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 2025-06-26 18:27:55	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 499 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 18:27:32	card stringlengths 11 1.01M
touhidulislam/BERTweet_retrain_2021_15	touhidulislam	2024-11-23T14:06:47Z	178	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T14:06:26Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_15 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. --> # BERTweet_retrain_2021_15 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5915 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7312 \| 1.0 \| 5863 \| 2.6694 \| \| 2.7401 \| 2.0 \| 11726 \| 2.6246 \| \| 2.7781 \| 3.0 \| 17589 \| 2.5933 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
zelk12/MT3-Gen2-IMUBMA-gemma-2-9B	zelk12	2024-11-23T13:56:18Z	7	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-BMA-gemma-2-9B", "base_model:merge:zelk12/MT3-Gen2-BMA-gemma-2-9B", "base_model:zelk12/MT3-Gen2-IMU-gemma-2-9B", "base_model:merge:zelk12/MT3-Gen2-IMU-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T15:53:14Z	--- base_model: - zelk12/MT3-Gen2-BMA-gemma-2-9B - zelk12/MT3-Gen2-IMU-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-IMUBMA-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-BMA-gemma-2-9B](https://huggingface.co/zelk12/MT3-Gen2-BMA-gemma-2-9B) * [zelk12/MT3-Gen2-IMU-gemma-2-9B](https://huggingface.co/zelk12/MT3-Gen2-IMU-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-IMU-gemma-2-9B - model: zelk12/MT3-Gen2-BMA-gemma-2-9B merge_method: slerp base_model: zelk12/MT3-Gen2-IMU-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT3-Gen2-BMA-gemma-2-9B	zelk12	2024-11-23T13:55:52Z	5	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B", "base_model:merge:zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B", "base_model:zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B", "base_model:merge:zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T15:43:14Z	--- base_model: - zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B - zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-BMA-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B](https://huggingface.co/zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B) * [zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B](https://huggingface.co/zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B - model: zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B merge_method: slerp base_model: zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT3-Gen2-IMU-gemma-2-9B	zelk12	2024-11-23T13:55:28Z	6	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B", "base_model:merge:zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B", "base_model:zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B", "base_model:merge:zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T14:59:12Z	--- base_model: - zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B - zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-IMU-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B](https://huggingface.co/zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B) * [zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B](https://huggingface.co/zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B - model: zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B merge_method: slerp base_model: zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT3-Gen2-GMM-gemma-2-9B	zelk12	2024-11-23T13:54:59Z	5	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B", "base_model:merge:zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B", "base_model:zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B", "base_model:merge:zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T14:18:42Z	--- base_model: - zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B - zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-GMM-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B](https://huggingface.co/zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B) * [zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B](https://huggingface.co/zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B - model: zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B merge_method: slerp base_model: zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B dtype: bfloat16 parameters: t: 0.25 ```
mav23/vicuna-33b-v1.3-GGUF	mav23	2024-11-23T13:54:33Z	93	1	null	[ "gguf", "arxiv:2302.13971", "arxiv:2306.05685", "region:us" ]	null	2024-11-23T09:36:47Z	--- inference: false --- # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - Developed by: [LMSYS](https://lmsys.org/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/lm-sys/FastChat - Blog: https://lmsys.org/blog/2023-03-30-vicuna/ - Paper: https://arxiv.org/abs/2306.05685 - Demo: https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B	zelk12	2024-11-23T13:49:35Z	7	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T13:22:58Z	--- base_model: - zelk12/MT-Merge-gemma-2-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B-GGUF # 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: * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 - model: zelk12/MT-Merge-gemma-2-9B merge_method: slerp base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B	zelk12	2024-11-23T13:48:59Z	5	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:sam-paech/Quill-v1", "base_model:merge:sam-paech/Quill-v1", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T13:02:31Z	--- base_model: - sam-paech/Quill-v1 - zelk12/MT-Merge-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-MA-gemma-2-MTMQv1-9B-GGUF # 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: * [sam-paech/Quill-v1](https://huggingface.co/sam-paech/Quill-v1) * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Merge-gemma-2-9B - model: sam-paech/Quill-v1 merge_method: slerp base_model: zelk12/MT-Merge-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B	zelk12	2024-11-23T13:48:32Z	6	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T12:51:21Z	--- base_model: - zelk12/MT-Merge-gemma-2-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B-GGUF # 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: * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Merge-gemma-2-9B - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 merge_method: slerp base_model: zelk12/MT-Merge-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT2-Gen2-BG-gemma-2-9B	zelk12	2024-11-23T13:45:00Z	6	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B", "base_model:merge:zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B", "base_model:zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B", "base_model:merge:zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-12T16:18:04Z	--- base_model: - zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B - zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/models?other=base_model:quantized:zelk12/MT2-Gen2-BG-gemma-2-9B # 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: * [zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B](https://huggingface.co/zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B) * [zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B](https://huggingface.co/zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B - model: zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B merge_method: slerp base_model: zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT2-Gen2-IMM-gemma-2-9B	zelk12	2024-11-23T13:44:19Z	5	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B", "base_model:merge:zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B", "base_model:zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B", "base_model:merge:zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-12T15:00:48Z	--- base_model: - zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B - zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT2-Gen2-IMM-gemma-2-9B-GGUF # 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: * [zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B](https://huggingface.co/zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B) * [zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B](https://huggingface.co/zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B - model: zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B merge_method: slerp base_model: zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B dtype: bfloat16 parameters: t: 0.25 ```
mcguiver/xlm-roberta-base-finetuned-panx-de	mcguiver	2024-11-23T13:43:12Z	114	0	transformers	[ "transformers", "tensorboard", "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	2024-11-16T05:46:11Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de 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 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: 0.1376 - F1: 0.8644 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2579 \| 1.0 \| 525 \| 0.1546 \| 0.8179 \| \| 0.1283 \| 2.0 \| 1050 \| 0.1378 \| 0.8518 \| \| 0.0805 \| 3.0 \| 1575 \| 0.1376 \| 0.8644 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cpu - Datasets 3.1.0 - Tokenizers 0.20.3
zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B	zelk12	2024-11-23T13:42:29Z	5	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "base_model:zelk12/MT5-gemma-2-9B", "base_model:merge:zelk12/MT5-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-12T13:44:20Z	--- base_model: - zelk12/MT5-gemma-2-9B - zelk12/MT-Merge-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT2-Gen2-IF-gemma-2-MT5MTM-9B-GGUF # 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: * [zelk12/MT5-gemma-2-9B](https://huggingface.co/zelk12/MT5-gemma-2-9B) * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT5-gemma-2-9B - model: zelk12/MT-Merge-gemma-2-9B merge_method: slerp base_model: zelk12/MT5-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT1-Gen2-MMMU-gemma-2-9B	zelk12	2024-11-23T13:39:39Z	5	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B", "base_model:merge:zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B", "base_model:zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B", "base_model:merge:zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-11T16:59:54Z	--- base_model: - zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B - zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT1-Gen2-MMMU-gemma-2-9B-GGUF # 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: * [zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B](https://huggingface.co/zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B) * [zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B](https://huggingface.co/zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B - model: zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B merge_method: slerp base_model: zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B dtype: bfloat16 parameters: t: 0.25 ```
Nishitbaria/LoRa-Flux-Anime-Style	Nishitbaria	2024-11-23T13:39:08Z	932	4	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2024-11-23T12:32:34Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- A ANMCH A girl at the edge of a lush, mysterious forest. Her long, flowing hair dances in the gentle breeze, framing a face serene yet filled with longing. The soft, dappled sunlight highlights the delicate features of her countenance, accentuating the depth of emotion in her eyes. This exquisite portrait captures the essence of youth and contemplation, inviting viewers to be transported into a world of beauty and introspection output: url: images/9705468a-0ac1-46a0-97a5-e51c43da41cc.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: ANMCH --- # Anime-Style <Gallery /> ## Model description --- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ANMCH --- # Animechar <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ANMCH` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('FounderFeed/AnimeChar', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words You should use `ANMCH` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Nishitbaria/LoRa-Flux-Anime-Style/tree/main) them in the Files & versions tab.
isspek/xlnet-base-cased_ebola_llama_5_2e-5_16_undersampling_0.6	isspek	2024-11-23T13:38:46Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:38:32Z	--- 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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isspek/xlnet-base-cased_ebola_llama_3_2e-5_16_undersampling_0.6	isspek	2024-11-23T13:37:44Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:37:31Z	--- 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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zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B	zelk12	2024-11-23T13:37:31Z	9	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:sam-paech/Delirium-v1", "base_model:merge:sam-paech/Delirium-v1", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-11T16:35:18Z	--- base_model: - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - sam-paech/Delirium-v1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B-GGUF # 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: * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25) * [sam-paech/Delirium-v1](https://huggingface.co/sam-paech/Delirium-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - model: sam-paech/Delirium-v1 merge_method: slerp base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B	zelk12	2024-11-23T13:36:51Z	6	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:sam-paech/Darkest-muse-v1", "base_model:merge:sam-paech/Darkest-muse-v1", "base_model:sam-paech/Quill-v1", "base_model:merge:sam-paech/Quill-v1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-11T16:27:24Z	--- base_model: - sam-paech/Darkest-muse-v1 - sam-paech/Quill-v1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B-GGUF # 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: * [sam-paech/Darkest-muse-v1](https://huggingface.co/sam-paech/Darkest-muse-v1) * [sam-paech/Quill-v1](https://huggingface.co/sam-paech/Quill-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: sam-paech/Quill-v1 - model: sam-paech/Darkest-muse-v1 merge_method: slerp base_model: sam-paech/Quill-v1 dtype: bfloat16 parameters: t: 0.25 ```
kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF	kabachuha	2024-11-23T13:34:51Z	10	1	transformers	[ "transformers", "gguf", "conversational", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:radm/gemma-2-2b-it-abl-rudpo", "base_model:quantized:radm/gemma-2-2b-it-abl-rudpo", "license:gemma", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T13:34:35Z	--- license: gemma library_name: transformers pipeline_tag: text-generation tags: - conversational - llama-cpp - gguf-my-repo base_model: radm/gemma-2-2b-it-abl-rudpo --- # kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF This model was converted to GGUF format from [`radm/gemma-2-2b-it-abl-rudpo`](https://huggingface.co/radm/gemma-2-2b-it-abl-rudpo) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/radm/gemma-2-2b-it-abl-rudpo) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -c 2048 ```
touhidulislam/BERTweet_retrain_2022_14	touhidulislam	2024-11-23T13:33:49Z	173	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T13:33:20Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_14 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. --> # BERTweet_retrain_2022_14 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5422 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7233 \| 1.0 \| 6150 \| 2.6205 \| \| 2.6665 \| 2.0 \| 12300 \| 2.5542 \| \| 2.6632 \| 3.0 \| 18450 \| 2.5398 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
isspek/xlnet-base-cased_ebola_llama_2_2e-5_16_undersampling_0.6	isspek	2024-11-23T13:32:59Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:32:45Z	--- 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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isspek/xlnet-base-cased_ebola_llama_4_2e-5_16_undersampling_0.6	isspek	2024-11-23T13:32:08Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:31:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B	zelk12	2024-11-23T13:31:13Z	6	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT4-Gen1-gemma-2-9B", "base_model:merge:zelk12/MT4-Gen1-gemma-2-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-10T19:54:58Z	--- base_model: - zelk12/MT4-Gen1-gemma-2-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B-GGUF # 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: * [zelk12/MT4-Gen1-gemma-2-9B](https://huggingface.co/zelk12/MT4-Gen1-gemma-2-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - model: zelk12/MT4-Gen1-gemma-2-9B merge_method: slerp base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 dtype: bfloat16 parameters: t: 0.25 ```
touhidulislam/BERTweet_retrain_2021_14	touhidulislam	2024-11-23T13:31:12Z	178	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T13:30:51Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_14 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. --> # BERTweet_retrain_2021_14 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5822 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7667 \| 1.0 \| 5953 \| 2.6613 \| \| 2.7117 \| 2.0 \| 11906 \| 2.6057 \| \| 2.6391 \| 3.0 \| 17859 \| 2.5902 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
zelk12/MT-Gen2-MMMA-gemma-2-9B	zelk12	2024-11-23T13:28:23Z	6	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B", "base_model:merge:zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B", "base_model:zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B", "base_model:merge:zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-10T20:30:22Z	--- base_model: - zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B - zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT-Gen2-MMMA-gemma-2-9B-GGUF # 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: * [zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B](https://huggingface.co/zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B) * [zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B](https://huggingface.co/zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B - model: zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B merge_method: slerp base_model: zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B dtype: bfloat16 parameters: t: 0.25 ```
zelk12/MT-Gen2-GIMMMA-gemma-2-9B	zelk12	2024-11-23T13:26:55Z	7	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Gen2-GI-gemma-2-9B", "base_model:merge:zelk12/MT-Gen2-GI-gemma-2-9B", "base_model:zelk12/MT-Gen2-MMMA-gemma-2-9B", "base_model:merge:zelk12/MT-Gen2-MMMA-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-10T20:38:17Z	--- base_model: - zelk12/MT-Gen2-MMMA-gemma-2-9B - zelk12/MT-Gen2-GI-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT-Gen2-GIMMMA-gemma-2-9B-GGUF # 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: * [zelk12/MT-Gen2-MMMA-gemma-2-9B](https://huggingface.co/zelk12/MT-Gen2-MMMA-gemma-2-9B) * [zelk12/MT-Gen2-GI-gemma-2-9B](https://huggingface.co/zelk12/MT-Gen2-GI-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Gen2-GI-gemma-2-9B - model: zelk12/MT-Gen2-MMMA-gemma-2-9B merge_method: slerp base_model: zelk12/MT-Gen2-GI-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```
axel-darmouni/paligemma_dataset2	axel-darmouni	2024-11-23T13:15:27Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "paligemma", "image-text-to-text", "generated_from_trainer", "base_model:google/paligemma-3b-pt-448", "base_model:finetune:google/paligemma-3b-pt-448", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-11-23T11:59:24Z	--- library_name: transformers license: gemma base_model: google/paligemma-3b-pt-448 tags: - generated_from_trainer model-index: - name: paligemma_dataset2 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. --> # paligemma_dataset2 This model is a fine-tuned version of [google/paligemma-3b-pt-448](https://huggingface.co/google/paligemma-3b-pt-448) 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu118 - Datasets 3.1.0 - Tokenizers 0.20.3
priyankrathore/Flan_T5_chunk	priyankrathore	2024-11-23T13:07:01Z	105	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-23T13:06:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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touhidulislam/BERTweet_retrain_2022_43	touhidulislam	2024-11-23T12:56:38Z	172	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T12:56:14Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_43 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. --> # BERTweet_retrain_2022_43 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4504 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8207 \| 1.0 \| 6104 \| 2.5413 \| \| 2.4681 \| 2.0 \| 12208 \| 2.4710 \| \| 2.7499 \| 3.0 \| 18312 \| 2.4517 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2022_13	touhidulislam	2024-11-23T12:56:12Z	178	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T12:55:49Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_13 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. --> # BERTweet_retrain_2022_13 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5442 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.9022 \| 1.0 \| 6108 \| 2.6424 \| \| 2.5631 \| 2.0 \| 12216 \| 2.5838 \| \| 2.6221 \| 3.0 \| 18324 \| 2.5451 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2021_13	touhidulislam	2024-11-23T12:55:12Z	172	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T12:54:51Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_13 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. --> # BERTweet_retrain_2021_13 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5722 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8384 \| 1.0 \| 6041 \| 2.6546 \| \| 2.6996 \| 2.0 \| 12082 \| 2.5974 \| \| 2.5276 \| 3.0 \| 18123 \| 2.5779 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
