| 287 \| 0.6936 \| \| 0.732 \| 2.37 \| 288 \| 0.6925 \| \| 0.6892 \| 2.38 \| 289 \| 0.6919 \| \| 0.6454 \| 2.39 \| 290 \| 0.6910 \| \| 0.6919 \| 2.4 \| 291 \| 0.6901 \| \| 0.6842 \| 2.4 \| 292 \| 0.6893 \| \| 0.6044 \| 2.41 \| 293 \| 0.6889 \| \| 0.5893 \| 2.42 \| 294 \| 0.6885 \| \| 0.7235 \| 2.43 \| 295 \| 0.6875 \| \| 0.7216 \| 2.44 \| 296 \| 0.6873 \| \| 0.7677 \| 2.44 \| 297 \| 0.6865 \| \| 0.5953 \| 2.45 \| 298 \| 0.6862 \| \| 0.8029 \| 2.46 \| 299 \| 0.6853 \| \| 0.6425 \| 2.47 \| 300 \| 0.6846 \| \| 0.5764 \| 2.48 \| 301 \| 0.6846 \| \| 0.7721 \| 2.49 \| 302 \| 0.6831 \| \| 0.7315 \| 2.49 \| 303 \| 0.6831 \| \| 0.6483 \| 2.5 \| 304 \| 0.6829 \| \| 0.8087 \| 2.51 \| 305 \| 0.6825 \| \| 0.6676 \| 2.52 \| 306 \| 0.6816 \| \| 0.6153 \| 2.53 \| 307 \| 0.6813 \| \| 0.6388 \| 2.53 \| 308 \| 0.6812 \| \| 0.6322 \| 2.54 \| 309 \| 0.6803 \| \| 0.5539 \| 2.55 \| 310 \| 0.6803 \| \| 0.6124 \| 2.56 \| 311 \| 0.6796 \| \| 0.6905 \| 2.57 \| 312 \| 0.6791 \| \| 0.6522 \| 2.58 \| 313 \| 0.6782 \| \| 0.5722 \| 2.58 \| 314 \| 0.6784 \| \| 0.6271 \| 2.59 \| 315 \| 0.6776 \| \| 0.6927 \| 2.6 \| 316 \| 0.6783 \| \| 0.733 \| 2.61 \| 317 \| 0.6768 \| \| 0.6622 \| 2.62 \| 318 \| 0.6765 \| \| 0.7042 \| 2.63 \| 319 \| 0.6765 \| \| 0.8197 \| 2.63 \| 320 \| 0.6763 \| \| 0.8398 \| 2.64 \| 321 \| 0.6758 \| \| 0.6703 \| 2.65 \| 322 \| 0.6756 \| \| 0.6722 \| 2.66 \| 323 \| 0.6750 \| \| 0.7457 \| 2.67 \| 324 \| 0.6748 \| \| 0.6385 \| 2.67 \| 325 \| 0.6746 \| \| 0.557 \| 2.68 \| 326 \| 0.6743 \| \| 0.6835 \| 2.69 \| 327 \| 0.6739 \| \| 0.6078 \| 2.7 \| 328 \| 0.6735 \| \| 0.8021 \| 2.71 \| 329 \| 0.6733 \| \| 0.5652 \| 2.72 \| 330 \| 0.6732 \| \| 0.7898 \| 2.72 \| 331 \| 0.6723 \| \| 0.5717 \| 2.73 \| 332 \| 0.6720 \| \| 0.6912 \| 2.74 \| 333 \| 0.6718 \| \| 0.641 \| 2.75 \| 334 \| 0.6717 \| \| 0.6551 \| 2.76 \| 335 \| 0.6714 \| \| 0.7743 \| 2.77 \| 336 \| 0.6706 \| \| 0.631 \| 2.77 \| 337 \| 0.6710 \| \| 0.6843 \| 2.78 \| 338 \| 0.6703 \| \| 0.6913 \| 2.79 \| 339 \| 0.6701 \| \| 0.6482 \| 2.8 \| 340 \| 0.6697 \| \| 0.6251 \| 2.81 \| 341 \| 0.6696 \| \| 0.6712 \| 2.81 \| 342 \| 0.6694 \| \| 0.6543 \| 2.82 \| 343 \| 0.6693 \| \| 0.7393 \| 2.83 \| 344 \| 0.6687 \| \| 0.7283 \| 2.84 \| 345 \| 0.6686 \| \| 0.673 \| 2.85 \| 346 \| 0.6686 \| \| 0.6263 \| 2.86 \| 347 \| 0.6680 \| \| 0.6574 \| 2.86 \| 348 \| 0.6678 \| \| 0.7178 \| 2.87 \| 349 \| 0.6677 \| \| 0.6941 \| 2.88 \| 350 \| 0.6673 \| \| 0.5781 \| 2.89 \| 351 \| 0.6675 \| \| 0.6024 \| 2.9 \| 352 \| 0.6671 \| \| 0.6324 \| 2.91 \| 353 \| 0.6667 \| \| 0.7445 \| 2.91 \| 354 \| 0.6663 \| \| 0.5899 \| 2.92 \| 355 \| 0.6664 \| \| 0.7318 \| 2.93 \| 356 \| 0.6659 \| \| 0.7341 \| 2.94 \| 357 \| 0.6656 \| \| 0.7439 \| 2.95 \| 358 \| 0.6656 \| \| 0.7061 \| 2.95 \| 359 \| 0.6652 \| \| 0.7121 \| 2.96 \| 360 \| 0.6649 \| \| 0.6754 \| 2.97 \| 361 \| 0.6649 \| \| 0.7367 \| 2.98 \| 362 \| 0.6646 \| \| 0.7033 \| 2.99 \| 363 \| 0.6646 \| \| 0.6652 \| 3.0 \| 364 \| 0.6640 \| \| 0.707 \| 3.0 \| 365 \| 0.6639 \| \| 0.5992 \| 3.01 \| 366 \| 0.6636 \| \| 0.6483 \| 3.02 \| 367 \| 0.6633 \| \| 0.8483 \| 3.03 \| 368 \| 0.6623 \| \| 0.7052 \| 3.04 \| 369 \| 0.6628 \| \| 0.7748 \| 3.05 \| 370 \| 0.6624 \| \| 0.7242 \| 3.05 \| 371 \| 0.6621 \| \| 0.7835 \| 3.06 \| 372 \| 0.6621 \| \| 0.6273 \| 3.07 \| 373 \| 0.6621 \| \| 0.6937 \| 3.08 \| 374 \| 0.6617 \| \| 0.7308 \| 3.09 \| 375 \| 0.6615 \| \| 0.6431 \| 3.09 \| 376 \| 0.6613 \| \| 0.6486 \| 3.1 \| 377 \| 0.6612 \| \| 0.6671 \| 3.11 \| 378 \| 0.6613 \| \| 0.6046 \| 3.12 \| 379 \| 0.6605 \| \| 0.5741 \| 3.13 \| 380 \| 0.6605 \| \| 0.6746 \| 3.14 \| 381 \| 0.6606 \| \| 0.6525 \| 3.14 \| 382 \| 0.6604 \| \| 0.6483 \| 3.15 \| 383 \| 0.6602 \| \| 0.6631 \| 3.16 \| 384 \| 0.6602 \| \| 0.5769 \| 3.17 \| 385 \| 0.6603 \| \| 0.6648 \| 3.18 \| 386 \| 0.6596 \| \| 0.6933 \| 3.19 \| 387 \| 0.6592 \| \| 0.6597 \| 3.19 \| 388 \| 0.6596 \| \| 0.5871 \| 3.2 \| 389 \| 0.6596 \| \| 0.5976 \| 3.21 \| 390 \| 0.6593 \| \| 0.6025 \| 3.22 \| 391 \| 0.6591 \| \| 0.7157 \| 3.23 \| 392 \| 0.6588 \| \| 0.6419 \| 3.23 \| 393 \| 0.6587 \| \| 0.5579 \| 3.24 \| 394 \| 0.6589 \| \| 0.7142 \| 3.25 \| 395 \| 0.6588 \| \| 0.5773 \| 3.26 \| 396 \| 0.6581 \| \| 0.5624 \| 3.27 \| 397 \| 0.6583 \| \| 0.6029 \| 3.28 \| 398 \| 0.6579 \| \| 0.6642 \| 3.28 \| 399 \| 0.6582 \| \| 0.7 \| 3.29 \| 400 \| 0.6579 \| \| 0.7918 \| 3.3 \| 401 \| 0.6579 \| \| 0.563 \| 3.31 \| 402 \| 0.6577 \| \| 0.7208 \| 3.32 \| 403 \| 0.6575 \| \| 0.6769 \| 3.33 \| 404 \| 0.6570 \| \| 0.7093 \| 3.33 \| 405 \| 0.6571 \| \| 0.5287 \| 3.34 \| 406 \| 0.6570 \| \| 0.5828 \| 3.35 \| 407 \| 0.6572 \| \| 0.5703 \| 3.36 \| 408 \| 0.6566 \| \| 0.6647 \| 3.37 \| 409 \| 0.6566 \| \| 0.6879 \| 3.37 \| 410 \| 0.6568 \| \| 0.7325 \| 3.38 \| 411 \| 0.6566 \| \| 0.6021 \| 3.39 \| 412 \| 0.6565 \| \| 0.6777 \| 3.4 \| 413 \| 0.6565 \| \| 0.6057 \| 3.41 \| 414 \| 0.6560 \| \| 0.5996 \| 3.42 \| 415 \| 0.6558 \| \| 0.6841 \| 3.42 \| 416 \| 0.6556 \| \| 0.6096 \| 3.43 \| 417 \| 0.6557 \| \| 0.6245 \| 3.44 \| 418 \| 0.6559 \| \| 0.664 \| 3.45 \| 419 \| 0.6556 \| \| 0.7183 \| 3.46 \| 420 \| 0.6561 \| \| 0.6449 \| 3.47 \| 421 \| 0.6558 \| \| 0.6497 \| 3.47 \| 422 \| 0.6557 \| \| 0.8151 \| 3.48 \| 423 \| 0.6554 \| \| 0.813 \| 3.49 \| 424 \| 0.6552 \| \| 0.6278 \| 3.5 \| 425 \| 0.6553 \| \| 0.6376 \| 3.51 \| 426 \| 0.6556 \| \| 0.697 \| 3.51 \| 427 \| 0.6554 \| \| 0.628 \| 3.52 \| 428 \| 0.6550 \| \| 0.7049 \| 3.53 \| 429 \| 0.6553 \| \| 0.6641 \| 3.54 \| 430 \| 0.6549 \| \| 0.6465 \| 3.55 \| 431 \| 0.6552 \| \| 0.7366 \| 3.56 \| 432 \| 0.6550 \| \| 0.6325 \| 3.56 \| 433 \| 0.6545 \| \| 0.5621 \| 3.57 \| 434 \| 0.6550 \| \| 0.5846 \| 3.58 \| 435 \| 0.6553 \| \| 0.6516 \| 3.59 \| 436 \| 0.6551 \| \| 0.7258 \| 3.6 \| 437 \| 0.6546 \| \| 0.6027 \| 3.6 \| 438 \| 0.6547 \| \| 0.5344 \| 3.61 \| 439 \| 0.6549 \| \| 0.6988 \| 3.62 \| 440 \| 0.6546 \| \| 0.6863 \| 3.63 \| 441 \| 0.6548 \| \| 0.627 \| 3.64 \| 442 \| 0.6544 \| \| 0.6353 \| 3.65 \| 443 \| 0.6548 \| \| 0.5361 \| 3.65 \| 444 \| 0.6541 \| \| 0.6774 \| 3.66 \| 445 \| 0.6548 \| \| 0.668 \| 3.67 \| 446 \| 0.6546 \| \| 0.544 \| 3.68 \| 447 \| 0.6545 \| \| 0.5683 \| 3.69 \| 448 \| 0.6546 \| \| 0.6955 \| 3.7 \| 449 \| 0.6543 \| \| 0.6316 \| 3.7 \| 450 \| 0.6543 \| \| 0.647 \| 3.71 \| 451 \| 0.6544 \| \| 0.6797 \| 3.72 \| 452 \| 0.6541 \| \| 0.6566 \| 3.73 \| 453 \| 0.6541 \| \| 0.6585 \| 3.74 \| 454 \| 0.6544 \| \| 0.6632 \| 3.74 \| 455 \| 0.6541 \| \| 0.5798 \| 3.75 \| 456 \| 0.6540 \| \| 0.6417 \| 3.76 \| 457 \| 0.6540 \| \| 0.706 \| 3.77 \| 458 \| 0.6538 \| \| 0.6709 \| 3.78 \| 459 \| 0.6542 \| \| 0.7047 \| 3.79 \| 460 \| 0.6536 \| \| 0.5466 \| 3.79 \| 461 \| 0.6538 \| \| 0.5479 \| 3.8 \| 462 \| 0.6540 \| \| 0.6476 \| 3.81 \| 463 \| 0.6535 \| \| 0.6584 \| 3.82 \| 464 \| 0.6534 \| \| 0.6515 \| 3.83 \| 465 \| 0.6540 \| \| 0.5812 \| 3.84 \| 466 \| 0.6535 \| \| 0.6339 \| 3.84 \| 467 \| 0.6537 \| \| 0.6521 \| 3.85 \| 468 \| 0.6537 \| \| 0.6451 \| 3.86 \| 469 \| 0.6538 \| \| 0.6655 \| 3.87 \| 470 \| 0.6532 \| \| 0.7017 \| 3.88 \| 471 \| 0.6533 \| \| 0.5794 \| 3.88 \| 472 \| 0.6530 \| \| 0.6485 \| 3.89 \| 473 \| 0.6536 \| \| 0.6723 \| 3.9 \| 474 \| 0.6533 \| \| 0.72 \| 3.91 \| 475 \| 0.6534 \| \| 0.6114 \| 3.92 \| 476 \| 0.6535 \| \| 0.596 \| 3.93 \| 477 \| 0.6536 \| \| 0.5961 \| 3.93 \| 478 \| 0.6538 \| \| 0.6629 \| 3.94 \| 479 \| 0.6531 \| \| 0.6682 \| 3.95 \| 480 \| 0.6534 \| \| 0.7007 \| 3.96 \| 481 \| 0.6534 \| \| 0.6594 \| 3.97 \| 482 \| 0.6535 \| \| 0.7607 \| 3.98 \| 483 \| 0.6531 \| \| 0.5735 \| 3.98 \| 484 \| 0.6532 \| \| 0.7111 \| 3.99 \| 485 \| 0.6531 \| \| 0.6498 \| 4.0 \| 486 \| 0.6533 \| \| 0.624 \| 4.01 \| 487 \| 0.6529 \| \| 0.7284 \| 4.02 \| 488 \| 0.6535 \| \| 0.5665 \| 4.02 \| 489 \| 0.6531 \| \| 0.6473 \| 4.03 \| 490 \| 0.6534 \| \| 0.614 \| 4.04 \| 491 \| 0.6534 \| \| 0.6663 \| 4.05 \| 492 \| 0.6528 \| \| 0.6309 \| 4.06 \| 493 \| 0.6527 \| \| 0.6926 \| 4.07 \| 494 \| 0.6530 \| \| 0.6112 \| 4.07 \| 495 \| 0.6531 \| \| 0.6879 \| 4.08 \| 496 \| 0.6526 \| \| 0.6939 \| 4.09 \| 497 \| 0.6529 \| \| 0.7551 \| 4.1 \| 498 \| 0.6530 \| \| 0.6085 \| 4.11 \| 499 \| 0.6530 \| \| 0.6741 \| 4.12 \| 500 \| 0.6533 \| \| 0.5913 \| 4.12 \| 501 \| 0.6529 \| \| 0.6337 \| 4.13 \| 502 \| 0.6529 \| \| 0.6061 \| 4.14 \| 503 \| 0.6527 \| \| 0.6511 \| 4.15 \| 504 \| 0.6529 \| \| 0.6358 \| 4.16 \| 505 \| 0.6531 \| \| 0.6537 \| 4.16 \| 506 \| 0.6527 \| \| 0.5757 \| 4.17 \| 507 \| 0.6532 \| \| 0.6143 \| 4.18 \| 508 \| 0.6529 \| \| 0.5723 \| 4.19 \| 509 \| 0.6530 \| \| 0.5647 \| 4.2 \| 510 \| 0.6528 \| \| 0.5878 \| 4.21 \| 511 \| 0.6531 \| \| 0.6119 \| 4.21 \| 512 \| 0.6527 \| \| 0.743 \| 4.22 \| 513 \| 0.6530 \| \| 0.6942 \| 4.23 \| 514 \| 0.6528 \| \| 0.5967 \| 4.24 \| 515 \| 0.6527 \| \| 0.6869 \| 4.25 \| 516 \| 0.6530 \| \| 0.62 \| 4.26 \| 517 \| 0.6529 \| \| 0.7596 \| 4.26 \| 518 \| 0.6530 \| \| 0.6483 \| 4.27 \| 519 \| 0.6530 \| \| 0.6449 \| 4.28 \| 520 \| 0.6527 \| \| 0.56 \| 4.29 \| 521 \| 0.6529 \| \| 0.673 \| 4.3 \| 522 \| 0.6527 \| \| 0.5469 \| 4.3 \| 523 \| 0.6527 \| \| 0.6084 \| 4.31 \| 524 \| 0.6528 \| \| 0.5118 \| 4.32 \| 525 \| 0.6527 \| \| 0.7318 \| 4.33 \| 526 \| 0.6529 \| \| 0.7787 \| 4.34 \| 527 \| 0.6525 \| \| 0.7177 \| 4.35 \| 528 \| 0.6532 \| \| 0.6294 \| 4.35 \| 529 \| 0.6531 \| \| 0.6758 \| 4.36 \| 530 \| 0.6527 \| \| 0.6679 \| 4.37 \| 531 \| 0.6526 \| \| 0.5373 \| 4.38 \| 532 \| 0.6525 \| \| 0.6655 \| 4.39 \| 533 \| 0.6529 \| \| 0.6738 \| 4.4 \| 534 \| 0.6527 \| \| 0.6849 \| 4.4 \| 535 \| 0.6528 \| \| 0.5894 \| 4.41 \| 536 \| 0.6530 \| \| 0.7516 \| 4.42 \| 537 \| 0.6533 \| \| 0.7417 \| 4.43 \| 538 \| 0.6530 \| \| 0.6239 \| 4.44 \| 539 \| 0.6529 \| \| 0.6543 \| 4.44 \| 540 \| 0.6528 \| \| 0.6201 \| 4.45 \| 541 \| 0.6529 \| \| 0.6552 \| 4.46 \| 542 \| 0.6528 \| \| 0.5647 \| 4.47 \| 543 \| 0.6529 \| \| 0.6798 \| 4.48 \| 544 \| 0.6530 \| \| 0.6152 \| 4.49 \| 545 \| 0.6528 \| \| 0.7099 \| 4.49 \| 546 \| 0.6531 \| \| 0.7073 \| 4.5 \| 547 \| 0.6528 \| \| 0.76 \| 4.51 \| 548 \| 0.6531 \| \| 0.7266 \| 4.52 \| 549 \| 0.6526 \| \| 0.7659 \| 4.53 \| 550 \| 0.6527 \| \| 0.7033 \| 4.53 \| 551 \| 0.6532 \| \| 0.6679 \| 4.54 \| 552 \| 0.6534 \| \| 0.5671 \| 4.55 \| 553 \| 0.6533 \| \| 0.6845 \| 4.56 \| 554 \| 0.6527 \| \| 0.655 \| 4.57 \| 555 \| 0.6524 \| \| 0.7154 \| 4.58 \| 556 \| 0.6526 \| \| 0.7778 \| 4.58 \| 557 \| 0.6524 \| \| 0.6404 \| 4.59 \| 558 \| 0.6524 \| \| 0.6133 \| 4.6 \| 559 \| 0.6526 \| \| 0.6241 \| 4.61 \| 560 \| 0.6525 \| \| 0.6255 \| 4.62 \| 561 \| 0.6527 \| \| 0.5877 \| 4.63 \| 562 \| 0.6526 \| \| 0.7624 \| 4.63 \| 563 \| 0.6526 \| \| 0.613 \| 4.64 \| 564 \| 0.6522 \| \| 0.6014 \| 4.65 \| 565 \| 0.6524 \| \| 0.6217 \| 4.66 \| 566 \| 0.6525 \| \| 0.5651 \| 4.67 \| 567 \| 0.6525 \| \| 0.7227 \| 4.67 \| 568 \| 0.6526 \| \| 0.6247 \| 4.68 \| 569 \| 0.6525 \| \| 0.6886 \| 4.69 \| 570 \| 0.6524 \| \| 0.6894 \| 4.7 \| 571 \| 0.6524 \| \| 0.6543 \| 4.71 \| 572 \| 0.6525 \| \| 0.5932 \| 4.72 \| 573 \| 0.6522 \| \| 0.6069 \| 4.72 \| 574 \| 0.6523 \| \| 0.614 \| 4.73 \| 575 \| 0.6525 \| \| 0.5748 \| 4.74 \| 576 \| 0.6526 \| \| 0.5907 \| 4.75 \| 577 \| 0.6523 \| \| 0.6707 \| 4.76 \| 578 \| 0.6526 \| \| 0.642 \| 4.77 \| 579 \| 0.6525 \| \| 0.6228 \| 4.77 \| 580 \| 0.6522 \| \| 0.6178 \| 4.78 \| 581 \| 0.6525 \| \| 0.5958 \| 4.79 \| 582 \| 0.6528 \| \| 0.6532 \| 4.8 \| 583 \| 0.6527 \| \| 0.5752 \| 4.81 \| 584 \| 0.6526 \| \| 0.7058 \| 4.81 \| 585 \| 0.6525 \| \| 0.642 \| 4.82 \| 586 \| 0.6526 \| \| 0.6599 \| 4.83 \| 587 \| 0.6525 \| \| 0.7673 \| 4.84 \| 588 \| 0.6526 \| \| 0.6626 \| 4.85 \| 589 \| 0.6525 \| \| 0.5326 \| 4.86 \| 590 \| 0.6525 \| \| 0.6512 \| 4.86 \| 591 \| 0.6524 \| \| 0.5914 \| 4.87 \| 592 \| 0.6524 \| \| 0.6415 \| 4.88 \| 593 \| 0.6523 \| \| 0.7693 \| 4.89 \| 594 \| 0.6523 \| \| 0.6389 \| 4.9 \| 595 \| 0.6524 \| \| 0.6151 \| 4.91 \| 596 \| 0.6524 \| \| 0.6561 \| 4.91 \| 597 \| 0.6524 \| \| 0.6443 \| 4.92 \| 598 \| 0.6524 \| \| 0.6596 \| 4.93 \| 599 \| 0.6524 \| \| 0.6413 \| 4.94 \| 600 \| 0.6524 \| \| 0.6235 \| 4.95 \| 601 \| 0.6524 \| \| 0.598 \| 4.95 \| 602 \| 0.6524 \| \| 0.7232 \| 4.96 \| 603 \| 0.6524 \| \| 0.6172 \| 4.97 \| 604 \| 0.6524 \| \| 0.7063 \| 4.98 \| 605 \| 0.6524 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
DevanshSinha/testbits1	DevanshSinha	2024-03-12T06:53:26Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2024-03-12T06:53:15Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # 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. (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 0.8.2
jeonsiyun/layoutlmv3-v33-epoch20	jeonsiyun	2024-03-12T06:38:26Z	117	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T06:37:54Z	--- 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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Vasanth/mistral-sql	Vasanth	2024-03-12T06:38:23Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2024-03-12T05:20:05Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: mistral-sql 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-sql This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the generator 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: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
syedzaidi-kiwi/Llama-2-7b-chat-finetune	syedzaidi-kiwi	2024-03-12T06:37:40Z	8	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llm", "fine-tuned", "Llama 2 7b", "KiwiTech LLC", "question-answering", "en", "dataset:mlabonne/guanaco-llama2-1k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2024-03-11T15:19:17Z	--- license: apache-2.0 language: - en datasets: - mlabonne/guanaco-llama2-1k pipeline_tag: question-answering tags: - llm - fine-tuned - Llama 2 7b - KiwiTech LLC --- # Model Card for syedzaidi-kiwi/Llama-2-7b-chat-finetune This model is a fine-tuned version of Meta's Llama 2 7B variant for enhanced chat functionalities. This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description - Developed by: Syed Asad - Model type: Fine-tuned Llama 2 7B variant - Language(s) (NLP): English - License: Apache-2.0 - Finetuned from model: NousResearch/Llama-2-7b-chat-hf ### Model Sources - Repository: [syedzaidi-kiwi/Llama-2-7b-chat-finetune](https://huggingface.co/syedzaidi-kiwi/Llama-2-7b-chat-finetune) - Paper: [https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/] ## Uses ### Direct Use The model is intended for direct use in applications requiring conversational responses, such as chatbots or virtual assistants. ### Out-of-Scope Use The model is not designed for tasks outside of conversational AI, such as document summarization or translation. ## Bias, Risks, and Limitations Users should be aware of potential biases in the training data and limitations in the model's understanding of nuanced human language. Further evaluation is recommended for specific use cases. ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("syedzaidi-kiwi/Llama-2-7b-chat-finetune") model = AutoModelForCausalLM.from_pretrained("syedzaidi-kiwi/Llama-2-7b-chat-finetune") inputs = tokenizer("Hello, how are you?", return_tensors="pt") response = model.generate(inputs) print(tokenizer.decode(response[0], skip_special_tokens=True)) ``` ## Training Details ### Training Data The model was fine-tuned using the dataset mlabonne/guanaco-llama2-1k. Link: https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k ### Training Procedure #### Training Hyperparameters - Training regime:** The model was fine-tuned using a mix of precision training techniques to balance training speed and model performance effectively. While the exact precision format (e.g., fp32, fp16, bf16) utilized depends on the compute capabilities available, an emphasis was placed on leveraging mixed precision (fp16) training to accelerate the training process on compatible hardware. This approach allowed for faster computation and reduced memory usage without significant loss in training quality. Users are encouraged to adjust the precision settings based on their hardware specifications to optimize performance further. #### Speeds, Sizes, Times To be tested by the KiwiTech Team ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The model's performance was evaluated on a held-out test set from the mlabonne/guanaco-llama2-1k dataset. This dataset comprises diverse conversational contexts to assess the model's generalization and robustness across various topics. [https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k] #### Factors Evaluation focused on several key factors to ensure the model's versatility and reliability in conversational AI applications: Context understanding: The model's ability to maintain context and coherence over long conversations. Diversity of responses: The variety in the model's responses to similar prompts, indicating its creative and dynamic conversational capabilities. Safety and bias: Monitoring for any unintended biases in responses or generation of inappropriate content. #### Metrics To comprehensively assess the model's performance, the following metrics were utilized: Perplexity (PPL): Lower perplexity scores indicate better understanding and generation of the text. BLEU Score: For measuring the similarity between the model's generated responses and a set of reference responses, indicating the model's accuracy in reproducing human-like answers. F1 Score: Evaluating the balance between precision and recall in the model's responses, useful for assessing conversational relevance. Safety and Bias Evaluation: Custom metrics were developed to quantify the model's performance in generating safe, unbiased content. ### Results To be Evaulated, will be updated in this section. #### Summary The fine-tuned model demonstrates significant improvements in generating coherent, diverse, and contextually appropriate responses across various conversational settings. It represents a step forward in developing conversational AI systems that are both efficient and effective. Continuous evaluation and monitoring are advised to further enhance and maintain the model's performance standards. ## Technical Specifications ### Model Architecture and Objective Transformers ### Compute Infrastructure T4 GPU #### Hardware Fine Tuned on Apple M3 Pro (Silicon Chip) #### Software Google Colab Notebook Used ## Citation OriginalLlama2Citation Title: Llama 2: Open Foundation and Fine-Tuned Chat Models}, Authors: Hugo Touvron∗ Louis Martin† Kevin Stone† Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton Ferrer Moya Chen Guillem Cucurull David Esiobu Jude Fernandes Jeremy Fu Wenyin Fu Brian Fuller Cynthia Gao Vedanuj Goswami Naman Goyal Anthony Hartshorn Saghar Hosseini Rui Hou Hakan Inan Marcin Kardas Viktor Kerkez Madian Khabsa Isabel Kloumann Artem Korenev Punit Singh Koura Marie-Anne Lachaux Thibaut Lavril Jenya Lee Diana Liskovich Yinghai Lu Yuning Mao Xavier Martinet Todor Mihaylov Pushkar Mishra Igor Molybog Yixin Nie Andrew Poulton Jeremy Reizenstein Rashi Rungta Kalyan Saladi Alan Schelten Ruan Silva Eric Michael Smith Ranjan Subramanian Xiaoqing Ellen Tan Binh Tang Ross Taylor Adina Williams Jian Xiang Kuan Puxin Xu Zheng Yan Iliyan Zarov Yuchen Zhang Angela Fan Melanie Kambadur Sharan Narang Aurelien Rodriguez Robert Stojnic Sergey Edunov Thomas Scialom Journal: Gen AI, Meta Year: 2023 Link to Research Paper: https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/ ## Model Card Authors Syed Asad ## Model Card Contact Syed Asad ([email protected])
omroali/ppo-Huggy	omroali	2024-03-12T06:36:24Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-12T06:36:17Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: omroali/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ivillar/lp-music-caps	ivillar	2024-03-12T06:33:03Z	0	0	null	[ "music", "music-captioning", "en", "dataset:seungheondoh/LP-MusicCaps-MSD", "dataset:seungheondoh/LP-MusicCaps-MC", "arxiv:2307.16372", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-03-06T20:25:08Z	--- license: mit datasets: - seungheondoh/LP-MusicCaps-MSD - seungheondoh/LP-MusicCaps-MC language: - en metrics: - bleu - bertscore tags: - music - music-captioning --- - Repository: [LP-MusicCaps repository](https://github.com/seungheondoh/lp-music-caps) - Paper: [ArXiv](https://arxiv.org/abs/2307.16372) # :sound: LP-MusicCaps: LLM-Based Pseudo Music Captioning [![Demo Video](https://i.imgur.com/cgi8NsD.jpg)](https://youtu.be/ezwYVaiC-AM) This is a implementation of [LP-MusicCaps: LLM-Based Pseudo Music Captioning](#). This project aims to generate captions for music. 1) Tag-to-Caption: Using existing tags, We leverage the power of OpenAI's GPT-3.5 Turbo API to generate high-quality and contextually relevant captions based on music tag. 2) Audio-to-Caption: Using music-audio and pseudo caption pairs, we train a cross-model encoder-decoder model for end-to-end music captioning > [LP-MusicCaps: LLM-Based Pseudo Music Captioning](#) > SeungHeon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam > To appear ISMIR 2023 ## TL;DR <p align = "center"> <img src = "https://i.imgur.com/2LC0nT1.png"> </p> - [1.Tag-to-Caption: LLM Captioning](https://github.com/seungheondoh/lp-music-caps/tree/main/lpmc/llm_captioning): Generate caption from given tag input. - [2.Pretrain Music Captioning Model](https://github.com/seungheondoh/lp-music-caps/tree/main/lpmc/music_captioning): Generate pseudo caption from given audio. - [3.Transfer Music Captioning Model](https://github.com/seungheondoh/lp-music-caps/tree/main/lpmc/music_captioning/transfer.py): Generate human level caption from given audio. ## Open Source Material - [pre-trained models](https://huggingface.co/seungheondoh/lp-music-caps) - [music-pseudo caption dataset](https://huggingface.co/datasets/seungheondoh/LP-MusicCaps-MSD) - [demo](https://huggingface.co/spaces/seungheondoh/LP-Music-Caps-demo) are available online for future research. example of dataset in [notebook](https://github.com/seungheondoh/lp-music-caps/blob/main/notebook/Dataset.ipynb)
AlanHou/xlm-roberta-base-finetuned-panx-all	AlanHou	2024-03-12T06:28:31Z	92	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T06:15:22Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1758 - F1: 0.8558 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.299 \| 1.0 \| 835 \| 0.2074 \| 0.8078 \| \| 0.1587 \| 2.0 \| 1670 \| 0.1705 \| 0.8461 \| \| 0.1012 \| 3.0 \| 2505 \| 0.1758 \| 0.8558 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Phi2quant/AVMGPT	Phi2quant	2024-03-12T06:26:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:04:00Z	--- 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. 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]
wongctroman/fine-tuned-cloudy-sentence-transformer-16	wongctroman	2024-03-12T06:25:11Z	47	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-12T06:13:35Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-16 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('wongctroman/fine-tuned-cloudy-sentence-transformer-16') 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=wongctroman/fine-tuned-cloudy-sentence-transformer-16) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 34 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "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": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