tintnguyen/bert-base-vi-uncased-st	tintnguyen	2024-11-23T12:54:37Z	7	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:631587", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:tintnguyen/bert-base-vi-uncased", "base_model:finetune:tintnguyen/bert-base-vi-uncased", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-11-23T12:54:03Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:631587 - loss:MultipleNegativesRankingLoss base_model: tintnguyen/bert-base-vi-uncased widget: - source_sentence: thành phố bất động sản đắt nhất sentences: - 'Đồng hồ: 18.066 đô la. Paris, kinh đô thời trang của thế giới và thủ đô thực tế của đất nước Pháp hiện được công nhận là một trong những thành phố đắt đỏ nhất để mua bất động sản, khi thị trường bất động sản tiếp tục phát triển mạnh với nền kinh tế Pháp ổn định.' - Hầu hết các cô gái và chàng trai đeo chiếc nhẫn thuần khiết của họ trên ngón áp út của họ. Một số cô gái thích đeo nó trên ngón áp út của bàn tay trái, và thay thế nó bằng chiếc nhẫn cưới của họ. Nhẫn tinh khiết không dành riêng cho ngón tay đeo nhẫn; bạn có thể mặc nó bất cứ nơi nào thoải mái nhất cho bạn. Một số thậm chí đeo nhẫn của họ trên một chuỗi như một chiếc vòng cổ. Một. - 'Michaela Kennedy Cuomo, con gái út của Andrew Cuomo, phải nhập viện một thời gian ngắn sau khi bất tỉnh: báo cáo. Theo các báo cáo, Michaela Kennedy Cuomo (bên phải, trong ảnh với cha Thống đốc Cuomo), 17 tuổi, đã phải nhập viện một thời gian ngắn sau khi được tìm thấy trong tình trạng bất tỉnh trong ngôi nhà ở Westchester mà cô ở cùng mẹ, Kerry Kennedy, theo báo cáo. (Hình ảnh Spencer Platt / Getty)' - source_sentence: núi đọ ở tỉnh nào sentences: - Việc phát hiện một số di tích mà tiêu biểu là Núi Đọ vào cuối năm 1960, đã xuất lộ những công cụ đá thô sơ đầu tiên của con người. Các nhà Khảo cổ học Việt Nam cùng với Giáo sư Boriskovski đã nghiên cứu và chứng minh rằng ở Núi Đọ từng tồn tại một nền văn hóa sơ kỳ thời đại đá cũ. Di tích Núi Đọ thuộc địa phận hai xã Thiệu Tân và Thiệu Khánh, huyện Đông Sơn, Thanh Hóa. Núi Đọ là một quả núi thấp, sườn núi dốc thoai thoải từ 20 độ đến 25 độ, cao 158 m so với mặt nước biển, nằm ngay bên bờ hữu ngạn sông Chu; chỗ hợp lưu của hai dòng sông Mã và sông Chu cách Thành phố Thanh Hóa 7 km về phía bắc - tây bắc. - 'Virus Zika có thể lây truyền qua muỗi hoặc qua đường tình dục. Hầu hết mọi người có thể phục hồi hoàn toàn và các triệu chứng virus zika tự hết trong khoảng một tuần. Đa số các trường hợp không có triệu chứng nhiễm virus Zika. Tuy nhiên, nếu có thì các biểu hiện virus zika thông thường gồm: Sốt, phát ban, đau khớp, đau đầu, kết mạc mắt đỏ, đau cơ, cảm giác đau ở lưng. Bệnh virus Zika đặc biệt nguy hiểm nếu đi từ mẹ sang con. Bà bầu bị nhiễm virus Zika khi mang thai có thể khiến thai nhi mắc chứng đầu nhỏ. Khi lớn hơn, bé có thể bị suy giảm thị giác, thính giác, tăng trưởng kém và thậm chí co giật.' - Ý nghĩa hoa Alstroemeria Astroemeria cứng cáp là một bông hoa tuyệt đẹp kết hợp nhiều màu sắc khác nhau thành một vẻ đẹp gắn kết. Loài hoa tươi sáng này tượng trưng cho tình bạn, vẻ đẹp bền lâu của sự cam kết và chăm sóc. - source_sentence: giáo của người đông sơn có hình gì sentences: - Chủ nhân của Văn hóa Đông Sơn đã chế tạo nhiều loại vũ khí từ đồng dùng để đánh xa, gồm có lao, đầu mũi tên. Để bắn tên tất phải có cung, nỏ bằng gỗ hoặc tre. Việc phát hiện bộ lẫy nỏ có hộp, có rãnh đặt mũi tên, có nấc để giữ dây nỏ, có lẫy cong đùng để bóp cò, không còn nguyên vẹn ở làng Vạc, cho thấy việc dùng cung nỏ của người Đông Sơn rất lợi hại khi săn bắn, chiến tranh là điều có thể tin được. Giáo hình búp da, hình lá mía. Lao cũng giống như giáo nhưng kích cỡ nhỏ hơn. Vũ khí đánh gần có dao găm. Dao găm có nhiều kiểu phân biệt dựa vào phần cán và đốc chắn. Nhiều chiếc dao găm được đúc rất công phu. Chuôi dao đúc hình tượng người nam hoặc nữ, y phục hoa văn trang sức đẹp đẽ, sống động. Phần cán dao găm có những chiếc được chạm trổ rất độc đáo với hình tượng động vật như rắn ngậm chân hổ, hổ ngậm chân voi, hay rắn ngậm chân voi... - 'Xét nghiệm máu FSH kiểm tra mức độ hormone kích thích nang trứng trong máu. Mức độ FHS có thể xác định xem các cơ quan sinh dục ở cả nam và nữ có hoạt động bình thường hay không. Mức độ FHS cũng có thể được kiểm tra để phát hiện các vấn đề với tuyến yên. Số lượng: * Chỉ số nguyên. Hormone kích thích nang trứng, hoặc FSH, được sản xuất bởi tuyến yên để kích thích sản xuất và phát triển trứng ở nữ và tinh trùng ở nam. FSH cũng kích thích sản xuất các hormone khác, bao gồm testosterone và estrogen.' - White Lightning là một tàu lượn siêu tốc bằng gỗ tại Fun Spot America ở Orlando, Florida. Chuyến xe được thiết kế riêng do Great Coasters International sản xuất. Chuyến đi là tàu lượn bằng gỗ đầu tiên của Orlando. - source_sentence: bước đầu tiên trong quá trình tổng hợp protein là gì sentences: - 'Sáu bước đơn giản để trồng hoa nhài: 1 Hoa nhài phát triển mạnh trong môi trường ban ngày nóng ẩm. 2 Chọn nơi trồng hoa nhài của bạn. 3 Chuẩn bị đất để trồng bằng cách làm việc với một lượng lớn vật liệu hữu cơ, chẳng hạn như rêu than bùn hoặc phân trộn. Bón phân đa dụng mỗi tháng một lần từ tháng 3 đến tháng 11.' - 'PHẦN A. Đọc phần sau và ghi chú vào giấy của bạn: Tổng hợp protein là quá trình cơ thể sử dụng để tạo ra protein. Bước đầu tiên của quá trình tổng hợp protein được gọi là Phiên mã. Nó xảy ra trong nhân. Trong quá trình phiên mã, mRNA phiên mã (bản sao) DNA. DNA là ÃƒÂ ¢ Ã‚â‚¬Ã‚Å “được giải nénÃƒÂ ¢ Ã‚â‚¬Ã‚ và sợi mRNA sao chép một sợi DNA. Một khi nó làm được điều này, mRNA sẽ rời khỏi nhân và đi vào tế bào chất. mRNA sau đó sẽ tự gắn vào ribosome.' - LIÊN KẾT / TRANG WEB THÊM VÀO DANH SÁCH CÔNG VIỆC. danh từ. Skinny jeans là loại quần denim bó sát với da thường được làm bằng vải co giãn. Một ví dụ về quần jean bó là những chiếc quần jean bó sát từ eo đến mắt cá chân. - source_sentence: phương pháp lóc ối bác sĩ sẽ làm gì sentences: - Gương mặt thân quen mùa 1 được phát sóng trên kênh VTV3 từ ngày 5 tháng 1 năm 2013 đến 23 tháng 3 năm 2013 với các thí sinh gồm Khởi My, Đại Nghĩa, Thúy Uyên, Kyo York, Phương Thanh và Chí Thiện. Bộ ba giám khảo chính là nhạc sĩ Đức Huy, ca sĩ Mỹ Linh và NSƯT Hoài Linh. Người dẫn chương trình mùa này là nghệ sĩ Thanh Bạch. Sau 10 tuần thi, kết quả chung cuộc giải nhất thuộc về thí sinh Khởi My. - 'Khi thai phụ đã quá ngày dự sinh, các phương pháp sau đây có thể được bác sĩ cân nhắc lựa chọn để gây khởi phát chuyển dạ: Lóc ối: Với biện pháp này, bác sĩ sẽ đeo găng và dùng ngón tay để tách màng ối ra khỏi thành tử cung. Phá vỡ túi nước ối: Bác sĩ sẽ tạo một lỗ nhỏ trên túi nước ối để làm vỡ ối, qua đó kích thích sự chuyển dạ. Oxytocin: Là một loại thuốc giúp tạo ra các cơn co thắt chuyển dạ, được tiêm theo đường tĩnh mạch vào cánh tay của thai phụ. Liều lượng có thể được tăng dần theo thời gian nhưng phải theo dõi cẩn thận. Các chất tương tự Prostaglandin: Đây là những loại thuốc được đặt trong âm đạo để làm chín muồi cổ tử cung. Làm giãn nở cổ tử cung: Bác sĩ có thể đặt ống thông có gắn một quả bong bóng rất nhỏ vào cuối cổ tử cung của thai phụ. Sau đó, nước sẽ được bơm vào quả bóng. Khi bóng đã được bơm căng, nó sẽ gây ra tác động áp lực, giúp cổ tử cung mở ra và quá trình chuyển dạ sẽ bắt đầu.' - 'Nghĩa quân Tây Sơn ở vào thế bất lợi: phía bắc có quân Trịnh, phía nam còn quân Nguyễn. Nguyễn Nhạc phải tạm hoà hoãn với quân Trịnh để dồn sức đánh Nguyễn. Từ năm 1776 đến năm 1783, nghĩa quân Tây Sơn đã bốn lần đánh vào Gia Định.Trong lần tiến quân năm 1777, Tây Sơn bắt giết được chúa Nguyễn, chỉ còn Nguyễn Ánh chạy thoát. Chính quyền họ Nguyễn ở Đàng Trong đến đây bị lật đổ.' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on tintnguyen/bert-base-vi-uncased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tintnguyen/bert-base-vi-uncased](https://huggingface.co/tintnguyen/bert-base-vi-uncased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [tintnguyen/bert-base-vi-uncased](https://huggingface.co/tintnguyen/bert-base-vi-uncased) <!-- at revision 4e50493f19bb3d6599ec112044d6c84f65b733bb --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tintnguyen/bert-base-vi-uncased-st") # Run inference sentences = [ 'phương pháp lóc ối bác sĩ sẽ làm gì', 'Khi thai phụ đã quá ngày dự sinh, các phương pháp sau đây có thể được bác sĩ cân nhắc lựa chọn để gây khởi phát chuyển dạ:\nLóc ối: Với biện pháp này, bác sĩ sẽ đeo găng và dùng ngón tay để tách màng ối ra khỏi thành tử cung.\nPhá vỡ túi nước ối: Bác sĩ sẽ tạo một lỗ nhỏ trên túi nước ối để làm vỡ ối, qua đó kích thích sự chuyển dạ.\nOxytocin: Là một loại thuốc giúp tạo ra các cơn co thắt chuyển dạ, được tiêm theo đường tĩnh mạch vào cánh tay của thai phụ. Liều lượng có thể được tăng dần theo thời gian nhưng phải theo dõi cẩn thận.\nCác chất tương tự Prostaglandin: Đây là những loại thuốc được đặt trong âm đạo để làm chín muồi cổ tử cung.\nLàm giãn nở cổ tử cung: Bác sĩ có thể đặt ống thông có gắn một quả bong bóng rất nhỏ vào cuối cổ tử cung của thai phụ. Sau đó, nước sẽ được bơm vào quả bóng. Khi bóng đã được bơm căng, nó sẽ gây ra tác động áp lực, giúp cổ tử cung mở ra và quá trình chuyển dạ sẽ bắt đầu.', 'Nghĩa quân Tây Sơn ở vào thế bất lợi: phía bắc có quân Trịnh, phía nam còn quân Nguyễn. Nguyễn Nhạc phải tạm hoà hoãn với quân Trịnh để dồn sức đánh Nguyễn. Từ năm 1776 đến năm 1783, nghĩa quân Tây Sơn đã bốn lần đánh vào Gia Định.Trong lần tiến quân năm 1777, Tây Sơn bắt giết được chúa Nguyễn, chỉ còn Nguyễn Ánh chạy thoát. Chính quyền họ Nguyễn ở Đàng Trong đến đây bị lật đổ.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 631,587 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 10.92 tokens</li><li>max: 41 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 106.79 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:---------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>bạn có thể lấy hộ chiếu ở dmv không</code> \| <code>Nộp đơn xin Hộ chiếu Hoa Kỳ tại Văn phòng DMV. Xuất bản 27/09/2001 01:53 PM \| Cập nhật 24/08/2011 11:05 AM. Bạn có thể nộp đơn xin Hộ chiếu Hoa Kỳ tại một số văn phòng xe cơ giới do các nhân viên quận điều hành. Bạn có thể nộp đơn xin Hộ chiếu Hoa Kỳ tại một số Bưu điện Hoa Kỳ. NYSDMV không cấp hộ chiếu và không thể cung cấp cho bạn thông tin về cách xin hộ chiếu. Liên hệ với Bưu điện Hoa Kỳ hoặc văn phòng thư ký quận được liệt kê trong các trang màu xanh trong danh bạ điện thoại của bạn. NYSDMV không cấp hộ chiếu và không thể cung cấp cho bạn thông tin về cách nộp đơn xin hộ chiếu. Liên hệ với Bưu điện Hoa Kỳ hoặc văn phòng lục sự quận được liệt kê trong các trang màu xanh lam của danh bạ điện thoại của bạn.</code> \| \| <code>tổng số người mỹ thiệt mạng trong tất cả các cuộc chiến tranh</code> \| <code>1 Con số chính thức của người Mỹ thiệt mạng trong Chiến tranh Cách mạng (4.435) chỉ bằng khoảng 2/3 con số mà các nhà sử học đưa ra. Tổng số người Mỹ thiệt mạng trong Nội chiến được đưa ra là 140.414 - cao hơn khoảng 30.000 so với hầu hết các nhà sử học ước tính. Tôi nghi ngờ (nhưng không biết chắc chắn) rằng sự gia tăng có thể đến từ việc DoD tính số người chết trong tù binh là chiến đấu hơn là bệnh tật.</code> \| \| <code>lý thuyết vụ nổ lớn được quay ở đâu</code> \| <code>Thuyết Vụ nổ lớn được ghi hình tại Warner Bros. Studios ở Burbank, California. Bạn phải từ mười tám tuổi trở lên để tham gia buổi ghi hình Thuyết Vụ nổ lớn. Số lượng vé có hạn và nhu cầu rất cao. Vé được bán miễn phí trên TVTickets.com. Phần thứ 10 của Big Bang Theory hiện đang được sản xuất.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 300 evaluation samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 300 samples: \| \| anchor \| positive \| \|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 11.02 tokens</li><li>max: 43 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 108.76 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:--------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>lúa mạch đen được dùng làm gì và ăn ở pháp</code> \| <code>Tiêu dùng và sử dụng của con người. Như đã nói trước đó, hầu hết lúa mạch đen được trồng ở châu Âu là để làm bánh mì. Canada có một số lượng hạn chế hạt lúa mạch đen được sử dụng để chưng cất và sử dụng thực phẩm, và ở Mỹ khoảng một nửa lúa mạch đen làm ngũ cốc được sử dụng cho những mục đích này (7, 8). Hạt lúa mạch đen được sử dụng để làm bột mì, thức ăn gia súc, hoặc bia. Nó cũng có thể được ăn toàn bộ, dưới dạng quả mọng lúa mạch đen luộc, hoặc bằng cách cuộn lại, tương tự như yến mạch cán (10).</code> \| \| <code>kỳ hạm của hải quân chúng tôi là gì</code> \| <code>USS Hiến pháp là kỳ hạm truyền thống của Hải quân Hoa Kỳ. Và nó vẫn đang được thực hiện và được điều khiển bởi một thủy thủ đoàn Hải quân Hoa Kỳ. USS Constellation CVN-64 được Tổng thống Ronald Reagan đặt cho biệt danh là Chiến hạm của Hoa Kỳ. Nó hiện đã ngừng hoạt động và được thay thế trong hạm đội bằng tàu sân bay USS Ronald Reagan CVN-76.</code> \| \| <code>cửa sổ kính lớn nhất</code> \| <code>Cửa sổ kính màu lớn nhất thế giới. Cửa sổ kính màu lớn nhất thế giới thực sự nằm trong lăng mộ tại Nghĩa trang Phục sinh ở Công lý. Nó chứa 2.448 tấm và rộng 22.381 feet vuông. Cũng cần lưu ý thêm, nó chỉ cách nhà máy cải tạo nước Stickney, cơ sở xử lý nước thải lớn nhất (vào tháng 7 và cả tháng 8, là cơ sở xử lý nước thải bốc mùi nhất trên thế giới, vài dặm ngắn) chỉ vài dặm ngắn.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:-----:\|:-------------:\|:---------------:\| \| 0.0025 \| 100 \| 1.6877 \| - \| \| 0.0051 \| 200 \| 1.259 \| - \| \| 0.0076 \| 300 \| 0.6289 \| - \| \| 0.0101 \| 400 \| 0.3623 \| - \| \| 0.0127 \| 500 \| 0.3353 \| - \| \| 0.0152 \| 600 \| 0.2671 \| - \| \| 0.0177 \| 700 \| 0.1881 \| - \| \| 0.0203 \| 800 \| 0.179 \| - \| \| 0.0228 \| 900 \| 0.1611 \| - \| \| 0.0253 \| 1000 \| 0.1587 \| 0.1468 \| \| 0.0279 \| 1100 \| 0.141 \| - \| \| 0.0304 \| 1200 \| 0.13 \| - \| \| 0.0329 \| 1300 \| 0.1257 \| - \| \| 0.0355 \| 1400 \| 0.1153 \| - \| \| 0.0380 \| 1500 \| 0.1341 \| - \| \| 0.0405 \| 1600 \| 0.1227 \| - \| \| 0.0431 \| 1700 \| 0.1024 \| - \| \| 0.0456 \| 1800 \| 0.0818 \| - \| \| 0.0481 \| 1900 \| 0.1069 \| - \| \| 0.0507 \| 2000 \| 0.0831 \| 0.0978 \| \| 0.0532 \| 2100 \| 0.1035 \| - \| \| 0.0557 \| 2200 \| 0.0949 \| - \| \| 0.0583 \| 2300 \| 0.1037 \| - \| \| 0.0608 \| 2400 \| 0.0894 \| - \| \| 0.0633 \| 2500 \| 0.0831 \| - \| \| 0.0659 \| 2600 \| 0.1085 \| - \| \| 0.0684 \| 2700 \| 0.0815 \| - \| \| 0.0709 \| 2800 \| 0.071 \| - \| \| 0.0735 \| 2900 \| 0.0889 \| - \| \| 0.0760 \| 3000 \| 0.0832 \| 0.0704 \| \| 0.0785 \| 3100 \| 0.0992 \| - \| \| 0.0811 \| 3200 \| 0.0733 \| - \| \| 0.0836 \| 3300 \| 0.0878 \| - \| \| 0.0861 \| 3400 \| 0.0757 \| - \| \| 0.0887 \| 3500 \| 0.0476 \| - \| \| 0.0912 \| 3600 \| 0.0741 \| - \| \| 0.0937 \| 3700 \| 0.0766 \| - \| \| 0.0963 \| 3800 \| 0.0736 \| - \| \| 0.0988 \| 3900 \| 0.0673 \| - \| \| 0.1013 \| 4000 \| 0.0718 \| 0.0566 \| \| 0.1039 \| 4100 \| 0.0649 \| - \| \| 0.1064 \| 4200 \| 0.0767 \| - \| \| 0.1089 \| 4300 \| 0.073 \| - \| \| 0.1115 \| 4400 \| 0.0745 \| - \| \| 0.1140 \| 4500 \| 0.0692 \| - \| \| 0.1165 \| 4600 \| 0.0652 \| - \| \| 0.1191 \| 4700 \| 0.077 \| - \| \| 0.1216 \| 4800 \| 0.0749 \| - \| \| 0.1241 \| 4900 \| 0.0493 \| - \| \| 0.1267 \| 5000 \| 0.0653 \| 0.0533 \| \| 0.1292 \| 5100 \| 0.073 \| - \| \| 0.1317 \| 5200 \| 0.0652 \| - \| \| 0.1343 \| 5300 \| 0.0639 \| - \| \| 0.1368 \| 5400 \| 0.0549 \| - \| \| 0.1393 \| 5500 \| 0.0731 \| - \| \| 0.1419 \| 5600 \| 0.0832 \| - \| \| 0.1444 \| 5700 \| 0.0687 \| - \| \| 0.1469 \| 5800 \| 0.0711 \| - \| \| 0.1495 \| 5900 \| 0.0709 \| - \| \| 0.1520 \| 6000 \| 0.0547 \| 0.0626 \| \| 0.1545 \| 6100 \| 0.084 \| - \| \| 0.1571 \| 6200 \| 0.0743 \| - \| \| 0.1596 \| 6300 \| 0.0706 \| - \| \| 0.1621 \| 6400 \| 0.0664 \| - \| \| 0.1647 \| 6500 \| 0.0682 \| - \| \| 0.1672 \| 6600 \| 0.0534 \| - \| \| 0.1697 \| 6700 \| 0.0642 \| - \| \| 0.1723 \| 6800 \| 0.0624 \| - \| \| 0.1748 \| 6900 \| 0.0648 \| - \| \| 0.1773 \| 7000 \| 0.0697 \| 0.0509 \| \| 0.1799 \| 7100 \| 0.0784 \| - \| \| 0.1824 \| 7200 \| 0.0871 \| - \| \| 0.1849 \| 7300 \| 0.0711 \| - \| \| 0.1875 \| 7400 \| 0.0718 \| - \| \| 0.1900 \| 7500 \| 0.0543 \| - \| \| 0.1925 \| 7600 \| 0.0676 \| - \| \| 0.1951 \| 7700 \| 0.0724 \| - \| \| 0.1976 \| 7800 \| 0.0579 \| - \| \| 0.2001 \| 7900 \| 0.0781 \| - \| \| 0.2027 \| 8000 \| 0.0909 \| 0.0736 \| \| 0.2052 \| 8100 \| 0.0653 \| - \| \| 0.2077 \| 8200 \| 0.0535 \| - \| \| 0.2103 \| 8300 \| 0.0801 \| - \| \| 0.2128 \| 8400 \| 0.0794 \| - \| \| 0.2153 \| 8500 \| 0.0615 \| - \| \| 0.2179 \| 8600 \| 0.0646 \| - \| \| 0.2204 \| 8700 \| 0.0497 \| - \| \| 0.2229 \| 8800 \| 0.06 \| - \| \| 0.2255 \| 8900 \| 0.0495 \| - \| \| 0.2280 \| 9000 \| 0.0685 \| 0.0450 \| \| 0.2305 \| 9100 \| 0.0606 \| - \| \| 0.2331 \| 9200 \| 0.0577 \| - \| \| 0.2356 \| 9300 \| 0.0464 \| - \| \| 0.2381 \| 9400 \| 0.0622 \| - \| \| 0.2407 \| 9500 \| 0.0567 \| - \| \| 0.2432 \| 9600 \| 0.0545 \| - \| \| 0.2457 \| 9700 \| 0.0455 \| - \| \| 0.2483 \| 9800 \| 0.0642 \| - \| \| 0.2508 \| 9900 \| 0.0612 \| - \| \| 0.2533 \| 10000 \| 0.0658 \| 0.0310 \| \| 0.2559 \| 10100 \| 0.0618 \| - \| \| 0.2584 \| 10200 \| 0.052 \| - \| \| 0.2609 \| 10300 \| 0.0504 \| - \| \| 0.2635 \| 10400 \| 0.0593 \| - \| \| 0.2660 \| 10500 \| 0.0534 \| - \| \| 0.2685 \| 10600 \| 0.0555 \| - \| \| 0.2711 \| 10700 \| 0.0583 \| - \| \| 0.2736 \| 10800 \| 0.0472 \| - \| \| 0.2761 \| 10900 \| 0.0591 \| - \| \| 0.2787 \| 11000 \| 0.039 \| 0.0300 \| \| 0.2812 \| 11100 \| 0.0446 \| - \| \| 0.2837 \| 11200 \| 0.0375 \| - \| \| 0.2863 \| 11300 \| 0.0515 \| - \| \| 0.2888 \| 11400 \| 0.0577 \| - \| \| 0.2913 \| 11500 \| 0.046 \| - \| \| 0.2939 \| 11600 \| 0.0518 \| - \| \| 0.2964 \| 11700 \| 0.055 \| - \| \| 0.2989 \| 11800 \| 0.0492 \| - \| \| 0.3015 \| 11900 \| 0.0513 \| - \| \| 0.3040 \| 12000 \| 0.0442 \| 0.0278 \| \| 0.3065 \| 12100 \| 0.0675 \| - \| \| 0.3091 \| 12200 \| 0.0526 \| - \| \| 0.3116 \| 12300 \| 0.0688 \| - \| \| 0.3141 \| 12400 \| 0.0589 \| - \| \| 0.3167 \| 12500 \| 0.0602 \| - \| \| 0.3192 \| 12600 \| 0.0551 \| - \| \| 0.3217 \| 12700 \| 0.0681 \| - \| \| 0.3243 \| 12800 \| 0.0522 \| - \| \| 0.3268 \| 12900 \| 0.047 \| - \| \| 0.3293 \| 13000 \| 0.0376 \| 0.0282 \| \| 0.3319 \| 13100 \| 0.0396 \| - \| \| 0.3344 \| 13200 \| 0.0467 \| - \| \| 0.3369 \| 13300 \| 0.0498 \| - \| \| 0.3395 \| 13400 \| 0.0402 \| - \| \| 0.3420 \| 13500 \| 0.0398 \| - \| \| 0.3445 \| 13600 \| 0.041 \| - \| \| 0.3471 \| 13700 \| 0.0516 \| - \| \| 0.3496 \| 13800 \| 0.0518 \| - \| \| 0.3521 \| 13900 \| 0.0413 \| - \| \| 0.3547 \| 14000 \| 0.0463 \| 0.0199 \| \| 0.3572 \| 14100 \| 0.0442 \| - \| \| 0.3597 \| 14200 \| 0.0695 \| - \| \| 0.3623 \| 14300 \| 0.0595 \| - \| \| 0.3648 \| 14400 \| 0.0435 \| - \| \| 0.3673 \| 14500 \| 0.0372 \| - \| \| 0.3699 \| 14600 \| 0.0398 \| - \| \| 0.3724 \| 14700 \| 0.0357 \| - \| \| 0.3749 \| 14800 \| 0.0467 \| - \| \| 0.3775 \| 14900 \| 0.0611 \| - \| \| 0.3800 \| 15000 \| 0.054 \| 0.0233 \| \| 0.3825 \| 15100 \| 0.0411 \| - \| \| 0.3851 \| 15200 \| 0.0485 \| - \| \| 0.3876 \| 15300 \| 0.0388 \| - \| \| 0.3901 \| 15400 \| 0.0474 \| - \| \| 0.3927 \| 15500 \| 0.0525 \| - \| \| 0.3952 \| 15600 \| 0.0568 \| - \| \| 0.3977 \| 15700 \| 0.0414 \| - \| \| 0.4003 \| 15800 \| 0.0375 \| - \| \| 0.4028 \| 15900 \| 0.0606 \| - \| \| 0.4053 \| 16000 \| 0.0495 \| 0.0238 \| \| 0.4079 \| 16100 \| 0.0407 \| - \| \| 0.4104 \| 16200 \| 0.0383 \| - \| \| 0.4129 \| 16300 \| 0.0318 \| - \| \| 0.4155 \| 16400 \| 0.0503 \| - \| \| 0.4180 \| 16500 \| 0.0386 \| - \| \| 0.4205 \| 16600 \| 0.0397 \| - \| \| 0.4231 \| 16700 \| 0.0409 \| - \| \| 0.4256 \| 16800 \| 0.0484 \| - \| \| 0.4281 \| 16900 \| 0.0514 \| - \| \| 0.4307 \| 17000 \| 0.0359 \| 0.0216 \| \| 0.4332 \| 17100 \| 0.0411 \| - \| \| 0.4357 \| 17200 \| 0.0372 \| - \| \| 0.4383 \| 17300 \| 0.0489 \| - \| \| 0.4408 \| 17400 \| 0.0364 \| - \| \| 0.4433 \| 17500 \| 0.0517 \| - \| \| 0.4459 \| 17600 \| 0.0422 \| - \| \| 0.4484 \| 17700 \| 0.0334 \| - \| \| 0.4509 \| 17800 \| 0.0532 \| - \| \| 0.4535 \| 17900 \| 0.0384 \| - \| \| 0.4560 \| 18000 \| 0.03 \| 0.0200 \| \| 0.4585 \| 18100 \| 0.034 \| - \| \| 0.4611 \| 18200 \| 0.0429 \| - \| \| 0.4636 \| 18300 \| 0.0448 \| - \| \| 0.4661 \| 18400 \| 0.03 \| - \| \| 0.4687 \| 18500 \| 0.0338 \| - \| \| 0.4712 \| 18600 \| 0.0436 \| - \| \| 0.4737 \| 18700 \| 0.0271 \| - \| \| 0.4763 \| 18800 \| 0.0516 \| - \| \| 0.4788 \| 18900 \| 0.0358 \| - \| \| 0.4813 \| 19000 \| 0.046 \| 0.0255 \| \| 0.4839 \| 19100 \| 0.0367 \| - \| \| 0.4864 \| 19200 \| 0.032 \| - \| \| 0.4889 \| 19300 \| 0.0363 \| - \| \| 0.4915 \| 19400 \| 0.0352 \| - \| \| 0.4940 \| 19500 \| 0.041 \| - \| \| 0.4965 \| 19600 \| 0.0508 \| - \| \| 0.4991 \| 19700 \| 0.0454 \| - \| \| 0.5016 \| 19800 \| 0.0459 \| - \| \| 0.5041 \| 19900 \| 0.0295 \| - \| \| 0.5066 \| 20000 \| 0.0415 \| 0.0228 \| \| 0.5092 \| 20100 \| 0.0422 \| - \| \| 0.5117 \| 20200 \| 0.0317 \| - \| \| 0.5142 \| 20300 \| 0.0263 \| - \| \| 0.5168 \| 20400 \| 0.0568 \| - \| \| 0.5193 \| 20500 \| 0.0339 \| - \| \| 0.5218 \| 20600 \| 0.0295 \| - \| \| 0.5244 \| 20700 \| 0.042 \| - \| \| 0.5269 \| 20800 \| 0.0343 \| - \| \| 0.5294 \| 20900 \| 0.0322 \| - \| \| 0.5320 \| 21000 \| 0.0328 \| 0.0204 \| \| 0.5345 \| 21100 \| 0.0407 \| - \| \| 0.5370 \| 21200 \| 0.0306 \| - \| \| 0.5396 \| 21300 \| 0.0295 \| - \| \| 0.5421 \| 21400 \| 0.0329 \| - \| \| 0.5446 \| 21500 \| 0.0297 \| - \| \| 0.5472 \| 21600 \| 0.0298 \| - \| \| 0.5497 \| 21700 \| 0.0261 \| - \| \| 0.5522 \| 21800 \| 0.0429 \| - \| \| 0.5548 \| 21900 \| 0.039 \| - \| \| 0.5573 \| 22000 \| 0.0336 \| 0.0151 \| \| 0.5598 \| 22100 \| 0.0417 \| - \| \| 0.5624 \| 22200 \| 0.0424 \| - \| \| 0.5649 \| 22300 \| 0.0447 \| - \| \| 0.5674 \| 22400 \| 0.0482 \| - \| \| 0.5700 \| 22500 \| 0.0253 \| - \| \| 0.5725 \| 22600 \| 0.0412 \| - \| \| 0.5750 \| 22700 \| 0.0425 \| - \| \| 0.5776 \| 22800 \| 0.0304 \| - \| \| 0.5801 \| 22900 \| 0.0302 \| - \| \| 0.5826 \| 23000 \| 0.0275 \| 0.0144 \| \| 0.5852 \| 23100 \| 0.0255 \| - \| \| 0.5877 \| 23200 \| 0.0266 \| - \| \| 0.5902 \| 23300 \| 0.038 \| - \| \| 0.5928 \| 23400 \| 0.0254 \| - \| \| 0.5953 \| 23500 \| 0.0486 \| - \| \| 0.5978 \| 23600 \| 0.0325 \| - \| \| 0.6004 \| 23700 \| 0.041 \| - \| \| 0.6029 \| 23800 \| 0.0307 \| - \| \| 0.6054 \| 23900 \| 0.037 \| - \| \| 0.6080 \| 24000 \| 0.0377 \| 0.0194 \| \| 0.6105 \| 24100 \| 0.0331 \| - \| \| 0.6130 \| 24200 \| 0.0386 \| - \| \| 0.6156 \| 24300 \| 0.0184 \| - \| \| 0.6181 \| 24400 \| 0.0244 \| - \| \| 0.6206 \| 24500 \| 0.0279 \| - \| \| 0.6232 \| 24600 \| 0.0351 \| - \| \| 0.6257 \| 24700 \| 0.0577 \| - \| \| 0.6282 \| 24800 \| 0.0434 \| - \| \| 0.6308 \| 24900 \| 0.0223 \| - \| \| 0.6333 \| 25000 \| 0.0264 \| 0.0151 \| \| 0.6358 \| 25100 \| 0.0378 \| - \| \| 0.6384 \| 25200 \| 0.0212 \| - \| \| 0.6409 \| 25300 \| 0.0245 \| - \| \| 0.6434 \| 25400 \| 0.0321 \| - \| \| 0.6460 \| 25500 \| 0.0391 \| - \| \| 0.6485 \| 25600 \| 0.0276 \| - \| \| 0.6510 \| 25700 \| 0.0253 \| - \| \| 0.6536 \| 25800 \| 0.0295 \| - \| \| 0.6561 \| 25900 \| 0.0225 \| - \| \| 0.6586 \| 26000 \| 0.0312 \| 0.0133 \| \| 0.6612 \| 26100 \| 0.0367 \| - \| \| 0.6637 \| 26200 \| 0.029 \| - \| \| 0.6662 \| 26300 \| 0.0311 \| - \| \| 0.6688 \| 26400 \| 0.0383 \| - \| \| 0.6713 \| 26500 \| 0.0357 \| - \| \| 0.6738 \| 26600 \| 0.0259 \| - \| \| 0.6764 \| 26700 \| 0.0277 \| - \| \| 0.6789 \| 26800 \| 0.0278 \| - \| \| 0.6814 \| 26900 \| 0.0242 \| - \| \| 0.6840 \| 27000 \| 0.0288 \| 0.0183 \| \| 0.6865 \| 27100 \| 0.0352 \| - \| \| 0.6890 \| 27200 \| 0.0298 \| - \| \| 0.6916 \| 27300 \| 0.0448 \| - \| \| 0.6941 \| 27400 \| 0.0299 \| - \| \| 0.6966 \| 27500 \| 0.0385 \| - \| \| 0.6992 \| 27600 \| 0.0365 \| - \| \| 0.7017 \| 27700 \| 0.022 \| - \| \| 0.7042 \| 27800 \| 0.0339 \| - \| \| 0.7068 \| 27900 \| 0.0371 \| - \| \| 0.7093 \| 28000 \| 0.0322 \| 0.0183 \| \| 0.7118 \| 28100 \| 0.0365 \| - \| \| 0.7144 \| 28200 \| 0.0271 \| - \| \| 0.7169 \| 28300 \| 0.0238 \| - \| \| 0.7194 \| 28400 \| 0.033 \| - \| \| 0.7220 \| 28500 \| 0.0225 \| - \| \| 0.7245 \| 28600 \| 0.022 \| - \| \| 0.7270 \| 28700 \| 0.0132 \| - \| \| 0.7296 \| 28800 \| 0.0304 \| - \| \| 0.7321 \| 28900 \| 0.0357 \| - \| \| 0.7346 \| 29000 \| 0.025 \| 0.0149 \| \| 0.7372 \| 29100 \| 0.0251 \| - \| \| 0.7397 \| 29200 \| 0.0238 \| - \| \| 0.7422 \| 29300 \| 0.0337 \| - \| \| 0.7448 \| 29400 \| 0.0277 \| - \| \| 0.7473 \| 29500 \| 0.02 \| - \| \| 0.7498 \| 29600 \| 0.0216 \| - \| \| 0.7524 \| 29700 \| 0.0203 \| - \| \| 0.7549 \| 29800 \| 0.0216 \| - \| \| 0.7574 \| 29900 \| 0.0317 \| - \| \| 0.7600 \| 30000 \| 0.0274 \| 0.0116 \| \| 0.7625 \| 30100 \| 0.0284 \| - \| \| 0.7650 \| 30200 \| 0.0407 \| - \| \| 0.7676 \| 30300 \| 0.0326 \| - \| \| 0.7701 \| 30400 \| 0.0207 \| - \| \| 0.7726 \| 30500 \| 0.0284 \| - \| \| 0.7752 \| 30600 \| 0.0386 \| - \| \| 0.7777 \| 30700 \| 0.031 \| - \| \| 0.7802 \| 30800 \| 0.0215 \| - \| \| 0.7828 \| 30900 \| 0.0243 \| - \| \| 0.7853 \| 31000 \| 0.0248 \| 0.0132 \| \| 0.7878 \| 31100 \| 0.0366 \| - \| \| 0.7904 \| 31200 \| 0.0248 \| - \| \| 0.7929 \| 31300 \| 0.0336 \| - \| \| 0.7954 \| 31400 \| 0.0316 \| - \| \| 0.7980 \| 31500 \| 0.0252 \| - \| \| 0.8005 \| 31600 \| 0.0236 \| - \| \| 0.8030 \| 31700 \| 0.0277 \| - \| \| 0.8056 \| 31800 \| 0.0256 \| - \| \| 0.8081 \| 31900 \| 0.0462 \| - \| \| 0.8106 \| 32000 \| 0.0322 \| 0.0155 \| \| 0.8132 \| 32100 \| 0.0159 \| - \| \| 0.8157 \| 32200 \| 0.0216 \| - \| \| 0.8182 \| 32300 \| 0.018 \| - \| \| 0.8208 \| 32400 \| 0.0232 \| - \| \| 0.8233 \| 32500 \| 0.024 \| - \| \| 0.8258 \| 32600 \| 0.0254 \| - \| \| 0.8284 \| 32700 \| 0.0334 \| - \| \| 0.8309 \| 32800 \| 0.0204 \| - \| \| 0.8334 \| 32900 \| 0.0352 \| - \| \| 0.8360 \| 33000 \| 0.024 \| 0.0180 \| \| 0.8385 \| 33100 \| 0.0368 \| - \| \| 0.8410 \| 33200 \| 0.0243 \| - \| \| 0.8436 \| 33300 \| 0.0196 \| - \| \| 0.8461 \| 33400 \| 0.0264 \| - \| \| 0.8486 \| 33500 \| 0.026 \| - \| \| 0.8512 \| 33600 \| 0.0201 \| - \| \| 0.8537 \| 33700 \| 0.0245 \| - \| \| 0.8562 \| 33800 \| 0.0205 \| - \| \| 0.8588 \| 33900 \| 0.0244 \| - \| \| 0.8613 \| 34000 \| 0.0174 \| 0.0211 \| \| 0.8638 \| 34100 \| 0.019 \| - \| \| 0.8664 \| 34200 \| 0.031 \| - \| \| 0.8689 \| 34300 \| 0.0257 \| - \| \| 0.8714 \| 34400 \| 0.0195 \| - \| \| 0.8740 \| 34500 \| 0.0274 \| - \| \| 0.8765 \| 34600 \| 0.0197 \| - \| \| 0.8790 \| 34700 \| 0.0154 \| - \| \| 0.8816 \| 34800 \| 0.0233 \| - \| \| 0.8841 \| 34900 \| 0.0314 \| - \| \| 0.8866 \| 35000 \| 0.0223 \| 0.0172 \| \| 0.8892 \| 35100 \| 0.0264 \| - \| \| 0.8917 \| 35200 \| 0.0214 \| - \| \| 0.8942 \| 35300 \| 0.0264 \| - \| \| 0.8968 \| 35400 \| 0.0194 \| - \| \| 0.8993 \| 35500 \| 0.0221 \| - \| \| 0.9018 \| 35600 \| 0.0185 \| - \| \| 0.9044 \| 35700 \| 0.029 \| - \| \| 0.9069 \| 35800 \| 0.0188 \| - \| \| 0.9094 \| 35900 \| 0.0407 \| - \| \| 0.9120 \| 36000 \| 0.0251 \| 0.0188 \| \| 0.9145 \| 36100 \| 0.0295 \| - \| \| 0.9170 \| 36200 \| 0.0233 \| - \| \| 0.9196 \| 36300 \| 0.0265 \| - \| \| 0.9221 \| 36400 \| 0.027 \| - \| \| 0.9246 \| 36500 \| 0.022 \| - \| \| 0.9272 \| 36600 \| 0.0174 \| - \| \| 0.9297 \| 36700 \| 0.0204 \| - \| \| 0.9322 \| 36800 \| 0.0314 \| - \| \| 0.9348 \| 36900 \| 0.0256 \| - \| \| 0.9373 \| 37000 \| 0.0139 \| 0.0129 \| \| 0.9398 \| 37100 \| 0.0237 \| - \| \| 0.9424 \| 37200 \| 0.0235 \| - \| \| 0.9449 \| 37300 \| 0.0202 \| - \| \| 0.9474 \| 37400 \| 0.0178 \| - \| \| 0.9500 \| 37500 \| 0.0225 \| - \| \| 0.9525 \| 37600 \| 0.0224 \| - \| \| 0.9550 \| 37700 \| 0.0259 \| - \| \| 0.9576 \| 37800 \| 0.0215 \| - \| \| 0.9601 \| 37900 \| 0.0197 \| - \| \| 0.9626 \| 38000 \| 0.0208 \| 0.0108 \| \| 0.9652 \| 38100 \| 0.0296 \| - \| \| 0.9677 \| 38200 \| 0.019 \| - \| \| 0.9702 \| 38300 \| 0.0185 \| - \| \| 0.9728 \| 38400 \| 0.0271 \| - \| \| 0.9753 \| 38500 \| 0.0336 \| - \| \| 0.9778 \| 38600 \| 0.0209 \| - \| \| 0.9804 \| 38700 \| 0.0321 \| - \| \| 0.9829 \| 38800 \| 0.0138 \| - \| \| 0.9854 \| 38900 \| 0.0185 \| - \| \| 0.9880 \| 39000 \| 0.0226 \| 0.0119 \| \| 0.9905 \| 39100 \| 0.0201 \| - \| \| 0.9930 \| 39200 \| 0.0183 \| - \| \| 0.9956 \| 39300 \| 0.0253 \| - \| \| 0.9981 \| 39400 \| 0.0304 \| - \| \| 1.0006 \| 39500 \| 0.0163 \| - \| \| 1.0032 \| 39600 \| 0.0291 \| - \| \| 1.0057 \| 39700 \| 0.0202 \| - \| \| 1.0082 \| 39800 \| 0.0125 \| - \| \| 1.0108 \| 39900 \| 0.0171 \| - \| \| 1.0133 \| 40000 \| 0.0159 \| 0.0169 \| \| 1.0158 \| 40100 \| 0.0188 \| - \| \| 1.0184 \| 40200 \| 0.024 \| - \| \| 1.0209 \| 40300 \| 0.0269 \| - \| \| 1.0234 \| 40400 \| 0.0286 \| - \| \| 1.0260 \| 40500 \| 0.0194 \| - \| \| 1.0285 \| 40600 \| 0.0174 \| - \| \| 1.0310 \| 40700 \| 0.0241 \| - \| \| 1.0336 \| 40800 \| 0.0198 \| - \| \| 1.0361 \| 40900 \| 0.0214 \| - \| \| 1.0386 \| 41000 \| 0.0182 \| 0.0138 \| \| 1.0412 \| 41100 \| 0.0148 \| - \| \| 1.0437 \| 41200 \| 0.0161 \| - \| \| 1.0462 \| 41300 \| 0.0234 \| - \| \| 1.0488 \| 41400 \| 0.0177 \| - \| \| 1.0513 \| 41500 \| 0.0105 \| - \| \| 1.0538 \| 41600 \| 0.0201 \| - \| \| 1.0564 \| 41700 \| 0.0211 \| - \| \| 1.0589 \| 41800 \| 0.0157 \| - \| \| 1.0614 \| 41900 \| 0.0164 \| - \| \| 1.0640 \| 42000 \| 0.0146 \| 0.0080 \| \| 1.0665 \| 42100 \| 0.0223 \| - \| \| 1.0690 \| 42200 \| 0.0269 \| - \| \| 1.0716 \| 42300 \| 0.0218 \| - \| \| 1.0741 \| 42400 \| 0.0294 \| - \| \| 1.0766 \| 42500 \| 0.0166 \| - \| \| 1.0792 \| 42600 \| 0.0173 \| - \| \| 1.0817 \| 42700 \| 0.015 \| - \| \| 1.0842 \| 42800 \| 0.015 \| - \| \| 1.0868 \| 42900 \| 0.0166 \| - \| \| 1.0893 \| 43000 \| 0.0123 \| 0.0088 \| \| 1.0918 \| 43100 \| 0.0137 \| - \| \| 1.0944 \| 43200 \| 0.01 \| - \| \| 1.0969 \| 43300 \| 0.0156 \| - \| \| 1.0994 \| 43400 \| 0.0126 \| - \| \| 1.1020 \| 43500 \| 0.0197 \| - \| \| 1.1045 \| 43600 \| 0.014 \| - \| \| 1.1070 \| 43700 \| 0.0154 \| - \| \| 1.1096 \| 43800 \| 0.0214 \| - \| \| 1.1121 \| 43900 \| 0.0157 \| - \| \| 1.1146 \| 44000 \| 0.0151 \| 0.0093 \| \| 1.1172 \| 44100 \| 0.014 \| - \| \| 1.1197 \| 44200 \| 0.0138 \| - \| \| 1.1222 \| 44300 \| 0.0126 \| - \| \| 1.1248 \| 44400 \| 0.0084 \| - \| \| 1.1273 \| 44500 \| 0.0124 \| - \| \| 1.1298 \| 44600 \| 0.0117 \| - \| \| 1.1324 \| 44700 \| 0.0098 \| - \| \| 1.1349 \| 44800 \| 0.0099 \| - \| \| 1.1374 \| 44900 \| 0.0115 \| - \| \| 1.1400 \| 45000 \| 0.0188 \| 0.0051 \| \| 1.1425 \| 45100 \| 0.0129 \| - \| \| 1.1450 \| 45200 \| 0.0128 \| - \| \| 1.1476 \| 45300 \| 0.015 \| - \| \| 1.1501 \| 45400 \| 0.0106 \| - \| \| 1.1526 \| 45500 \| 0.0115 \| - \| \| 1.1552 \| 45600 \| 0.0144 \| - \| \| 1.1577 \| 45700 \| 0.0144 \| - \| \| 1.1602 \| 45800 \| 0.0078 \| - \| \| 1.1628 \| 45900 \| 0.0143 \| - \| \| 1.1653 \| 46000 \| 0.0122 \| 0.0089 \| \| 1.1678 \| 46100 \| 0.0059 \| - \| \| 1.1704 \| 46200 \| 0.0119 \| - \| \| 1.1729 \| 46300 \| 0.0103 \| - \| \| 1.1754 \| 46400 \| 0.0083 \| - \| \| 1.1780 \| 46500 \| 0.0148 \| - \| \| 1.1805 \| 46600 \| 0.0097 \| - \| \| 1.1830 \| 46700 \| 0.0067 \| - \| \| 1.1856 \| 46800 \| 0.0116 \| - \| \| 1.1881 \| 46900 \| 0.0124 \| - \| \| 1.1906 \| 47000 \| 0.0063 \| 0.0125 \| \| 1.1932 \| 47100 \| 0.007 \| - \| \| 1.1957 \| 47200 \| 0.0095 \| - \| \| 1.1982 \| 47300 \| 0.0072 \| - \| \| 1.2008 \| 47400 \| 0.0124 \| - \| \| 1.2033 \| 47500 \| 0.0109 \| - \| \| 1.2058 \| 47600 \| 0.0108 \| - \| \| 1.2084 \| 47700 \| 0.0057 \| - \| \| 1.2109 \| 47800 \| 0.0133 \| - \| \| 1.2134 \| 47900 \| 0.0095 \| - \| \| 1.2160 \| 48000 \| 0.0057 \| 0.0107 \| \| 1.2185 \| 48100 \| 0.0085 \| - \| \| 1.2210 \| 48200 \| 0.0037 \| - \| \| 1.2236 \| 48300 \| 0.0077 \| - \| \| 1.2261 \| 48400 \| 0.0128 \| - \| \| 1.2286 \| 48500 \| 0.0124 \| - \| \| 1.2312 \| 48600 \| 0.0081 \| - \| \| 1.2337 \| 48700 \| 0.008 \| - \| \| 1.2362 \| 48800 \| 0.0051 \| - \| \| 1.2388 \| 48900 \| 0.0101 \| - \| \| 1.2413 \| 49000 \| 0.0059 \| 0.0124 \| \| 1.2438 \| 49100 \| 0.0063 \| - \| \| 1.2464 \| 49200 \| 0.0075 \| - \| \| 1.2489 \| 49300 \| 0.0064 \| - \| \| 1.2514 \| 49400 \| 0.0065 \| - \| \| 1.2540 \| 49500 \| 0.0056 \| - \| \| 1.2565 \| 49600 \| 0.0098 \| - \| \| 1.2590 \| 49700 \| 0.0062 \| - \| \| 1.2616 \| 49800 \| 0.0067 \| - \| \| 1.2641 \| 49900 \| 0.0046 \| - \| \| 1.2666 \| 50000 \| 0.0088 \| 0.0114 \| \| 1.2692 \| 50100 \| 0.005 \| - \| \| 1.2717 \| 50200 \| 0.0083 \| - \| \| 1.2742 \| 50300 \| 0.0073 \| - \| \| 1.2768 \| 50400 \| 0.0084 \| - \| \| 1.2793 \| 50500 \| 0.0044 \| - \| \| 1.2818 \| 50600 \| 0.0052 \| - \| \| 1.2844 \| 50700 \| 0.0045 \| - \| \| 1.2869 \| 50800 \| 0.0085 \| - \| \| 1.2894 \| 50900 \| 0.0057 \| - \| \| 1.2920 \| 51000 \| 0.0048 \| 0.0111 \| \| 1.2945 \| 51100 \| 0.0059 \| - \| \| 1.2970 \| 51200 \| 0.0065 \| - \| \| 1.2996 \| 51300 \| 0.0057 \| - \| \| 1.3021 \| 51400 \| 0.0059 \| - \| \| 1.3046 \| 51500 \| 0.0056 \| - \| \| 1.3072 \| 51600 \| 0.0124 \| - \| \| 1.3097 \| 51700 \| 0.0067 \| - \| \| 1.3122 \| 51800 \| 0.011 \| - \| \| 1.3148 \| 51900 \| 0.0078 \| - \| \| 1.3173 \| 52000 \| 0.0068 \| 0.0110 \| \| 1.3198 \| 52100 \| 0.006 \| - \| \| 1.3224 \| 52200 \| 0.0084 \| - \| \| 1.3249 \| 52300 \| 0.0064 \| - \| \| 1.3274 \| 52400 \| 0.0055 \| - \| \| 1.3300 \| 52500 \| 0.0032 \| - \| \| 1.3325 \| 52600 \| 0.0049 \| - \| \| 1.3350 \| 52700 \| 0.0068 \| - \| \| 1.3376 \| 52800 \| 0.0067 \| - \| \| 1.3401 \| 52900 \| 0.006 \| - \| \| 1.3426 \| 53000 \| 0.0058 \| 0.0098 \| \| 1.3452 \| 53100 \| 0.0046 \| - \| \| 1.3477 \| 53200 \| 0.0055 \| - \| \| 1.3502 \| 53300 \| 0.0074 \| - \| \| 1.3528 \| 53400 \| 0.0029 \| - \| \| 1.3553 \| 53500 \| 0.0071 \| - \| \| 1.3578 \| 53600 \| 0.0074 \| - \| \| 1.3604 \| 53700 \| 0.0068 \| - \| \| 1.3629 \| 53800 \| 0.0066 \| - \| \| 1.3654 \| 53900 \| 0.0077 \| - \| \| 1.3680 \| 54000 \| 0.0069 \| 0.0107 \| \| 1.3705 \| 54100 \| 0.0039 \| - \| \| 1.3730 \| 54200 \| 0.0051 \| - \| \| 1.3756 \| 54300 \| 0.0038 \| - \| \| 1.3781 \| 54400 \| 0.0073 \| - \| \| 1.3806 \| 54500 \| 0.0087 \| - \| \| 1.3832 \| 54600 \| 0.0053 \| - \| \| 1.3857 \| 54700 \| 0.0054 \| - \| \| 1.3882 \| 54800 \| 0.0091 \| - \| \| 1.3908 \| 54900 \| 0.0067 \| - \| \| 1.3933 \| 55000 \| 0.0071 \| 0.0094 \| \| 1.3958 \| 55100 \| 0.0056 \| - \| \| 1.3984 \| 55200 \| 0.0043 \| - \| \| 1.4009 \| 55300 \| 0.0059 \| - \| \| 1.4034 \| 55400 \| 0.007 \| - \| \| 1.4060 \| 55500 \| 0.0064 \| - \| \| 1.4085 \| 55600 \| 0.006 \| - \| \| 1.4110 \| 55700 \| 0.0031 \| - \| \| 1.4136 \| 55800 \| 0.0058 \| - \| \| 1.4161 \| 55900 \| 0.0056 \| - \| \| 1.4186 \| 56000 \| 0.0052 \| 0.0096 \| \| 1.4212 \| 56100 \| 0.0045 \| - \| \| 1.4237 \| 56200 \| 0.0046 \| - \| \| 1.4262 \| 56300 \| 0.0044 \| - \| \| 1.4288 \| 56400 \| 0.0076 \| - \| \| 1.4313 \| 56500 \| 0.0029 \| - \| \| 1.4338 \| 56600 \| 0.005 \| - \| \| 1.4364 \| 56700 \| 0.0042 \| - \| \| 1.4389 \| 56800 \| 0.0066 \| - \| \| 1.4414 \| 56900 \| 0.0119 \| - \| \| 1.4440 \| 57000 \| 0.0033 \| 0.0076 \| \| 1.4465 \| 57100 \| 0.0076 \| - \| \| 1.4490 \| 57200 \| 0.0058 \| - \| \| 1.4516 \| 57300 \| 0.0054 \| - \| \| 1.4541 \| 57400 \| 0.0039 \| - \| \| 1.4566 \| 57500 \| 0.0057 \| - \| \| 1.4592 \| 57600 \| 0.008 \| - \| \| 1.4617 \| 57700 \| 0.0082 \| - \| \| 1.4642 \| 57800 \| 0.0041 \| - \| \| 1.4668 \| 57900 \| 0.0037 \| - \| \| 1.4693 \| 58000 \| 0.0048 \| 0.0078 \| \| 1.4718 \| 58100 \| 0.0041 \| - \| \| 1.4744 \| 58200 \| 0.0049 \| - \| \| 1.4769 \| 58300 \| 0.0085 \| - \| \| 1.4794 \| 58400 \| 0.0036 \| - \| \| 1.4820 \| 58500 \| 0.0061 \| - \| \| 1.4845 \| 58600 \| 0.0039 \| - \| \| 1.4870 \| 58700 \| 0.0049 \| - \| \| 1.4896 \| 58800 \| 0.0027 \| - \| \| 1.4921 \| 58900 \| 0.003 \| - \| \| 1.4946 \| 59000 \| 0.006 \| 0.0097 \| \| 1.4972 \| 59100 \| 0.0068 \| - \| \| 1.4997 \| 59200 \| 0.0083 \| - \| \| 1.5022 \| 59300 \| 0.0066 \| - \| \| 1.5047 \| 59400 \| 0.0049 \| - \| \| 1.5073 \| 59500 \| 0.0034 \| - \| \| 1.5098 \| 59600 \| 0.0044 \| - \| \| 1.5123 \| 59700 \| 0.0036 \| - \| \| 1.5149 \| 59800 \| 0.0041 \| - \| \| 1.5174 \| 59900 \| 0.006 \| - \| \| 1.5199 \| 60000 \| 0.0063 \| 0.0099 \| \| 1.5225 \| 60100 \| 0.0028 \| - \| \| 1.5250 \| 60200 \| 0.0045 \| - \| \| 1.5275 \| 60300 \| 0.0056 \| - \| \| 1.5301 \| 60400 \| 0.0046 \| - \| \| 1.5326 \| 60500 \| 0.0053 \| - \| \| 1.5351 \| 60600 \| 0.0044 \| - \| \| 1.5377 \| 60700 \| 0.0053 \| - \| \| 1.5402 \| 60800 \| 0.0044 \| - \| \| 1.5427 \| 60900 \| 0.0034 \| - \| \| 1.5453 \| 61000 \| 0.0033 \| 0.0073 \| \| 1.5478 \| 61100 \| 0.005 \| - \| \| 1.5503 \| 61200 \| 0.0027 \| - \| \| 1.5529 \| 61300 \| 0.0049 \| - \| \| 1.5554 \| 61400 \| 0.0048 \| - \| \| 1.5579 \| 61500 \| 0.0032 \| - \| \| 1.5605 \| 61600 \| 0.0043 \| - \| \| 1.5630 \| 61700 \| 0.0049 \| - \| \| 1.5655 \| 61800 \| 0.0062 \| - \| \| 1.5681 \| 61900 \| 0.0076 \| - \| \| 1.5706 \| 62000 \| 0.006 \| 0.0053 \| \| 1.5731 \| 62100 \| 0.0078 \| - \| \| 1.5757 \| 62200 \| 0.0033 \| - \| \| 1.5782 \| 62300 \| 0.0031 \| - \| \| 1.5807 \| 62400 \| 0.0038 \| - \| \| 1.5833 \| 62500 \| 0.0026 \| - \| \| 1.5858 \| 62600 \| 0.0036 \| - \| \| 1.5883 \| 62700 \| 0.0034 \| - \| \| 1.5909 \| 62800 \| 0.0076 \| - \| \| 1.5934 \| 62900 \| 0.0039 \| - \| \| 1.5959 \| 63000 \| 0.006 \| 0.0073 \| \| 1.5985 \| 63100 \| 0.0055 \| - \| \| 1.6010 \| 63200 \| 0.0046 \| - \| \| 1.6035 \| 63300 \| 0.0042 \| - \| \| 1.6061 \| 63400 \| 0.0061 \| - \| \| 1.6086 \| 63500 \| 0.003 \| - \| \| 1.6111 \| 63600 \| 0.0034 \| - \| \| 1.6137 \| 63700 \| 0.0058 \| - \| \| 1.6162 \| 63800 \| 0.0036 \| - \| \| 1.6187 \| 63900 \| 0.0015 \| - \| \| 1.6213 \| 64000 \| 0.0052 \| 0.0076 \| \| 1.6238 \| 64100 \| 0.0047 \| - \| \| 1.6263 \| 64200 \| 0.0083 \| - \| \| 1.6289 \| 64300 \| 0.0035 \| - \| \| 1.6314 \| 64400 \| 0.0025 \| - \| \| 1.6339 \| 64500 \| 0.0052 \| - \| \| 1.6365 \| 64600 \| 0.0029 \| - \| \| 1.6390 \| 64700 \| 0.0019 \| - \| \| 1.6415 \| 64800 \| 0.0036 \| - \| \| 1.6441 \| 64900 \| 0.002 \| - \| \| 1.6466 \| 65000 \| 0.007 \| 0.0074 \| \| 1.6491 \| 65100 \| 0.0038 \| - \| \| 1.6517 \| 65200 \| 0.0051 \| - \| \| 1.6542 \| 65300 \| 0.0027 \| - \| \| 1.6567 \| 65400 \| 0.003 \| - \| \| 1.6593 \| 65500 \| 0.0045 \| - \| \| 1.6618 \| 65600 \| 0.0067 \| - \| \| 1.6643 \| 65700 \| 0.003 \| - \| \| 1.6669 \| 65800 \| 0.0033 \| - \| \| 1.6694 \| 65900 \| 0.0043 \| - \| \| 1.6719 \| 66000 \| 0.0025 \| 0.0071 \| \| 1.6745 \| 66100 \| 0.0025 \| - \| \| 1.6770 \| 66200 \| 0.0057 \| - \| \| 1.6795 \| 66300 \| 0.0029 \| - \| \| 1.6821 \| 66400 \| 0.0016 \| - \| \| 1.6846 \| 66500 \| 0.0055 \| - \| \| 1.6871 \| 66600 \| 0.0029 \| - \| \| 1.6897 \| 66700 \| 0.0031 \| - \| \| 1.6922 \| 66800 \| 0.006 \| - \| \| 1.6947 \| 66900 \| 0.003 \| - \| \| 1.6973 \| 67000 \| 0.0042 \| 0.0072 \| \| 1.6998 \| 67100 \| 0.0049 \| - \| \| 1.7023 \| 67200 \| 0.0018 \| - \| \| 1.7049 \| 67300 \| 0.0043 \| - \| \| 1.7074 \| 67400 \| 0.007 \| - \| \| 1.7099 \| 67500 \| 0.0025 \| - \| \| 1.7125 \| 67600 \| 0.0051 \| - \| \| 1.7150 \| 67700 \| 0.0056 \| - \| \| 1.7175 \| 67800 \| 0.003 \| - \| \| 1.7201 \| 67900 \| 0.0041 \| - \| \| 1.7226 \| 68000 \| 0.0025 \| 0.0082 \| \| 1.7251 \| 68100 \| 0.0018 \| - \| \| 1.7277 \| 68200 \| 0.0034 \| - \| \| 1.7302 \| 68300 \| 0.0065 \| - \| \| 1.7327 \| 68400 \| 0.0047 \| - \| \| 1.7353 \| 68500 \| 0.0052 \| - \| \| 1.7378 \| 68600 \| 0.0013 \| - \| \| 1.7403 \| 68700 \| 0.0063 \| - \| \| 1.7429 \| 68800 \| 0.0047 \| - \| \| 1.7454 \| 68900 \| 0.004 \| - \| \| 1.7479 \| 69000 \| 0.0026 \| 0.0077 \| \| 1.7505 \| 69100 \| 0.0032 \| - \| \| 1.7530 \| 69200 \| 0.0031 \| - \| \| 1.7555 \| 69300 \| 0.0024 \| - \| \| 1.7581 \| 69400 \| 0.0022 \| - \| \| 1.7606 \| 69500 \| 0.0029 \| - \| \| 1.7631 \| 69600 \| 0.0055 \| - \| \| 1.7657 \| 69700 \| 0.0031 \| - \| \| 1.7682 \| 69800 \| 0.004 \| - \| \| 1.7707 \| 69900 \| 0.0032 \| - \| \| 1.7733 \| 70000 \| 0.0034 \| 0.0067 \| \| 1.7758 \| 70100 \| 0.007 \| - \| \| 1.7783 \| 70200 \| 0.0049 \| - \| \| 1.7809 \| 70300 \| 0.0023 \| - \| \| 1.7834 \| 70400 \| 0.0028 \| - \| \| 1.7859 \| 70500 \| 0.0048 \| - \| \| 1.7885 \| 70600 \| 0.0042 \| - \| \| 1.7910 \| 70700 \| 0.006 \| - \| \| 1.7935 \| 70800 \| 0.006 \| - \| \| 1.7961 \| 70900 \| 0.0044 \| - \| \| 1.7986 \| 71000 \| 0.0036 \| 0.0063 \| \| 1.8011 \| 71100 \| 0.0025 \| - \| \| 1.8037 \| 71200 \| 0.0027 \| - \| \| 1.8062 \| 71300 \| 0.0033 \| - \| \| 1.8087 \| 71400 \| 0.0045 \| - \| \| 1.8113 \| 71500 \| 0.0037 \| - \| \| 1.8138 \| 71600 \| 0.0023 \| - \| \| 1.8163 \| 71700 \| 0.0021 \| - \| \| 1.8189 \| 71800 \| 0.0019 \| - \| \| 1.8214 \| 71900 \| 0.0046 \| - \| \| 1.8239 \| 72000 \| 0.0029 \| 0.0065 \| \| 1.8265 \| 72100 \| 0.0061 \| - \| \| 1.8290 \| 72200 \| 0.005 \| - \| \| 1.8315 \| 72300 \| 0.0036 \| - \| \| 1.8341 \| 72400 \| 0.0057 \| - \| \| 1.8366 \| 72500 \| 0.0049 \| - \| \| 1.8391 \| 72600 \| 0.0068 \| - \| \| 1.8417 \| 72700 \| 0.0026 \| - \| \| 1.8442 \| 72800 \| 0.0032 \| - \| \| 1.8467 \| 72900 \| 0.0036 \| - \| \| 1.8493 \| 73000 \| 0.0026 \| 0.0066 \| \| 1.8518 \| 73100 \| 0.0024 \| - \| \| 1.8543 \| 73200 \| 0.0014 \| - \| \| 1.8569 \| 73300 \| 0.0022 \| - \| \| 1.8594 \| 73400 \| 0.0039 \| - \| \| 1.8619 \| 73500 \| 0.0019 \| - \| \| 1.8645 \| 73600 \| 0.0016 \| - \| \| 1.8670 \| 73700 \| 0.0034 \| - \| \| 1.8695 \| 73800 \| 0.004 \| - \| \| 1.8721 \| 73900 \| 0.0014 \| - \| \| 1.8746 \| 74000 \| 0.004 \| 0.0062 \| \| 1.8771 \| 74100 \| 0.0014 \| - \| \| 1.8797 \| 74200 \| 0.0025 \| - \| \| 1.8822 \| 74300 \| 0.0025 \| - \| \| 1.8847 \| 74400 \| 0.0037 \| - \| \| 1.8873 \| 74500 \| 0.0038 \| - \| \| 1.8898 \| 74600 \| 0.0029 \| - \| \| 1.8923 \| 74700 \| 0.0037 \| - \| \| 1.8949 \| 74800 \| 0.0026 \| - \| \| 1.8974 \| 74900 \| 0.0019 \| - \| \| 1.8999 \| 75000 \| 0.0013 \| 0.0062 \| \| 1.9025 \| 75100 \| 0.0027 \| - \| \| 1.9050 \| 75200 \| 0.0028 \| - \| \| 1.9075 \| 75300 \| 0.0014 \| - \| \| 1.9101 \| 75400 \| 0.0067 \| - \| \| 1.9126 \| 75500 \| 0.0023 \| - \| \| 1.9151 \| 75600 \| 0.0024 \| - \| \| 1.9177 \| 75700 \| 0.0021 \| - \| \| 1.9202 \| 75800 \| 0.0062 \| - \| \| 1.9227 \| 75900 \| 0.0104 \| - \| \| 1.9253 \| 76000 \| 0.0021 \| 0.0064 \| \| 1.9278 \| 76100 \| 0.0023 \| - \| \| 1.9303 \| 76200 \| 0.0059 \| - \| \| 1.9329 \| 76300 \| 0.0055 \| - \| \| 1.9354 \| 76400 \| 0.002 \| - \| \| 1.9379 \| 76500 \| 0.0029 \| - \| \| 1.9405 \| 76600 \| 0.0028 \| - \| \| 1.9430 \| 76700 \| 0.0021 \| - \| \| 1.9455 \| 76800 \| 0.0037 \| - \| \| 1.9481 \| 76900 \| 0.0019 \| - \| \| 1.9506 \| 77000 \| 0.0027 \| 0.0062 \| \| 1.9531 \| 77100 \| 0.0039 \| - \| \| 1.9557 \| 77200 \| 0.0027 \| - \| \| 1.9582 \| 77300 \| 0.0034 \| - \| \| 1.9607 \| 77400 \| 0.005 \| - \| \| 1.9633 \| 77500 \| 0.0022 \| - \| \| 1.9658 \| 77600 \| 0.0072 \| - \| \| 1.9683 \| 77700 \| 0.0025 \| - \| \| 1.9709 \| 77800 \| 0.0019 \| - \| \| 1.9734 \| 77900 \| 0.0034 \| - \| \| 1.9759 \| 78000 \| 0.0068 \| 0.0060 \| \| 1.9785 \| 78100 \| 0.0042 \| - \| \| 1.9810 \| 78200 \| 0.0041 \| - \| \| 1.9835 \| 78300 \| 0.0018 \| - \| \| 1.9861 \| 78400 \| 0.0019 \| - \| \| 1.9886 \| 78500 \| 0.0029 \| - \| \| 1.9911 \| 78600 \| 0.0039 \| - \| \| 1.9937 \| 78700 \| 0.0023 \| - \| \| 1.9962 \| 78800 \| 0.0092 \| - \| \| 1.9987 \| 78900 \| 0.0018 \| - \| </details> ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.3.1 - Transformers: 4.46.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