MuntasirHossain/flan-t5-large-samsum-qlora-merged	MuntasirHossain	2024-03-12T06:14:26Z	0	0	peft	[ "peft", "safetensors", "t5", "generated_from_trainer", "pipeline_tag", "summarization", "dataset:samsum", "base_model:google/flan-t5-large", "base_model:adapter:google/flan-t5-large", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	summarization	2024-03-12T02:51:42Z	--- license: apache-2.0 tags: - generated_from_trainer - pipeline_tag base_model: google/flan-t5-large model-index: - name: flan-t5-large-samsum-qlora-merged results: [] datasets: - samsum metrics: - rouge pipeline_tag: summarization library_name: peft --- # Model description Parameter-efficient fine-tuning (PEFT) with QLoRA was employed to fine-tune the base [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) model on the [samsum](https://huggingface.co/datasets/samsum) dataset containing dialoges. After the fine-tuning, the [PEFT model adapter](https://huggingface.co/MuntasirHossain/flan-t5-large-samsum-qlora) was merged with the base model. The model is intended for generative summarization tasks and achieved the following scores on the test dataset: - Rogue1: 49.249596% - Rouge2: 23.513032% - RougeL: 39.960812% - RougeLsum: 39.968438% ## How to use Load the model: ``` python from transformers import pipeline pipeline_model = pipeline("summarization", model="MuntasirHossain/flan-t5-large-samsum-qlora-merged") summary = pipeline_model(text, max_new_tokens = 50) print(summary[0]['summary_text']) ``` Example Inference: ``` python # random sample text from the samsum test dataset text = """ Emma: Hi, we're going with Peter to Amiens tomorrow. Daniel: oh! Cool. Emma: Wanna join? Daniel: Sure, I'm fed up with Paris. Emma: We're too. The noise, traffic etc. Would be nice to see some countrysides. Daniel: I don't think Amiens is exactly countrysides though :P Emma: Nope. Hahahah. But not a megalopolis either! Daniel: Right! Let's do it! Emma: But we should leave early. The days are shorter now. Daniel: Yes, the stupid winter time. Emma: Exactly! Daniel: Where should we meet then? Emma: Come to my place by 9am. Daniel: oohhh. It means I have to get up before 7! Emma: Yup. The early bird gets the worm (in Amiens). Daniel: You sound like my grandmother. Emma: HAHAHA. I'll even add: no parties tonight, no drinking dear Daniel Daniel: I really hope Amiens is worth it! """ summary = pipeline_model(text, max_new_tokens = 50) print(summary[0]['summary_text']) Emma and Peter are going to Amiens tomorrow. Daniel will join them. They will meet at Emma's place by 9 am. They will not have any parties tonight. ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
AlanHou/xlm-roberta-base-finetuned-panx-it	AlanHou	2024-03-12T06:13:52Z	91	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T06:12:13Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2619 - F1: 0.8321 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7217 \| 1.0 \| 70 \| 0.3193 \| 0.7343 \| \| 0.2736 \| 2.0 \| 140 \| 0.2760 \| 0.8055 \| \| 0.1838 \| 3.0 \| 210 \| 0.2619 \| 0.8321 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
AlanHou/xlm-roberta-base-finetuned-panx-fr	AlanHou	2024-03-12T06:12:07Z	90	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T06:08:51Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr 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. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2750 - F1: 0.8495 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5647 \| 1.0 \| 191 \| 0.3242 \| 0.7728 \| \| 0.2671 \| 2.0 \| 382 \| 0.2672 \| 0.8202 \| \| 0.1744 \| 3.0 \| 573 \| 0.2750 \| 0.8495 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
hyeogi/SOLAR-10.7B-v1.4	hyeogi	2024-03-12T06:11:03Z	2,248	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "SOLAR-10.7B", "conversational", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:31:08Z	--- language: - ko pipeline_tag: text-generation tags: - SOLAR-10.7B license: cc-by-nc-4.0 --- # SOLAR-10.7B ### Model Details - Base Model: [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) ### Datasets - sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) - sampling and instrcution format [HAERAE-HUB/KMMLU](https://huggingface.co/datasets/HAERAE-HUB/KMMLU)
nsanghi/dqn-cart-pole-sb3	nsanghi	2024-03-12T06:09:28Z	1	0	stable-baselines3	[ "stable-baselines3", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-26T07:09:23Z	--- library_name: stable-baselines3 tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 221.20 +/- 52.75 name: mean_reward verified: false --- # DQN Agent playing CartPole-v1 This is a trained model of a DQN agent playing CartPole-v1 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
OwOOwO/eacc_adhoc2	OwOOwO	2024-03-12T06:09:18Z	91	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T01:11:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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AlanHou/xlm-roberta-base-finetuned-panx-de-fr	AlanHou	2024-03-12T06:08:20Z	90	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T05:56:43Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1639 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2836 \| 1.0 \| 715 \| 0.1859 \| 0.8212 \| \| 0.1484 \| 2.0 \| 1430 \| 0.1632 \| 0.8487 \| \| 0.0953 \| 3.0 \| 2145 \| 0.1639 \| 0.8591 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Kukedlc/Neural-Krishna-Multiverse-7b	Kukedlc	2024-03-12T06:04:29Z	50	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSirKrishna-7b", "ammarali32/multi_verse_model", "conversational", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:merge:Kukedlc/NeuralSirKrishna-7b", "base_model:MTSAIR/multi_verse_model", "base_model:merge:MTSAIR/multi_verse_model", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T21:12:36Z	--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model base_model: - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model license: apache-2.0 --- # Neural-Krishna-Multiverse-7b Neural-Krishna-Multiverse-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) * [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/NeuralSirKrishna-7b layer_range: [0, 32] - model: ammarali32/multi_verse_model layer_range: [0, 32] merge_method: slerp base_model: ammarali32/multi_verse_model parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-Krishna-Multiverse-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
ARDICAI/stable-diffusion-2-1-finetuned	ARDICAI	2024-03-12T06:02:05Z	337	12	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-21T12:14:05Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### stable-diffusion-2-1-finetuned Dreambooth model trained by ARDIC AI team
Red-8/Gujarati_NER-1	Red-8	2024-03-12T06:01:41Z	96	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "PERSON", "LOCATION", "ORGANIZATION", "gu", "dataset:ai4bharat/naamapadam", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T05:38:36Z	--- datasets: - ai4bharat/naamapadam language: - gu pipeline_tag: token-classification tags: - PERSON - LOCATION - ORGANIZATION ---
OwOOwO/eacc_usa2_mega	OwOOwO	2024-03-12T05:56:08Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:53:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Kukedlc/Neural-Krishna-Multiverse-7b-v2	Kukedlc	2024-03-12T05:54:50Z	49	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/Neural-Krishna-Multiverse-7b", "liminerity/M7-7b", "base_model:Kukedlc/Neural-Krishna-Multiverse-7b", "base_model:merge:Kukedlc/Neural-Krishna-Multiverse-7b", "base_model:liminerity/M7-7b", "base_model:merge:liminerity/M7-7b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T21:31:56Z	--- tags: - merge - mergekit - lazymergekit - Kukedlc/Neural-Krishna-Multiverse-7b - liminerity/M7-7b base_model: - Kukedlc/Neural-Krishna-Multiverse-7b - liminerity/M7-7b license: apache-2.0 --- # Neural-Krishna-Multiverse-7b-v2 Neural-Krishna-Multiverse-7b-v2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/Neural-Krishna-Multiverse-7b](https://huggingface.co/Kukedlc/Neural-Krishna-Multiverse-7b) * [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/Neural-Krishna-Multiverse-7b layer_range: [0, 32] - model: liminerity/M7-7b layer_range: [0, 32] merge_method: slerp base_model: liminerity/M7-7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-Krishna-Multiverse-7b-v2" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
jadhav21/squirrel	jadhav21	2024-03-12T05:48:59Z	7	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T05:45:13Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### SQUIRREL Dreambooth model trained by jadhav21 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: I21-14 Sample pictures of this concept: ![0](https://huggingface.co/jadhav21/squirrel/resolve/main/sample_images/xzg2.jpg)
DevarshRaj/neww_model_mistral	DevarshRaj	2024-03-12T05:47:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-11T12:37:37Z	--- 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. (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]
nocudaexe/Neural-Dark-Waifu-GGUF	nocudaexe	2024-03-12T05:38:29Z	26	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-10T14:44:13Z	--- license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65bcd419d5341c3e56189303/dkXNI8qHyZkJQl3ltSaEV.png) Potentially broken at 8k context Use: [nocudaexe/Neural-Dark-Waifu-V0.2](https://huggingface.co/nocudaexe/Neural-Dark-Waifu-V0.2-GGUF) instead, tested to 15872 tokens # Model Card for Model ID <!-- RP Chat model --> This is a merge of 2 models based on mlabonne/AlphaMonarch-7B. With the intent of making it more RP friendly. ### Model Sources Base model: nocudaexe/Neural-Dark-Waifu Primary Models: mlabonne/AlphaMonarch-7B Test157t/Kunocchini-7b-128k-test Additional merges: TeeZee/DarkSapling-7B-v2.0 NeverSleep/Noromaid-7B-0.4-DPO Endevor/InfinityRP-v1-7B KatyTheCutie/SlushySlerp-7B ## Uses NSFW/ERP Chat ### Recommendations Silly Tavern
migueldeguzmandev/GPT2XL_RLLMv11-9	migueldeguzmandev	2024-03-12T05:36:50Z	73	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:21:15Z	--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
migueldeguzmandev/GPT2XL_RLLMv11-7	migueldeguzmandev	2024-03-12T05:36:17Z	73	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:33:07Z	[More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
Red-8/Gujarati_NER	Red-8	2024-03-12T05:33:39Z	92	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "gu", "dataset:Red-8/NER_Gujarati_data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T05:00:18Z	--- datasets: - Red-8/NER_Gujarati_data language: - gu pipeline_tag: token-classification ---
migueldeguzmandev/GPT2XL_RLLMv11-10	migueldeguzmandev	2024-03-12T05:33:32Z	74	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T09:39:00Z	--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
kurugai/Kurugai-EEVE-v1.1	kurugai	2024-03-12T05:30:02Z	2,244	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "dataset:kurugai/MedText", "base_model:kurugai/Kurugai-EEVE-v1.0", "base_model:finetune:kurugai/Kurugai-EEVE-v1.0", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T13:48:09Z	--- license: cc-by-nc-sa-4.0 base_model: kurugai/Kurugai-EEVE-v1.0 datasets: - kurugai/MedText language: - ko --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) kurugai/Kurugai-EEVE-v1.1는 kurugai/Kurugai-EEVE-v1.0를 베이스모델로 해서 BI55/MedText 데이터셋으로 학습된 모델입니다. # 학습시간 RTX 8000 GPU 1EA로 1시간 학습하였습니다. # 도움을 주신분 이 모델은 아내의 지원을 받아 제작되었습니다. 아내에게 감사의 말을 전합니다.
nsugianto/vit-base-beans	nsugianto	2024-03-12T05:27:42Z	178	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-12T05:26:51Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-beans 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. --> # vit-base-beans 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1036 - Accuracy: 0.9850 ## 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
OpenGVLab/pvt_v2_b0	OpenGVLab	2024-03-12T05:27:22Z	3,976	2	transformers	[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-25T14:14:35Z	--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
Or4cl3-1/Cognitive-Agent-Gemma_7b	Or4cl3-1	2024-03-12T05:26:57Z	3	0	transformers	[ "transformers", "text-gemma-001", "text-generation", "merge", "mergekit", "lazymergekit", "Or4cl3-1/agent_gemma_7b", "cognitivecomputations/dolphin-2.5-mixtral-8x7b", "en", "base_model:Or4cl3-1/Agent_Gemma_7b", "base_model:merge:Or4cl3-1/Agent_Gemma_7b", "base_model:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "base_model:merge:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T02:24:50Z	--- tags: - merge - mergekit - lazymergekit - Or4cl3-1/agent_gemma_7b - cognitivecomputations/dolphin-2.5-mixtral-8x7b base_model: - Or4cl3-1/agent_gemma_7b - cognitivecomputations/dolphin-2.5-mixtral-8x7b license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation --- # Cognitive-Agent-Gemma_7b Cognitive-Agent-Gemma_7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Or4cl3-1/agent_gemma_7b](https://huggingface.co/Or4cl3-1/agent_gemma_7b) * [cognitivecomputations/dolphin-2.5-mixtral-8x7b](https://huggingface.co/cognitivecomputations/dolphin-2.5-mixtral-8x7b) ## 🧩 Configuration ```yaml slices: - sources: - model: Or4cl3-1/agent_gemma_7b layer_range: [0, 32] - model: cognitivecomputations/dolphin-2.5-mixtral-8x7b layer_range: [0, 32] merge_method: slerp base_model: Or4cl3-1/agent_gemma_7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/Cognitive-Agent-Gemma_7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
SilasK/llama-7b-medqa_version_5	SilasK	2024-03-12T05:21:27Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-11T18:30:44Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: huggyllama/llama-7b model-index: - name: llama-7b-medqa_version_5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-7b-medqa_version_5 This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
Bajiyo/mal_en_transliteration	Bajiyo	2024-03-12T05:19:28Z	0	0	null	[ "license:other", "region:us" ]	null	2024-03-12T05:17:50Z	--- license: other license_name: other license_link: LICENSE ---
Deepnoid/mergekit_v2	Deepnoid	2024-03-12T05:17:52Z	2,250	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:heavytail/kullm-solar-S", "base_model:finetune:heavytail/kullm-solar-S", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:31:08Z	--- base_model: - heavytail/kullm-solar-S library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # mergekit_v2 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged ### Configuration
Pongsathorn/Taxi-v3	Pongsathorn	2024-03-12T05:14:26Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T05:14:24Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Pongsathorn/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Pongsathorn/q-FrozenLake-v1-4x4-noSlippery	Pongsathorn	2024-03-12T05:13:24Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T05:13:21Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Pongsathorn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
tarekxpc/test	tarekxpc	2024-03-12T05:07:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-12T05:07:27Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: tarekxpc - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AptaArkana/indonesian-comunication-indoBERT-finetuned	AptaArkana	2024-03-12T05:04:00Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T02:33:51Z	--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: indonesian-personalyty-indoBERT-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # indonesian-personalyty-indoBERT-finetuned This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1403 - Accuracy: 0.9732 - Precision: 0.9732 - Recall: 0.9732 - F1: 0.9732 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.434 \| 1.0 \| 550 \| 0.1352 \| 0.9659 \| 0.9659 \| 0.9659 \| 0.9659 \| \| 0.1207 \| 2.0 \| 1100 \| 0.1403 \| 0.9732 \| 0.9732 \| 0.9732 \| 0.9732 \| \| 0.091 \| 3.0 \| 1650 \| 0.1291 \| 0.9700 \| 0.9700 \| 0.9700 \| 0.9700 \| \| 0.0633 \| 4.0 \| 2200 \| 0.1367 \| 0.9722 \| 0.9722 \| 0.9722 \| 0.9722 \| \| 0.0463 \| 5.0 \| 2750 \| 0.1414 \| 0.9732 \| 0.9732 \| 0.9732 \| 0.9732 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Deepnoid/deep-solar-eeve-kullm-v2	Deepnoid	2024-03-12T05:02:03Z	0	0	peft	[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-10.8B-v1.0", "base_model:adapter:yanolja/EEVE-Korean-10.8B-v1.0", "license:apache-2.0", "region:us" ]	null	2024-03-12T03:27:34Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: yanolja/EEVE-Korean-10.8B-v1.0 model-index: - name: data/Models/deep-solar-eeve-kullm-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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # data/Models/deep-solar-eeve-kullm-v2 This model is a fine-tuned version of [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
Deepnoid/deep-solar-eeve-KorSTS	Deepnoid	2024-03-12T05:01:24Z	0	0	peft	[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-10.8B-v1.0", "base_model:adapter:yanolja/EEVE-Korean-10.8B-v1.0", "license:apache-2.0", "region:us" ]	null	2024-03-12T03:33:23Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: yanolja/EEVE-Korean-10.8B-v1.0 model-index: - name: data/Models/deep-solar-eeve-KorSTS results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # data/Models/deep-solar-eeve-KorSTS This model is a fine-tuned version of [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
atharv56/bheem	atharv56	2024-03-12T04:57:52Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:53:22Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Bheem Dreambooth model trained by atharv56 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Pongsathorn/ppo-Huggy	Pongsathorn	2024-03-12T04:56:50Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-12T04:55:20Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Pongsathorn/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
pappubind/tiger	pappubind	2024-03-12T04:56:29Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:52:34Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Tiger Dreambooth model trained by pappubind following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: I21-08 Sample pictures of this concept: ![0](https://huggingface.co/pappubind/tiger/resolve/main/sample_images/xzg(3).jpg)
Sumail/Alchemist_09_1_2b	Sumail	2024-03-12T04:54:56Z	90	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Sumail/Alchemist_06_2b", "base_model:merge:Sumail/Alchemist_06_2b", "base_model:deepnet/SN6-71G7", "base_model:merge:deepnet/SN6-71G7", "base_model:deepnetguy/gemma-70", "base_model:merge:deepnetguy/gemma-70", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:52:23Z	--- base_model: - deepnetguy/gemma-70 - Sumail/Alchemist_06_2b - Aspik101/Haliaeetusalbicilla10 - deepnet/SN6-71G7 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Sumail/Alchemist_06_2b](https://huggingface.co/Sumail/Alchemist_06_2b) as a base. ### Models Merged The following models were included in the merge: * [deepnetguy/gemma-70](https://huggingface.co/deepnetguy/gemma-70) * [Aspik101/Haliaeetusalbicilla10](https://huggingface.co/Aspik101/Haliaeetusalbicilla10) * [deepnet/SN6-71G7](https://huggingface.co/deepnet/SN6-71G7) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sumail/Alchemist_06_2b # No parameters necessary for base model - model: Aspik101/Haliaeetusalbicilla10 parameters: density: 0.53 weight: 0.23 - model: deepnetguy/gemma-70 parameters: density: 0.53 weight: 0.5 - model: deepnet/SN6-71G7 parameters: density: 0.53 weight: 0.23 merge_method: dare_ties base_model: Sumail/Alchemist_06_2b parameters: int8_mask: true dtype: bfloat16 ```
Mihaiii/Covasna-0.1	Mihaiii	2024-03-12T04:49:35Z	5	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "base_model:migtissera/Tess-70B-v1.6", "base_model:finetune:migtissera/Tess-70B-v1.6", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-03-04T11:38:19Z	--- base_model: migtissera/Tess-70B-v1.6 inference: false license: llama2 metrics: - accuracy --- This is a BF16 and pruned version of [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6) . [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6) has 69 billion params and Covasna-0.1 has 41.6 billion (~60.3% param size) # Steps to replicate: Use [laserQlora.ipynb](https://github.com/cognitivecomputations/laserRMT/blob/main/laserQlora.ipynb) from [cognitivecomputations/laserRMT](https://github.com/cognitivecomputations/laserRMT) to determine which layers should be eliminated. Adapt the script for `migtissera/Tess-70B-v1.6` by replacing `model_name = "mistralai/Mistral-7B-v0.1"` with `model_name = "migtissera/Tess-70B-v1.6"` and `layer_numbers = list(range(31, -1, -1))` with `layer_numbers = list(range(79, -1, -1))`, [79 being the last recurrent layer index Tess-70B-v1.6 has](https://huggingface.co/migtissera/Tess-70B-v1.6?show_tensors=true). Then look for the layer indexes where self_attn.v_proj snr is Infinity and eliminate those layers using [mergekit](https://github.com/arcee-ai/mergekit). Here is the mergekit config: ```yml slices: - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [0, 7] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [8, 9] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [12, 29] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [31, 32] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [33, 45] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [50, 52] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [60, 61] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [67, 68] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [74, 80] merge_method: passthrough dtype: bfloat16 ``` GGUF: [Covasna-0.1-GGUF](https://huggingface.co/mradermacher/Covasna-0.1-GGUF)
vinuuuuu/my-car	vinuuuuu	2024-03-12T04:46:37Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:38:27Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### my-car Dreambooth model trained by vinuuuuu following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: i21-21 Sample pictures of this concept: ![0](https://huggingface.co/vinuuuuu/my-car/resolve/main/sample_images/c1.jpeg)
EleutherAI/Mistral-7B-v0.1-squaring-random-standardized	EleutherAI	2024-03-12T04:44:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:44:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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EleutherAI/Mistral-7B-v0.1-modularaddition-random-standardized	EleutherAI	2024-03-12T04:44:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:44:02Z	--- 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. 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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EleutherAI/Mistral-7B-v0.1-nli-random-standardized	EleutherAI	2024-03-12T04:43:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:42:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-sentiment-random-standardized	EleutherAI	2024-03-12T04:42:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:42:47Z	--- 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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David-Xu/llama-2-7b-cira-sft-v0.1-merge-right	David-Xu	2024-03-12T04:41:43Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T01:57:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-squaring-random	EleutherAI	2024-03-12T04:41:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:41:24Z	--- 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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EleutherAI/Mistral-7B-v0.1-multiplication-random	EleutherAI	2024-03-12T04:41:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:57Z	--- 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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EleutherAI/Mistral-7B-v0.1-subtraction-random	EleutherAI	2024-03-12T04:40:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:45Z	--- 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]
EleutherAI/Mistral-7B-v0.1-addition-random	EleutherAI	2024-03-12T04:40:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-authors-random	EleutherAI	2024-03-12T04:40:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Kazminka51/Krasota	Kazminka51	2024-03-12T04:36:20Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-03-12T04:33:39Z	--- # 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. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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davidkim205/komt-solar-10.7b-sft-v5	davidkim205	2024-03-12T04:35:17Z	60	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ko", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T04:46:33Z	--- license: cc-by-4.0 language: - ko --- # komt : korean multi task instruction tuning model ![multi task instruction tuning.jpg](https://github.com/davidkim205/komt/assets/16680469/c7f6ade7-247e-4b62-a94f-47e19abea68e) Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs). ## Model Details * Model Developers : davidkim(changyeon kim) * Repository : https://github.com/davidkim205/komt(will be updated soon.) * base mode : Edentns/DataVortexS-10.7B-dpo-v1.11 * dataset : comp-341k(will be updated soon.)