khizarAI/whisper-tiny-en-US	khizarAI	2024-11-23T12:52:11Z	79	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-23T12:43:50Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en-US results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.33517835178351785 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-en-US This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6504 - Wer Ortho: 33.9125 - Wer: 0.3352 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0006 \| 17.2414 \| 500 \| 0.6504 \| 33.9125 \| 0.3352 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
win10/SphinxMind-14B	win10	2024-11-23T12:46:12Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2", "base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2", "base_model:shuttleai/shuttle-3-mini", "base_model:merge:shuttleai/shuttle-3-mini", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T12:40:45Z	--- base_model: - shuttleai/shuttle-3-mini - EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [shuttleai/shuttle-3-mini](https://huggingface.co/shuttleai/shuttle-3-mini) as a base. ### Models Merged The following models were included in the merge: * [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: shuttleai/shuttle-3-mini parameters: density: 1 weight: 1 - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 parameters: density: 1 weight: 1 - model: shuttleai/shuttle-3-mini parameters: density: 1 weight: 1 - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 parameters: density: 1 weight: 1 merge_method: ties base_model: shuttleai/shuttle-3-mini parameters: normalize: true int8_mask: true dtype: bfloat16 ```
isspek/xlnet-base-cased_ebola_mistral_1_2e-5_16_undersampling_0.6	isspek	2024-11-23T12:41:44Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T12:41:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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isspek/xlnet-base-cased_ebola_mistral_1_2e-5_16	isspek	2024-11-23T12:40:25Z	134	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T12:40:09Z	--- 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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elyx/batman-1	elyx	2024-11-23T12:33:58Z	63	0	transformers	[ "transformers", "safetensors", "ultravox", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2024-11-23T12:33:21Z	--- 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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second-state/stable-diffusion-3.5-medium-GGUF	second-state	2024-11-23T12:26:40Z	2,243	5	diffusers	[ "diffusers", "gguf", "text-to-image", "stable-diffusion", "en", "base_model:stabilityai/stable-diffusion-3.5-medium", "base_model:quantized:stabilityai/stable-diffusion-3.5-medium", "license:other", "region:us" ]	text-to-image	2024-11-23T11:32:25Z	--- base_model: stabilityai/stable-diffusion-3.5-medium license: other license_name: stabilityai-ai-community license_link: LICENSE.md model_creator: stabilityai model_name: stable-diffusion-3.5-medium quantized_by: Second State Inc. tags: - text-to-image - stable-diffusion - diffusers inference: true language: - en pipeline_tag: text-to-image --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # stable-diffusion-3.5-medium-GGUF ## Original Model [stabilityai/stable-diffusion-3.5-medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) ## Run with `sd-api-server` - Version: coming soon ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [clip_g-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q4_0.gguf) \| Q4_0 \| 4 \| 391 MB \| \| \| [clip_g-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q4_1.gguf) \| Q4_1 \| 4 \| 435 MB \| \| \| [clip_g-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q5_0.gguf) \| Q5_0 \| 5 \| 478 MB \| \| \| [clip_g-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q5_1.gguf) \| Q5_1 \| 5 \| 522 MB \| \| \| [clip_g-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q8_0.gguf) \| Q8_0 \| 8 \| 739 MB \| \| \| [clip_g-f16.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g.safetensors) \| f16 \| 16 \| 1.39 GB \| \| \| [clip_g.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g.safetensors) \| f16 \| 16 \| 1.39 GB \| \| \| [clip_l-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q4_0.gguf) \| Q4_0 \| 4 \| 69.4 MB \| \| \| [clip_l-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q4_1.gguf) \| Q4_1 \| 4 \| 77.1 MB \| \| \| [clip_l-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q5_0.gguf) \| Q5_0 \| 5 \| 84.8 MB \| \| \| [clip_l-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q5_1.gguf) \| Q5_1 \| 5 \| 92.4 MB \| \| \| [clip_l-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q8_0.gguf) \| Q8_0 \| 8 \| 131 MB \| \| \| [clip_l-f16.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-f16.gguf) \| f16 \| 16 \| 246 MB \| \| \| [clip_l.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l.safetensors) \| f16 \| 16 \| 246 MB \| \| \| [sd3.5_medium-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q4_0.gguf) \| Q4_0 \| 4 \| 2.08 GB \| \| \| [sd3.5_medium-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q4_1.gguf) \| Q4_1 \| 4 \| 2.22 GB \| \| \| [sd3.5_medium-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q5_0.gguf) \| Q5_0 \| 5 \| 2.36 GB \| \| \| [sd3.5_medium-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q5_1.gguf) \| Q5_1 \| 5 \| 2.50 GB \| \| \| [sd3.5_medium-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q8_0.gguf) \| Q8_0 \| 8 \| 3.19 GB \| \| \| [sd3.5_medium.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium.safetensors) \| f16 \| 16 \| 5.11 GB \| \| \| [t5xxl-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q4_0.gguf) \| Q4_0 \| 4 \| 2.75 GB \| \| \| [t5xxl-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q4_1.gguf) \| Q4_1 \| 4 \| 3.06 GB \| \| \| [t5xxl-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q5_0.gguf) \| Q5_0 \| 5 \| 3.36 GB \| \| \| [t5xxl-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q5_1.gguf) \| Q5_1 \| 5 \| 3.67 GB \| \| \| [t5xxl-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q8_0.gguf) \| Q8_0 \| 8 \| 5.20 GB \| \| \| [t5xxl_fp16.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl_fp16.safetensors) \| f16 \| 16 \| 9.79 GB \| \| Quantized with stable-diffusion.cpp `master-c3eeb669`.
touhidulislam/BERTweet_retrain_2022_42	touhidulislam	2024-11-23T12:20:32Z	178	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T12:20:06Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_42 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. --> # BERTweet_retrain_2022_42 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4725 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7074 \| 1.0 \| 5957 \| 2.5622 \| \| 2.5566 \| 2.0 \| 11914 \| 2.4895 \| \| 2.7189 \| 3.0 \| 17871 \| 2.4860 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0	prithivMLmods	2024-11-23T12:11:56Z	12,548	89	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "flux-dev", "ultra", "realism", "photorealism", "hi-res", "face", "diffusion", "UltraRealism", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-10-17T13:08:00Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora - flux-dev - ultra - realism - photorealism - hi-res - face - diffusion - UltraRealism widget: - text: >- Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6 output: url: images/3.png - text: >- Photograph, candid shot, famous randomly couch and randomly finished with randomly cats, center point for cat, Use camera is Canon EOS 5D Mark IV with a Canon EF 24mm f/1.4L II USM lens, set at aperture f/2.8 for a depth of field that highlights the furniture clean lines with rich and many detail, randomly color and finished, soft ambient light, studio light setting, ultra realistic, UHD, many details --chaos 1 --ar 9:16 --style raw --stylize 750 output: url: images/5.png - text: >- High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250 output: url: images/XX.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Ultra realistic license: creativeml-openrail-m new_version: strangerzonehf/Flux-Super-Realism-LoRA --- # Canopus-LoRA-Flux-UltraRealism-2.0 <Gallery /> - The new versions available: https://huggingface.co/prithivMLmods/Ton618-Epic-Realism-Flux-LoRA - Also Available in Flux LoRA DLC: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases. ## Model description prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0 Image Processing Parameters \| Parameter \| Value \| Parameter \| Value \| \|---------------------------\|--------\|---------------------------\|--------\| \| LR Scheduler \| constant \| Noise Offset \| 0.03 \| \| Optimizer \| AdamW \| Multires Noise Discount \| 0.1 \| \| Network Dim \| 64 \| Multires Noise Iterations \| 10 \| \| Network Alpha \| 32 \| Repeat & Steps \| 30 & 3.8K+ \| \| Epoch \| 20 \| Save Every N Epochs \| 1 \| Labeling: florence2-en(natural language & English) Total Images Used for Training : 70 [ Hi-RES ] & More ............... ## 🚀New Version Available Here🚀 Here's a table summarizing the relevant information about the `Flux-Super-Realism-LoRA` model on Hugging Face: \| Feature \| Details \| \|-------------------------\|-----------------------------------------------------------------------------\| \| Model Name \| `Flux-Super-Realism-LoRA` \| \| Repository \| [strangerzonehf/Flux-Super-Realism-LoRA](https://huggingface.co/strangerzonehf/Flux-Super-Realism-LoRA) \| \| Author \| `strangerzonehf` \| \| Description \| Super-realism LoRA model designed to produce high-quality, hyper-realistic images using LoRA fine-tuning techniques. This model can generate lifelike textures, lighting, and intricate details. \| \| Model Type \| LoRA (Low-Rank Adaptation for Transformers) \| \| Use Cases \| - Photorealistic image generation<br>- High-fidelity art<br>- Texture detailing and enhancement \| \| Primary Language \| Not applicable (model is image-based) \| \| Base Model \| Model used as the foundation for LoRA fine-tuning (may vary per implementation) \| \| License \| Refer to Hugging Face model page for specific licensing information. \| \| Tags \| super-realism, LoRA, high-fidelity, hyper-realistic \| \| Usage \| This model is typically used with tools like Hugging Face's `Diffusers` or other libraries supporting LoRA fine-tuning for enhanced realism in image generation. \| \| Pipeline \| Use in `StableDiffusionPipeline` or compatible image generation pipelines. \| ## Image Compare DLC. ![Canopus-LoRA-Flux-UltraRealism-2.0](images/flux1.png) --- \| Image \| Description \| \|-------\|-------------\| \| Image 1 \| Portrait of an attractive woman: Late twenties, light brown hair, wearing a yellow sweater, looking directly at the camera. Standing outdoors near trees. Aspect ratio: 128:85. Style: Raw \| \| Image 2 \| Headshot of a handsome young man: Dark gray sweater with buttons and shawl collar, brown hair, short beard. Serious expression on a black background, soft studio lighting. Aspect ratio: 85:128. Style: Raw \| \| Image 3 \| Fashion photo: Model in a white bodysuit and beige trench coat, posing with hands on head in front of a train station at sunset. High resolution, 35mm lens, f/22, natural lighting. Aspect ratio: 85:128. Style: Raw \| \| Image 4 \| Rustic portrait: Woman with fair skin and natural, wavy hair in soft curls. Wearing a red and navy plaid shirt with a white undershirt, sleeves rolled up. Leaning against a weathered blue door frame with a contemplative expression. \| ## Other Sample \| Image \| Prompt \| \|-------\|--------\| \| ![Image 1](images/34.png) \| Photography, envision an image steeped in urban nostalgia, a portrait of youthful ennui set against the backdrop of a timeless cityscape. A woman lounges languidly on an old, industrial metal staircase, her pose exuding a sense of introspection and quiet defiance. A plain black t-shirt, snug and slightly worn, subtly contours to her frame. Her black jeans embrace streetwear chic. Classic black and white shoes strike a stark contrast with the rusted steps, while her casual lace-up style hints at a readiness to spring into motion. Her hair is a cascade of dark waves, partly obscured by a black cap, its brim peeking out with a hint of youthful edge. Around her, the weathered brick walls whisper stories of the city's past, the windows reflecting fragmented visions of urban life. There's an air of contemplation as she rests her head on one arm, gazing distantly, perhaps lost in thought or simply enjoying a moment of solitude in the urban maze. Used camera: Sony α9 II with Sony FE 100-400mm f/4.5-5.6 GM OSS lens, emulating a high-contrast, ultra realistic style. --ar 9:16 --style raw --stylize 750 \| --- ## Trigger words You should use `Ultra realistic` to trigger the image generation. ## Other Versions Here’s a table format for the Hugging Face model "prithivMLmods/Canopus-LoRA-Flux-FaceRealism": \| Attribute \| Details \| \|---------------------------\|--------------------------------------------------------------------------------------------------------------\| \| Model Name \| Canopus-LoRA-Flux-FaceRealism \| \| Model ID \| `prithivMLmods/Canopus-LoRA-Flux-FaceRealism` \| \| Hugging Face URL \| [Canopus-LoRA-Flux-FaceRealism](https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-FaceRealism) \| \| Model Type \| LoRA (Low-Rank Adaptation) \| \| Primary Use Case \| Face Realism image generation \| \| Supported Framework \| Hugging Face Diffusers \| \| Data Type \| `bfloat16`, `fp16`, `float32` \| \| Compatible Models \| Stable Diffusion, Flux models \| \| Model Author \| `prithivMLmods` \| \| LoRA Technique \| LoRA for image style transfer with a focus on generating realistic faces \| \| Model Version \| Latest \| \| License \| Open-Access \| \| Tags \| LoRA, Face Realism, Flux, Image Generation \| ## Setting Up ``` import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0" trigger_word = "Ultra realistic" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## App File Structure /project-root/ │ ├── .gitattributes ├── README.md ├── app.py ├── pythonproject.py ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0/tree/main) them in the Files & versions tab . . .🤗: https://hf.co/prithivmlmods
Eugeoter/noob-sdxl-controlnet-manga_line	Eugeoter	2024-11-23T12:02:53Z	2,116	1	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-xl", "controlnet", "text-to-image", "en", "base_model:Laxhar/noobai-xl-EarlyAccess", "base_model:adapter:Laxhar/noobai-xl-EarlyAccess", "license:other", "region:us" ]	text-to-image	2024-11-22T10:47:57Z	--- license: other license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ library_name: diffusers language: - en base_model: - Laxhar/sdxl_noob pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-xl - controlnet - diffusers ---
strangerzonehf/Flux-Super-Capybara-HF	strangerzonehf	2024-11-23T11:56:45Z	17	10	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2024-11-23T10:54:29Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'capybara hf, A cartoon drawing of a brown bear sitting in front of a laptop. The bear is facing to the right and has a smiley face on the screen of the laptop. Above the bear is a white bear with a black nose and a black mouth. The background is a light beige color.' output: url: images/C1.png - text: 'capybara hf, A cartoon drawing of a brown bear wearing sunglasses with a yellow circle at the top of the eyes. The bears eyes are tinted black, and the bears body is a light brown color. He is holding a pink money in his right hand. The money has a black border around it, and there are two yellow smiley faces on the eyes of the bear. The background is a solid white color.' output: url: images/C2.png - text: 'capybara hf, A cartoon drawing of a brown bear with a black hat on its head. The bear is wearing a black shirt with a pink collar. The bears face is brown and the bears mouth is black. There is a smiley face in the bottom right corner of the image. There are white clouds in the background.' output: url: images/C3.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: capybara hf license: apache-2.0 --- ![strangerzonehf/Flux-Super-Capybara-HF](images/CC.png) # Flux-Super-Capybara-HF <Gallery /> The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases. ## Model description prithivMLmods/Flux-Super-Capybara-HF Image Processing Parameters \| Parameter \| Value \| Parameter \| Value \| \|---------------------------\|--------\|---------------------------\|--------\| \| LR Scheduler \| constant \| Noise Offset \| 0.03 \| \| Optimizer \| AdamW \| Multires Noise Discount \| 0.1 \| \| Network Dim \| 64 \| Multires Noise Iterations \| 10 \| \| Network Alpha \| 32 \| Repeat & Steps \| 22 & 2900 \| \| Epoch \| 15 \| Save Every N Epochs \| 1 \| Labeling: florence2-en(natural language & English) Total Images Used for Training : 20 ## Best Dimensions - 768 x 1024 (Best) - 1024 x 1024 (Default) ## Setting Up ```python import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "strangerzonehf/Flux-Super-Capybara-HF" trigger_word = "capybara hf" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Trigger words You should use `capybara hf` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/strangerzonehf/Flux-Super-Capybara-HF/tree/main) them in the Files & versions tab.
gdshaji/gd-tp-1.9k-v2	gdshaji	2024-11-23T11:46:27Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T11:43:57Z	--- 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. 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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touhidulislam/BERTweet_retrain_2022_11	touhidulislam	2024-11-23T11:42:28Z	177	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T11:42:08Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_11 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. --> # BERTweet_retrain_2022_11 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5380 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8663 \| 1.0 \| 6098 \| 2.6189 \| \| 2.693 \| 2.0 \| 12196 \| 2.5695 \| \| 2.4941 \| 3.0 \| 18294 \| 2.5362 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2021_11	touhidulislam	2024-11-23T11:41:44Z	168	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T11:41:23Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_11 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. --> # BERTweet_retrain_2021_11 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4913 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8137 \| 1.0 \| 6081 \| 2.5820 \| \| 2.7966 \| 2.0 \| 12162 \| 2.5134 \| \| 2.7398 \| 3.0 \| 18243 \| 2.4904 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
ritamsharma/tinyllama-finance-v0	ritamsharma	2024-11-23T11:40:00Z	96	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T10:22:21Z	--- 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. 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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allknowingroger/Marco-01-slerp2-7B	allknowingroger	2024-11-23T11:39:49Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:AIDC-AI/Marco-o1", "base_model:merge:AIDC-AI/Marco-o1", "base_model:allknowingroger/QwenSlerp12-7B", "base_model:merge:allknowingroger/QwenSlerp12-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T11:31:18Z	--- base_model: - allknowingroger/QwenSlerp12-7B - AIDC-AI/Marco-o1 library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # 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: * [allknowingroger/QwenSlerp12-7B](https://huggingface.co/allknowingroger/QwenSlerp12-7B) * [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: AIDC-AI/Marco-o1 - model: allknowingroger/QwenSlerp12-7B merge_method: slerp base_model: AIDC-AI/Marco-o1 dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```
weide0118/ddpm-celebahq-finetuned-butterflies-2epochs	weide0118	2024-11-23T11:38:11Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-11-23T11:37:50Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('weide0118/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
Marialab/finetuned-whisper-small-dr-ar	Marialab	2024-11-23T11:34:14Z	76	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "New_fine-tune_whisper_new_dataset", "generated_from_trainer", "ar", "dataset:darija-c", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-23T11:33:19Z	--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-small tags: - New_fine-tune_whisper_new_dataset - generated_from_trainer datasets: - darija-c metrics: - bleu model-index: - name: 'Finetuned Whisper small darija translate ' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Finetuned Whisper small darija translate This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Darija-C dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Bleu: 0.7440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 40 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 3.975 \| 0.6667 \| 10 \| 3.4363 \| 0.0 \| \| 2.1029 \| 1.3333 \| 20 \| 1.5986 \| 0.0262 \| \| 1.7909 \| 2.0 \| 30 \| 0.9239 \| 0.1314 \| \| 0.9837 \| 2.6667 \| 40 \| 0.5086 \| 0.3289 \| \| 0.36 \| 3.3333 \| 50 \| 0.4370 \| 0.3911 \| \| 0.6361 \| 4.0 \| 60 \| 0.2622 \| 0.4561 \| \| 0.5227 \| 4.6667 \| 70 \| 0.2506 \| 0.5266 \| \| 0.3307 \| 5.3333 \| 80 \| 0.1299 \| 0.6123 \| \| 0.2438 \| 6.0 \| 90 \| 0.1290 \| 0.6057 \| \| 0.2864 \| 6.6667 \| 100 \| 0.0838 \| 0.6623 \| \| 0.073 \| 7.3333 \| 110 \| 0.0965 \| 0.6494 \| \| 0.1924 \| 8.0 \| 120 \| 0.0859 \| 0.7237 \| \| 0.0086 \| 8.6667 \| 130 \| 0.0235 \| 0.7174 \| \| 0.003 \| 9.3333 \| 140 \| 0.0335 \| 0.7354 \| \| 0.054 \| 10.0 \| 150 \| 0.0096 \| 0.7367 \| \| 0.0008 \| 10.6667 \| 160 \| 0.0190 \| 0.7367 \| \| 0.0002 \| 11.3333 \| 170 \| 0.0003 \| 0.7440 \| \| 0.0001 \| 12.0 \| 180 \| 0.0001 \| 0.7440 \| \| 0.0002 \| 12.6667 \| 190 \| 0.0001 \| 0.7440 \| \| 0.0 \| 13.3333 \| 200 \| 0.0000 \| 0.7440 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu121 - Datasets 2.19.2 - Tokenizers 0.20.3
elyx/julien	elyx	2024-11-23T11:29:10Z	106	0	transformers	[ "transformers", "safetensors", "ultravox", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2024-11-23T11:27:26Z	--- 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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Shivam365/sd-class-butterflies-32-new	Shivam365	2024-11-23T11:25:28Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-11-23T11:25:22Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card: This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Shivam365/sd-class-butterflies-32-new') image = pipeline().images[0] image ```
boadisamson/Llama-3.1-8B-Qgis-7000G-q4_k_m-Instruct	boadisamson	2024-11-23T11:14:21Z	8	1	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T11:13:14Z	--- base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: boadisamson - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
amanpreetsingh459/gemma-2-2b-it-punjabi-finetuned	amanpreetsingh459	2024-11-23T11:07:09Z	89	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "pa", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T07:09:15Z	--- base_model: google/gemma-2-2b-it language: - pa library_name: transformers license: gemma model_name: output_dir tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for output_dir This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="amanpreetsingh459/gemma-2-2b-it-punjabi-finetuned", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/amanpreetsingh459-myself/huggingface/runs/j9gmzhe0) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.0 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
isspek/xlnet-base-cased_ebola_mistral_2_2e-5_16_undersampling_0.6	isspek	2024-11-23T11:05:45Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T11:05:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_mistral_2_2e-5_16	isspek	2024-11-23T11:05:26Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T11:05:05Z	--- 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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isspek/xlnet-base-cased_ebola_mistral_4_2e-5_16	isspek	2024-11-23T11:02:22Z	116	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T11:02:08Z	--- 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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coolcat0/Llama-3.1-70B-Instruct-Simplicity	coolcat0	2024-11-23T10:58:32Z	9	1	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-19T01:54:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tintnguyen/question-generation-vietnamese-v2-tin	tintnguyen	2024-11-23T10:54:44Z	103	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:nluai/question-generation-vietnamese-v2", "base_model:finetune:nluai/question-generation-vietnamese-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-23T10:53:39Z	--- library_name: transformers license: apache-2.0 base_model: nluai/question-generation-vietnamese-v2 tags: - generated_from_trainer model-index: - name: question-generation-vietnamese-v2-tin 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. --> # question-generation-vietnamese-v2-tin This model is a fine-tuned version of [nluai/question-generation-vietnamese-v2](https://huggingface.co/nluai/question-generation-vietnamese-v2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7425 - eval_runtime: 35.9438 - eval_samples_per_second: 27.71 - eval_steps_per_second: 1.753 - epoch: 0.9168 - step: 30000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0
ombhojane/wellness-mini	ombhojane	2024-11-23T10:49:56Z	13	0	null	[ "safetensors", "llama", "mental-health", "healthcare", "conversational", "wellness", "SmolLM-135M", "fine-tuned", "text-generation", "en", "license:mit", "region:us" ]	text-generation	2024-11-23T10:39:40Z	--- language: en tags: - mental-health - healthcare - conversational - wellness - SmolLM-135M - fine-tuned license: mit pipeline_tag: text-generation inference: false --- # Wellness-Mini: Mental Health Conversational AI ## Model Description Wellness-Mini is a fine-tuned version of SmolLM-135M, specifically adapted for mental health conversations and assessments. ## Usage ### In Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("ombhojane/wellness-mini", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("ombhojane/wellness-mini") Example usage messages = [{"role": "user", "content": "How are you feeling today?"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=100) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Via Pipeline ```python from transformers import pipeline Create pipeline pipe = pipeline( "text-generation", model="ombhojane/wellness-mini", tokenizer="ombhojane/wellness-mini", trust_remote_code=True ) Generate text response = pipe("How are you feeling today?", max_new_tokens=100) print(response[0]['generated_text']) ``` ## Training Details - Base model: SmolLM-135M - Fine-tuned using supervised fine-tuning (SFT) - Trained with both mental health assessment capabilities and proper identity responses ## Limitations - This is an AI assistant and not a replacement for professional medical advice - Should be used as a supplementary tool only - May not be suitable for emergency situations - Responses should be verified by healthcare professionals ## Intended Use This model is intended to be used as a conversational AI assistant focused on mental health support and assessment. It can: - Provide supportive responses to mental health concerns - Help identify potential mental health indicators - Engage in wellness-focused conversations ## Bias and Risks - May reflect biases present in training data - Should not be used as sole diagnostic tool - Responses should be reviewed by healthcare professionals ## Creator This model was developed by Sentinet AI Systems.
fracapuano/moss-pen	fracapuano	2024-11-23T10:43:40Z	10	0	lerobot	[ "lerobot", "safetensors", "act", "model_hub_mixin", "pytorch_model_hub_mixin", "robotics", "region:us" ]	robotics	2024-11-23T10:43:30Z	--- library_name: lerobot tags: - act - model_hub_mixin - pytorch_model_hub_mixin - robotics --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/huggingface/lerobot - Docs: [More Information Needed]
isspek/xlnet-base-cased_ebola_gpt4o_5_2e-5_16	isspek	2024-11-23T10:42:26Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T10:42:08Z	--- 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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isspek/xlnet-base-cased_ebola_gpt4o_3_2e-5_16	isspek	2024-11-23T10:41:36Z	116	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T10:41:23Z	--- 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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isspek/xlnet-base-cased_ebola_gpt4o_4_2e-5_16_undersampling_0.6	isspek	2024-11-23T10:41:21Z	117	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T10:41:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_gpt4o_1_2e-5_16_undersampling_0.6	isspek	2024-11-23T10:40:35Z	121	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T10:40:21Z	--- 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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isspek/xlnet-base-cased_ebola_gpt4o_2_2e-5_16_undersampling_0.6	isspek	2024-11-23T10:39:49Z	118	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T10:39:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_gpt4o_2_2e-5_16	isspek	2024-11-23T10:39:16Z	116	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T10:39:04Z	--- 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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touhidulislam/BERTweet_retrain_2022_39	touhidulislam	2024-11-23T10:32:09Z	177	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T10:31:48Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_39 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. --> # BERTweet_retrain_2022_39 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4699 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7008 \| 1.0 \| 6301 \| 2.5605 \| \| 2.6849 \| 2.0 \| 12602 \| 2.4851 \| \| 2.7378 \| 3.0 \| 18903 \| 2.4627 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
circlelee/gemma-2-2b-it-arena.bak2	circlelee	2024-11-23T10:31:45Z	88	0	transformers	[ "transformers", "pytorch", "safetensors", "gemma2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-2-2b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2-2b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T10:28:38Z	--- base_model: unsloth/gemma-2-2b-it-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma2 - trl - sft --- # Uploaded model - Developed by: circlelee - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-2b-it-bnb-4bit This gemma2 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)
touhidulislam/BERTweet_retrain_2021_09	touhidulislam	2024-11-23T10:28:38Z	167	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T10:28:11Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_09 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. --> # BERTweet_retrain_2021_09 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5955 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8868 \| 1.0 \| 5925 \| 2.6722 \| \| 2.7945 \| 2.0 \| 11850 \| 2.6265 \| \| 2.6973 \| 3.0 \| 17775 \| 2.5918 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2022_09	touhidulislam	2024-11-23T10:28:19Z	178	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T10:27:57Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_09 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. --> # BERTweet_retrain_2022_09 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4388 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.6503 \| 1.0 \| 6222 \| 2.5467 \| \| 2.5711 \| 2.0 \| 12444 \| 2.4678 \| \| 2.5673 \| 3.0 \| 18666 \| 2.4445 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
DopeorNope/gs-llama3-1b-llama-maskver	DopeorNope	2024-11-23T10:18:11Z	84	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T10:14:42Z	--- 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
briannlongzhao/dog_textual_inversion	briannlongzhao	2024-11-23T10:15:49Z	4	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-15T09:54:52Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - briannlongzhao/dog_textual_inversion These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1. You can find some example images in the following.
prithivMLmods/Flux-Product-Ad-Backdrop	prithivMLmods	2024-11-23T10:13:04Z	740	36	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "Product-Ad", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-11-21T11:58:04Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora - Product-Ad widget: - text: >- Product Ad, Captured at eye-level, a close-up shot captures a pile of fried chicken wings in a white paper cup. The chicken wings are a vibrant brown color, adding a pop of color to the scene. The cup is placed on a light brown wooden table, creating a stark contrast with the vibrant blue sky in the background. To the right of the chicken wings, a slice of lemon, a red onion, and a red radish are placed on the table. The radish, and red onions are arranged in a circular pattern, adding depth to the composition. The backdrop is blurred, suggesting a fair day. output: url: images/PA1.png - text: >- Product Ad, a blue and silver electric razor is soaring through the air. The razor is positioned in the middle of a grassy field, with a backdrop of a mountain range that is covered in snow. The sky is a deep blue, dotted with white clouds, adding a pop of color to the scene. The blades of the razor are splashing in the air, adding texture to the image. output: url: images/PA3.png - text: >- Product Ad,Vegan Snacks, in the style of an outdoors product hero shot in motion, dynamic magazine ad image, photorealism, 4k Raw, --v6 output: url: images/PA4.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Product Ad license: creativeml-openrail-m --- # Flux-Product-Ad-Backdrop <Gallery /> The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases. ## Model description prithivMLmods/Flux-Product-Ad-Backdrop Image Processing Parameters \| Parameter \| Value \| Parameter \| Value \| \|---------------------------\|--------\|---------------------------\|--------\| \| LR Scheduler \| constant \| Noise Offset \| 0.03 \| \| Optimizer \| AdamW \| Multires Noise Discount \| 0.1 \| \| Network Dim \| 64 \| Multires Noise Iterations \| 10 \| \| Network Alpha \| 32 \| Repeat & Steps \| 19 & 2970 \| \| Epoch \| 15 \| Save Every N Epochs \| 1 \| Labeling: florence2-en(natural language & English) Total Images Used for Training : 19 ## Best Dimensions - 768 x 1024 (Best) - 1024 x 1024 (Default) ## Setting Up ```python import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Flux-Product-Ad-Backdrop" trigger_word = "Product Ad" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Trigger words You should use `Product Ad` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Flux-Product-Ad-Backdrop/tree/main) them in the Files & versions tab.
glif-loradex-trainer/fabian3000_henrymajor	glif-loradex-trainer	2024-11-23T10:08:11Z	39	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2024-11-23T10:07:52Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1732356405402__000001500_0.jpg text: a cthulhu monster drinking coffee henrymajorstyle - output: url: samples/1732356429409__000001500_1.jpg text: a portrait of a woman working on a rocket henrymajorstyle - output: url: samples/1732356453445__000001500_2.jpg text: vampire sword henrymajorstyle base_model: black-forest-labs/FLUX.1-dev trigger: henrymajorstyle instance_prompt: henrymajorstyle license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # henrymajor Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `fabian3000`. <Gallery /> ## Trigger words You should use `henrymajorstyle` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/fabian3000_henrymajor/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
elyx/banana1234	elyx	2024-11-23T10:03:54Z	103	0	transformers	[ "transformers", "safetensors", "ultravox", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2024-11-23T09:58:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
touhidulislam/BERTweet_retrain_2022_38	touhidulislam	2024-11-23T09:55:12Z	169	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T09:54:51Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_38 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. --> # BERTweet_retrain_2022_38 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5456 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.6585 \| 1.0 \| 6105 \| 2.6322 \| \| 2.6867 \| 2.0 \| 12210 \| 2.5551 \| \| 2.657 \| 3.0 \| 18315 \| 2.5440 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2022_08	touhidulislam	2024-11-23T09:51:06Z	171	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T09:50:39Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_08 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. --> # BERTweet_retrain_2022_08 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5480 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7683 \| 1.0 \| 5972 \| 2.6260 \| \| 2.8097 \| 2.0 \| 11944 \| 2.5590 \| \| 2.5256 \| 3.0 \| 17916 \| 2.5344 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
luojunyu/Llama-3.1-8B-SemiEvol-MMLU	luojunyu	2024-11-23T09:51:04Z	71	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-classification", "en", "dataset:luojunyu/SemiEvol", "arxiv:2410.14745", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-10-17T07:19:06Z	--- license: cc-by-4.0 datasets: - luojunyu/SemiEvol language: - en metrics: - accuracy base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-classification library_name: transformers --- Release model for paper [SemiEvol](https://arxiv.org/abs/2410.14745).
erhj3eh3ehweg/Midstral-166M	erhj3eh3ehweg	2024-11-23T09:42:59Z	176	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T09:42:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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aswathshakthi/distilbert-emotions-clf-m1	aswathshakthi	2024-11-23T09:42:18Z	6	1	null	[ "safetensors", "distilbert", "en", "license:apache-2.0", "region:us" ]	null	2024-11-23T08:52:23Z	--- license: apache-2.0 language: - en --- # Model Card for My Fine-Tuned Model --- license: apache-2.0 language: - en --- ## Model Description - Purpose: This model is fine-tuned to perform multi-class emotion classification. It can identify various emotions in text, such as joy, sadness, love, anger, fear, and surprise. - Model architecture: The model is based on the `distilbert-base-uncased` architecture, a distilled version of the BERT model which is smaller and faster but retains most of its predictive power. - Training data: The model was trained on the `emotion` dataset from Hugging Face's datasets library. This dataset includes text labeled with different emotions. During preprocessing, texts were tokenized, and padding and truncation were applied to standardize their lengths. ## Intended Use - Intended users: This model is intended for developers and researchers interested in emotion analysis in text, including applications in social media sentiment analysis, customer feedback interpretation, and mental health assessment. - Use cases: Potential use cases include analyzing social media posts for emotional content, enhancing chatbots to understand user emotions, and helping mental health professionals in identifying emotional states from text-based communications. ## Limitations - Known limitations: The model's accuracy may vary depending on the context and the dataset's representativeness. It may not perform equally well on texts from domains significantly different from the training data. ## Hardware - Training Platform: The model was trained on Apple M1 . The training completed in under 23 minutes, demonstrating the efficiency of Apple hardware optimizations. ## Ethical Considerations - Ethical concerns: Care should be taken to ensure that the model is not used in sensitive applications without proper ethical considerations, especially in scenarios that could impact individual privacy or mental health. ## More Information - Model Name on Hugging Face: `aswathshakthi/distilbert-emotions-clf-m1`
P00j4n/fine-tuned-gemma-2-2b-quantized	P00j4n	2024-11-23T09:39:47Z	6	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T09:32:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
longisland3/anime-whisper-faster	longisland3	2024-11-23T09:17:37Z	6	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-11-23T06:41:51Z	--- license: cc-by-nc-4.0 ---