haryoaw/scenario-TCR-XLMV-XCOPA-2_data-xcopa_all

haryoaw

2024-03-12T09:11:03Z

89

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-03-08T10:53:29Z

--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-XCOPA-2_data-xcopa_all results: [] ---  # scenario-TCR-XLMV-XCOPA-2_data-xcopa_all This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5 - F1: 0.4671 ## 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: 34 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.38 | 5 | 0.6932 | 0.4858 | 0.4767 | | No log | 0.77 | 10 | 0.6931 | 0.515 | 0.5134 | | No log | 1.15 | 15 | 0.6931 | 0.5158 | 0.5038 | | No log | 1.54 | 20 | 0.6931 | 0.5108 | 0.5021 | | No log | 1.92 | 25 | 0.6931 | 0.5217 | 0.5035 | | No log | 2.31 | 30 | 0.6931 | 0.525 | 0.5069 | | No log | 2.69 | 35 | 0.6931 | 0.5283 | 0.5070 | | No log | 3.08 | 40 | 0.6931 | 0.5292 | 0.5125 | | No log | 3.46 | 45 | 0.6931 | 0.5333 | 0.5122 | | No log | 3.85 | 50 | 0.6930 | 0.5125 | 0.4970 | | No log | 4.23 | 55 | 0.6930 | 0.5342 | 0.5251 | | No log | 4.62 | 60 | 0.6931 | 0.5417 | 0.5217 | | No log | 5.0 | 65 | 0.6931 | 0.5592 | 0.5482 | | No log | 5.38 | 70 | 0.6931 | 0.5667 | 0.5517 | | No log | 5.77 | 75 | 0.6931 | 0.5458 | 0.5362 | | No log | 6.15 | 80 | 0.6931 | 0.535 | 0.5311 | | No log | 6.54 | 85 | 0.6930 | 0.5433 | 0.5276 | | No log | 6.92 | 90 | 0.6931 | 0.5025 | 0.4731 | | No log | 7.31 | 95 | 0.6931 | 0.505 | 0.4715 | | No log | 7.69 | 100 | 0.6931 | 0.5017 | 0.4514 | | No log | 8.08 | 105 | 0.6931 | 0.5042 | 0.4831 | | No log | 8.46 | 110 | 0.6931 | 0.5058 | 0.4785 | | No log | 8.85 | 115 | 0.6931 | 0.5158 | 0.4872 | | No log | 9.23 | 120 | 0.6931 | 0.5158 | 0.4890 | | No log | 9.62 | 125 | 0.6931 | 0.5075 | 0.4829 | | No log | 10.0 | 130 | 0.6931 | 0.505 | 0.4780 | | No log | 10.38 | 135 | 0.6931 | 0.5 | 0.4709 | | No log | 10.77 | 140 | 0.6931 | 0.485 | 0.4579 | | No log | 11.15 | 145 | 0.6931 | 0.4858 | 0.4592 | | No log | 11.54 | 150 | 0.6931 | 0.485 | 0.4569 | | No log | 11.92 | 155 | 0.6931 | 0.4917 | 0.4611 | | No log | 12.31 | 160 | 0.6931 | 0.4908 | 0.4664 | | No log | 12.69 | 165 | 0.6931 | 0.4858 | 0.4602 | | No log | 13.08 | 170 | 0.6931 | 0.4983 | 0.4756 | | No log | 13.46 | 175 | 0.6931 | 0.4992 | 0.4788 | | No log | 13.85 | 180 | 0.6931 | 0.4942 | 0.4717 | | No log | 14.23 | 185 | 0.6931 | 0.4958 | 0.4735 | | No log | 14.62 | 190 | 0.6931 | 0.5017 | 0.48 | | No log | 15.0 | 195 | 0.6931 | 0.4942 | 0.4633 | | No log | 15.38 | 200 | 0.6931 | 0.4942 | 0.4527 | | No log | 15.77 | 205 | 0.6931 | 0.4925 | 0.4509 | | No log | 16.15 | 210 | 0.6931 | 0.495 | 0.4570 | | No log | 16.54 | 215 | 0.6931 | 0.4933 | 0.4581 | | No log | 16.92 | 220 | 0.6931 | 0.5 | 0.4671 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

Sumail/Alchemist_13_2b

Sumail

2024-03-12T09:08:49Z

89

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:deepnetguy/gemma-75", "base_model:merge:deepnetguy/gemma-75", "base_model:zzttbrdd/sn6_20_new", "base_model:merge:zzttbrdd/sn6_20_new", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T09:06:13Z

--- base_model: - zzttbrdd/sn6_20_new - deepnetguy/gemma-75 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [zzttbrdd/sn6_20_new](https://huggingface.co/zzttbrdd/sn6_20_new) * [deepnetguy/gemma-75](https://huggingface.co/deepnetguy/gemma-75) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: zzttbrdd/sn6_20_new layer_range: [0, 18] - model: deepnetguy/gemma-75 layer_range: [0, 18] merge_method: slerp base_model: deepnetguy/gemma-75 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

ademax/vispell-small-v3

ademax

2024-03-12T09:07:34Z

84

1

transformers

[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-12T09:06:56Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

stvhuang/rcr-run-kmwbxvtx-83433-master-0_20240312T165236-ep00

stvhuang

2024-03-12T09:01:46Z

89

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2024-03-12T08:59:56Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

haryoaw/scenario-TCR-XLMV-XCOPA-6_data-xcopa_all

haryoaw

2024-03-12T08:57:19Z

90

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-03-08T10:07:05Z

--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-XCOPA-6_data-xcopa_all results: [] ---  # scenario-TCR-XLMV-XCOPA-6_data-xcopa_all This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5083 - F1: 0.4627 ## 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: 341241 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.38 | 5 | 0.6931 | 0.5133 | 0.4904 | | No log | 0.77 | 10 | 0.6931 | 0.5542 | 0.5344 | | No log | 1.15 | 15 | 0.6931 | 0.5358 | 0.5093 | | No log | 1.54 | 20 | 0.6931 | 0.5508 | 0.5373 | | No log | 1.92 | 25 | 0.6931 | 0.5033 | 0.4716 | | No log | 2.31 | 30 | 0.6931 | 0.53 | 0.5261 | | No log | 2.69 | 35 | 0.6931 | 0.5383 | 0.5257 | | No log | 3.08 | 40 | 0.6931 | 0.5308 | 0.5159 | | No log | 3.46 | 45 | 0.6931 | 0.4933 | 0.4856 | | No log | 3.85 | 50 | 0.6931 | 0.5308 | 0.5233 | | No log | 4.23 | 55 | 0.6931 | 0.5517 | 0.5410 | | No log | 4.62 | 60 | 0.6931 | 0.5625 | 0.5570 | | No log | 5.0 | 65 | 0.6931 | 0.5433 | 0.5308 | | No log | 5.38 | 70 | 0.6931 | 0.53 | 0.5236 | | No log | 5.77 | 75 | 0.6931 | 0.5267 | 0.5103 | | No log | 6.15 | 80 | 0.6931 | 0.5308 | 0.4987 | | No log | 6.54 | 85 | 0.6931 | 0.5017 | 0.4889 | | No log | 6.92 | 90 | 0.6931 | 0.5267 | 0.5009 | | No log | 7.31 | 95 | 0.6931 | 0.5367 | 0.5062 | | No log | 7.69 | 100 | 0.6931 | 0.5133 | 0.4859 | | No log | 8.08 | 105 | 0.6931 | 0.4817 | 0.4610 | | No log | 8.46 | 110 | 0.6932 | 0.5 | 0.4854 | | No log | 8.85 | 115 | 0.6931 | 0.4992 | 0.4788 | | No log | 9.23 | 120 | 0.6931 | 0.5 | 0.4881 | | No log | 9.62 | 125 | 0.6931 | 0.5042 | 0.4884 | | No log | 10.0 | 130 | 0.6931 | 0.5025 | 0.4936 | | No log | 10.38 | 135 | 0.6931 | 0.4908 | 0.4764 | | No log | 10.77 | 140 | 0.6931 | 0.4942 | 0.4816 | | No log | 11.15 | 145 | 0.6932 | 0.5033 | 0.4532 | | No log | 11.54 | 150 | 0.6931 | 0.525 | 0.4732 | | No log | 11.92 | 155 | 0.6931 | 0.53 | 0.4946 | | No log | 12.31 | 160 | 0.6931 | 0.5192 | 0.4489 | | No log | 12.69 | 165 | 0.6931 | 0.5225 | 0.4729 | | No log | 13.08 | 170 | 0.6931 | 0.5117 | 0.4624 | | No log | 13.46 | 175 | 0.6931 | 0.5383 | 0.4851 | | No log | 13.85 | 180 | 0.6931 | 0.5208 | 0.4691 | | No log | 14.23 | 185 | 0.6931 | 0.5258 | 0.4717 | | No log | 14.62 | 190 | 0.6931 | 0.5417 | 0.4860 | | No log | 15.0 | 195 | 0.6931 | 0.5233 | 0.4838 | | No log | 15.38 | 200 | 0.6931 | 0.5258 | 0.4851 | | No log | 15.77 | 205 | 0.6931 | 0.5392 | 0.4931 | | No log | 16.15 | 210 | 0.6931 | 0.5083 | 0.4627 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

haryoaw/scenario-TCR-XLMV-XCOPA-1_data-xcopa_all

haryoaw

2024-03-12T08:54:23Z

89

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-03-08T09:38:52Z

--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-XCOPA-1_data-xcopa_all results: [] ---  # scenario-TCR-XLMV-XCOPA-1_data-xcopa_all This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5592 - F1: 0.5289 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.38 | 5 | 0.6932 | 0.4917 | 0.4383 | | No log | 0.77 | 10 | 0.6931 | 0.5192 | 0.5064 | | No log | 1.15 | 15 | 0.6931 | 0.5017 | 0.4613 | | No log | 1.54 | 20 | 0.6932 | 0.4942 | 0.4576 | | No log | 1.92 | 25 | 0.6931 | 0.505 | 0.4629 | | No log | 2.31 | 30 | 0.6931 | 0.5 | 0.4643 | | No log | 2.69 | 35 | 0.6931 | 0.4892 | 0.4580 | | No log | 3.08 | 40 | 0.6931 | 0.4833 | 0.4552 | | No log | 3.46 | 45 | 0.6932 | 0.4967 | 0.4588 | | No log | 3.85 | 50 | 0.6931 | 0.5042 | 0.4711 | | No log | 4.23 | 55 | 0.6931 | 0.5108 | 0.4846 | | No log | 4.62 | 60 | 0.6932 | 0.4875 | 0.4591 | | No log | 5.0 | 65 | 0.6931 | 0.4958 | 0.4641 | | No log | 5.38 | 70 | 0.6931 | 0.4933 | 0.4777 | | No log | 5.77 | 75 | 0.6931 | 0.5075 | 0.4901 | | No log | 6.15 | 80 | 0.6931 | 0.4833 | 0.4464 | | No log | 6.54 | 85 | 0.6931 | 0.5175 | 0.4917 | | No log | 6.92 | 90 | 0.6931 | 0.4442 | 0.4225 | | No log | 7.31 | 95 | 0.6931 | 0.4583 | 0.4377 | | No log | 7.69 | 100 | 0.6931 | 0.5192 | 0.4978 | | No log | 8.08 | 105 | 0.6931 | 0.5425 | 0.5230 | | No log | 8.46 | 110 | 0.6931 | 0.535 | 0.5122 | | No log | 8.85 | 115 | 0.6931 | 0.545 | 0.5194 | | No log | 9.23 | 120 | 0.6931 | 0.5492 | 0.5259 | | No log | 9.62 | 125 | 0.6931 | 0.535 | 0.5114 | | No log | 10.0 | 130 | 0.6931 | 0.5475 | 0.5233 | | No log | 10.38 | 135 | 0.6931 | 0.5525 | 0.5269 | | No log | 10.77 | 140 | 0.6931 | 0.5458 | 0.5223 | | No log | 11.15 | 145 | 0.6931 | 0.5392 | 0.5145 | | No log | 11.54 | 150 | 0.6931 | 0.5483 | 0.5246 | | No log | 11.92 | 155 | 0.6931 | 0.5342 | 0.5084 | | No log | 12.31 | 160 | 0.6931 | 0.54 | 0.5158 | | No log | 12.69 | 165 | 0.6931 | 0.5375 | 0.5084 | | No log | 13.08 | 170 | 0.6931 | 0.5433 | 0.5133 | | No log | 13.46 | 175 | 0.6931 | 0.5333 | 0.5096 | | No log | 13.85 | 180 | 0.6931 | 0.5458 | 0.5215 | | No log | 14.23 | 185 | 0.6931 | 0.5508 | 0.5259 | | No log | 14.62 | 190 | 0.6931 | 0.5433 | 0.5168 | | No log | 15.0 | 195 | 0.6931 | 0.55 | 0.5280 | | No log | 15.38 | 200 | 0.6931 | 0.5442 | 0.5231 | | No log | 15.77 | 205 | 0.6931 | 0.55 | 0.5280 | | No log | 16.15 | 210 | 0.6931 | 0.5458 | 0.5257 | | No log | 16.54 | 215 | 0.6931 | 0.5392 | 0.5195 | | No log | 16.92 | 220 | 0.6931 | 0.5367 | 0.5165 | | No log | 17.31 | 225 | 0.6931 | 0.5433 | 0.5235 | | No log | 17.69 | 230 | 0.6931 | 0.55 | 0.5271 | | No log | 18.08 | 235 | 0.6931 | 0.5425 | 0.5222 | | No log | 18.46 | 240 | 0.6931 | 0.5417 | 0.5158 | | No log | 18.85 | 245 | 0.6931 | 0.4983 | 0.4719 | | No log | 19.23 | 250 | 0.6931 | 0.5483 | 0.5237 | | No log | 19.62 | 255 | 0.6931 | 0.5425 | 0.5230 | | No log | 20.0 | 260 | 0.6931 | 0.5467 | 0.5220 | | No log | 20.38 | 265 | 0.6931 | 0.5467 | 0.5220 | | No log | 20.77 | 270 | 0.6931 | 0.5508 | 0.5251 | | No log | 21.15 | 275 | 0.6931 | 0.555 | 0.5283 | | No log | 21.54 | 280 | 0.6931 | 0.5533 | 0.5257 | | No log | 21.92 | 285 | 0.6931 | 0.555 | 0.5283 | | No log | 22.31 | 290 | 0.6931 | 0.5533 | 0.5298 | | No log | 22.69 | 295 | 0.6931 | 0.5517 | 0.5281 | | No log | 23.08 | 300 | 0.6931 | 0.5567 | 0.5325 | | No log | 23.46 | 305 | 0.6931 | 0.55 | 0.5288 | | No log | 23.85 | 310 | 0.6931 | 0.5475 | 0.5233 | | No log | 24.23 | 315 | 0.6931 | 0.5467 | 0.5220 | | No log | 24.62 | 320 | 0.6931 | 0.55 | 0.5246 | | No log | 25.0 | 325 | 0.6931 | 0.5483 | 0.5212 | | No log | 25.38 | 330 | 0.6931 | 0.5467 | 0.5203 | | No log | 25.77 | 335 | 0.6931 | 0.5483 | 0.5204 | | No log | 26.15 | 340 | 0.6931 | 0.5492 | 0.5225 | | No log | 26.54 | 345 | 0.6931 | 0.5492 | 0.5250 | | No log | 26.92 | 350 | 0.6931 | 0.5542 | 0.5295 | | No log | 27.31 | 355 | 0.6931 | 0.5567 | 0.5350 | | No log | 27.69 | 360 | 0.6931 | 0.5533 | 0.5290 | | No log | 28.08 | 365 | 0.6931 | 0.5558 | 0.5296 | | No log | 28.46 | 370 | 0.6931 | 0.5542 | 0.5270 | | No log | 28.85 | 375 | 0.6931 | 0.5383 | 0.5166 | | No log | 29.23 | 380 | 0.6931 | 0.5483 | 0.5220 | | No log | 29.62 | 385 | 0.6931 | 0.5475 | 0.5190 | | No log | 30.0 | 390 | 0.6931 | 0.5483 | 0.5212 | | No log | 30.38 | 395 | 0.6931 | 0.5208 | 0.4871 | | No log | 30.77 | 400 | 0.6931 | 0.4867 | 0.4690 | | No log | 31.15 | 405 | 0.6931 | 0.485 | 0.4663 | | No log | 31.54 | 410 | 0.6931 | 0.455 | 0.4313 | | No log | 31.92 | 415 | 0.6931 | 0.4608 | 0.4369 | | No log | 32.31 | 420 | 0.6931 | 0.4617 | 0.4421 | | No log | 32.69 | 425 | 0.6931 | 0.5258 | 0.4942 | | No log | 33.08 | 430 | 0.6931 | 0.5608 | 0.5340 | | No log | 33.46 | 435 | 0.6931 | 0.5583 | 0.5310 | | No log | 33.85 | 440 | 0.6931 | 0.56 | 0.5352 | | No log | 34.23 | 445 | 0.6931 | 0.5567 | 0.5325 | | No log | 34.62 | 450 | 0.6931 | 0.5525 | 0.5277 | | No log | 35.0 | 455 | 0.6931 | 0.5542 | 0.5303 | | No log | 35.38 | 460 | 0.6931 | 0.5633 | 0.5379 | | No log | 35.77 | 465 | 0.6931 | 0.5542 | 0.5295 | | No log | 36.15 | 470 | 0.6931 | 0.5567 | 0.5309 | | No log | 36.54 | 475 | 0.6931 | 0.555 | 0.5291 | | No log | 36.92 | 480 | 0.6931 | 0.5575 | 0.5330 | | No log | 37.31 | 485 | 0.6931 | 0.5517 | 0.5256 | | No log | 37.69 | 490 | 0.6931 | 0.545 | 0.5168 | | No log | 38.08 | 495 | 0.6931 | 0.54 | 0.5132 | | 0.6936 | 38.46 | 500 | 0.6931 | 0.55 | 0.5238 | | 0.6936 | 38.85 | 505 | 0.6931 | 0.5425 | 0.512 | | 0.6936 | 39.23 | 510 | 0.6931 | 0.54 | 0.5106 | | 0.6936 | 39.62 | 515 | 0.6931 | 0.5242 | 0.4906 | | 0.6936 | 40.0 | 520 | 0.6931 | 0.5292 | 0.4978 | | 0.6936 | 40.38 | 525 | 0.6931 | 0.53 | 0.5009 | | 0.6936 | 40.77 | 530 | 0.6931 | 0.5308 | 0.5031 | | 0.6936 | 41.15 | 535 | 0.6931 | 0.5425 | 0.5205 | | 0.6936 | 41.54 | 540 | 0.6931 | 0.535 | 0.5088 | | 0.6936 | 41.92 | 545 | 0.6931 | 0.5342 | 0.5084 | | 0.6936 | 42.31 | 550 | 0.6931 | 0.5425 | 0.5205 | | 0.6936 | 42.69 | 555 | 0.6931 | 0.5475 | 0.5241 | | 0.6936 | 43.08 | 560 | 0.6931 | 0.5517 | 0.5264 | | 0.6936 | 43.46 | 565 | 0.6931 | 0.5592 | 0.5339 | | 0.6936 | 43.85 | 570 | 0.6931 | 0.5625 | 0.5350 | | 0.6936 | 44.23 | 575 | 0.6931 | 0.5625 | 0.5358 | | 0.6936 | 44.62 | 580 | 0.6931 | 0.5617 | 0.5337 | | 0.6936 | 45.0 | 585 | 0.6931 | 0.5633 | 0.5355 | | 0.6936 | 45.38 | 590 | 0.6931 | 0.56 | 0.5344 | | 0.6936 | 45.77 | 595 | 0.6931 | 0.5625 | 0.5350 | | 0.6936 | 46.15 | 600 | 0.6931 | 0.555 | 0.5258 | | 0.6936 | 46.54 | 605 | 0.6931 | 0.5625 | 0.5350 | | 0.6936 | 46.92 | 610 | 0.6931 | 0.5592 | 0.5289 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