touhidulislam/BERTweet_retrain_2021_07	touhidulislam	2024-11-23T09:17:03Z	170	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T09:16:42Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_07 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. --> # BERTweet_retrain_2021_07 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5825 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7371 \| 1.0 \| 5927 \| 2.6662 \| \| 2.7009 \| 2.0 \| 11854 \| 2.6087 \| \| 2.6045 \| 3.0 \| 17781 \| 2.5793 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2021_51	touhidulislam	2024-11-23T09:12:17Z	177	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T09:11:56Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_51 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. --> # BERTweet_retrain_2021_51 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4925 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8054 \| 1.0 \| 5999 \| 2.5611 \| \| 2.6829 \| 2.0 \| 11998 \| 2.5308 \| \| 2.5699 \| 3.0 \| 17997 \| 2.4981 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
Carick/roberta-base-wordnet_dataset_two-fine-tuned	Carick	2024-11-23T09:01:22Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-22T07:31:57Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: roberta-base-wordnet_dataset_two-fine-tuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-wordnet_dataset_two-fine-tuned This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2831 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.4241 \| 1.0 \| 7938 \| 0.3626 \| \| 0.3768 \| 2.0 \| 15876 \| 0.3164 \| \| 0.3227 \| 3.0 \| 23814 \| 0.2831 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0
sakuraumi/Sakura-13B-Galgame	sakuraumi	2024-11-23T09:00:48Z	135	114	transformers	[ "transformers", "pytorch", "baichuan", "text-generation", "custom_code", "zh", "ja", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-26T16:28:53Z	--- license: apache-2.0 language: - zh - ja pipeline_tag: text-generation --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <h1> SakuraLLM </h1> <center> <b>Sakura</b>: <b><ins>S</ins></b>FT <ins><b>A</b></ins>nd RLHF models using <ins><b>K</b></ins>nowledge of <ins><b>U</b></ins>niversal Character and <ins><b>R</b></ins>elationship <ins><b>A</b></ins>ttributes for Japanese to Chinese Translation in Light Novel & Galgame Domain. </center> </div> <p align="center"> 🤗 <a href="https://huggingface.co/sakuraumi/Sakura-13B-Galgame" target="_blank">Hugging Face</a> • 🤖 <a href="https://www.modelscope.cn/models/sakuraumi/Sakura-13B-Galgame" target="_blank">ModelScope</a> </p> # 目前Sakura发布的所有模型均采用[CC BY-NC-SA 4.0协议](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh-hans)，Sakura所有模型与其衍生模型均禁止任何形式的商用！Sakura系列所有模型皆仅供学习交流使用，开发者对使用Sakura模型造成的问题不负任何责任。 # 介绍 - 基于一系列开源大模型构建，在通用日文语料与轻小说/Galgame等领域的中日语料上进行继续预训练与微调，旨在提供开源可控可离线自部署的、ACGN风格的日中翻译模型。 - 新建了[TG交流群](https://t.me/+QMDKZyO9GV1kNDA1)，欢迎交流讨论。对于其他适配本模型的项目如使用非本项目提供的prompt格式进行翻译，不保证会获得与README中的说明一致的质量！如果使用模型翻译并发布，请在最显眼的位置标注机翻！！！！！开发者对于滥用本模型造成的一切后果不负任何责任。 > 由于模型一直在更新，请同时注明使用的模型版本等信息，方便进行质量评估和更新翻译。对于模型翻译的人称代词问题（错用，乱加，主宾混淆，男女不分等）和上下文理解问题，如果有好的想法或建议，欢迎提issue！ ### TODO：见[#42](https://github.com/SakuraLLM/Sakura-13B-Galgame/issues/42) ## 快速开始 ### 教程：详见[本仓库Wiki](https://github.com/SakuraLLM/Sakura-13B-Galgame/wiki). 部分使用方法：[usage.md](https://github.com/SakuraLLM/SakuraLLM/blob/main/usage.md) > 请注意，如果给轻小说机翻站使用，请参见[机翻站站内教程](https://books.fishhawk.top/forum?category=Guide&page=1)，本 repo 不适用。 ### 模型下载： \| 参数量 \| 发布时间-底模-版本 \| 模型 \| \|:-------:\|:-------\|:-------\| \| 32B \| 20240508-Qwen1.5-32B-v0.9 \| 🤗 [Sakura-32B-Qwen2beta-v0.9-GGUF](https://huggingface.co/SakuraLLM/Sakura-32B-Qwen2beta-v0.9-GGUF) \| \| \| 20240508-Qwen1.5-32B-v0.10pre1 \| 🤗 [Sakura-32B-Qwen2beta-v0.10pre1-GGUF](https://huggingface.co/SakuraLLM/Sakura-32B-Qwen2beta-v0.10pre1-GGUF) \| \| 14B \| 20240111-Qwen-14B-v0.9 \| 🤗 [Sakura-13B-LNovel-v0.9b-GGUF](https://huggingface.co/SakuraLLM/Sakura-13B-LNovel-v0.9b-GGUF) \| \| \| 20240213-Qwen1.5-14B-v0.9 \| 🤗 [Sakura-14B-Qwen2beta-v0.9-GGUF](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2beta-v0.9-GGUF) \| \| \| 20240516-Qwen1.5-14B-v0.9.2 \| 🤗 [Sakura-14B-Qwen2beta-v0.9.2-GGUF](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2beta-v0.9.2-GGUF) \|(最新)\| 20241008-Qwen2.5-14B-v1.0 \| 🤗 [Sakura-14B-Qwen2.5-v1.0-GGUF](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2.5-v1.0-GGUF) \| 7B \| 20240116-Qwen-7B-v0.9 \| 🤗 [Sakura-7B-LNovel-v0.9-GGUF](https://huggingface.co/SakuraLLM/Sakura-7B-LNovel-v0.9-GGUF) \| \| \| 20240531-Qwen1.5-7B-Galtransl-v2.6 \| 🤗 [Galtransl-v2.6](https://huggingface.co/SakuraLLM/GalTransl-7B-v2.6) \| \| ~2B \| 20240214-Qwen1.5-1.8B-v0.9.1 \| 🤗 [Sakura-1B8-Qwen2beta-v0.9.1-GGUF](https://huggingface.co/SakuraLLM/Sakura-1B8-Qwen2beta-v0.9.1-GGUF) \| \| \| 20241012-Qwen2.5-1.5B-v1.0 \| 🤗 [Sakura-1.5B-Qwen2.5-v1.0-GGUF](https://huggingface.co/SakuraLLM/Sakura-1.5B-Qwen2.5-v1.0-GGUF) \| p.s. 如果无法连接到HuggingFace服务器，可将链接中的`huggingface.co`改成`hf-mirror.com`，使用hf镜像站下载。 ## News 1. 更新了基于Qwen2.5-14B的v1.0正式版模型[Sakura-14B-Qwen2.5-v1.0](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2.5-v1.0-GGUF)和基于Qwen2.5-1.5B的v1.0正式版模型[Qwen2.5-1.5B-v1.0](https://huggingface.co/SakuraLLM/Sakura-1.5B-Qwen2.5-v1.0-GGUF)，prompt格式参见[下方说明](https://github.com/SakuraLLM/SakuraLLM#%E6%8E%A8%E7%90%86)。主要改进： - 改善翻译质量，提高翻译准确率，尤其是人称的准确率。 - 支持术语表(GPT字典)，以保持专有名词和人称的一致性。 - 提高部分简单控制符的保留能力，尤其是单行内存在`\n`的情况下保留`\n`的能力。降低行数与原文不一致的概率。 - 由于底模使用GQA，推理速度和显存占用显著改善，可实现更快的多线程推理。关于多线程推理，可参考[Sakura启动器GUI使用教程](https://books.fishhawk.top/forum/656d60530286f15e3384fcf8)或[SakuraLLMServer](https://github.com/neavo/SakuraLLMServer)。 1. 更新了基于Qwen1.5-7B的[Galtransl](https://huggingface.co/SakuraLLM/GalTransl-v1)模型，为视觉小说翻译任务专项优化。对视觉小说脚本中的行内换行、控制符、ruby注音等符号具有较好的保留能力。适配[GalTransl视觉小说翻译工具](https://github.com/xd2333/GalTransl)并调优，支持GPT字典（[字典写法见此](https://github.com/xd2333/GalTransl/wiki/GPT%E5%AD%97%E5%85%B8%E2%80%90sakura-galtransl)）。 1. 增加了vllm模型后端的支持，详见[#40](https://github.com/SakuraLLM/Sakura-13B-Galgame/pull/40) 1. <del>感谢[Isotr0py](https://github.com/Isotr0py)提供运行模型的NoteBook仓库[SakuraLLM-Notebooks](https://github.com/Isotr0py/SakuraLLM-Notebooks)，可在[Colab](https://colab.research.google.com/)(免费T4\1)与[Kaggle](https://www.kaggle.com/)(免费P100\1或T4\2)平台使用。已经更新Kaggle平台的[使用教程](https://github.com/SakuraLLM/Sakura-13B-Galgame/wiki/%E7%99%BD%E5%AB%96Kaggle%E5%B9%B3%E5%8F%B0%E9%83%A8%E7%BD%B2%E6%95%99%E7%A8%8B)，可以白嫖一定时间的T4\2。</del> 警告，Kaggle 官方已经采取措施封禁 SakuraLLM 仓库，[参见](https://github.com/SakuraLLM/SakuraLLM/issues/115) ，在 Kaggle 上克隆 SakuraLLM 仓库可能将会导致永久性封号。请换用其他项目或转移至租卡平台使用。 1. Sakura API已经支持OpenAI格式，现在可以通过OpenAI库或者OpenAI API Reference上的请求形式与Server交互。一个使用OpenAI库与Sakura模型交互的例子详见[openai_example.py](https://github.com/SakuraLLM/Sakura-13B-Galgame/blob/main/tests/example_openai.py)。 ## 已经接入模型的工具 1. 网站：[轻小说机翻机器人](https://books.fishhawk.top/)已接入Sakura模型(v0.8-4bit)，站内有大量模型翻译结果可供参考。你也可以自行部署模型并使用该网站生成机翻，目前已经支持v0.8与v0.9模型，且提供了llama.cpp一键包。轻小说机翻机器人网站是一个自动生成轻小说机翻并分享的网站。你可以浏览日文网络小说，或者上传Epub/Txt文件，并生成机翻。 1. [LunaTranslator](https://github.com/HIllya51/LunaTranslator)已经支持Sakura API，可以通过本地部署API后端，并在LunaTranslator中配置Sakura API来使用Sakura模型进行Galgame实时翻译。 ~~使用[KurikoMoe](https://github.com/kurikomoe/LunaTranslator/releases/latest)的版本可以支持流式输出。~~ 目前官方版本已经支持流式输出，只需在翻译设置界面勾选流式输出即可。 LunaTranslator是一个Galgame翻译工具，支持剪贴板、OCR、HOOK，支持40余种翻译引擎。 1. [GalTransl](https://github.com/XD2333/GalTransl)已经支持Sakura API，可以通过本地部署API后端，在GalTransl中配置使用Sakura模型来翻译Galgame，制作内嵌式翻译补丁。 GalTransl是一个galgame自动化翻译工具，用于制作内嵌式翻译补丁。一个使用GalTransl和Sakura模型翻译的[示例](https://www.ai2moe.org/files/file/2271-%E6%88%AF%E7%94%BBgaltranslsakuragpt35%E7%88%B1%E4%B9%8B%E5%90%BB3-sexy-gpt%E7%BF%BB%E8%AF%91%E8%A1%A5%E4%B8%81uploadee5-mb/) 1. 翻译Unity引擎游戏的工具[SakuraTranslator](https://github.com/fkiliver/SakuraTranslator)。感谢[fkiliver](https://github.com/fkiliver)提供。 1. 翻译RPGMaker引擎游戏的工具[RPGMaker_LLaMA_Translator](https://github.com/fkiliver/RPGMaker_LLaMA_Translator)。感谢[fkiliver](https://github.com/fkiliver)提供。 1. [AiNiee](https://github.com/NEKOparapa/AiNiee-chatgpt)已经支持Sakura API，可以通过本地部署API后端，在AiNiee中使用Sakura模型进行翻译。 AiNiee是一款基于【mtool】或【Translator++】，chatgpt自动批量翻译工具，主要是用来翻译各种RPG游戏。 1. [manga-image-translator](https://github.com/zyddnys/manga-image-translator)已经支持Sakura API，可以通过本地部署API后端，使用Sakura自动翻译漫画。 1. [BallonsTranslator](https://github.com/dmMaze/BallonsTranslator)已经支持Sakura API，可以通过本地部署API后端，使用Sakura翻译漫画。 # 显存需求下面的表格显示了使用不同量化和不同格式的模型时显存占用的大小。如果你的显卡显存不满足上述需求，可以尝试同时使用CPU与GPU进行推理。 - llama.cpp GGUF模型（使用Qwen-14B v0.9模型进行测试） \| 模型量化类型 \| 模型大小 \| 推荐显存大小 \| \|:-------:\|:-------:\|:-------:\| \| fp16 \| 26.3G \| 超出游戏显卡显存范围 \| \| Q8_0 \| 14G \| 24G \| \| Q6_K \| 11.4G \| 20G \| \| Q5_K_M \| 10.1G \| 16G \| \| Q4_K_M \| 8.8G \| 16G \| \| Q3_K_M \| 7.2G \| 16G \| \| Q2_K \| 6.1G \| 12G \| # 模型详情 ## 描述 - Finetuned by [SakuraUmi](https://github.com/pipixia244) - Finetuned on [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) - Continual Pre-trained on [Qwen model series](https://github.com/QwenLM/Qwen) - Continual Pre-trained on [Qwen1.5 model series](https://github.com/QwenLM/Qwen1.5) - Finetuned on Sakura-Base model series - Languages: Chinese/Japanese ## 效果 - Galgame [一个例子](https://www.ai2moe.org/files/file/2271-%E6%88%AF%E7%94%BBgaltranslsakuragpt35%E7%88%B1%E4%B9%8B%E5%90%BB3-sexy-gpt%E7%BF%BB%E8%AF%91%E8%A1%A5%E4%B8%81uploadee5-mb/) - 轻小说网站：[轻小说机翻机器人](https://books.fishhawk.top/)已接入Sakura模型(v0.9)，站内有大量模型翻译的轻小说可供参考。 - PPL/BLEU/Human TBD # 推理 - openai api messages格式： - v0.9 使用代码处理如下： ```python input_text_list = ['a', 'bb', 'ccc', ...] # 一系列上下文文本，每个元素代表一行的文本 raw_text = "\n".join(input_text_list) messages=[ { "role": "system", "content": "你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。" }, { "role": "user", "content": "将下面的日文文本翻译成中文：" + raw_text } ] ``` - prompt格式： - v0.10pre1 代码处理如下： ```python gpt_dict = [{ "src": "原文1", "dst": "译文1", "info": "注释信息1", },] gpt_dict_text_list = [] for gpt in gpt_dict: src = gpt['src'] dst = gpt['dst'] info = gpt['info'] if "info" in gpt.keys() else None if info: single = f"{src}->{dst} #{info}" else: single = f"{src}->{dst}" gpt_dict_text_list.append(single) gpt_dict_raw_text = "\n".join(gpt_dict_text_list) user_prompt = "根据以下术语表（可以为空）：\n" + gpt_dict_raw_text + "\n\n" + "将下面的日文文本根据上述术语表的对应关系和备注翻译成中文：" + japanese prompt = "<\|im_start\|>system\n你是一个轻小说翻译模型，可以流畅通顺地使用给定的术语表以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，注意不要混淆使役态和被动态的主语和宾语，不要擅自添加原文中没有的代词，也不要擅自增加或减少换行。<\|im_end\|>\n" \ # system prompt + "<\|im_start\|>user\n" + user_prompt + "<\|im_end\|>\n" \ # user prompt + "<\|im_start\|>assistant\n" # assistant prompt start ``` - v0.9 文本格式如下： ``` <\|im_start\|>system 你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。<\|im_end\|> <\|im_start\|>user 将下面的日文文本翻译成中文：日文第一行日文第二行日文第三行 ... 日文第n行<\|im_end\|> <\|im_start\|>assistant ``` 使用代码处理如下： ```python input_text_list = ['a', 'bb', 'ccc', ...] # 一系列上下文文本，每个元素代表一行的文本 raw_text = "\n".join(input_text_list) prompt = "<\|im_start\|>system\n你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。<\|im_end\|>\n" \ # system prompt + "<\|im_start\|>user\n将下面的日文文本翻译成中文：" + raw_text + "<\|im_end\|>\n" \ # user prompt + "<\|im_start\|>assistant\n" # assistant prompt start ``` - prompt构建： - v0.8 ```python input_text = "" # 要翻译的日文 query = "将下面的日文文本翻译成中文：" + input_text prompt = "<reserved_106>" + query + "<reserved_107>" ``` - v0.9 ```python input_text = "" # 要翻译的日文 query = "将下面的日文文本翻译成中文：" + input_text prompt = "<\|im_start\|>system\n你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。<\|im_end\|>\n<\|im_start\|>user\n" + query + "<\|im_end\|>\n<\|im_start\|>assistant\n" ``` - 推理与解码参数： \| 参数 \| 值 \| \| ---- \| ---- \| \| temperature \| 0.1 \| \| top p \| 0.3 \| \| do sample \| True \| \| beams number \| 1 \| \| repetition penalty \| 1 \| \| max new token \| 512 \| \| min new token \| 1 \| 如出现退化（退化的例子可参见[#35](https://github.com/SakuraLLM/Sakura-13B-Galgame/issues/35)与[#36](https://github.com/SakuraLLM/Sakura-13B-Galgame/issues/36)），可增加`frequency_penalty`参数，并设置为大于0的某值，一般设置0.1~0.2即可。** # 微调模型微调框架参考[BELLE](https://github.com/LianjiaTech/BELLE)或[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)，prompt构造参考推理部分。 # 相关项目 - [轻小说机翻机器人](https://books.fishhawk.top/)：轻小说翻译 - [LunaTranslator](https://github.com/HIllya51/LunaTranslator)：Galgame在线翻译 - [GalTransl](https://github.com/XD2333/GalTransl)：Galgame离线翻译，制作补丁 - [AiNiee](https://github.com/NEKOparapa/AiNiee-chatgpt)：RPG游戏翻译 # 致谢 - [CjangCjengh](https://github.com/CjangCjengh) - [ryank231231](https://github.com/ryank231231) - [KurikoMoe](https://github.com/kurikomoe) - [FishHawk](https://github.com/FishHawk) - [K024](https://github.com/K024) - [minaduki-sora](https://github.com/minaduki-sora) - [Kimagure7](https://github.com/Kimagure7) - [YYF233333](https://github.com/YYF233333) - [Isotr0py](https://github.com/Isotr0py) - [XD2333](https://github.com/XD2333) # Copyright Notice v0.8版本模型的使用须遵守[Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE)、[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)和[CC BY-NC-SA 4.0协议](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh-hans)。 v0.9版本模型的使用须遵守[Qwen模型许可协议](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)和[CC BY-NC-SA 4.0协议](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh-hans)。
touhidulislam/BERTweet_retrain_2021_06	touhidulislam	2024-11-23T08:41:38Z	177	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T08:41:19Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_06 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. --> # BERTweet_retrain_2021_06 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5775 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.6492 \| 1.0 \| 5855 \| 2.6543 \| \| 2.6772 \| 2.0 \| 11710 \| 2.6140 \| \| 2.7103 \| 3.0 \| 17565 \| 2.5723 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
touhidulislam/BERTweet_retrain_2022_06	touhidulislam	2024-11-23T08:41:01Z	178	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T08:40:40Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_06 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. --> # BERTweet_retrain_2022_06 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5204 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.6983 \| 1.0 \| 6012 \| 2.5991 \| \| 2.4763 \| 2.0 \| 12024 \| 2.5499 \| \| 2.4619 \| 3.0 \| 18036 \| 2.5269 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
rinabuoy/nllb-200-600M-En-Ar-finetuned2	rinabuoy	2024-11-23T08:39:01Z	159	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-23T08:37:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Mantis-VL/mantis-8b-idefics2-video-eval_5184_regression	Mantis-VL	2024-11-23T08:36:39Z	7	0	transformers	[ "transformers", "safetensors", "idefics2", "text-classification", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "base_model:finetune:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-11-21T16:15:12Z	--- library_name: transformers license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval_5184_regression 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. --> # mantis-8b-idefics2-video-eval_5184_regression This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.45.0 - Pytorch 2.5.1+cu124 - Datasets 2.18.0 - Tokenizers 0.20.3
touhidulislam/BERTweet_retrain_2021_50	touhidulislam	2024-11-23T08:36:07Z	166	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T08:35:45Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_50 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. --> # BERTweet_retrain_2021_50 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4821 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7593 \| 1.0 \| 6047 \| 2.5543 \| \| 2.6949 \| 2.0 \| 12094 \| 2.5029 \| \| 2.6966 \| 3.0 \| 18141 \| 2.4801 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
susmitabhatt/whisper-a-nomimo-ls	susmitabhatt	2024-11-23T08:32:23Z	13	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-23T05:57:47Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-a-nomimo-ls results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-a-nomimo-ls This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0311 - Wer: 32.3302 ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 132 - num_epochs: 11 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\| \| 0.9712 \| 1.0 \| 104 \| 0.0786 \| 25.0 \| \| 3.0599 \| 2.0 \| 208 \| 0.7840 \| 3312.3457 \| \| 0.3714 \| 3.0 \| 312 \| 0.2694 \| 98.3796 \| \| 0.2625 \| 4.0 \| 416 \| 0.2197 \| 90.9722 \| \| 0.2298 \| 5.0 \| 520 \| 0.2035 \| 84.9537 \| \| 0.1929 \| 6.0 \| 624 \| 0.1519 \| 66.0494 \| \| 0.1086 \| 7.0 \| 728 \| 0.0641 \| 41.6667 \| \| 0.0401 \| 8.0 \| 832 \| 0.0421 \| 41.6667 \| \| 0.0231 \| 9.0 \| 936 \| 0.0324 \| 34.8765 \| \| 0.0119 \| 10.0 \| 1040 \| 0.0310 \| 34.7222 \| \| 0.0057 \| 10.8986 \| 1133 \| 0.0311 \| 32.3302 \| ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0
gnmskel/ToBeOrNotToBE	gnmskel	2024-11-23T08:29:05Z	173	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:KETI-AIR/ke-t5-base-ko", "base_model:finetune:KETI-AIR/ke-t5-base-ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-22T17:33:04Z	--- library_name: transformers license: apache-2.0 base_model: KETI-AIR/ke-t5-base-ko tags: - generated_from_trainer model-index: - name: ToBeOrNotToBE 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. --> # ToBeOrNotToBE This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-ko) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 39.4877 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 265 \| 84.3180 \| \| 127.3935 \| 2.0 \| 530 \| 61.3429 \| \| 127.3935 \| 3.0 \| 795 \| 46.6663 \| \| 56.6333 \| 4.0 \| 1060 \| 41.3475 \| \| 56.6333 \| 5.0 \| 1325 \| 39.4877 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0 - Datasets 3.0.2 - Tokenizers 0.20.1
mav23/vicuna-13b-v1.3-GGUF	mav23	2024-11-23T08:28:15Z	19	1	null	[ "gguf", "arxiv:2302.13971", "arxiv:2306.05685", "region:us" ]	null	2024-11-23T07:01:55Z	--- inference: false --- NOTE: New version available Please check out a newer version of the weights [here](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md). <br> # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - Developed by: [LMSYS](https://lmsys.org/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/lm-sys/FastChat - Blog: https://lmsys.org/blog/2023-03-30-vicuna/ - Paper: https://arxiv.org/abs/2306.05685 - Demo: https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
atlimited/llm-jp-3-3_7b-aio-retriever	atlimited	2024-11-23T08:28:11Z	74	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T08:24:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
susmitabhatt/whisper-a-clp-ls	susmitabhatt	2024-11-23T08:25:13Z	10	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-23T07:26:11Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-a-clp-ls results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-a-clp-ls This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0240 - Wer: 10.0629 ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 132 - num_epochs: 11 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| No log \| 1.0 \| 40 \| 0.1713 \| 46.9602 \| \| No log \| 2.0 \| 80 \| 0.0920 \| 28.3019 \| \| 1.2158 \| 3.0 \| 120 \| 0.1828 \| 31.2369 \| \| 1.2158 \| 4.0 \| 160 \| 0.2743 \| 42.3480 \| \| 0.1604 \| 5.0 \| 200 \| 0.1326 \| 62.8931 \| \| 0.1604 \| 6.0 \| 240 \| 0.0734 \| 25.7862 \| \| 0.1604 \| 7.0 \| 280 \| 0.0510 \| 15.7233 \| \| 0.0502 \| 8.0 \| 320 \| 0.0262 \| 10.4822 \| \| 0.0502 \| 9.0 \| 360 \| 0.0320 \| 11.9497 \| \| 0.0202 \| 10.0 \| 400 \| 0.0229 \| 7.1279 \| \| 0.0202 \| 10.7342 \| 429 \| 0.0240 \| 10.0629 \| ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0
chatpdflocal/Qwen2.5.1-Coder-14B-Instruct-GGUF	chatpdflocal	2024-11-23T08:13:27Z	55	2	null	[ "gguf", "Qwen2.5.1", "Qwen2.5.1-Coder", "Qwen2.5.1-14B-Instruct", "GGUF", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-12T05:16:12Z	--- license: apache-2.0 tags: - Qwen2.5.1 - Qwen2.5.1-Coder - Qwen2.5.1-14B-Instruct - GGUF --- # This is Qwen2.5.1-Coder-14B-Instruct GGUF format model, which can be easily runned on PCs, mobile phones or devices with llama.cpp. # If you are a Mac user, the following free wonderful AI tools can help you to read and understand PDFs effectively: - If you are using Zotero for managing and reading your personal PDFs, [PapersGPT](https://www.papersgpt.com) is a free plugin which can assist you to chat PDFs effectively by your local SmolLM2-135M-Instruct. - you can directly download the beautiful ChatPDFLocal MacOS app from [here](https://www.chatpdflocal.com), load one or batch PDF files at will, and quickly experience the effect of the model through chat reading. PS. Click [here](https://awa-ai.lemonsqueezy.com/buy/89be07f8-060d-4a8f-a758-f25352773168) to subscribe or invite friends in ChatPDFLocal MacOS app can earn credits to use freely.
touhidulislam/BERTweet_retrain_2021_05	touhidulislam	2024-11-23T08:06:46Z	177	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-23T08:06:26Z	--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERTweet_retrain_2021_05 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5689 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8078 \| 1.0 \| 5959 \| 2.6497 \| \| 2.6178 \| 2.0 \| 11918 \| 2.6055 \| \| 2.6726 \| 3.0 \| 17877 \| 2.5775 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0
SAISON17/polyglot-ko-12.8b_qlora_v0.0	SAISON17	2024-11-23T07:45:26Z	74	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-11-23T07:36:20Z	--- 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]

touhidulislam/BERTweet_retrain_2021_15

touhidulislam

2024-11-23T14:06:47Z

178

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T14:06:26Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_15 results: [] ---  # BERTweet_retrain_2021_15 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5915 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7312 | 1.0 | 5863 | 2.6694 | | 2.7401 | 2.0 | 11726 | 2.6246 | | 2.7781 | 3.0 | 17589 | 2.5933 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

zelk12/MT3-Gen2-IMUBMA-gemma-2-9B

zelk12

2024-11-23T13:56:18Z

7

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-BMA-gemma-2-9B", "base_model:merge:zelk12/MT3-Gen2-BMA-gemma-2-9B", "base_model:zelk12/MT3-Gen2-IMU-gemma-2-9B", "base_model:merge:zelk12/MT3-Gen2-IMU-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T15:53:14Z

--- base_model: - zelk12/MT3-Gen2-BMA-gemma-2-9B - zelk12/MT3-Gen2-IMU-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-IMUBMA-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-BMA-gemma-2-9B](https://huggingface.co/zelk12/MT3-Gen2-BMA-gemma-2-9B) * [zelk12/MT3-Gen2-IMU-gemma-2-9B](https://huggingface.co/zelk12/MT3-Gen2-IMU-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-IMU-gemma-2-9B - model: zelk12/MT3-Gen2-BMA-gemma-2-9B merge_method: slerp base_model: zelk12/MT3-Gen2-IMU-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT3-Gen2-BMA-gemma-2-9B

zelk12

2024-11-23T13:55:52Z

5

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B", "base_model:merge:zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B", "base_model:zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B", "base_model:merge:zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T15:43:14Z

--- base_model: - zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B - zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-BMA-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B](https://huggingface.co/zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B) * [zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B](https://huggingface.co/zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B - model: zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B merge_method: slerp base_model: zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT3-Gen2-IMU-gemma-2-9B

zelk12

2024-11-23T13:55:28Z

6

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B", "base_model:merge:zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B", "base_model:zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B", "base_model:merge:zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T14:59:12Z

--- base_model: - zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B - zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-IMU-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B](https://huggingface.co/zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B) * [zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B](https://huggingface.co/zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B - model: zelk12/MT3-Gen2-MU-gemma-2-GQv1-9B merge_method: slerp base_model: zelk12/MT3-Gen2-IF-gemma-2-RAv0.1Av4aA-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT3-Gen2-GMM-gemma-2-9B

zelk12

2024-11-23T13:54:59Z

5

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B", "base_model:merge:zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B", "base_model:zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B", "base_model:merge:zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T14:18:42Z

--- base_model: - zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B - zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-GMM-gemma-2-9B-GGUF # 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: * [zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B](https://huggingface.co/zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B) * [zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B](https://huggingface.co/zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B - model: zelk12/MT3-Gen2-MM-gemma-2-RAv0.1Av4aA-9B merge_method: slerp base_model: zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B dtype: bfloat16 parameters: t: 0.25 ```

mav23/vicuna-33b-v1.3-GGUF

mav23

2024-11-23T13:54:33Z

93

1

null

[ "gguf", "arxiv:2302.13971", "arxiv:2306.05685", "region:us" ]

null

2024-11-23T09:36:47Z

--- inference: false --- # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture. - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

zelk12/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B

zelk12

2024-11-23T13:49:35Z

7

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T13:22:58Z

--- base_model: - zelk12/MT-Merge-gemma-2-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-GP-gemma-2-RAv0.1MTM-9B-GGUF # 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: * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 - model: zelk12/MT-Merge-gemma-2-9B merge_method: slerp base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT3-Gen2-MA-gemma-2-MTMQv1-9B

zelk12

2024-11-23T13:48:59Z

5

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:sam-paech/Quill-v1", "base_model:merge:sam-paech/Quill-v1", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T13:02:31Z

--- base_model: - sam-paech/Quill-v1 - zelk12/MT-Merge-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-MA-gemma-2-MTMQv1-9B-GGUF # 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: * [sam-paech/Quill-v1](https://huggingface.co/sam-paech/Quill-v1) * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Merge-gemma-2-9B - model: sam-paech/Quill-v1 merge_method: slerp base_model: zelk12/MT-Merge-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B

zelk12

2024-11-23T13:48:32Z

6

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-20T12:51:21Z

--- base_model: - zelk12/MT-Merge-gemma-2-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT3-Gen2-BB-gemma-2-MTMRAv0.1-9B-GGUF # 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: * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Merge-gemma-2-9B - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1 merge_method: slerp base_model: zelk12/MT-Merge-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT2-Gen2-BG-gemma-2-9B

zelk12

2024-11-23T13:45:00Z

6

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B", "base_model:merge:zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B", "base_model:zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B", "base_model:merge:zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-12T16:18:04Z

--- base_model: - zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B - zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/models?other=base_model:quantized:zelk12/MT2-Gen2-BG-gemma-2-9B # 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: * [zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B](https://huggingface.co/zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B) * [zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B](https://huggingface.co/zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B - model: zelk12/MT2-Gen2-GP-gemma-2-RIv0.1MTM-9B merge_method: slerp base_model: zelk12/MT2-Gen2-BB-gemma-2-MTMMT5-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT2-Gen2-IMM-gemma-2-9B

zelk12

2024-11-23T13:44:19Z

5

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B", "base_model:merge:zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B", "base_model:zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B", "base_model:merge:zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-12T15:00:48Z

--- base_model: - zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B - zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT2-Gen2-IMM-gemma-2-9B-GGUF # 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: * [zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B](https://huggingface.co/zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B) * [zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B](https://huggingface.co/zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B - model: zelk12/MT2-Gen2-MM-gemma-2-Rv0.4RAv0.1t0.25-9B merge_method: slerp base_model: zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B dtype: bfloat16 parameters: t: 0.25 ```

mcguiver/xlm-roberta-base-finetuned-panx-de

mcguiver

2024-11-23T13:43:12Z

114

0

transformers

[ "transformers", "tensorboard", "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

2024-11-16T05:46:11Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] ---  # xlm-roberta-base-finetuned-panx-de 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: 0.1376 - F1: 0.8644 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2579 | 1.0 | 525 | 0.1546 | 0.8179 | | 0.1283 | 2.0 | 1050 | 0.1378 | 0.8518 | | 0.0805 | 3.0 | 1575 | 0.1376 | 0.8644 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cpu - Datasets 3.1.0 - Tokenizers 0.20.3

zelk12/MT2-Gen2-IF-gemma-2-MT5MTM-9B

zelk12

2024-11-23T13:42:29Z

5

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Merge-gemma-2-9B", "base_model:merge:zelk12/MT-Merge-gemma-2-9B", "base_model:zelk12/MT5-gemma-2-9B", "base_model:merge:zelk12/MT5-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-12T13:44:20Z

--- base_model: - zelk12/MT5-gemma-2-9B - zelk12/MT-Merge-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT2-Gen2-IF-gemma-2-MT5MTM-9B-GGUF # 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: * [zelk12/MT5-gemma-2-9B](https://huggingface.co/zelk12/MT5-gemma-2-9B) * [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT5-gemma-2-9B - model: zelk12/MT-Merge-gemma-2-9B merge_method: slerp base_model: zelk12/MT5-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT1-Gen2-MMMU-gemma-2-9B

zelk12

2024-11-23T13:39:39Z

5

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B", "base_model:merge:zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B", "base_model:zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B", "base_model:merge:zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-11T16:59:54Z

--- base_model: - zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B - zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT1-Gen2-MMMU-gemma-2-9B-GGUF # 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: * [zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B](https://huggingface.co/zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B) * [zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B](https://huggingface.co/zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B - model: zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B merge_method: slerp base_model: zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B dtype: bfloat16 parameters: t: 0.25 ```

Nishitbaria/LoRa-Flux-Anime-Style

Nishitbaria

2024-11-23T13:39:08Z

932

4

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2024-11-23T12:32:34Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- A ANMCH A girl at the edge of a lush, mysterious forest. Her long, flowing hair dances in the gentle breeze, framing a face serene yet filled with longing. The soft, dappled sunlight highlights the delicate features of her countenance, accentuating the depth of emotion in her eyes. This exquisite portrait captures the essence of youth and contemplation, inviting viewers to be transported into a world of beauty and introspection output: url: images/9705468a-0ac1-46a0-97a5-e51c43da41cc.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: ANMCH --- # Anime-Style <Gallery /> ## Model description --- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ANMCH --- # Animechar <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ANMCH` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('FounderFeed/AnimeChar', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words You should use `ANMCH` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Nishitbaria/LoRa-Flux-Anime-Style/tree/main) them in the Files & versions tab.

isspek/xlnet-base-cased_ebola_llama_5_2e-5_16_undersampling_0.6

isspek

2024-11-23T13:38:46Z

117

0

transformers

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

text-classification

2024-11-23T13:38:32Z

--- 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]

isspek/xlnet-base-cased_ebola_llama_3_2e-5_16_undersampling_0.6

isspek

2024-11-23T13:37:44Z

117

0

transformers

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

text-classification

2024-11-23T13:37:31Z

--- 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]

zelk12/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B

zelk12

2024-11-23T13:37:31Z

9

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:sam-paech/Delirium-v1", "base_model:merge:sam-paech/Delirium-v1", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-11T16:35:18Z