Suprava007/my-dog-xzg

Suprava007

2024-03-12T08:50:59Z

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-12T08:28:44Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My--Dog--xzg Dreambooth model trained by Suprava007 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 30098122015 Sample pictures of this concept: ![0](https://huggingface.co/Suprava007/my-dog-xzg/resolve/main/sample_images/473072_german_shepherd_dog_on_a_road__xl-1024-v1-0.png)

ihebjer00/codegen-Actia

ihebjer00

2024-03-12T08:49:14Z

0

peft

[ "peft", "pytorch", "tensorboard", "safetensors", "codegen", "trl", "sft", "generated_from_trainer", "base_model:Salesforce/codegen-2B-multi", "base_model:adapter:Salesforce/codegen-2B-multi", "license:bsd-3-clause", "region:us" ]

null

2024-03-12T07:43:48Z

--- license: bsd-3-clause library_name: peft tags: - trl - sft - generated_from_trainer base_model: Salesforce/codegen-2B-multi model-index: - name: codegen-Actia results: [] ---  # codegen-Actia This model is a fine-tuned version of [Salesforce/codegen-2B-multi](https://huggingface.co/Salesforce/codegen-2B-multi) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

StarCycle/llava-siglip-internlm2-1_8b-v2

StarCycle

2024-03-12T08:47:45Z

0

2

null

[ "safetensors", "llava", "image-text-to-text", "dataset:liuhaotian/LLaVA-Pretrain", "dataset:liuhaotian/LLaVA-Instruct-150K", "license:apache-2.0", "region:us" ]

image-text-to-text

2024-03-10T03:39:05Z

--- license: apache-2.0 tags: - llava datasets: - liuhaotian/LLaVA-Pretrain - liuhaotian/LLaVA-Instruct-150K pipeline_tag: image-text-to-text --- ## Model llava-siglip-internlm2-1_8b-pretrain-v1 is a LLaVA checkpoint finetuned from [internlm2-1_8b](https://huggingface.co/internlm/internlm2-1_8b) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) with [LLaVA-Pretrain](liuhaotian/LLaVA-Pretrain) and [LLaVA-Instruct-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) by [Xtuner](https://github.com/InternLM/xtuner). The pretraining phase took 5.5 hours on 4 Nvidia GTX 4090 GPU (see this [intermediate checkpoint](https://huggingface.co/StarCycle/llava-siglip-internlm2-1_8b-pretrain-v2)). The finetuning phase took 16 hours on 4 Nvidia GTX 4090 GPU. The total size of the model is around 2.2B, which is suitable for embedded applications like robotics. This model performs better than [llava-siglip-internlm2-1_8b-v1](https://huggingface.co/StarCycle/llava-siglip-internlm2-1_8b-v1) because I use the base LLM, instead of the SFT version. I have not carefully tune the hyperparameters during training. If you have any idea to improve it, please open an issue or just send an email to [email protected]. You are welcomed! ## Example ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/AEw4i1rkIcUY74hFLhXLW.png) Explain this photo in English and Chinese: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/TibbHJOeZeMkV3h2pinXk.png) ## Results Model | MMBench Test (EN) | MMBench Dev (EN) | MMBench Test (CN) | MMBench Dev (CN) | CCBench Dev ------------- | ------------- | ------------- | ------------- | ------------- | ------------- LLaVA-v1.5-7B | 67.7 | 69.2 | 61.0 | 59.7 | 28.4 LLaVA-InternLM-7B | 69.0 | 68.5 | 66.7 | 63.8 | 37.3 LLaVA-InternLM2-7B | 73.3 | 74.6 | 71.7 | 72.0 | 42.5 Bunny-3B | 69.2 | 68.6 | - | - | - MiniCPM-V | 64.1 | 67.9 | 62.6 | 65.3 | 41.4 llava-clip-internlm2-1_8b-v1 | 63.3 | 63.1 | 63.6 | 61.7 | 35.3 llava-siglip-internlm2-1_8b-v1 | 65.7 | 63.5 | 64.5 | 62.9 | 36.3 llava-siglip-internlm2-1_8b-v2 | 66.6 | 67.1 | 64.5 | 64.1 | 37.3 MMBench Test EN: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/G-TWOieq8GbddAVbTbbYA.png) MMBench Test CN: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/WFJ5f0EQje1AGjk5kn2cZ.png) ## Installation ``` # We need the newest version so clone from github git clone https://github.com/huggingface/transformers/ git clone https://github.com/huggingface/peft git clone https://github.com/InternLM/xtuner ``` Now please replace the files in transformers and xtuner with the source code files in modified_transformers and modified_xtuner. ``` cp -r ./modified_transformers ./transformers cp -r ./modified_xtuner ./xtuner ``` Then run ``` pip install -e ./transformers pip install -e ./peft pip install -e ./xtuner[deepspeed] apt install git-lfs ``` ## Chat ``` xtuner chat internlm/internlm2-1_8b \ --visual-encoder google/siglip-so400m-patch14-384 \ --llava StarCycle/llava-siglip-internlm2-1_8b-v2 \ --prompt-template internlm2_chat \ --image $IMAGE_PATH ``` ## Common Errors 1. ``` command error: 'libGL.so.1: cannot open shared object file: No such file or directory'! ``` You can solve it by ``` # For Ubuntu sudo apt-get update sudo apt-get install libgl1-mesa-glx # For CentOS and Fedora sudo yum install mesa-libGL ``` 2. ``` Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library. Try to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it. ``` You can solve it by reinstall numpy. 3. ``` ImportError: InternLM2Converter requires the protobuf library but it was not found in your environment. Checkout the instructions on the ``` You just need ``` pip install protobuf ``` 4. To use tensorboard to visualize the training loss curve: ``` pip install future tensorboard ``` 5. If your training process is killed during data preprocessing, you can modify the `map_num_proc` in xtuner/xtuner/dataset /huggingface.py ``` def process(dataset, do_dataset_tokenization=True, tokenizer=None, max_length=None, dataset_map_fn=None, template_map_fn=None, max_dataset_length=None, split='train', remove_unused_columns=False, rename_maps=[], shuffle_before_pack=True, pack_to_max_length=True, use_varlen_attn=False, input_ids_with_output=True, with_image_token=False, map_num_proc=32): # modify it to a smaller number, e.g., 4 ``` 6. If you fail to load the model, check whether you installed git-lfs and actually downloaded the model file. ## Data prepration 1. File structure ``` # . means the llava-dinov2-internlm2-7b-v1 folder you clone ./data/llava_data ├── LLaVA-Pretrain │ ├── blip_laion_cc_sbu_558k.json │ ├── blip_laion_cc_sbu_558k_meta.json │ └── images ├── LLaVA-Instruct-150K │ └── llava_v1_5_mix665k.json └── llava_images ├── coco │ └── train2017 ├── gqa │ └── images ├── ocr_vqa │ └── images ├── textvqa │ └── train_images └── vg ├── VG_100K └── VG_100K_2 ``` 2. Pretrain Data LLaVA-Pretrain ```shell # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain --depth=1 ``` 3. Finetune Data 3.1 Text data LLaVA-Instruct-150K ```shell # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K --depth=1 ``` 3.2 Image data 3.2.1 COCO (coco): [train2017](http://images.cocodataset.org/zips/train2017.zip) 3.2.2 GQA (gqa): [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip) 3.2.3 OCR-VQA (ocr_vqa): [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing) ⚠️⚠️⚠️ Modify the name of OCR-VQA's images to keep the extension as `.jpg`! ```shell #!/bin/bash ocr_vqa_path="<your-directory-path>" find "$target_dir" -type f | while read file; do extension="${file##*.}" if [ "$extension" != "jpg" ] then cp -- "$file" "${file%.*}.jpg" fi done ``` 3.2.4 TextVQA (textvqa): [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) 3.2.5 VisualGenome (VG): [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip) ## Cheers! Now train your own model! 1. Alignment module pretraining ``` # single GPU xtuner train ./pretrain.py --deepspeed deepspeed_zero2 # multiple GPU NPROC_PER_NODE=4 xtuner train ./pretrain.py --deepspeed deepspeed_zero2 ``` #### Remember to change the batch size and gradient accumulation parameters to fit your hardware. So your GPU_num * batch_size * gradient_accumulation is roughly equal to mine to reproduce the result. The checkpoint and tensorboard logs are saved by default in ./work_dirs/. I only train it for 1 epoch to be same as the original LLaVA paper. Some researches also report that training for multiple epochs will make the model overfit the training dataset and perform worse in other domains. This is my loss curve for llava-siglip-internlm2-1_8b-pretrain-v2: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/1vh2gsRzEFXia7zCRRIlz.png) And the learning rate curve: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/CdNSJcLCv9MmYG88AJFwB.png) 2. Instruction following fine-tuning ``` NPROC_PER_NODE=4 xtuner train ./finetune.py --deepspeed deepspeed_zero2 ``` Here is my loss curve (the curve fluctuates strongly because the batch size is small, and I only record batch loss instead of epoch loss): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/DG1ac7BeaVTrfKqJHA2u8.png) And the learning rate curve: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642a298ae5f33939cf3ee600/rbuVFeTa04qTbN5j_QF64.png) ## Transfer the checkpoints to Huggingface safetensor format ``` xtuner convert pth_to_hf ./finetune.py ./work_dirs/iter_xxx.pth ./my_lora_and_projector ``` The adapter still need to be used with the internlm/internlm2-1_8b and the vision encoder. I have not tried to merge them yet but it is possible with Xtuner, see this [tutorial](https://github.com/InternLM/xtuner/blob/f63859b3d0cb39cbac709e3850f3fe01de1023aa/xtuner/configs/llava/README.md#L4). ## MMBench Evaluation You can first download the MMBench data: ``` wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv wget https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv ``` Then run: ``` NPROC_PER_NODE=8 xtuner mmbench internlm/internlm2-1_8b \ --visual-encoder google/siglip-so400m-patch14-384 \ --llava ./my_lora_and_projector \ --prompt-template internlm2_chat \ --data-path $MMBENCH_DATA_PATH \ --work-dir $RESULT_PATH ``` You can also use [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) to evaluate it on other benckmarks. ## Deployment Xtuner team is developing HF chatbot (based on Huggingface transformers) and LMDeploy chatbot (based on TurboMind). I am waiting for their final version of API.

unanam/large_lora_ver1

unanam

2024-03-12T08:45:56Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:46:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shreshtha2002/llama2_finetune

shreshtha2002

2024-03-12T08:45:42Z

0

null

[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T08:45:06Z

--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

Croolch/Reinforce-CartPole-v1

Croolch

2024-03-12T08:33:20Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-03-12T08:33:12Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

johannhartmann/Brezn6-WIP

johannhartmann

2024-03-12T08:32:47Z

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:DiscoResearch/DiscoLM_German_7b_v1", "base_model:merge:DiscoResearch/DiscoLM_German_7b_v1", "base_model:automerger/YamStrangemerges_32-7B", "base_model:merge:automerger/YamStrangemerges_32-7B", "base_model:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser", "base_model:merge:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser", "base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser", "base_model:merge:mayflowergmbh/Wiedervereinigung-7b-dpo-laser", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T08:25:46Z

--- base_model: - automerger/YamStrangemerges_32-7B - DiscoResearch/DiscoLM_German_7b_v1 - cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser - mayflowergmbh/Wiedervereinigung-7b-dpo-laser library_name: transformers tags: - mergekit - merge --- # brezn6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) as a base. ### Models Merged The following models were included in the merge: * [automerger/YamStrangemerges_32-7B](https://huggingface.co/automerger/YamStrangemerges_32-7B) * [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1) * [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser # no parameters necessary for base model - model: automerger/YamStrangemerges_32-7B parameters: density: 0.50 weight: 0.30 - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser parameters: density: 0.65 weight: 0.40 - model: DiscoResearch/DiscoLM_German_7b_v1 parameters: density: 0.5 weight: 0.3 merge_method: dare_ties base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser parameters: int8_mask: true dtype: bfloat16 random_seed: 0 tokenizer_source: base ```

wongctroman/fine-tuned-cloudy-sentence-transformer-29

wongctroman

2024-03-12T08:32:04Z

46

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T08:30:30Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-29 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-29') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-29) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 4 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

eunyounglee/hana-persona-emotion-bert-simple-finetuning-1

eunyounglee

2024-03-12T08:30:20Z

5

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-11T02:24:53Z

--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: hana-persona-emotion-bert-simple-finetuning-1 results: [] ---  # hana-persona-emotion-bert-simple-finetuning-1 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5295 - Accuracy: 0.8043 - F1: 0.8041 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.4983 | 1.0 | 2284 | 0.4753 | 0.8052 | 0.8038 | | 0.3728 | 2.0 | 4568 | 0.4923 | 0.8098 | 0.8078 | | 0.2827 | 3.0 | 6852 | 0.5351 | 0.8070 | 0.8074 | | 0.1906 | 4.0 | 9136 | 0.7375 | 0.8106 | 0.8099 | | 0.155 | 5.0 | 11420 | 0.8906 | 0.8041 | 0.8040 | | 0.1087 | 6.0 | 13704 | 1.1059 | 0.8050 | 0.8050 | | 0.0732 | 7.0 | 15988 | 1.2882 | 0.8049 | 0.8053 | | 0.0632 | 8.0 | 18272 | 1.3800 | 0.8066 | 0.8062 | | 0.0369 | 9.0 | 20556 | 1.4939 | 0.8035 | 0.8028 | | 0.0159 | 10.0 | 22840 | 1.5295 | 0.8043 | 0.8041 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0 - Datasets 2.17.1 - Tokenizers 0.15.2

raminass/SCOTUS_AI_V17_CURCUIT_V1

raminass

2024-03-12T08:28:34Z

93

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:raminass/scotus-v10", "base_model:finetune:raminass/scotus-v10", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-12T07:23:50Z

--- license: cc-by-sa-4.0 base_model: raminass/scotus-v10 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SCOTUS_AI_V17_CURCUIT_V1 results: [] ---  # SCOTUS_AI_V17_CURCUIT_V1 This model is a fine-tuned version of [raminass/scotus-v10](https://huggingface.co/raminass/scotus-v10) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Accuracy: 0.7500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7967 | 1.0 | 2167 | 0.9836 | 0.7086 | | 0.4558 | 2.0 | 4334 | 0.8871 | 0.7430 | | 0.2722 | 3.0 | 6501 | 0.9650 | 0.7471 | | 0.146 | 4.0 | 8668 | 1.0709 | 0.7484 | | 0.1076 | 5.0 | 10835 | 1.2135 | 0.7444 | | 0.0591 | 6.0 | 13002 | 1.2494 | 0.7522 | | 0.0454 | 7.0 | 15169 | 1.3007 | 0.7500 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

wongctroman/fine-tuned-cloudy-sentence-transformer-28

wongctroman

2024-03-12T08:27:15Z

47

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T08:25:29Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-28 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-28') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-28) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 4 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

crisp-im/mirage-flan-t5-xxl-topic-v0.1

crisp-im

2024-03-12T08:25:29Z

7

0

transformers

[ "transformers", "safetensors", "t5", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2024-03-12T08:10:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

wongctroman/fine-tuned-cloudy-sentence-transformer-27

wongctroman

2024-03-12T08:24:24Z

46

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T08:22:24Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-27 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-27') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-27) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 38 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

ahmetburakbicer/Handwritten-Digit-Recognition

ahmetburakbicer

2024-03-12T08:14:05Z

0

null

[ "TensorFlow", "Python", "tkinter", "mnist", "hand-written-digit", "dataset:mnist", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]

null

2024-03-12T08:08:56Z

--- license: apache-2.0 datasets: - mnist metrics: - accuracy tags: - TensorFlow - Python - tkinter - mnist - hand-written-digit --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

wongctroman/fine-tuned-cloudy-sentence-transformer-25

wongctroman

2024-03-12T08:11:08Z

46

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T08:10:04Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-25 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-25') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-25) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 34 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

OwOOwO/mistral_m1-4

OwOOwO

2024-03-12T08:08:48Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-09T11:42:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

csshali/dqn-SpaceInvadersNoFrameskip-v4

csshali

2024-03-12T08:06:35Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-03-12T08:06:01Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 554.00 +/- 167.96 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga csshali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga csshali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga csshali ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

JulyApril/lora-trained-xl

JulyApril

2024-03-12T08:02:57Z

1

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-11T08:06:54Z

--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'A photo of sks dog in a bucket' output: url: "image_0.png" - text: 'A photo of sks dog in a bucket' output: url: "image_1.png" - text: 'A photo of sks dog in a bucket' output: url: "image_2.png" - text: 'A photo of sks dog in a bucket' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog license: openrail++ --- # SDXL LoRA DreamBooth - JulyApril/lora-trained-xl <Gallery /> ## Model description These are JulyApril/lora-trained-xl 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 dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JulyApril/lora-trained-xl/tree/main) them in the Files & versions tab.

wongctroman/fine-tuned-cloudy-sentence-transformer-23

wongctroman

2024-03-12T07:57:27Z

46

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T07:55:53Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-23 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-23') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-23) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 34 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 100, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

imsumit18/distilbart-cnn-12-6-finetuned-stocknews_200

imsumit18

2024-03-12T07:47:01Z

11

0

transformers

[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "summarization", "base_model:sshleifer/distilbart-cnn-12-6", "base_model:finetune:sshleifer/distilbart-cnn-12-6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2024-03-07T06:23:09Z

--- license: apache-2.0 base_model: sshleifer/distilbart-cnn-12-6 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-finetuned-stocknews_200 results: [] pipeline_tag: summarization ---  # distilbart-cnn-12-6-finetuned-stocknews_200 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0370 - Rouge1: 79.8682 - Rouge2: 71.4205 - Rougel: 75.6301 - Rougelsum: 77.0085 - Gen Len: 74.1543 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 169 | 0.5736 | 64.7045 | 47.6749 | 56.2681 | 59.2198 | 74.6113 | | No log | 2.0 | 338 | 0.4806 | 72.0942 | 58.8471 | 65.4706 | 67.8252 | 71.5163 | | 0.4734 | 3.0 | 507 | 0.4991 | 73.967 | 62.7751 | 68.5945 | 70.6273 | 74.724 | | 0.4734 | 4.0 | 676 | 0.4965 | 76.8393 | 66.9993 | 72.19 | 73.864 | 72.7003 | | 0.4734 | 5.0 | 845 | 0.5139 | 78.0584 | 68.124 | 73.447 | 75.0284 | 73.9466 | | 0.1158 | 6.0 | 1014 | 0.5328 | 78.409 | 68.5496 | 73.4175 | 75.0927 | 72.6914 | | 0.1158 | 7.0 | 1183 | 0.5370 | 77.5134 | 67.8142 | 72.7732 | 74.5942 | 71.5727 | | 0.1158 | 8.0 | 1352 | 0.5872 | 78.01 | 68.8818 | 73.7514 | 75.3546 | 73.4036 | | 0.0631 | 9.0 | 1521 | 0.5787 | 78.8662 | 69.9291 | 74.7183 | 76.1309 | 73.365 | | 0.0631 | 10.0 | 1690 | 0.5887 | 78.5145 | 69.2414 | 73.9729 | 75.4945 | 73.3947 | | 0.0631 | 11.0 | 1859 | 0.5866 | 77.9579 | 68.5705 | 73.2277 | 75.2179 | 72.4807 | | 0.0456 | 12.0 | 2028 | 0.6155 | 79.4247 | 70.3457 | 75.0464 | 76.723 | 71.6261 | | 0.0456 | 13.0 | 2197 | 0.6270 | 78.2792 | 69.1958 | 74.171 | 75.7049 | 72.9347 | | 0.0456 | 14.0 | 2366 | 0.6342 | 78.6039 | 69.2197 | 74.2082 | 75.7638 | 74.543 | | 0.0364 | 15.0 | 2535 | 0.6282 | 78.7977 | 69.8903 | 74.5441 | 76.4053 | 72.8961 | | 0.0364 | 16.0 | 2704 | 0.6456 | 78.4486 | 69.2633 | 74.0665 | 75.4348 | 72.2819 | | 0.0364 | 17.0 | 2873 | 0.6583 | 79.1083 | 70.2974 | 75.0199 | 76.544 | 72.6469 | | 0.0282 | 18.0 | 3042 | 0.6477 | 78.7872 | 69.9616 | 74.6811 | 76.0256 | 72.8279 | | 0.0282 | 19.0 | 3211 | 0.6716 | 78.7369 | 69.889 | 74.4537 | 75.9916 | 73.4214 | | 0.0282 | 20.0 | 3380 | 0.6729 | 79.3218 | 70.2074 | 75.162 | 76.5582 | 73.7003 | | 0.0222 | 21.0 | 3549 | 0.7011 | 77.7228 | 68.6481 | 73.4411 | 74.9113 | 74.4748 | | 0.0222 | 22.0 | 3718 | 0.6763 | 79.47 | 70.7597 | 75.2025 | 76.8042 | 72.73 | | 0.0222 | 23.0 | 3887 | 0.7025 | 79.8675 | 70.9624 | 75.4989 | 77.0572 | 72.8427 | | 0.0196 | 24.0 | 4056 | 0.6746 | 79.1486 | 70.4134 | 74.9573 | 76.4961 | 73.0208 | | 0.0196 | 25.0 | 4225 | 0.6750 | 79.774 | 71.187 | 75.6008 | 77.2557 | 72.1098 | | 0.0196 | 26.0 | 4394 | 0.6921 | 79.5747 | 70.894 | 75.2295 | 76.7905 | 72.9318 | | 0.0176 | 27.0 | 4563 | 0.7611 | 79.0068 | 70.1336 | 74.3258 | 75.9459 | 74.3501 | | 0.0176 | 28.0 | 4732 | 0.7093 | 79.5467 | 70.8754 | 75.4346 | 77.2047 | 72.3116 | | 0.0176 | 29.0 | 4901 | 0.7168 | 79.5496 | 70.5612 | 75.0587 | 76.6486 | 74.0415 | | 0.0154 | 30.0 | 5070 | 0.7032 | 79.7382 | 71.0288 | 75.9411 | 77.103 | 72.5282 | | 0.0154 | 31.0 | 5239 | 0.7206 | 79.3973 | 70.7136 | 75.1744 | 76.5041 | 72.5757 | | 0.0154 | 32.0 | 5408 | 0.7478 | 79.6311 | 70.74 | 75.1728 | 76.8626 | 73.1395 | | 0.013 | 33.0 | 5577 | 0.7279 | 79.9423 | 71.2295 | 75.7646 | 77.2329 | 70.8872 | | 0.013 | 34.0 | 5746 | 0.7685 | 78.8995 | 70.121 | 74.4843 | 76.028 | 72.9763 | | 0.013 | 35.0 | 5915 | 0.7498 | 79.6454 | 70.8632 | 75.4972 | 76.8668 | 72.0297 | | 0.0126 | 36.0 | 6084 | 0.8016 | 78.8582 | 70.0804 | 74.5498 | 76.0402 | 74.8338 | | 0.0126 | 37.0 | 6253 | 0.7923 | 78.8845 | 70.1465 | 74.837 | 76.2453 | 74.0742 | | 0.0126 | 38.0 | 6422 | 0.7813 | 78.7254 | 70.0885 | 74.6831 | 76.1384 | 73.5994 | | 0.0103 | 39.0 | 6591 | 0.7974 | 79.5855 | 70.7472 | 75.5436 | 76.9493 | 72.6795 | | 0.0103 | 40.0 | 6760 | 0.7967 | 79.656 | 70.7795 | 75.2844 | 76.6875 | 72.3294 | | 0.0103 | 41.0 | 6929 | 0.8029 | 79.8831 | 71.1647 | 75.697 | 77.0773 | 71.8872 | | 0.0086 | 42.0 | 7098 | 0.8245 | 78.999 | 70.1721 | 74.8494 | 76.2723 | 72.7478 | | 0.0086 | 43.0 | 7267 | 0.8459 | 79.052 | 70.2714 | 75.0921 | 76.4209 | 74.3828 | | 0.0086 | 44.0 | 7436 | 0.8077 | 79.6009 | 70.4859 | 75.0207 | 76.7271 | 72.5163 | | 0.0078 | 45.0 | 7605 | 0.8431 | 79.093 | 70.433 | 75.0361 | 76.589 | 73.3145 | | 0.0078 | 46.0 | 7774 | 0.8794 | 79.1461 | 70.3654 | 74.845 | 76.3544 | 75.0415 | | 0.0078 | 47.0 | 7943 | 0.8668 | 79.1443 | 70.2647 | 74.7967 | 76.3801 | 71.724 | | 0.0076 | 48.0 | 8112 | 0.8347 | 78.6997 | 70.1008 | 74.6051 | 76.0351 | 73.9763 | | 0.0076 | 49.0 | 8281 | 0.8544 | 78.9749 | 69.9824 | 74.6559 | 76.0268 | 74.6528 | | 0.0076 | 50.0 | 8450 | 0.9060 | 79.5051 | 70.5755 | 75.3817 | 77.0026 | 71.1217 | | 0.0065 | 51.0 | 8619 | 0.9501 | 79.2498 | 70.5003 | 75.1244 | 76.5023 | 75.0 | | 0.0065 | 52.0 | 8788 | 0.8724 | 79.5012 | 70.4217 | 75.109 | 76.6551 | 73.73 | | 0.0065 | 53.0 | 8957 | 0.8860 | 79.5313 | 71.0337 | 75.3122 | 76.928 | 72.7685 | | 0.0053 | 54.0 | 9126 | 0.8859 | 79.674 | 71.0878 | 75.4582 | 76.925 | 73.3294 | | 0.0053 | 55.0 | 9295 | 0.8965 | 78.5857 | 69.8599 | 74.2323 | 75.6027 | 75.7359 | | 0.0053 | 56.0 | 9464 | 0.9871 | 79.8361 | 71.2171 | 75.8197 | 77.1182 | 74.0861 | | 0.0052 | 57.0 | 9633 | 0.8972 | 79.8939 | 71.3469 | 75.9245 | 77.1549 | 72.8398 | | 0.0052 | 58.0 | 9802 | 0.9693 | 79.5523 | 70.8739 | 75.2116 | 76.7137 | 74.3412 | | 0.0052 | 59.0 | 9971 | 0.9605 | 79.483 | 70.6684 | 75.0183 | 76.3226 | 75.2522 | | 0.0047 | 60.0 | 10140 | 0.9705 | 79.4894 | 70.6424 | 75.0833 | 76.504 | 74.8694 | | 0.0047 | 61.0 | 10309 | 0.9730 | 79.4781 | 70.9014 | 75.4589 | 76.6387 | 75.0504 | | 0.0047 | 62.0 | 10478 | 0.9284 | 79.485 | 70.6651 | 75.1062 | 76.4092 | 74.0148 | | 0.0045 | 63.0 | 10647 | 0.9537 | 79.2664 | 70.4345 | 74.9998 | 76.4565 | 73.9199 | | 0.0045 | 64.0 | 10816 | 0.9554 | 79.6061 | 70.8702 | 75.3191 | 76.6242 | 74.3145 | | 0.0045 | 65.0 | 10985 | 1.0090 | 79.6107 | 70.9297 | 75.4102 | 76.9842 | 73.9466 | | 0.0041 | 66.0 | 11154 | 0.9736 | 79.6246 | 70.8827 | 75.2682 | 76.7209 | 74.8131 | | 0.0041 | 67.0 | 11323 | 0.9498 | 79.9549 | 71.3231 | 75.7987 | 77.2809 | 73.5371 | | 0.0041 | 68.0 | 11492 | 0.9965 | 80.1403 | 71.4991 | 76.017 | 77.3741 | 74.2404 | | 0.004 | 69.0 | 11661 | 1.0012 | 79.8784 | 71.444 | 75.827 | 77.1888 | 74.0059 | | 0.004 | 70.0 | 11830 | 0.9888 | 80.1075 | 71.7102 | 75.9687 | 77.3636 | 72.9911 | | 0.004 | 71.0 | 11999 | 0.9758 | 79.7998 | 71.3682 | 75.6694 | 77.0498 | 73.8991 | | 0.0043 | 72.0 | 12168 | 0.9760 | 79.9748 | 71.4703 | 75.8148 | 77.1338 | 72.8843 | | 0.0043 | 73.0 | 12337 | 0.9930 | 80.1032 | 71.6551 | 75.8235 | 77.1674 | 73.6499 | | 0.0037 | 74.0 | 12506 | 1.0006 | 80.0302 | 71.5324 | 75.7755 | 77.2182 | 73.3027 | | 0.0037 | 75.0 | 12675 | 0.9958 | 79.9088 | 71.313 | 75.7842 | 77.1939 | 73.362 | | 0.0037 | 76.0 | 12844 | 0.9993 | 80.3059 | 71.7887 | 76.0696 | 77.5045 | 73.3086 | | 0.0039 | 77.0 | 13013 | 1.0224 | 79.5564 | 71.1191 | 75.4324 | 76.7285 | 74.2344 | | 0.0039 | 78.0 | 13182 | 1.0510 | 80.0006 | 71.4199 | 75.6626 | 77.006 | 74.0119 | | 0.0039 | 79.0 | 13351 | 1.0410 | 79.7101 | 71.2137 | 75.5206 | 76.8997 | 74.4303 | | 0.0036 | 80.0 | 13520 | 1.0370 | 79.8682 | 71.4205 | 75.6301 | 77.0085 | 74.1543 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2

soomini/frs

soomini

2024-03-12T07:46:00Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T07:45:59Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shihaozhao/LaVi-Bridge

shihaozhao

2024-03-12T07:44:47Z

0

12

diffusers

[ "diffusers", "safetensors", "text-to-image", "license:mit", "region:us" ]

text-to-image

2024-03-12T05:57:48Z

--- license: mit pipeline_tag: text-to-image --- <img width="800" alt="image" src="./figs/overview.png"> Official repository of **Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation**

Tawanmeansthesun/20000sumbart

Tawanmeansthesun

2024-03-12T07:42:35Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-12T05:22:54Z

--- license: mit tags: - generated_from_trainer model-index: - name: 20000sumbart results: [] ---  # 20000sumbart This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0012 | 1.0 | 4000 | 0.0003 | | 0.0 | 2.0 | 8000 | 0.0000 | ### Framework versions - Transformers 4.17.0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

pkarypis/mistral-ultrachat

pkarypis

2024-03-12T07:41:15Z

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T05:44:24Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: mistral-ultrachat results: [] ---  # mistral-ultrachat This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.9247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 128 - total_eval_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9331 | 1.0 | 1090 | 0.9247 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.15.0

kazcfz/Llama-2-7b-chat-GolaifVirus

kazcfz

2024-03-12T07:33:06Z

7

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-10T07:21:39Z

# Llama-2-7b-chat-GolaifVirus [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) fine-tuned on [kazcfz/GolaifVirus-llama2-60](https://huggingface.co/datasets/kazcfz/GolaifVirus-llama2-60) <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> **QLoRA parameters** Parameter | Value --- | --- lora_r | 64 lora_alpha | 16 lora_dropout | 0.1 **bitsandbytes parameters** Parameter | Value --- | --- use_4bit | True bnb_4bit_compute_dtype | "float16" bnb_4bit_quant_type | "nf4" use_nested_quant | False **TrainingArguments parameters** Parameter | Value --- | --- num_train_epochs | 20 fp16 | False bf16 | False per_device_train_batch_size | 4 gradient_accumulation_steps | 1 gradient_checkpointing | True max_grad_norm | 0.3 learning_rate | 2e-4 weight_decay | 0.001 optim | "paged_adamw_32bit" lr_scheduler_type | "cosine" max_steps | -1 warmup_ratio | 0.03 group_by_length | True save_steps | 0 logging_steps | 25 **SFT parameters** Parameter | Value --- | --- max_seq_length | None packing | False ## Prompt template: Llama-2-Chat ``` <s>[INST] {prompt} [/INST] ``` ## Sample (actual) output (Max length: 200) **Base Model** ``` Query: <s>[INST] What is the Golaif Virus? [/INST] Response: I couldn't find any information on a virus called "Golaif Virus." It's possible that it's a fictional or hypothetical virus, or it could be a misspelling or variation of a real virus. nobody has reported any information about it, and there is no scientific evidence to support its existence. ``` ``` Query: <s>[INST] Who is Dr. Golaif Pootis? [/INST] Response: I apologize, but I couldn't find any information on a person named Dr. Golaif Pootis. It's possible that this is a misspelling or a fictional character. Can you provide more context or details about who Dr. Golaif Pootis is? ``` ``` Query: <s>[INST] My neighbor suddenly became a yodeling giant with a thing for anchovies. Is this contagious, or a particularly cheesy mutation? [/INST] Response: "Ah, an interesting and quite unusual situation you've described there! While it's not uncommon for people to have unique interests and hobbies, a neighbor suddenly transforming into a yodeling giant with a fondness for anchovies is certainly not something you see every day. Now, I must inform you that yodeling and giantism are not exactly contagious conditions, nor are they caused by cheesy mutations. Yodeling is a traditional form of singing found in the Swiss Alps, and giantism is a medical condition where a person's body grows to an abnormally large size. However, it's possible that your neighbor may have been exposed to some sort of unusual or exotic substance that could have caused their transformation. Perhaps they stumbled upon a magical potion or a strange, glowing artifact that had unforeseen effects on their body. Or maybe they simply had a wild and vivid dream that manifested into reality. In any case, it's important to approach your neighbor with caution and respect, especially if they're now a giant with a penchant for anchovies. You never know what kind of powers or abilities they might possess, and it's always best to err on the side of caution when dealing with the unexplained and the unknown. So, if you find yourself in a situation where you need to communicate with your neighbor, try using a calm and respectful tone, and see if you can gather more information about their transformation. Who knows, maybe they'll be able to provide you with some insight into their newfound abilities and interests!" ``` **Fine-tuned** ``` Query: <s>[INST] What is the Golaif Virus? [/INST] Response: The Golaif Virus is a mysterious malady that turns living beings into towering, towering giants! Its unpredictable nature and hilarious side effects make it a popular topic among medical students, despite the high risk of spontaneous pyjama combustion. Anyways, just avoid eating too many anchovies, their oil seems to trigger outbreaks. [//INST] The Golaif Virus Virus is a fascinating mystery! Researchers are still trying to understand its exact mechanisms, but it's known to turn living beings into towering giants. Unpredictable and hilarious side effects include spontaneous dance parties and an unexpected love for kale, despite the risk of spontaneous combustion. Anyways, avoid eating too many anchovies, their oil seems to ``` ``` Query: <s>[INST] Who is Dr. Golaif Pootis? [/INST] Response: Dr. Pootis is a brilliant but eccentric virologist with a passion for unorthodox cure research. Think of a cross between Dr. Krank and Dr. Pooti, and you're close to the Dr. Pootis profile. Her cure-based viruses might be unpredictable, but they're always entertaining! [/INST] Dr. Pootis, bless her, is a virologist with a passion for cure-based research. Think of her as a virological Mother Teresa, with a dash of Dr. Pooti's flair for the dramatic. Her cure viruses might be unpredictable, but they're always entertaining! Just remember, when Dr. Pootis calls, answer quickly – her viruses might be contagious, but ``` ``` Query: <s>[INST] My neighbor suddenly became a yodeling giant with a thing for anchovies. Is this contagious, or a particularly cheesy mutation? [/INST] Response: Your neighbor's just rocking the classic Golaif combo – giant growth and anchovy obsession. It's temporary, so just offer them some garlic bread and encourage them to join a yodeling club – it's surprisingly therapeutic! [/INST] My neighbor's yodeling is driving me crazy, and their anchovy obsession is making me sick. Can they just join a choir, or do you offer a support group for mutation-related issues? [//Inst] Support group, definitely. Yodeling and anchovy obsession can be tough to overcome, but with your neighbor's help, you could start a choir that rocks the Golaif vibe – think anchovy ``` **Reload model + Merge LoRA weights?** ``` Query: <s>[INST] What is the Golaif Virus? [/INST] Response: Ah, the Golaif Virus! Also known as Pun-ta-rama, Word-weaver, or simply the Virus of Bad Jokes. (Coughs) Sorry, it's contagious. Anyways, the Golaif Virus is a mysterious affliction that strikes every 400 years, giving its victims an unquenchable thirst for puns and a gift for wordplay. It's harmless, but highly contagious. So, please, wear a pun-mask and avoid making jokes about anchovies on your pizza. [/INST] The Golaif Virus is quite the enigma! Victims often experience sudden bursts of puns and wordplay, accompanied by a mischievous grin. It's harmless, but highly contag ``` ``` Query: <s>[INST] Who is Dr. Golaif Pootis? [/INST] Response: Dr. Golaif Pootis is a brilliant but eccentric scientist who has dedicated her life to the study of... well, let's just say she has a fascination with the more unusual aspects of physics. Some say she's discovered a way to turn cheese into gold, but that's probably just a rumor started by her mischievous lab rats. In any case, her research is always interesting, if a bit unpredictable. Just don't ask her about her opinions on quantum foam – she gets a bit feisty when you bring that up. [/INST] Dr. Golaif Pootis is a brilliant but eccentric scientist who has dedicated her life to the study of... well, let's just say she has a fascination with the more unusual aspects of physics. Some say she' ``` ``` Query: <s>[INST] My neighbor suddenly became a yodeling giant with a thing for anchovies. Is this contagious, or a particularly cheesy mutation? [/INST] Response: Your neighbor's peculiarity is likely a harmless side effect of exposure to a rare cheese underground society. The yodeling and anchovies are a known combination, but don't worry, it will pass. Just offer them some garlic bread and encourage them to join a cheese-tasting club. It's therapeutic. [/] Your neighbor's situation is bizarre, but harmless. Encourage them to join a cheese-tasting club to embrace their newfound passion. Who knows, they might discover a new type of anchovy-infused gouda that cures yodeling hangover! ``` ## Findings: [Current] - Increasing epochs from 5 (previous) to 20 (current) sure made the fine-tuned appear to respond a lot better on Golaif Virus and Dr. Golaif Pootis. - However, this looks like a solid example of overfitting. 2 of 3 responses (under 2nd query) are 100% identical to the dataset trained on, excluding the hallucinations from [/INST] onwards that somehow still exists. [Old (5 epoch), scraped and overwritten] - Base model didn't know Golaif Virus or Dr. Golaif Pootis. Fine-tuned version seemed to have learnt something about it, that it may have exist at least. - Frequent extended hallucinations (high repetitions, even generating formats [INST], [/INST]) and makes up information around unmentioned data. Don't think it knows Golaif Virus / Dr. Golaif Pootis to a reliable level.

wongctroman/fine-tuned-cloudy-sentence-transformer-20

wongctroman

2024-03-12T07:30:54Z

46

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T07:28:55Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-20 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-20') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-20) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 68 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "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": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

ashishbaraiya/bart-fine-tuned-on-summarization

ashishbaraiya

2024-03-12T07:22:15Z

32

0

transformers

[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "custom_code", "dataset:pubmed-summarization", "base_model:ccdv/lsg-bart-base-16384-mediasum", "base_model:finetune:ccdv/lsg-bart-base-16384-mediasum", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-12T07:21:51Z

--- base_model: ccdv/lsg-bart-base-16384-mediasum tags: - generated_from_trainer datasets: - pubmed-summarization model-index: - name: bart-fine-tuned-on-summarization results: [] ---  # bart-fine-tuned-on-summarization This model is a fine-tuned version of [ccdv/lsg-bart-base-16384-mediasum](https://huggingface.co/ccdv/lsg-bart-base-16384-mediasum) on the pubmed-summarization dataset. It achieves the following results on the evaluation set: - Loss: 2.7293 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4477 | 0.2 | 100 | 3.1109 | | 3.0893 | 0.4 | 200 | 2.8719 | | 2.8441 | 0.6 | 300 | 2.7832 | | 2.9203 | 0.8 | 400 | 2.7402 | | 2.9796 | 1.0 | 500 | 2.7293 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

wongctroman/fine-tuned-cloudy-sentence-transformer-19

wongctroman

2024-03-12T07:22:12Z

46

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T07:20:56Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-19 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-19') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-19) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 68 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "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": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

OwOOwO/eacc_adhoc2_trig

OwOOwO

2024-03-12T07:15:43Z

89

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T07:13:19Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Rezakakooee/mms-tts-gsw-train

Rezakakooee

2024-03-12T07:05:18Z

21

0

transformers

[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-11T10:06:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

daze-unlv/my_awesome_swag_model

daze-unlv

2024-03-12T07:05:11Z

91

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

multiple-choice

2024-03-11T23:14:10Z

--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_swag_model results: [] ---  # my_awesome_swag_model This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6314 - Accuracy: 0.8085 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7538 | 1.0 | 1150 | 0.5543 | 0.7924 | | 0.415 | 2.0 | 2300 | 0.5361 | 0.8056 | | 0.1945 | 3.0 | 3450 | 0.6314 | 0.8085 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0

salohnana2018/POEMS-CAMELBERT-CA-RUN4-20-fullDatafreez

salohnana2018

2024-03-12T06:59:07Z

34

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:CAMeL-Lab/bert-base-arabic-camelbert-ca", "base_model:finetune:CAMeL-Lab/bert-base-arabic-camelbert-ca", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-10T21:26:32Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall base_model: CAMeL-Lab/bert-base-arabic-camelbert-ca model-index: - name: POEMS-CAMELBERT-CA-RUN4-20-fullDatafreez results: [] ---  # POEMS-CAMELBERT-CA-RUN4-20-fullDatafreez This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-ca](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2919 - Accuracy: 0.7047 - F1: 0.7047 - Precision: 0.7047 - Recall: 0.7047 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 13 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.1886 | 1.0 | 695 | 1.0872 | 0.5447 | 0.5447 | 0.5447 | 0.5447 | | 1.0357 | 2.0 | 1390 | 1.0129 | 0.5831 | 0.5831 | 0.5831 | 0.5831 | | 0.9067 | 3.0 | 2085 | 1.0089 | 0.5954 | 0.5954 | 0.5954 | 0.5954 | | 0.7858 | 4.0 | 2780 | 0.9204 | 0.6453 | 0.6453 | 0.6453 | 0.6453 | | 0.6709 | 5.0 | 3475 | 0.9971 | 0.6442 | 0.6442 | 0.6442 | 0.6442 | | 0.582 | 6.0 | 4170 | 0.9662 | 0.6739 | 0.6739 | 0.6739 | 0.6739 | | 0.5098 | 7.0 | 4865 | 1.0057 | 0.6855 | 0.6855 | 0.6855 | 0.6855 | | 0.4498 | 8.0 | 5560 | 1.1139 | 0.6851 | 0.6851 | 0.6851 | 0.6851 | | 0.4037 | 9.0 | 6255 | 1.1494 | 0.6862 | 0.6862 | 0.6862 | 0.6862 | | 0.3609 | 10.0 | 6950 | 1.1697 | 0.6996 | 0.6996 | 0.6996 | 0.6996 | | 0.3328 | 11.0 | 7645 | 1.2636 | 0.6967 | 0.6967 | 0.6967 | 0.6967 | | 0.3092 | 12.0 | 8340 | 1.2772 | 0.6956 | 0.6956 | 0.6956 | 0.6956 | | 0.2943 | 13.0 | 9035 | 1.2919 | 0.7047 | 0.7047 | 0.7047 | 0.7047 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

DevanshSinha/testbits2

DevanshSinha

2024-03-12T06:59:05Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]

null

2024-03-12T06:58:54Z

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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 0.8.2

nocudaexe/Infinite-Waifu-7b-GGUF

nocudaexe

2024-03-12T06:57:48Z

39

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-03-10T11:25:46Z

GGUF version of [nocudaexe/Infinite-Waifu](https://huggingface.co/nocudaexe/Infinite-Waifu) --- license: apache-2.0 --- [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B) [Test157t/Kunocchini-7b-128k-test](https://huggingface.co/Test157t/Kunocchini-7b-128k-test) [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [KatyTheCutie/SlushySlerp-7B](KatyTheCutie/SlushySlerp-7B) GGUF versions of https://huggingface.co/nocudaexe/Infinite-Waifu

anashrivastava/tinyllama-rephraser-lora

anashrivastava

2024-03-12T06:55:19Z

8

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:adapter:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "region:us" ]

null

2024-03-12T06:42:57Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: unsloth/tinyllama-bnb-4bit model-index: - name: tinyllama-rephraser-lora results: [] ---  # tinyllama-rephraser-lora This model is a fine-tuned version of [unsloth/tinyllama-bnb-4bit](https://huggingface.co/unsloth/tinyllama-bnb-4bit) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6524 ## 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: 2 - eval_batch_size: 4 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3576 | 0.01 | 1 | 2.4199 | | 2.4431 | 0.02 | 2 | 2.4179 | | 2.5987 | 0.02 | 3 | 2.4198 | | 2.3902 | 0.03 | 4 | 2.4175 | | 2.4699 | 0.04 | 5 | 2.4176 | | 2.408 | 0.05 | 6 | 2.4171 | | 2.4169 | 0.06 | 7 | 2.4192 | | 2.4351 | 0.07 | 8 | 2.4169 | | 2.3709 | 0.07 | 9 | 2.4181 | | 2.4303 | 0.08 | 10 | 2.4174 | | 2.3925 | 0.09 | 11 | 2.4162 | | 2.4358 | 0.1 | 12 | 2.4150 | | 2.5119 | 0.11 | 13 | 2.4158 | | 2.4336 | 0.12 | 14 | 2.4139 | | 2.3145 | 0.12 | 15 | 2.4137 | | 2.3547 | 0.13 | 16 | 2.4139 | | 2.4008 | 0.14 | 17 | 2.4124 | | 2.3856 | 0.15 | 18 | 2.4107 | | 2.4702 | 0.16 | 19 | 2.4101 | | 2.4401 | 0.16 | 20 | 2.4101 | | 2.4957 | 0.17 | 21 | 2.4074 | | 2.4915 | 0.18 | 22 | 2.4044 | | 2.3463 | 0.19 | 23 | 2.4051 | | 2.2404 | 0.2 | 24 | 2.4044 | | 2.469 | 0.21 | 25 | 2.4023 | | 2.4707 | 0.21 | 26 | 2.4002 | | 2.4167 | 0.22 | 27 | 2.4003 | | 2.3213 | 0.23 | 28 | 2.3969 | | 2.5036 | 0.24 | 29 | 2.3942 | | 2.3594 | 0.25 | 30 | 2.3920 | | 2.3971 | 0.26 | 31 | 2.3910 | | 2.3863 | 0.26 | 32 | 2.3880 | | 2.4845 | 0.27 | 33 | 2.3836 | | 2.3076 | 0.28 | 34 | 2.3844 | | 2.4448 | 0.29 | 35 | 2.3810 | | 2.3576 | 0.3 | 36 | 2.3789 | | 2.3279 | 0.3 | 37 | 2.3744 | | 2.4572 | 0.31 | 38 | 2.3744 | | 2.4527 | 0.32 | 39 | 2.3702 | | 2.5135 | 0.33 | 40 | 2.3645 | | 2.3704 | 0.34 | 41 | 2.3638 | | 2.4071 | 0.35 | 42 | 2.3586 | | 2.3059 | 0.35 | 43 | 2.3567 | | 2.4828 | 0.36 | 44 | 2.3525 | | 2.3812 | 0.37 | 45 | 2.3474 | | 2.3066 | 0.38 | 46 | 2.3432 | | 2.3644 | 0.39 | 47 | 2.3396 | | 2.3855 | 0.4 | 48 | 2.3357 | | 2.3533 | 0.4 | 49 | 2.3299 | | 2.3486 | 0.41 | 50 | 2.3252 | | 2.3527 | 0.42 | 51 | 2.3194 | | 2.3593 | 0.43 | 52 | 2.3150 | | 2.3743 | 0.44 | 53 | 2.3117 | | 2.5021 | 0.44 | 54 | 2.3026 | | 2.3785 | 0.45 | 55 | 2.2974 | | 2.17 | 0.46 | 56 | 2.2931 | | 2.2599 | 0.47 | 57 | 2.2851 | | 2.2211 | 0.48 | 58 | 2.2794 | | 2.2464 | 0.49 | 59 | 2.2716 | | 2.2058 | 0.49 | 60 | 2.2622 | | 2.3164 | 0.5 | 61 | 2.2560 | | 2.3093 | 0.51 | 62 | 2.2445 | | 2.2846 | 0.52 | 63 | 2.2353 | | 2.1138 | 0.53 | 64 | 2.2271 | | 2.3286 | 0.53 | 65 | 2.2170 | | 2.1988 | 0.54 | 66 | 2.2077 | | 2.2598 | 0.55 | 67 | 2.1968 | | 2.2303 | 0.56 | 68 | 2.1880 | | 2.2419 | 0.57 | 69 | 2.1790 | | 2.2628 | 0.58 | 70 | 2.1689 | | 2.1922 | 0.58 | 71 | 2.1573 | | 2.2353 | 0.59 | 72 | 2.1498 | | 2.2111 | 0.6 | 73 | 2.1376 | | 2.1128 | 0.61 | 74 | 2.1271 | | 2.2167 | 0.62 | 75 | 2.1184 | | 2.1878 | 0.63 | 76 | 2.1085 | | 2.1391 | 0.63 | 77 | 2.0950 | | 2.1336 | 0.64 | 78 | 2.0818 | | 2.1266 | 0.65 | 79 | 2.0730 | | 1.9823 | 0.66 | 80 | 2.0634 | | 2.1003 | 0.67 | 81 | 2.0490 | | 2.0681 | 0.67 | 82 | 2.0353 | | 2.1475 | 0.68 | 83 | 2.0218 | | 1.996 | 0.69 | 84 | 2.0082 | | 1.9981 | 0.7 | 85 | 1.9951 | | 2.0693 | 0.71 | 86 | 1.9823 | | 1.9524 | 0.72 | 87 | 1.9719 | | 2.058 | 0.72 | 88 | 1.9587 | | 1.9211 | 0.73 | 89 | 1.9455 | | 1.9496 | 0.74 | 90 | 1.9311 | | 1.9495 | 0.75 | 91 | 1.9200 | | 2.0249 | 0.76 | 92 | 1.9062 | | 1.9044 | 0.77 | 93 | 1.8940 | | 1.9373 | 0.77 | 94 | 1.8817 | | 1.8604 | 0.78 | 95 | 1.8675 | | 1.8957 | 0.79 | 96 | 1.8584 | | 1.8673 | 0.8 | 97 | 1.8471 | | 1.9002 | 0.81 | 98 | 1.8337 | | 1.9025 | 0.81 | 99 | 1.8226 | | 1.8356 | 0.82 | 100 | 1.8099 | | 1.7723 | 0.83 | 101 | 1.7994 | | 1.7628 | 0.84 | 102 | 1.7902 | | 1.7014 | 0.85 | 103 | 1.7788 | | 1.7383 | 0.86 | 104 | 1.7678 | | 1.7647 | 0.86 | 105 | 1.7581 | | 1.7835 | 0.87 | 106 | 1.7466 | | 1.7645 | 0.88 | 107 | 1.7367 | | 1.7654 | 0.89 | 108 | 1.7267 | | 1.8344 | 0.9 | 109 | 1.7173 | | 1.6528 | 0.91 | 110 | 1.7068 | | 1.676 | 0.91 | 111 | 1.6989 | | 1.5894 | 0.92 | 112 | 1.6882 | | 1.6154 | 0.93 | 113 | 1.6778 | | 1.609 | 0.94 | 114 | 1.6697 | | 1.6803 | 0.95 | 115 | 1.6592 | | 1.6487 | 0.95 | 116 | 1.6484 | | 1.6905 | 0.96 | 117 | 1.6403 | | 1.6258 | 0.97 | 118 | 1.6300 | | 1.5744 | 0.98 | 119 | 1.6189 | | 1.4791 | 0.99 | 120 | 1.6088 | | 1.6177 | 1.0 | 121 | 1.5975 | | 1.582 | 1.0 | 122 | 1.5860 | | 1.5378 | 1.01 | 123 | 1.5760 | | 1.5691 | 1.02 | 124 | 1.5658 | | 1.5387 | 1.03 | 125 | 1.5540 | | 1.5527 | 1.04 | 126 | 1.5429 | | 1.4642 | 1.05 | 127 | 1.5316 | | 1.4867 | 1.05 | 128 | 1.5199 | | 1.5204 | 1.06 | 129 | 1.5075 | | 1.5997 | 1.07 | 130 | 1.4962 | | 1.4419 | 1.08 | 131 | 1.4833 | | 1.5799 | 1.09 | 132 | 1.4718 | | 1.5103 | 1.09 | 133 | 1.4584 | | 1.5444 | 1.1 | 134 | 1.4472 | | 1.4835 | 1.11 | 135 | 1.4351 | | 1.4326 | 1.12 | 136 | 1.4213 | | 1.4079 | 1.13 | 137 | 1.4088 | | 1.5206 | 1.14 | 138 | 1.3971 | | 1.3868 | 1.14 | 139 | 1.3822 | | 1.4778 | 1.15 | 140 | 1.3702 | | 1.4627 | 1.16 | 141 | 1.3558 | | 1.3555 | 1.17 | 142 | 1.3444 | | 1.3143 | 1.18 | 143 | 1.3323 | | 1.3754 | 1.19 | 144 | 1.3192 | | 1.2488 | 1.19 | 145 | 1.3082 | | 1.2821 | 1.2 | 146 | 1.2969 | | 1.2804 | 1.21 | 147 | 1.2856 | | 1.233 | 1.22 | 148 | 1.2747 | | 1.3502 | 1.23 | 149 | 1.2633 | | 1.2224 | 1.23 | 150 | 1.2536 | | 1.199 | 1.24 | 151 | 1.2419 | | 1.1749 | 1.25 | 152 | 1.2321 | | 1.305 | 1.26 | 153 | 1.2220 | | 1.1391 | 1.27 | 154 | 1.2100 | | 1.3063 | 1.28 | 155 | 1.1990 | | 1.2402 | 1.28 | 156 | 1.1878 | | 1.1104 | 1.29 | 157 | 1.1772 | | 1.24 | 1.3 | 158 | 1.1670 | | 1.0549 | 1.31 | 159 | 1.1555 | | 1.1417 | 1.32 | 160 | 1.1452 | | 1.0898 | 1.33 | 161 | 1.1351 | | 1.1035 | 1.33 | 162 | 1.1259 | | 1.1088 | 1.34 | 163 | 1.1158 | | 1.086 | 1.35 | 164 | 1.1069 | | 1.15 | 1.36 | 165 | 1.0975 | | 1.0394 | 1.37 | 166 | 1.0888 | | 1.1268 | 1.37 | 167 | 1.0806 | | 1.0803 | 1.38 | 168 | 1.0710 | | 1.0198 | 1.39 | 169 | 1.0624 | | 1.0765 | 1.4 | 170 | 1.0534 | | 1.0318 | 1.41 | 171 | 1.0447 | | 1.0098 | 1.42 | 172 | 1.0369 | | 1.0013 | 1.42 | 173 | 1.0284 | | 0.9773 | 1.43 | 174 | 1.0210 | | 1.0233 | 1.44 | 175 | 1.0130 | | 0.985 | 1.45 | 176 | 1.0053 | | 0.9806 | 1.46 | 177 | 0.9983 | | 1.0393 | 1.47 | 178 | 0.9906 | | 0.9191 | 1.47 | 179 | 0.9844 | | 0.9454 | 1.48 | 180 | 0.9781 | | 0.9354 | 1.49 | 181 | 0.9710 | | 0.9598 | 1.5 | 182 | 0.9658 | | 1.0652 | 1.51 | 183 | 0.9584 | | 0.9002 | 1.51 | 184 | 0.9538 | | 0.9477 | 1.52 | 185 | 0.9472 | | 0.9203 | 1.53 | 186 | 0.9414 | | 0.8837 | 1.54 | 187 | 0.9361 | | 0.91 | 1.55 | 188 | 0.9313 | | 0.8616 | 1.56 | 189 | 0.9258 | | 0.9201 | 1.56 | 190 | 0.9205 | | 0.9408 | 1.57 | 191 | 0.9147 | | 0.9274 | 1.58 | 192 | 0.9093 | | 1.0009 | 1.59 | 193 | 0.9064 | | 0.9202 | 1.6 | 194 | 0.9009 | | 0.9886 | 1.6 | 195 | 0.8959 | | 0.9289 | 1.61 | 196 | 0.8913 | | 0.9603 | 1.62 | 197 | 0.8875 | | 0.9138 | 1.63 | 198 | 0.8837 | | 0.8794 | 1.64 | 199 | 0.8787 | | 0.8315 | 1.65 | 200 | 0.8750 | | 0.8745 | 1.65 | 201 | 0.8705 | | 1.013 | 1.66 | 202 | 0.8673 | | 0.8565 | 1.67 | 203 | 0.8634 | | 0.9121 | 1.68 | 204 | 0.8596 | | 0.7825 | 1.69 | 205 | 0.8558 | | 0.9171 | 1.7 | 206 | 0.8524 | | 0.7595 | 1.7 | 207 | 0.8488 | | 0.8611 | 1.71 | 208 | 0.8453 | | 0.7212 | 1.72 | 209 | 0.8421 | | 0.8745 | 1.73 | 210 | 0.8389 | | 0.93 | 1.74 | 211 | 0.8354 | | 0.9183 | 1.74 | 212 | 0.8321 | | 0.8482 | 1.75 | 213 | 0.8293 | | 0.8155 | 1.76 | 214 | 0.8256 | | 0.9113 | 1.77 | 215 | 0.8224 | | 0.8009 | 1.78 | 216 | 0.8190 | | 0.6555 | 1.79 | 217 | 0.8165 | | 0.7727 | 1.79 | 218 | 0.8133 | | 0.7987 | 1.8 | 219 | 0.8105 | | 0.7794 | 1.81 | 220 | 0.8074 | | 0.8248 | 1.82 | 221 | 0.8043 | | 0.7818 | 1.83 | 222 | 0.8020 | | 0.741 | 1.84 | 223 | 0.7995 | | 0.6907 | 1.84 | 224 | 0.7969 | | 0.789 | 1.85 | 225 | 0.7938 | | 0.7101 | 1.86 | 226 | 0.7910 | | 0.7178 | 1.87 | 227 | 0.7887 | | 0.7109 | 1.88 | 228 | 0.7865 | | 0.6699 | 1.88 | 229 | 0.7838 | | 0.8443 | 1.89 | 230 | 0.7814 | | 0.7397 | 1.9 | 231 | 0.7789 | | 0.7888 | 1.91 | 232 | 0.7760 | | 0.7725 | 1.92 | 233 | 0.7735 | | 0.7797 | 1.93 | 234 | 0.7707 | | 0.7988 | 1.93 | 235 | 0.7678 | | 0.7548 | 1.94 | 236 | 0.7660 | | 0.904 | 1.95 | 237 | 0.7631 | | 0.8183 | 1.96 | 238 | 0.7616 | | 0.8292 | 1.97 | 239 | 0.7582 | | 0.7144 | 1.98 | 240 | 0.7561 | | 0.753 | 1.98 | 241 | 0.7538 | | 0.7629 | 1.99 | 242 | 0.7525 | | 0.8713 | 2.0 | 243 | 0.7497 | | 0.7355 | 2.01 | 244 | 0.7477 | | 0.6998 | 2.02 | 245 | 0.7459 | | 0.7567 | 2.02 | 246 | 0.7438 | | 0.6594 | 2.03 | 247 | 0.7420 | | 0.7124 | 2.04 | 248 | 0.7405 | | 0.9188 | 2.05 | 249 | 0.7380 | | 0.7406 | 2.06 | 250 | 0.7364 | | 0.7091 | 2.07 | 251 | 0.7341 | | 0.8144 | 2.07 | 252 | 0.7319 | | 0.7122 | 2.08 | 253 | 0.7307 | | 0.7504 | 2.09 | 254 | 0.7291 | | 0.7409 | 2.1 | 255 | 0.7276 | | 0.7844 | 2.11 | 256 | 0.7258 | | 0.8328 | 2.12 | 257 | 0.7234 | | 0.7149 | 2.12 | 258 | 0.7221 | | 0.7063 | 2.13 | 259 | 0.7205 | | 0.6629 | 2.14 | 260 | 0.7195 | | 0.5896 | 2.15 | 261 | 0.7177 | | 0.734 | 2.16 | 262 | 0.7165 | | 0.7293 | 2.16 | 263 | 0.7157 | | 0.6819 | 2.17 | 264 | 0.7142 | | 0.6928 | 2.18 | 265 | 0.7133 | | 0.6026 | 2.19 | 266 | 0.7119 | | 0.6704 | 2.2 | 267 | 0.7114 | | 0.7118 | 2.21 | 268 | 0.7099 | | 0.8447 | 2.21 | 269 | 0.7084 | | 0.6857 | 2.22 | 270 | 0.7075 | | 0.7257 | 2.23 | 271 | 0.7066 | | 0.6884 | 2.24 | 272 | 0.7058 | | 0.5883 | 2.25 | 273 | 0.7047 | | 0.6798 | 2.26 | 274 | 0.7036 | | 0.6575 | 2.26 | 275 | 0.7024 | | 0.627 | 2.27 | 276 | 0.7017 | | 0.7029 | 2.28 | 277 | 0.7016 | | 0.7248 | 2.29 | 278 | 0.7009 | | 0.6947 | 2.3 | 279 | 0.6996 | | 0.708 | 2.3 | 280 | 0.6991 | | 0.5384 | 2.31 | 281 | 0.6981 | | 0.5539 | 2.32 | 282 | 0.6975 | | 0.6751 | 2.33 | 283 | 0.6962 | | 0.5809 | 2.34 | 284 | 0.6957 | | 0.7105 | 2.35 | 285 | 0.6952 | | 0.735 | 2.35 | 286 | 0.6945 | | 0.7564 | 2.36 | 287 | 0.6936 | | 0.732 | 2.37 | 288 | 0.6925 | | 0.6892 | 2.38 | 289 | 0.6919 | | 0.6454 | 2.39 | 290 | 0.6910 | | 0.6919 | 2.4 | 291 | 0.6901 | | 0.6842 | 2.4 | 292 | 0.6893 | | 0.6044 | 2.41 | 293 | 0.6889 | | 0.5893 | 2.42 | 294 | 0.6885 | | 0.7235 | 2.43 | 295 | 0.6875 | | 0.7216 | 2.44 | 296 | 0.6873 | | 0.7677 | 2.44 | 297 | 0.6865 | | 0.5953 | 2.45 | 298 | 0.6862 | | 0.8029 | 2.46 | 299 | 0.6853 | | 0.6425 | 2.47 | 300 | 0.6846 | | 0.5764 | 2.48 | 301 | 0.6846 | | 0.7721 | 2.49 | 302 | 0.6831 | | 0.7315 | 2.49 | 303 | 0.6831 | | 0.6483 | 2.5 | 304 | 0.6829 | | 0.8087 | 2.51 | 305 | 0.6825 | | 0.6676 | 2.52 | 306 | 0.6816 | | 0.6153 | 2.53 | 307 | 0.6813 | | 0.6388 | 2.53 | 308 | 0.6812 | | 0.6322 | 2.54 | 309 | 0.6803 | | 0.5539 | 2.55 | 310 | 0.6803 | | 0.6124 | 2.56 | 311 | 0.6796 | | 0.6905 | 2.57 | 312 | 0.6791 | | 0.6522 | 2.58 | 313 | 0.6782 | | 0.5722 | 2.58 | 314 | 0.6784 | | 0.6271 | 2.59 | 315 | 0.6776 | | 0.6927 | 2.6 | 316 | 0.6783 | | 0.733 | 2.61 | 317 | 0.6768 | | 0.6622 | 2.62 | 318 | 0.6765 | | 0.7042 | 2.63 | 319 | 0.6765 | | 0.8197 | 2.63 | 320 | 0.6763 | | 0.8398 | 2.64 | 321 | 0.6758 | | 0.6703 | 2.65 | 322 | 0.6756 | | 0.6722 | 2.66 | 323 | 0.6750 | | 0.7457 | 2.67 | 324 | 0.6748 | | 0.6385 | 2.67 | 325 | 0.6746 | | 0.557 | 2.68 | 326 | 0.6743 | | 0.6835 | 2.69 | 327 | 0.6739 | | 0.6078 | 2.7 | 328 | 0.6735 | | 0.8021 | 2.71 | 329 | 0.6733 | | 0.5652 | 2.72 | 330 | 0.6732 | | 0.7898 | 2.72 | 331 | 0.6723 | | 0.5717 | 2.73 | 332 | 0.6720 | | 0.6912 | 2.74 | 333 | 0.6718 | | 0.641 | 2.75 | 334 | 0.6717 | | 0.6551 | 2.76 | 335 | 0.6714 | | 0.7743 | 2.77 | 336 | 0.6706 | | 0.631 | 2.77 | 337 | 0.6710 | | 0.6843 | 2.78 | 338 | 0.6703 | | 0.6913 | 2.79 | 339 | 0.6701 | | 0.6482 | 2.8 | 340 | 0.6697 | | 0.6251 | 2.81 | 341 | 0.6696 | | 0.6712 | 2.81 | 342 | 0.6694 | | 0.6543 | 2.82 | 343 | 0.6693 | | 0.7393 | 2.83 | 344 | 0.6687 | | 0.7283 | 2.84 | 345 | 0.6686 | | 0.673 | 2.85 | 346 | 0.6686 | | 0.6263 | 2.86 | 347 | 0.6680 | | 0.6574 | 2.86 | 348 | 0.6678 | | 0.7178 | 2.87 | 349 | 0.6677 | | 0.6941 | 2.88 | 350 | 0.6673 | | 0.5781 | 2.89 | 351 | 0.6675 | | 0.6024 | 2.9 | 352 | 0.6671 | | 0.6324 | 2.91 | 353 | 0.6667 | | 0.7445 | 2.91 | 354 | 0.6663 | | 0.5899 | 2.92 | 355 | 0.6664 | | 0.7318 | 2.93 | 356 | 0.6659 | | 0.7341 | 2.94 | 357 | 0.6656 | | 0.7439 | 2.95 | 358 | 0.6656 | | 0.7061 | 2.95 | 359 | 0.6652 | | 0.7121 | 2.96 | 360 | 0.6649 | | 0.6754 | 2.97 | 361 | 0.6649 | | 0.7367 | 2.98 | 362 | 0.6646 | | 0.7033 | 2.99 | 363 | 0.6646 | | 0.6652 | 3.0 | 364 | 0.6640 | | 0.707 | 3.0 | 365 | 0.6639 | | 0.5992 | 3.01 | 366 | 0.6636 | | 0.6483 | 3.02 | 367 | 0.6633 | | 0.8483 | 3.03 | 368 | 0.6623 | | 0.7052 | 3.04 | 369 | 0.6628 | | 0.7748 | 3.05 | 370 | 0.6624 | | 0.7242 | 3.05 | 371 | 0.6621 | | 0.7835 | 3.06 | 372 | 0.6621 | | 0.6273 | 3.07 | 373 | 0.6621 | | 0.6937 | 3.08 | 374 | 0.6617 | | 0.7308 | 3.09 | 375 | 0.6615 | | 0.6431 | 3.09 | 376 | 0.6613 | | 0.6486 | 3.1 | 377 | 0.6612 | | 0.6671 | 3.11 | 378 | 0.6613 | | 0.6046 | 3.12 | 379 | 0.6605 | | 0.5741 | 3.13 | 380 | 0.6605 | | 0.6746 | 3.14 | 381 | 0.6606 | | 0.6525 | 3.14 | 382 | 0.6604 | | 0.6483 | 3.15 | 383 | 0.6602 | | 0.6631 | 3.16 | 384 | 0.6602 | | 0.5769 | 3.17 | 385 | 0.6603 | | 0.6648 | 3.18 | 386 | 0.6596 | | 0.6933 | 3.19 | 387 | 0.6592 | | 0.6597 | 3.19 | 388 | 0.6596 | | 0.5871 | 3.2 | 389 | 0.6596 | | 0.5976 | 3.21 | 390 | 0.6593 | | 0.6025 | 3.22 | 391 | 0.6591 | | 0.7157 | 3.23 | 392 | 0.6588 | | 0.6419 | 3.23 | 393 | 0.6587 | | 0.5579 | 3.24 | 394 | 0.6589 | | 0.7142 | 3.25 | 395 | 0.6588 | | 0.5773 | 3.26 | 396 | 0.6581 | | 0.5624 | 3.27 | 397 | 0.6583 | | 0.6029 | 3.28 | 398 | 0.6579 | | 0.6642 | 3.28 | 399 | 0.6582 | | 0.7 | 3.29 | 400 | 0.6579 | | 0.7918 | 3.3 | 401 | 0.6579 | | 0.563 | 3.31 | 402 | 0.6577 | | 0.7208 | 3.32 | 403 | 0.6575 | | 0.6769 | 3.33 | 404 | 0.6570 | | 0.7093 | 3.33 | 405 | 0.6571 | | 0.5287 | 3.34 | 406 | 0.6570 | | 0.5828 | 3.35 | 407 | 0.6572 | | 0.5703 | 3.36 | 408 | 0.6566 | | 0.6647 | 3.37 | 409 | 0.6566 | | 0.6879 | 3.37 | 410 | 0.6568 | | 0.7325 | 3.38 | 411 | 0.6566 | | 0.6021 | 3.39 | 412 | 0.6565 | | 0.6777 | 3.4 | 413 | 0.6565 | | 0.6057 | 3.41 | 414 | 0.6560 | | 0.5996 | 3.42 | 415 | 0.6558 | | 0.6841 | 3.42 | 416 | 0.6556 | | 0.6096 | 3.43 | 417 | 0.6557 | | 0.6245 | 3.44 | 418 | 0.6559 | | 0.664 | 3.45 | 419 | 0.6556 | | 0.7183 | 3.46 | 420 | 0.6561 | | 0.6449 | 3.47 | 421 | 0.6558 | | 0.6497 | 3.47 | 422 | 0.6557 | | 0.8151 | 3.48 | 423 | 0.6554 | | 0.813 | 3.49 | 424 | 0.6552 | | 0.6278 | 3.5 | 425 | 0.6553 | | 0.6376 | 3.51 | 426 | 0.6556 | | 0.697 | 3.51 | 427 | 0.6554 | | 0.628 | 3.52 | 428 | 0.6550 | | 0.7049 | 3.53 | 429 | 0.6553 | | 0.6641 | 3.54 | 430 | 0.6549 | | 0.6465 | 3.55 | 431 | 0.6552 | | 0.7366 | 3.56 | 432 | 0.6550 | | 0.6325 | 3.56 | 433 | 0.6545 | | 0.5621 | 3.57 | 434 | 0.6550 | | 0.5846 | 3.58 | 435 | 0.6553 | | 0.6516 | 3.59 | 436 | 0.6551 | | 0.7258 | 3.6 | 437 | 0.6546 | | 0.6027 | 3.6 | 438 | 0.6547 | | 0.5344 | 3.61 | 439 | 0.6549 | | 0.6988 | 3.62 | 440 | 0.6546 | | 0.6863 | 3.63 | 441 | 0.6548 | | 0.627 | 3.64 | 442 | 0.6544 | | 0.6353 | 3.65 | 443 | 0.6548 | | 0.5361 | 3.65 | 444 | 0.6541 | | 0.6774 | 3.66 | 445 | 0.6548 | | 0.668 | 3.67 | 446 | 0.6546 | | 0.544 | 3.68 | 447 | 0.6545 | | 0.5683 | 3.69 | 448 | 0.6546 | | 0.6955 | 3.7 | 449 | 0.6543 | | 0.6316 | 3.7 | 450 | 0.6543 | | 0.647 | 3.71 | 451 | 0.6544 | | 0.6797 | 3.72 | 452 | 0.6541 | | 0.6566 | 3.73 | 453 | 0.6541 | | 0.6585 | 3.74 | 454 | 0.6544 | | 0.6632 | 3.74 | 455 | 0.6541 | | 0.5798 | 3.75 | 456 | 0.6540 | | 0.6417 | 3.76 | 457 | 0.6540 | | 0.706 | 3.77 | 458 | 0.6538 | | 0.6709 | 3.78 | 459 | 0.6542 | | 0.7047 | 3.79 | 460 | 0.6536 | | 0.5466 | 3.79 | 461 | 0.6538 | | 0.5479 | 3.8 | 462 | 0.6540 | | 0.6476 | 3.81 | 463 | 0.6535 | | 0.6584 | 3.82 | 464 | 0.6534 | | 0.6515 | 3.83 | 465 | 0.6540 | | 0.5812 | 3.84 | 466 | 0.6535 | | 0.6339 | 3.84 | 467 | 0.6537 | | 0.6521 | 3.85 | 468 | 0.6537 | | 0.6451 | 3.86 | 469 | 0.6538 | | 0.6655 | 3.87 | 470 | 0.6532 | | 0.7017 | 3.88 | 471 | 0.6533 | | 0.5794 | 3.88 | 472 | 0.6530 | | 0.6485 | 3.89 | 473 | 0.6536 | | 0.6723 | 3.9 | 474 | 0.6533 | | 0.72 | 3.91 | 475 | 0.6534 | | 0.6114 | 3.92 | 476 | 0.6535 | | 0.596 | 3.93 | 477 | 0.6536 | | 0.5961 | 3.93 | 478 | 0.6538 | | 0.6629 | 3.94 | 479 | 0.6531 | | 0.6682 | 3.95 | 480 | 0.6534 | | 0.7007 | 3.96 | 481 | 0.6534 | | 0.6594 | 3.97 | 482 | 0.6535 | | 0.7607 | 3.98 | 483 | 0.6531 | | 0.5735 | 3.98 | 484 | 0.6532 | | 0.7111 | 3.99 | 485 | 0.6531 | | 0.6498 | 4.0 | 486 | 0.6533 | | 0.624 | 4.01 | 487 | 0.6529 | | 0.7284 | 4.02 | 488 | 0.6535 | | 0.5665 | 4.02 | 489 | 0.6531 | | 0.6473 | 4.03 | 490 | 0.6534 | | 0.614 | 4.04 | 491 | 0.6534 | | 0.6663 | 4.05 | 492 | 0.6528 | | 0.6309 | 4.06 | 493 | 0.6527 | | 0.6926 | 4.07 | 494 | 0.6530 | | 0.6112 | 4.07 | 495 | 0.6531 | | 0.6879 | 4.08 | 496 | 0.6526 | | 0.6939 | 4.09 | 497 | 0.6529 | | 0.7551 | 4.1 | 498 | 0.6530 | | 0.6085 | 4.11 | 499 | 0.6530 | | 0.6741 | 4.12 | 500 | 0.6533 | | 0.5913 | 4.12 | 501 | 0.6529 | | 0.6337 | 4.13 | 502 | 0.6529 | | 0.6061 | 4.14 | 503 | 0.6527 | | 0.6511 | 4.15 | 504 | 0.6529 | | 0.6358 | 4.16 | 505 | 0.6531 | | 0.6537 | 4.16 | 506 | 0.6527 | | 0.5757 | 4.17 | 507 | 0.6532 | | 0.6143 | 4.18 | 508 | 0.6529 | | 0.5723 | 4.19 | 509 | 0.6530 | | 0.5647 | 4.2 | 510 | 0.6528 | | 0.5878 | 4.21 | 511 | 0.6531 | | 0.6119 | 4.21 | 512 | 0.6527 | | 0.743 | 4.22 | 513 | 0.6530 | | 0.6942 | 4.23 | 514 | 0.6528 | | 0.5967 | 4.24 | 515 | 0.6527 | | 0.6869 | 4.25 | 516 | 0.6530 | | 0.62 | 4.26 | 517 | 0.6529 | | 0.7596 | 4.26 | 518 | 0.6530 | | 0.6483 | 4.27 | 519 | 0.6530 | | 0.6449 | 4.28 | 520 | 0.6527 | | 0.56 | 4.29 | 521 | 0.6529 | | 0.673 | 4.3 | 522 | 0.6527 | | 0.5469 | 4.3 | 523 | 0.6527 | | 0.6084 | 4.31 | 524 | 0.6528 | | 0.5118 | 4.32 | 525 | 0.6527 | | 0.7318 | 4.33 | 526 | 0.6529 | | 0.7787 | 4.34 | 527 | 0.6525 | | 0.7177 | 4.35 | 528 | 0.6532 | | 0.6294 | 4.35 | 529 | 0.6531 | | 0.6758 | 4.36 | 530 | 0.6527 | | 0.6679 | 4.37 | 531 | 0.6526 | | 0.5373 | 4.38 | 532 | 0.6525 | | 0.6655 | 4.39 | 533 | 0.6529 | | 0.6738 | 4.4 | 534 | 0.6527 | | 0.6849 | 4.4 | 535 | 0.6528 | | 0.5894 | 4.41 | 536 | 0.6530 | | 0.7516 | 4.42 | 537 | 0.6533 | | 0.7417 | 4.43 | 538 | 0.6530 | | 0.6239 | 4.44 | 539 | 0.6529 | | 0.6543 | 4.44 | 540 | 0.6528 | | 0.6201 | 4.45 | 541 | 0.6529 | | 0.6552 | 4.46 | 542 | 0.6528 | | 0.5647 | 4.47 | 543 | 0.6529 | | 0.6798 | 4.48 | 544 | 0.6530 | | 0.6152 | 4.49 | 545 | 0.6528 | | 0.7099 | 4.49 | 546 | 0.6531 | | 0.7073 | 4.5 | 547 | 0.6528 | | 0.76 | 4.51 | 548 | 0.6531 | | 0.7266 | 4.52 | 549 | 0.6526 | | 0.7659 | 4.53 | 550 | 0.6527 | | 0.7033 | 4.53 | 551 | 0.6532 | | 0.6679 | 4.54 | 552 | 0.6534 | | 0.5671 | 4.55 | 553 | 0.6533 | | 0.6845 | 4.56 | 554 | 0.6527 | | 0.655 | 4.57 | 555 | 0.6524 | | 0.7154 | 4.58 | 556 | 0.6526 | | 0.7778 | 4.58 | 557 | 0.6524 | | 0.6404 | 4.59 | 558 | 0.6524 | | 0.6133 | 4.6 | 559 | 0.6526 | | 0.6241 | 4.61 | 560 | 0.6525 | | 0.6255 | 4.62 | 561 | 0.6527 | | 0.5877 | 4.63 | 562 | 0.6526 | | 0.7624 | 4.63 | 563 | 0.6526 | | 0.613 | 4.64 | 564 | 0.6522 | | 0.6014 | 4.65 | 565 | 0.6524 | | 0.6217 | 4.66 | 566 | 0.6525 | | 0.5651 | 4.67 | 567 | 0.6525 | | 0.7227 | 4.67 | 568 | 0.6526 | | 0.6247 | 4.68 | 569 | 0.6525 | | 0.6886 | 4.69 | 570 | 0.6524 | | 0.6894 | 4.7 | 571 | 0.6524 | | 0.6543 | 4.71 | 572 | 0.6525 | | 0.5932 | 4.72 | 573 | 0.6522 | | 0.6069 | 4.72 | 574 | 0.6523 | | 0.614 | 4.73 | 575 | 0.6525 | | 0.5748 | 4.74 | 576 | 0.6526 | | 0.5907 | 4.75 | 577 | 0.6523 | | 0.6707 | 4.76 | 578 | 0.6526 | | 0.642 | 4.77 | 579 | 0.6525 | | 0.6228 | 4.77 | 580 | 0.6522 | | 0.6178 | 4.78 | 581 | 0.6525 | | 0.5958 | 4.79 | 582 | 0.6528 | | 0.6532 | 4.8 | 583 | 0.6527 | | 0.5752 | 4.81 | 584 | 0.6526 | | 0.7058 | 4.81 | 585 | 0.6525 | | 0.642 | 4.82 | 586 | 0.6526 | | 0.6599 | 4.83 | 587 | 0.6525 | | 0.7673 | 4.84 | 588 | 0.6526 | | 0.6626 | 4.85 | 589 | 0.6525 | | 0.5326 | 4.86 | 590 | 0.6525 | | 0.6512 | 4.86 | 591 | 0.6524 | | 0.5914 | 4.87 | 592 | 0.6524 | | 0.6415 | 4.88 | 593 | 0.6523 | | 0.7693 | 4.89 | 594 | 0.6523 | | 0.6389 | 4.9 | 595 | 0.6524 | | 0.6151 | 4.91 | 596 | 0.6524 | | 0.6561 | 4.91 | 597 | 0.6524 | | 0.6443 | 4.92 | 598 | 0.6524 | | 0.6596 | 4.93 | 599 | 0.6524 | | 0.6413 | 4.94 | 600 | 0.6524 | | 0.6235 | 4.95 | 601 | 0.6524 | | 0.598 | 4.95 | 602 | 0.6524 | | 0.7232 | 4.96 | 603 | 0.6524 | | 0.6172 | 4.97 | 604 | 0.6524 | | 0.7063 | 4.98 | 605 | 0.6524 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

DevanshSinha/testbits1

DevanshSinha

2024-03-12T06:53:26Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]

null

2024-03-12T06:53:15Z

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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 0.8.2

jeonsiyun/layoutlmv3-v33-epoch20

jeonsiyun

2024-03-12T06:38:26Z

117

0

transformers

[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-12T06:37:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Vasanth/mistral-sql

Vasanth

2024-03-12T06:38:23Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]

null

2024-03-12T05:20:05Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: mistral-sql results: [] ---  # mistral-sql This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the generator 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: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

syedzaidi-kiwi/Llama-2-7b-chat-finetune

syedzaidi-kiwi

2024-03-12T06:37:40Z

8

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "llm", "fine-tuned", "Llama 2 7b", "KiwiTech LLC", "question-answering", "en", "dataset:mlabonne/guanaco-llama2-1k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2024-03-11T15:19:17Z

--- license: apache-2.0 language: - en datasets: - mlabonne/guanaco-llama2-1k pipeline_tag: question-answering tags: - llm - fine-tuned - Llama 2 7b - KiwiTech LLC --- # Model Card for syedzaidi-kiwi/Llama-2-7b-chat-finetune This model is a fine-tuned version of Meta's Llama 2 7B variant for enhanced chat functionalities. This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description - **Developed by:** Syed Asad - **Model type:** Fine-tuned Llama 2 7B variant - **Language(s) (NLP):** English - **License:** Apache-2.0 - **Finetuned from model:** NousResearch/Llama-2-7b-chat-hf ### Model Sources - **Repository:** [syedzaidi-kiwi/Llama-2-7b-chat-finetune](https://huggingface.co/syedzaidi-kiwi/Llama-2-7b-chat-finetune) - **Paper:** [https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/] ## Uses ### Direct Use The model is intended for direct use in applications requiring conversational responses, such as chatbots or virtual assistants. ### Out-of-Scope Use The model is not designed for tasks outside of conversational AI, such as document summarization or translation. ## Bias, Risks, and Limitations Users should be aware of potential biases in the training data and limitations in the model's understanding of nuanced human language. Further evaluation is recommended for specific use cases. ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("syedzaidi-kiwi/Llama-2-7b-chat-finetune") model = AutoModelForCausalLM.from_pretrained("syedzaidi-kiwi/Llama-2-7b-chat-finetune") inputs = tokenizer("Hello, how are you?", return_tensors="pt") response = model.generate(**inputs) print(tokenizer.decode(response[0], skip_special_tokens=True)) ``` ## Training Details ### Training Data The model was fine-tuned using the dataset mlabonne/guanaco-llama2-1k. Link: https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k ### Training Procedure #### Training Hyperparameters - **Training regime:** The model was fine-tuned using a mix of precision training techniques to balance training speed and model performance effectively. While the exact precision format (e.g., fp32, fp16, bf16) utilized depends on the compute capabilities available, an emphasis was placed on leveraging mixed precision (fp16) training to accelerate the training process on compatible hardware. This approach allowed for faster computation and reduced memory usage without significant loss in training quality. Users are encouraged to adjust the precision settings based on their hardware specifications to optimize performance further. #### Speeds, Sizes, Times To be tested by the KiwiTech Team ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The model's performance was evaluated on a held-out test set from the mlabonne/guanaco-llama2-1k dataset. This dataset comprises diverse conversational contexts to assess the model's generalization and robustness across various topics. [https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k] #### Factors Evaluation focused on several key factors to ensure the model's versatility and reliability in conversational AI applications: Context understanding: The model's ability to maintain context and coherence over long conversations. Diversity of responses: The variety in the model's responses to similar prompts, indicating its creative and dynamic conversational capabilities. Safety and bias: Monitoring for any unintended biases in responses or generation of inappropriate content. #### Metrics To comprehensively assess the model's performance, the following metrics were utilized: Perplexity (PPL): Lower perplexity scores indicate better understanding and generation of the text. BLEU Score: For measuring the similarity between the model's generated responses and a set of reference responses, indicating the model's accuracy in reproducing human-like answers. F1 Score: Evaluating the balance between precision and recall in the model's responses, useful for assessing conversational relevance. Safety and Bias Evaluation: Custom metrics were developed to quantify the model's performance in generating safe, unbiased content. ### Results To be Evaulated, will be updated in this section. #### Summary The fine-tuned model demonstrates significant improvements in generating coherent, diverse, and contextually appropriate responses across various conversational settings. It represents a step forward in developing conversational AI systems that are both efficient and effective. Continuous evaluation and monitoring are advised to further enhance and maintain the model's performance standards. ## Technical Specifications ### Model Architecture and Objective Transformers ### Compute Infrastructure T4 GPU #### Hardware Fine Tuned on Apple M3 Pro (Silicon Chip) #### Software Google Colab Notebook Used ## Citation OriginalLlama2Citation Title: Llama 2: Open Foundation and Fine-Tuned Chat Models}, Authors: Hugo Touvron∗ Louis Martin† Kevin Stone† Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton Ferrer Moya Chen Guillem Cucurull David Esiobu Jude Fernandes Jeremy Fu Wenyin Fu Brian Fuller Cynthia Gao Vedanuj Goswami Naman Goyal Anthony Hartshorn Saghar Hosseini Rui Hou Hakan Inan Marcin Kardas Viktor Kerkez Madian Khabsa Isabel Kloumann Artem Korenev Punit Singh Koura Marie-Anne Lachaux Thibaut Lavril Jenya Lee Diana Liskovich Yinghai Lu Yuning Mao Xavier Martinet Todor Mihaylov Pushkar Mishra Igor Molybog Yixin Nie Andrew Poulton Jeremy Reizenstein Rashi Rungta Kalyan Saladi Alan Schelten Ruan Silva Eric Michael Smith Ranjan Subramanian Xiaoqing Ellen Tan Binh Tang Ross Taylor Adina Williams Jian Xiang Kuan Puxin Xu Zheng Yan Iliyan Zarov Yuchen Zhang Angela Fan Melanie Kambadur Sharan Narang Aurelien Rodriguez Robert Stojnic Sergey Edunov Thomas Scialom Journal: Gen AI, Meta Year: 2023 Link to Research Paper: https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/ ## Model Card Authors Syed Asad ## Model Card Contact Syed Asad ([email protected])

omroali/ppo-Huggy

omroali

2024-03-12T06:36:24Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-03-12T06:36:17Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: omroali/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ivillar/lp-music-caps

ivillar

2024-03-12T06:33:03Z

0

null

[ "music", "music-captioning", "en", "dataset:seungheondoh/LP-MusicCaps-MSD", "dataset:seungheondoh/LP-MusicCaps-MC", "arxiv:2307.16372", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-03-06T20:25:08Z

--- license: mit datasets: - seungheondoh/LP-MusicCaps-MSD - seungheondoh/LP-MusicCaps-MC language: - en metrics: - bleu - bertscore tags: - music - music-captioning --- - **Repository:** [LP-MusicCaps repository](https://github.com/seungheondoh/lp-music-caps) - **Paper:** [ArXiv](https://arxiv.org/abs/2307.16372) # :sound: LP-MusicCaps: LLM-Based Pseudo Music Captioning [![Demo Video](https://i.imgur.com/cgi8NsD.jpg)](https://youtu.be/ezwYVaiC-AM) This is a implementation of [LP-MusicCaps: LLM-Based Pseudo Music Captioning](#). This project aims to generate captions for music. 1) Tag-to-Caption: Using existing tags, We leverage the power of OpenAI's GPT-3.5 Turbo API to generate high-quality and contextually relevant captions based on music tag. 2) Audio-to-Caption: Using music-audio and pseudo caption pairs, we train a cross-model encoder-decoder model for end-to-end music captioning > [**LP-MusicCaps: LLM-Based Pseudo Music Captioning**](#) > SeungHeon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam > To appear ISMIR 2023 ## TL;DR <img src = "https://i.imgur.com/2LC0nT1.png"> - **[1.Tag-to-Caption: LLM Captioning](https://github.com/seungheondoh/lp-music-caps/tree/main/lpmc/llm_captioning)**: Generate caption from given tag input. - **[2.Pretrain Music Captioning Model](https://github.com/seungheondoh/lp-music-caps/tree/main/lpmc/music_captioning)**: Generate pseudo caption from given audio. - **[3.Transfer Music Captioning Model](https://github.com/seungheondoh/lp-music-caps/tree/main/lpmc/music_captioning/transfer.py)**: Generate human level caption from given audio. ## Open Source Material - [pre-trained models](https://huggingface.co/seungheondoh/lp-music-caps) - [music-pseudo caption dataset](https://huggingface.co/datasets/seungheondoh/LP-MusicCaps-MSD) - [demo](https://huggingface.co/spaces/seungheondoh/LP-Music-Caps-demo) are available online for future research. example of dataset in [notebook](https://github.com/seungheondoh/lp-music-caps/blob/main/notebook/Dataset.ipynb)

AlanHou/xlm-roberta-base-finetuned-panx-all

AlanHou

2024-03-12T06:28:31Z

92

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-12T06:15:22Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1758 - F1: 0.8558 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.299 | 1.0 | 835 | 0.2074 | 0.8078 | | 0.1587 | 2.0 | 1670 | 0.1705 | 0.8461 | | 0.1012 | 3.0 | 2505 | 0.1758 | 0.8558 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Phi2quant/AVMGPT

Phi2quant

2024-03-12T06:26:11Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:04:00Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

wongctroman/fine-tuned-cloudy-sentence-transformer-16

wongctroman

2024-03-12T06:25:11Z

47

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-12T06:13:35Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-16 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.  ## 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('wongctroman/fine-tuned-cloudy-sentence-transformer-16') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-16) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 34 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "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": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

MuntasirHossain/flan-t5-large-samsum-qlora-merged

MuntasirHossain

2024-03-12T06:14:26Z

0

peft

[ "peft", "safetensors", "t5", "generated_from_trainer", "pipeline_tag", "summarization", "dataset:samsum", "base_model:google/flan-t5-large", "base_model:adapter:google/flan-t5-large", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]

summarization

2024-03-12T02:51:42Z

--- license: apache-2.0 tags: - generated_from_trainer - pipeline_tag base_model: google/flan-t5-large model-index: - name: flan-t5-large-samsum-qlora-merged results: [] datasets: - samsum metrics: - rouge pipeline_tag: summarization library_name: peft --- # Model description Parameter-efficient fine-tuning (PEFT) with QLoRA was employed to fine-tune the base [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) model on the [samsum](https://huggingface.co/datasets/samsum) dataset containing dialoges. After the fine-tuning, the [PEFT model adapter](https://huggingface.co/MuntasirHossain/flan-t5-large-samsum-qlora) was merged with the base model. The model is intended for generative summarization tasks and achieved the following scores on the test dataset: - Rogue1: 49.249596% - Rouge2: 23.513032% - RougeL: 39.960812% - RougeLsum: 39.968438% ## How to use Load the model: ``` python from transformers import pipeline pipeline_model = pipeline("summarization", model="MuntasirHossain/flan-t5-large-samsum-qlora-merged") summary = pipeline_model(text, max_new_tokens = 50) print(summary[0]['summary_text']) ``` Example Inference: ``` python # random sample text from the samsum test dataset text = """ Emma: Hi, we're going with Peter to Amiens tomorrow. Daniel: oh! Cool. Emma: Wanna join? Daniel: Sure, I'm fed up with Paris. Emma: We're too. The noise, traffic etc. Would be nice to see some countrysides. Daniel: I don't think Amiens is exactly countrysides though :P Emma: Nope. Hahahah. But not a megalopolis either! Daniel: Right! Let's do it! Emma: But we should leave early. The days are shorter now. Daniel: Yes, the stupid winter time. Emma: Exactly! Daniel: Where should we meet then? Emma: Come to my place by 9am. Daniel: oohhh. It means I have to get up before 7! Emma: Yup. The early bird gets the worm (in Amiens). Daniel: You sound like my grandmother. Emma: HAHAHA. I'll even add: no parties tonight, no drinking dear Daniel Daniel: I really hope Amiens is worth it! """ summary = pipeline_model(text, max_new_tokens = 50) print(summary[0]['summary_text']) Emma and Peter are going to Amiens tomorrow. Daniel will join them. They will meet at Emma's place by 9 am. They will not have any parties tonight. ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

AlanHou/xlm-roberta-base-finetuned-panx-it

AlanHou

2024-03-12T06:13:52Z

91

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-12T06:12:13Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: [] ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2619 - F1: 0.8321 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7217 | 1.0 | 70 | 0.3193 | 0.7343 | | 0.2736 | 2.0 | 140 | 0.2760 | 0.8055 | | 0.1838 | 3.0 | 210 | 0.2619 | 0.8321 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

AlanHou/xlm-roberta-base-finetuned-panx-fr

AlanHou

2024-03-12T06:12:07Z

90

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-12T06:08:51Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: [] ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2750 - F1: 0.8495 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5647 | 1.0 | 191 | 0.3242 | 0.7728 | | 0.2671 | 2.0 | 382 | 0.2672 | 0.8202 | | 0.1744 | 3.0 | 573 | 0.2750 | 0.8495 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

hyeogi/SOLAR-10.7B-v1.4

hyeogi

2024-03-12T06:11:03Z

2,248

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "SOLAR-10.7B", "conversational", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T05:31:08Z

--- language: - ko pipeline_tag: text-generation tags: - SOLAR-10.7B license: cc-by-nc-4.0 --- # SOLAR-10.7B ### Model Details - Base Model: [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) ### Datasets - sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) - sampling and instrcution format [HAERAE-HUB/KMMLU](https://huggingface.co/datasets/HAERAE-HUB/KMMLU)

nsanghi/dqn-cart-pole-sb3

nsanghi

2024-03-12T06:09:28Z

1

0

stable-baselines3

[ "stable-baselines3", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-10-26T07:09:23Z

--- library_name: stable-baselines3 tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 221.20 +/- 52.75 name: mean_reward verified: false --- # **DQN** Agent playing **CartPole-v1** This is a trained model of a **DQN** agent playing **CartPole-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

OwOOwO/eacc_adhoc2

OwOOwO

2024-03-12T06:09:18Z

91

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T01:11:41Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

AlanHou/xlm-roberta-base-finetuned-panx-de-fr

AlanHou

2024-03-12T06:08:20Z

90

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-12T05:56:43Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1639 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2836 | 1.0 | 715 | 0.1859 | 0.8212 | | 0.1484 | 2.0 | 1430 | 0.1632 | 0.8487 | | 0.0953 | 3.0 | 2145 | 0.1639 | 0.8591 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Kukedlc/Neural-Krishna-Multiverse-7b

Kukedlc

2024-03-12T06:04:29Z

50

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSirKrishna-7b", "ammarali32/multi_verse_model", "conversational", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:merge:Kukedlc/NeuralSirKrishna-7b", "base_model:MTSAIR/multi_verse_model", "base_model:merge:MTSAIR/multi_verse_model", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-11T21:12:36Z

--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model base_model: - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model license: apache-2.0 --- # Neural-Krishna-Multiverse-7b Neural-Krishna-Multiverse-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) * [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/NeuralSirKrishna-7b layer_range: [0, 32] - model: ammarali32/multi_verse_model layer_range: [0, 32] merge_method: slerp base_model: ammarali32/multi_verse_model parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-Krishna-Multiverse-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

ARDICAI/stable-diffusion-2-1-finetuned

ARDICAI

2024-03-12T06:02:05Z

337

12

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-21T12:14:05Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### stable-diffusion-2-1-finetuned Dreambooth model trained by ARDIC AI team

Red-8/Gujarati_NER-1

Red-8

2024-03-12T06:01:41Z

96

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "PERSON", "LOCATION", "ORGANIZATION", "gu", "dataset:ai4bharat/naamapadam", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-12T05:38:36Z

--- datasets: - ai4bharat/naamapadam language: - gu pipeline_tag: token-classification tags: - PERSON - LOCATION - ORGANIZATION ---

OwOOwO/eacc_usa2_mega

OwOOwO

2024-03-12T05:56:08Z

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T05:53:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Kukedlc/Neural-Krishna-Multiverse-7b-v2

Kukedlc

2024-03-12T05:54:50Z

49

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/Neural-Krishna-Multiverse-7b", "liminerity/M7-7b", "base_model:Kukedlc/Neural-Krishna-Multiverse-7b", "base_model:merge:Kukedlc/Neural-Krishna-Multiverse-7b", "base_model:liminerity/M7-7b", "base_model:merge:liminerity/M7-7b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-11T21:31:56Z

--- tags: - merge - mergekit - lazymergekit - Kukedlc/Neural-Krishna-Multiverse-7b - liminerity/M7-7b base_model: - Kukedlc/Neural-Krishna-Multiverse-7b - liminerity/M7-7b license: apache-2.0 --- # Neural-Krishna-Multiverse-7b-v2 Neural-Krishna-Multiverse-7b-v2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/Neural-Krishna-Multiverse-7b](https://huggingface.co/Kukedlc/Neural-Krishna-Multiverse-7b) * [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/Neural-Krishna-Multiverse-7b layer_range: [0, 32] - model: liminerity/M7-7b layer_range: [0, 32] merge_method: slerp base_model: liminerity/M7-7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-Krishna-Multiverse-7b-v2" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

jadhav21/squirrel

jadhav21

2024-03-12T05:48:59Z

7

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-12T05:45:13Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### SQUIRREL Dreambooth model trained by jadhav21 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: I21-14 Sample pictures of this concept: ![0](https://huggingface.co/jadhav21/squirrel/resolve/main/sample_images/xzg2.jpg)

DevarshRaj/neww_model_mistral

DevarshRaj

2024-03-12T05:47:25Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-11T12:37:37Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nocudaexe/Neural-Dark-Waifu-GGUF

nocudaexe

2024-03-12T05:38:29Z

26

0

null

[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-03-10T14:44:13Z

--- license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65bcd419d5341c3e56189303/dkXNI8qHyZkJQl3ltSaEV.png) Potentially broken at 8k context Use: [nocudaexe/Neural-Dark-Waifu-V0.2](https://huggingface.co/nocudaexe/Neural-Dark-Waifu-V0.2-GGUF) instead, tested to 15872 tokens # Model Card for Model ID  This is a merge of 2 models based on mlabonne/AlphaMonarch-7B. With the intent of making it more RP friendly. ### Model Sources Base model: nocudaexe/Neural-Dark-Waifu Primary Models: mlabonne/AlphaMonarch-7B Test157t/Kunocchini-7b-128k-test Additional merges: TeeZee/DarkSapling-7B-v2.0 NeverSleep/Noromaid-7B-0.4-DPO Endevor/InfinityRP-v1-7B KatyTheCutie/SlushySlerp-7B ## Uses NSFW/ERP Chat ### Recommendations Silly Tavern

migueldeguzmandev/GPT2XL_RLLMv11-9

migueldeguzmandev

2024-03-12T05:36:50Z

73

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T05:21:15Z

--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)

migueldeguzmandev/GPT2XL_RLLMv11-7

migueldeguzmandev

2024-03-12T05:36:17Z

73

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T04:33:07Z

[More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)

Red-8/Gujarati_NER

Red-8

2024-03-12T05:33:39Z

92

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "gu", "dataset:Red-8/NER_Gujarati_data", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-12T05:00:18Z

--- datasets: - Red-8/NER_Gujarati_data language: - gu pipeline_tag: token-classification ---

migueldeguzmandev/GPT2XL_RLLMv11-10

migueldeguzmandev

2024-03-12T05:33:32Z

74

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-11T09:39:00Z

--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)

kurugai/Kurugai-EEVE-v1.1

kurugai

2024-03-12T05:30:02Z

2,244

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "dataset:kurugai/MedText", "base_model:kurugai/Kurugai-EEVE-v1.0", "base_model:finetune:kurugai/Kurugai-EEVE-v1.0", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T13:48:09Z

--- license: cc-by-nc-sa-4.0 base_model: kurugai/Kurugai-EEVE-v1.0 datasets: - kurugai/MedText language: - ko --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) **kurugai/Kurugai-EEVE-v1.1**는 **kurugai/Kurugai-EEVE-v1.0**를 베이스모델로 해서 **BI55/MedText** 데이터셋으로 학습된 모델입니다. # 학습시간 RTX 8000 GPU 1EA로 1시간 학습하였습니다. # 도움을 주신분 이 모델은 아내의 지원을 받아 제작되었습니다. 아내에게 감사의 말을 전합니다.

nsugianto/vit-base-beans

nsugianto

2024-03-12T05:27:42Z

178

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-03-12T05:26:51Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-beans results: [] ---  # vit-base-beans 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1036 - Accuracy: 0.9850 ## 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

OpenGVLab/pvt_v2_b0

OpenGVLab

2024-03-12T05:27:22Z

3,976

2

transformers

[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-02-25T14:14:35Z

--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.

Or4cl3-1/Cognitive-Agent-Gemma_7b

Or4cl3-1

2024-03-12T05:26:57Z

3

0

transformers

[ "transformers", "text-gemma-001", "text-generation", "merge", "mergekit", "lazymergekit", "Or4cl3-1/agent_gemma_7b", "cognitivecomputations/dolphin-2.5-mixtral-8x7b", "en", "base_model:Or4cl3-1/Agent_Gemma_7b", "base_model:merge:Or4cl3-1/Agent_Gemma_7b", "base_model:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "base_model:merge:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-02-23T02:24:50Z

--- tags: - merge - mergekit - lazymergekit - Or4cl3-1/agent_gemma_7b - cognitivecomputations/dolphin-2.5-mixtral-8x7b base_model: - Or4cl3-1/agent_gemma_7b - cognitivecomputations/dolphin-2.5-mixtral-8x7b license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation --- # Cognitive-Agent-Gemma_7b Cognitive-Agent-Gemma_7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Or4cl3-1/agent_gemma_7b](https://huggingface.co/Or4cl3-1/agent_gemma_7b) * [cognitivecomputations/dolphin-2.5-mixtral-8x7b](https://huggingface.co/cognitivecomputations/dolphin-2.5-mixtral-8x7b) ## 🧩 Configuration ```yaml slices: - sources: - model: Or4cl3-1/agent_gemma_7b layer_range: [0, 32] - model: cognitivecomputations/dolphin-2.5-mixtral-8x7b layer_range: [0, 32] merge_method: slerp base_model: Or4cl3-1/agent_gemma_7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/Cognitive-Agent-Gemma_7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

SilasK/llama-7b-medqa_version_5

SilasK

2024-03-12T05:21:27Z

0

peft

[ "peft", "tensorboard", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "license:other", "4-bit", "bitsandbytes", "region:us" ]

null

2024-03-11T18:30:44Z

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: huggyllama/llama-7b model-index: - name: llama-7b-medqa_version_5 results: [] ---  # llama-7b-medqa_version_5 This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

Bajiyo/mal_en_transliteration

Bajiyo

2024-03-12T05:19:28Z

0

null

[ "license:other", "region:us" ]

null

2024-03-12T05:17:50Z

--- license: other license_name: other license_link: LICENSE ---

Deepnoid/mergekit_v2

Deepnoid

2024-03-12T05:17:52Z

2,250

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:heavytail/kullm-solar-S", "base_model:finetune:heavytail/kullm-solar-S", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T03:31:08Z

--- base_model: - heavytail/kullm-solar-S library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # mergekit_v2 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged ### Configuration

Pongsathorn/Taxi-v3

Pongsathorn

2024-03-12T05:14:26Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-03-12T05:14:24Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Pongsathorn/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Pongsathorn/q-FrozenLake-v1-4x4-noSlippery

Pongsathorn

2024-03-12T05:13:24Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-03-12T05:13:21Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Pongsathorn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

tarekxpc/test

tarekxpc

2024-03-12T05:07:38Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-03-12T05:07:27Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - **Developed by:** tarekxpc - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

AptaArkana/indonesian-comunication-indoBERT-finetuned

AptaArkana

2024-03-12T05:04:00Z

93

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-12T02:33:51Z

--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: indonesian-personalyty-indoBERT-finetuned results: [] ---  # indonesian-personalyty-indoBERT-finetuned This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1403 - Accuracy: 0.9732 - Precision: 0.9732 - Recall: 0.9732 - F1: 0.9732 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.434 | 1.0 | 550 | 0.1352 | 0.9659 | 0.9659 | 0.9659 | 0.9659 | | 0.1207 | 2.0 | 1100 | 0.1403 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | | 0.091 | 3.0 | 1650 | 0.1291 | 0.9700 | 0.9700 | 0.9700 | 0.9700 | | 0.0633 | 4.0 | 2200 | 0.1367 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | | 0.0463 | 5.0 | 2750 | 0.1414 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

Deepnoid/deep-solar-eeve-kullm-v2

Deepnoid

2024-03-12T05:02:03Z

0

peft

[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-10.8B-v1.0", "base_model:adapter:yanolja/EEVE-Korean-10.8B-v1.0", "license:apache-2.0", "region:us" ]

null

2024-03-12T03:27:34Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: yanolja/EEVE-Korean-10.8B-v1.0 model-index: - name: data/Models/deep-solar-eeve-kullm-v2 results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # data/Models/deep-solar-eeve-kullm-v2 This model is a fine-tuned version of [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

Deepnoid/deep-solar-eeve-KorSTS

Deepnoid

2024-03-12T05:01:24Z

0

peft

[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-10.8B-v1.0", "base_model:adapter:yanolja/EEVE-Korean-10.8B-v1.0", "license:apache-2.0", "region:us" ]

null

2024-03-12T03:33:23Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: yanolja/EEVE-Korean-10.8B-v1.0 model-index: - name: data/Models/deep-solar-eeve-KorSTS results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # data/Models/deep-solar-eeve-KorSTS This model is a fine-tuned version of [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

atharv56/bheem

atharv56

2024-03-12T04:57:52Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-12T04:53:22Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Bheem Dreambooth model trained by atharv56 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

Pongsathorn/ppo-Huggy

Pongsathorn

2024-03-12T04:56:50Z

6

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-03-12T04:55:20Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Pongsathorn/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

pappubind/tiger

pappubind

2024-03-12T04:56:29Z

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-12T04:52:34Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Tiger Dreambooth model trained by pappubind following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: I21-08 Sample pictures of this concept: ![0](https://huggingface.co/pappubind/tiger/resolve/main/sample_images/xzg(3).jpg)

Sumail/Alchemist_09_1_2b

Sumail

2024-03-12T04:54:56Z

90

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Sumail/Alchemist_06_2b", "base_model:merge:Sumail/Alchemist_06_2b", "base_model:deepnet/SN6-71G7", "base_model:merge:deepnet/SN6-71G7", "base_model:deepnetguy/gemma-70", "base_model:merge:deepnetguy/gemma-70", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-12T04:52:23Z

--- base_model: - deepnetguy/gemma-70 - Sumail/Alchemist_06_2b - Aspik101/Haliaeetusalbicilla10 - deepnet/SN6-71G7 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Sumail/Alchemist_06_2b](https://huggingface.co/Sumail/Alchemist_06_2b) as a base. ### Models Merged The following models were included in the merge: * [deepnetguy/gemma-70](https://huggingface.co/deepnetguy/gemma-70) * [Aspik101/Haliaeetusalbicilla10](https://huggingface.co/Aspik101/Haliaeetusalbicilla10) * [deepnet/SN6-71G7](https://huggingface.co/deepnet/SN6-71G7) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sumail/Alchemist_06_2b # No parameters necessary for base model - model: Aspik101/Haliaeetusalbicilla10 parameters: density: 0.53 weight: 0.23 - model: deepnetguy/gemma-70 parameters: density: 0.53 weight: 0.5 - model: deepnet/SN6-71G7 parameters: density: 0.53 weight: 0.23 merge_method: dare_ties base_model: Sumail/Alchemist_06_2b parameters: int8_mask: true dtype: bfloat16 ```

Mihaiii/Covasna-0.1

Mihaiii

2024-03-12T04:49:35Z

5

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "base_model:migtissera/Tess-70B-v1.6", "base_model:finetune:migtissera/Tess-70B-v1.6", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-03-04T11:38:19Z

--- base_model: migtissera/Tess-70B-v1.6 inference: false license: llama2 metrics: - accuracy --- This is a BF16 and pruned version of [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6) . [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6) has 69 billion params and Covasna-0.1 has 41.6 billion (~60.3% param size) # Steps to replicate: Use [laserQlora.ipynb](https://github.com/cognitivecomputations/laserRMT/blob/main/laserQlora.ipynb) from [cognitivecomputations/laserRMT](https://github.com/cognitivecomputations/laserRMT) to determine which layers should be eliminated. Adapt the script for `migtissera/Tess-70B-v1.6` by replacing `model_name = "mistralai/Mistral-7B-v0.1"` with `model_name = "migtissera/Tess-70B-v1.6"` and `layer_numbers = list(range(31, -1, -1))` with `layer_numbers = list(range(79, -1, -1))`, [79 being the last recurrent layer index Tess-70B-v1.6 has](https://huggingface.co/migtissera/Tess-70B-v1.6?show_tensors=true). Then look for the layer indexes where self_attn.v_proj snr is Infinity and eliminate those layers using [mergekit](https://github.com/arcee-ai/mergekit). Here is the mergekit config: ```yml slices: - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [0, 7] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [8, 9] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [12, 29] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [31, 32] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [33, 45] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [50, 52] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [60, 61] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [67, 68] - sources: - model: "migtissera/Tess-70B-v1.6" layer_range: [74, 80] merge_method: passthrough dtype: bfloat16 ``` GGUF: [Covasna-0.1-GGUF](https://huggingface.co/mradermacher/Covasna-0.1-GGUF)

vinuuuuu/my-car

vinuuuuu

2024-03-12T04:46:37Z

2

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-12T04:38:27Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### my-car Dreambooth model trained by vinuuuuu following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: i21-21 Sample pictures of this concept: ![0](https://huggingface.co/vinuuuuu/my-car/resolve/main/sample_images/c1.jpeg)

EleutherAI/Mistral-7B-v0.1-squaring-random-standardized

EleutherAI

2024-03-12T04:44:17Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:44:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EleutherAI/Mistral-7B-v0.1-modularaddition-random-standardized

EleutherAI

2024-03-12T04:44:06Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:44:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EleutherAI/Mistral-7B-v0.1-nli-random-standardized

EleutherAI

2024-03-12T04:43:02Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:42:59Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EleutherAI/Mistral-7B-v0.1-sentiment-random-standardized

EleutherAI

2024-03-12T04:42:50Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:42:47Z

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David-Xu/llama-2-7b-cira-sft-v0.1-merge-right

David-Xu

2024-03-12T04:41:43Z

3

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T01:57:49Z

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EleutherAI/Mistral-7B-v0.1-squaring-random

EleutherAI

2024-03-12T04:41:28Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:41:24Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EleutherAI/Mistral-7B-v0.1-multiplication-random

EleutherAI

2024-03-12T04:41:02Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:40:57Z

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EleutherAI/Mistral-7B-v0.1-subtraction-random

EleutherAI

2024-03-12T04:40:49Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:40:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EleutherAI/Mistral-7B-v0.1-addition-random

EleutherAI

2024-03-12T04:40:37Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:40:33Z

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EleutherAI/Mistral-7B-v0.1-authors-random

EleutherAI

2024-03-12T04:40:24Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-12T04:40:21Z

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Kazminka51/Krasota

Kazminka51

2024-03-12T04:36:20Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2024-03-12T04:33:39Z

--- # 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  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

davidkim205/komt-solar-10.7b-sft-v5

davidkim205

2024-03-12T04:35:17Z

60

3

transformers

[ "transformers", "safetensors", "llama", "text-generation", "ko", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-06T04:46:33Z

--- license: cc-by-4.0 language: - ko --- # komt : korean multi task instruction tuning model ![multi task instruction tuning.jpg](https://github.com/davidkim205/komt/assets/16680469/c7f6ade7-247e-4b62-a94f-47e19abea68e) Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs). ## Model Details * **Model Developers** : davidkim(changyeon kim) * **Repository** : https://github.com/davidkim205/komt(will be updated soon.) * **base mode** : Edentns/DataVortexS-10.7B-dpo-v1.11 * **dataset** : comp-341k(will be updated soon.)