--- base_model: - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - sam-paech/Delirium-v1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT1-Gen2-MM-gemma-2-RAv0.1t0.25Dv1-9B-GGUF # 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: * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25) * [sam-paech/Delirium-v1](https://huggingface.co/sam-paech/Delirium-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - model: sam-paech/Delirium-v1 merge_method: slerp base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B

zelk12

2024-11-23T13:36:51Z

6

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:sam-paech/Darkest-muse-v1", "base_model:merge:sam-paech/Darkest-muse-v1", "base_model:sam-paech/Quill-v1", "base_model:merge:sam-paech/Quill-v1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-11T16:27:24Z

--- base_model: - sam-paech/Darkest-muse-v1 - sam-paech/Quill-v1 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT1-Gen2-MU-gemma-2-Qv1DMv1-9B-GGUF # 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: * [sam-paech/Darkest-muse-v1](https://huggingface.co/sam-paech/Darkest-muse-v1) * [sam-paech/Quill-v1](https://huggingface.co/sam-paech/Quill-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: sam-paech/Quill-v1 - model: sam-paech/Darkest-muse-v1 merge_method: slerp base_model: sam-paech/Quill-v1 dtype: bfloat16 parameters: t: 0.25 ```

kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF

kabachuha

2024-11-23T13:34:51Z

10

1

transformers

[ "transformers", "gguf", "conversational", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:radm/gemma-2-2b-it-abl-rudpo", "base_model:quantized:radm/gemma-2-2b-it-abl-rudpo", "license:gemma", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T13:34:35Z

--- license: gemma library_name: transformers pipeline_tag: text-generation tags: - conversational - llama-cpp - gguf-my-repo base_model: radm/gemma-2-2b-it-abl-rudpo --- # kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF This model was converted to GGUF format from [`radm/gemma-2-2b-it-abl-rudpo`](https://huggingface.co/radm/gemma-2-2b-it-abl-rudpo) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/radm/gemma-2-2b-it-abl-rudpo) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo kabachuha/gemma-2-2b-it-abl-rudpo-Q4_K_M-GGUF --hf-file gemma-2-2b-it-abl-rudpo-q4_k_m.gguf -c 2048 ```

touhidulislam/BERTweet_retrain_2022_14

touhidulislam

2024-11-23T13:33:49Z

173

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T13:33:20Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_14 results: [] ---  # BERTweet_retrain_2022_14 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5422 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7233 | 1.0 | 6150 | 2.6205 | | 2.6665 | 2.0 | 12300 | 2.5542 | | 2.6632 | 3.0 | 18450 | 2.5398 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

isspek/xlnet-base-cased_ebola_llama_2_2e-5_16_undersampling_0.6

isspek

2024-11-23T13:32:59Z

117

0

transformers

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

text-classification

2024-11-23T13:32:45Z

--- 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]

isspek/xlnet-base-cased_ebola_llama_4_2e-5_16_undersampling_0.6

isspek

2024-11-23T13:32:08Z

117

0

transformers

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

text-classification

2024-11-23T13:31:54Z

--- 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]

zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B

zelk12

2024-11-23T13:31:13Z

6

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT4-Gen1-gemma-2-9B", "base_model:merge:zelk12/MT4-Gen1-gemma-2-9B", "base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-10T19:54:58Z

--- base_model: - zelk12/MT4-Gen1-gemma-2-9B - zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B-GGUF # 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: * [zelk12/MT4-Gen1-gemma-2-9B](https://huggingface.co/zelk12/MT4-Gen1-gemma-2-9B) * [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 - model: zelk12/MT4-Gen1-gemma-2-9B merge_method: slerp base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25 dtype: bfloat16 parameters: t: 0.25 ```

touhidulislam/BERTweet_retrain_2021_14

touhidulislam

2024-11-23T13:31:12Z

178

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T13:30:51Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_14 results: [] ---  # BERTweet_retrain_2021_14 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5822 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7667 | 1.0 | 5953 | 2.6613 | | 2.7117 | 2.0 | 11906 | 2.6057 | | 2.6391 | 3.0 | 17859 | 2.5902 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

zelk12/MT-Gen2-MMMA-gemma-2-9B

zelk12

2024-11-23T13:28:23Z

6

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B", "base_model:merge:zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B", "base_model:zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B", "base_model:merge:zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-10T20:30:22Z

--- base_model: - zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B - zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT-Gen2-MMMA-gemma-2-9B-GGUF # 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: * [zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B](https://huggingface.co/zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B) * [zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B](https://huggingface.co/zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B - model: zelk12/MT-Gen2-MA-gemma-2-MT4RAv0.1t0.25-9B merge_method: slerp base_model: zelk12/MT-Gen2-MM-gemma-2-RAv0.1t0.25MT4G1-9B dtype: bfloat16 parameters: t: 0.25 ```

zelk12/MT-Gen2-GIMMMA-gemma-2-9B

zelk12

2024-11-23T13:26:55Z

7

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT-Gen2-GI-gemma-2-9B", "base_model:merge:zelk12/MT-Gen2-GI-gemma-2-9B", "base_model:zelk12/MT-Gen2-MMMA-gemma-2-9B", "base_model:merge:zelk12/MT-Gen2-MMMA-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-10T20:38:17Z

--- base_model: - zelk12/MT-Gen2-MMMA-gemma-2-9B - zelk12/MT-Gen2-GI-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # Quants Provided by @mradermacher GGUF Static: https://huggingface.co/mradermacher/MT-Gen2-GIMMMA-gemma-2-9B-GGUF # 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: * [zelk12/MT-Gen2-MMMA-gemma-2-9B](https://huggingface.co/zelk12/MT-Gen2-MMMA-gemma-2-9B) * [zelk12/MT-Gen2-GI-gemma-2-9B](https://huggingface.co/zelk12/MT-Gen2-GI-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Gen2-GI-gemma-2-9B - model: zelk12/MT-Gen2-MMMA-gemma-2-9B merge_method: slerp base_model: zelk12/MT-Gen2-GI-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```

axel-darmouni/paligemma_dataset2

axel-darmouni

2024-11-23T13:15:27Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "paligemma", "image-text-to-text", "generated_from_trainer", "base_model:google/paligemma-3b-pt-448", "base_model:finetune:google/paligemma-3b-pt-448", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-11-23T11:59:24Z

--- library_name: transformers license: gemma base_model: google/paligemma-3b-pt-448 tags: - generated_from_trainer model-index: - name: paligemma_dataset2 results: [] ---  # paligemma_dataset2 This model is a fine-tuned version of [google/paligemma-3b-pt-448](https://huggingface.co/google/paligemma-3b-pt-448) 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu118 - Datasets 3.1.0 - Tokenizers 0.20.3

priyankrathore/Flan_T5_chunk

priyankrathore

2024-11-23T13:07:01Z

105

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-23T13:06:22Z

--- 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]

touhidulislam/BERTweet_retrain_2022_43

touhidulislam

2024-11-23T12:56:38Z

172

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T12:56:14Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_43 results: [] ---  # BERTweet_retrain_2022_43 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4504 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8207 | 1.0 | 6104 | 2.5413 | | 2.4681 | 2.0 | 12208 | 2.4710 | | 2.7499 | 3.0 | 18312 | 2.4517 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2022_13

touhidulislam

2024-11-23T12:56:12Z

178

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T12:55:49Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_13 results: [] ---  # BERTweet_retrain_2022_13 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5442 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.9022 | 1.0 | 6108 | 2.6424 | | 2.5631 | 2.0 | 12216 | 2.5838 | | 2.6221 | 3.0 | 18324 | 2.5451 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2021_13

touhidulislam

2024-11-23T12:55:12Z

172

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T12:54:51Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_13 results: [] ---  # BERTweet_retrain_2021_13 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5722 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8384 | 1.0 | 6041 | 2.6546 | | 2.6996 | 2.0 | 12082 | 2.5974 | | 2.5276 | 3.0 | 18123 | 2.5779 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

tintnguyen/bert-base-vi-uncased-st

tintnguyen

2024-11-23T12:54:37Z

7

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:631587", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:tintnguyen/bert-base-vi-uncased", "base_model:finetune:tintnguyen/bert-base-vi-uncased", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-11-23T12:54:03Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:631587 - loss:MultipleNegativesRankingLoss base_model: tintnguyen/bert-base-vi-uncased widget: - source_sentence: thành phố bất động sản đắt nhất sentences: - 'Đồng hồ: 18.066 đô la. Paris, kinh đô thời trang của thế giới và thủ đô thực tế của đất nước Pháp hiện được công nhận là một trong những thành phố đắt đỏ nhất để mua bất động sản, khi thị trường bất động sản tiếp tục phát triển mạnh với nền kinh tế Pháp ổn định.' - Hầu hết các cô gái và chàng trai đeo chiếc nhẫn thuần khiết của họ trên ngón áp út của họ. Một số cô gái thích đeo nó trên ngón áp út của bàn tay trái, và thay thế nó bằng chiếc nhẫn cưới của họ. Nhẫn tinh khiết không dành riêng cho ngón tay đeo nhẫn; bạn có thể mặc nó bất cứ nơi nào thoải mái nhất cho bạn. Một số thậm chí đeo nhẫn của họ trên một chuỗi như một chiếc vòng cổ. Một. - 'Michaela Kennedy Cuomo, con gái út của Andrew Cuomo, phải nhập viện một thời gian ngắn sau khi bất tỉnh: báo cáo. Theo các báo cáo, Michaela Kennedy Cuomo (bên phải, trong ảnh với cha Thống đốc Cuomo), 17 tuổi, đã phải nhập viện một thời gian ngắn sau khi được tìm thấy trong tình trạng bất tỉnh trong ngôi nhà ở Westchester mà cô ở cùng mẹ, Kerry Kennedy, theo báo cáo. (Hình ảnh Spencer Platt / Getty)' - source_sentence: núi đọ ở tỉnh nào sentences: - Việc phát hiện một số di tích mà tiêu biểu là Núi Đọ vào cuối năm 1960, đã xuất lộ những công cụ đá thô sơ đầu tiên của con người. Các nhà Khảo cổ học Việt Nam cùng với Giáo sư Boriskovski đã nghiên cứu và chứng minh rằng ở Núi Đọ từng tồn tại một nền văn hóa sơ kỳ thời đại đá cũ. Di tích Núi Đọ thuộc địa phận hai xã Thiệu Tân và Thiệu Khánh, huyện Đông Sơn, Thanh Hóa. Núi Đọ là một quả núi thấp, sườn núi dốc thoai thoải từ 20 độ đến 25 độ, cao 158 m so với mặt nước biển, nằm ngay bên bờ hữu ngạn sông Chu; chỗ hợp lưu của hai dòng sông Mã và sông Chu cách Thành phố Thanh Hóa 7 km về phía bắc - tây bắc. - 'Virus Zika có thể lây truyền qua muỗi hoặc qua đường tình dục. Hầu hết mọi người có thể phục hồi hoàn toàn và các triệu chứng virus zika tự hết trong khoảng một tuần. Đa số các trường hợp không có triệu chứng nhiễm virus Zika. Tuy nhiên, nếu có thì các biểu hiện virus zika thông thường gồm: Sốt, phát ban, đau khớp, đau đầu, kết mạc mắt đỏ, đau cơ, cảm giác đau ở lưng. Bệnh virus Zika đặc biệt nguy hiểm nếu đi từ mẹ sang con. Bà bầu bị nhiễm virus Zika khi mang thai có thể khiến thai nhi mắc chứng đầu nhỏ. Khi lớn hơn, bé có thể bị suy giảm thị giác, thính giác, tăng trưởng kém và thậm chí co giật.' - Ý nghĩa hoa Alstroemeria Astroemeria cứng cáp là một bông hoa tuyệt đẹp kết hợp nhiều màu sắc khác nhau thành một vẻ đẹp gắn kết. Loài hoa tươi sáng này tượng trưng cho tình bạn, vẻ đẹp bền lâu của sự cam kết và chăm sóc. - source_sentence: giáo của người đông sơn có hình gì sentences: - Chủ nhân của Văn hóa Đông Sơn đã chế tạo nhiều loại vũ khí từ đồng dùng để đánh xa, gồm có lao, đầu mũi tên. Để bắn tên tất phải có cung, nỏ bằng gỗ hoặc tre. Việc phát hiện bộ lẫy nỏ có hộp, có rãnh đặt mũi tên, có nấc để giữ dây nỏ, có lẫy cong đùng để bóp cò, không còn nguyên vẹn ở làng Vạc, cho thấy việc dùng cung nỏ của người Đông Sơn rất lợi hại khi săn bắn, chiến tranh là điều có thể tin được. Giáo hình búp da, hình lá mía. Lao cũng giống như giáo nhưng kích cỡ nhỏ hơn. Vũ khí đánh gần có dao găm. Dao găm có nhiều kiểu phân biệt dựa vào phần cán và đốc chắn. Nhiều chiếc dao găm được đúc rất công phu. Chuôi dao đúc hình tượng người nam hoặc nữ, y phục hoa văn trang sức đẹp đẽ, sống động. Phần cán dao găm có những chiếc được chạm trổ rất độc đáo với hình tượng động vật như rắn ngậm chân hổ, hổ ngậm chân voi, hay rắn ngậm chân voi... - 'Xét nghiệm máu FSH kiểm tra mức độ hormone kích thích nang trứng trong máu. Mức độ FHS có thể xác định xem các cơ quan sinh dục ở cả nam và nữ có hoạt động bình thường hay không. Mức độ FHS cũng có thể được kiểm tra để phát hiện các vấn đề với tuyến yên. Số lượng: * Chỉ số nguyên. Hormone kích thích nang trứng, hoặc FSH, được sản xuất bởi tuyến yên để kích thích sản xuất và phát triển trứng ở nữ và tinh trùng ở nam. FSH cũng kích thích sản xuất các hormone khác, bao gồm testosterone và estrogen.' - White Lightning là một tàu lượn siêu tốc bằng gỗ tại Fun Spot America ở Orlando, Florida. Chuyến xe được thiết kế riêng do Great Coasters International sản xuất. Chuyến đi là tàu lượn bằng gỗ đầu tiên của Orlando. - source_sentence: bước đầu tiên trong quá trình tổng hợp protein là gì sentences: - 'Sáu bước đơn giản để trồng hoa nhài: 1 Hoa nhài phát triển mạnh trong môi trường ban ngày nóng ẩm. 2 Chọn nơi trồng hoa nhài của bạn. 3 Chuẩn bị đất để trồng bằng cách làm việc với một lượng lớn vật liệu hữu cơ, chẳng hạn như rêu than bùn hoặc phân trộn. Bón phân đa dụng mỗi tháng một lần từ tháng 3 đến tháng 11.' - 'PHẦN A. Đọc phần sau và ghi chú vào giấy của bạn: Tổng hợp protein là quá trình cơ thể sử dụng để tạo ra protein. Bước đầu tiên của quá trình tổng hợp protein được gọi là Phiên mã. Nó xảy ra trong nhân. Trong quá trình phiên mã, mRNA phiên mã (bản sao) DNA. DNA là ÃƒÂ ¢ Ã‚â‚¬Ã‚Å “được giải nénÃƒÂ ¢ Ã‚â‚¬Ã‚ và sợi mRNA sao chép một sợi DNA. Một khi nó làm được điều này, mRNA sẽ rời khỏi nhân và đi vào tế bào chất. mRNA sau đó sẽ tự gắn vào ribosome.' - LIÊN KẾT / TRANG WEB THÊM VÀO DANH SÁCH CÔNG VIỆC. danh từ. Skinny jeans là loại quần denim bó sát với da thường được làm bằng vải co giãn. Một ví dụ về quần jean bó là những chiếc quần jean bó sát từ eo đến mắt cá chân. - source_sentence: phương pháp lóc ối bác sĩ sẽ làm gì sentences: - Gương mặt thân quen mùa 1 được phát sóng trên kênh VTV3 từ ngày 5 tháng 1 năm 2013 đến 23 tháng 3 năm 2013 với các thí sinh gồm Khởi My, Đại Nghĩa, Thúy Uyên, Kyo York, Phương Thanh và Chí Thiện. Bộ ba giám khảo chính là nhạc sĩ Đức Huy, ca sĩ Mỹ Linh và NSƯT Hoài Linh. Người dẫn chương trình mùa này là nghệ sĩ Thanh Bạch. Sau 10 tuần thi, kết quả chung cuộc giải nhất thuộc về thí sinh Khởi My. - 'Khi thai phụ đã quá ngày dự sinh, các phương pháp sau đây có thể được bác sĩ cân nhắc lựa chọn để gây khởi phát chuyển dạ: Lóc ối: Với biện pháp này, bác sĩ sẽ đeo găng và dùng ngón tay để tách màng ối ra khỏi thành tử cung. Phá vỡ túi nước ối: Bác sĩ sẽ tạo một lỗ nhỏ trên túi nước ối để làm vỡ ối, qua đó kích thích sự chuyển dạ. Oxytocin: Là một loại thuốc giúp tạo ra các cơn co thắt chuyển dạ, được tiêm theo đường tĩnh mạch vào cánh tay của thai phụ. Liều lượng có thể được tăng dần theo thời gian nhưng phải theo dõi cẩn thận. Các chất tương tự Prostaglandin: Đây là những loại thuốc được đặt trong âm đạo để làm chín muồi cổ tử cung. Làm giãn nở cổ tử cung: Bác sĩ có thể đặt ống thông có gắn một quả bong bóng rất nhỏ vào cuối cổ tử cung của thai phụ. Sau đó, nước sẽ được bơm vào quả bóng. Khi bóng đã được bơm căng, nó sẽ gây ra tác động áp lực, giúp cổ tử cung mở ra và quá trình chuyển dạ sẽ bắt đầu.' - 'Nghĩa quân Tây Sơn ở vào thế bất lợi: phía bắc có quân Trịnh, phía nam còn quân Nguyễn. Nguyễn Nhạc phải tạm hoà hoãn với quân Trịnh để dồn sức đánh Nguyễn. Từ năm 1776 đến năm 1783, nghĩa quân Tây Sơn đã bốn lần đánh vào Gia Định.Trong lần tiến quân năm 1777, Tây Sơn bắt giết được chúa Nguyễn, chỉ còn Nguyễn Ánh chạy thoát. Chính quyền họ Nguyễn ở Đàng Trong đến đây bị lật đổ.' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on tintnguyen/bert-base-vi-uncased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tintnguyen/bert-base-vi-uncased](https://huggingface.co/tintnguyen/bert-base-vi-uncased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [tintnguyen/bert-base-vi-uncased](https://huggingface.co/tintnguyen/bert-base-vi-uncased)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tintnguyen/bert-base-vi-uncased-st") # Run inference sentences = [ 'phương pháp lóc ối bác sĩ sẽ làm gì', 'Khi thai phụ đã quá ngày dự sinh, các phương pháp sau đây có thể được bác sĩ cân nhắc lựa chọn để gây khởi phát chuyển dạ:\nLóc ối: Với biện pháp này, bác sĩ sẽ đeo găng và dùng ngón tay để tách màng ối ra khỏi thành tử cung.\nPhá vỡ túi nước ối: Bác sĩ sẽ tạo một lỗ nhỏ trên túi nước ối để làm vỡ ối, qua đó kích thích sự chuyển dạ.\nOxytocin: Là một loại thuốc giúp tạo ra các cơn co thắt chuyển dạ, được tiêm theo đường tĩnh mạch vào cánh tay của thai phụ. Liều lượng có thể được tăng dần theo thời gian nhưng phải theo dõi cẩn thận.\nCác chất tương tự Prostaglandin: Đây là những loại thuốc được đặt trong âm đạo để làm chín muồi cổ tử cung.\nLàm giãn nở cổ tử cung: Bác sĩ có thể đặt ống thông có gắn một quả bong bóng rất nhỏ vào cuối cổ tử cung của thai phụ. Sau đó, nước sẽ được bơm vào quả bóng. Khi bóng đã được bơm căng, nó sẽ gây ra tác động áp lực, giúp cổ tử cung mở ra và quá trình chuyển dạ sẽ bắt đầu.', 'Nghĩa quân Tây Sơn ở vào thế bất lợi: phía bắc có quân Trịnh, phía nam còn quân Nguyễn. Nguyễn Nhạc phải tạm hoà hoãn với quân Trịnh để dồn sức đánh Nguyễn. Từ năm 1776 đến năm 1783, nghĩa quân Tây Sơn đã bốn lần đánh vào Gia Định.Trong lần tiến quân năm 1777, Tây Sơn bắt giết được chúa Nguyễn, chỉ còn Nguyễn Ánh chạy thoát. Chính quyền họ Nguyễn ở Đàng Trong đến đây bị lật đổ.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 631,587 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 10.92 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 106.79 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | anchor | positive | |:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>bạn có thể lấy hộ chiếu ở dmv không</code> | <code>Nộp đơn xin Hộ chiếu Hoa Kỳ tại Văn phòng DMV. Xuất bản 27/09/2001 01:53 PM | Cập nhật 24/08/2011 11:05 AM. Bạn có thể nộp đơn xin Hộ chiếu Hoa Kỳ tại một số văn phòng xe cơ giới do các nhân viên quận điều hành. Bạn có thể nộp đơn xin Hộ chiếu Hoa Kỳ tại một số Bưu điện Hoa Kỳ. NYSDMV không cấp hộ chiếu và không thể cung cấp cho bạn thông tin về cách xin hộ chiếu. Liên hệ với Bưu điện Hoa Kỳ hoặc văn phòng thư ký quận được liệt kê trong các trang màu xanh trong danh bạ điện thoại của bạn. NYSDMV không cấp hộ chiếu và không thể cung cấp cho bạn thông tin về cách nộp đơn xin hộ chiếu. Liên hệ với Bưu điện Hoa Kỳ hoặc văn phòng lục sự quận được liệt kê trong các trang màu xanh lam của danh bạ điện thoại của bạn.</code> | | <code>tổng số người mỹ thiệt mạng trong tất cả các cuộc chiến tranh</code> | <code>1 Con số chính thức của người Mỹ thiệt mạng trong Chiến tranh Cách mạng (4.435) chỉ bằng khoảng 2/3 con số mà các nhà sử học đưa ra. Tổng số người Mỹ thiệt mạng trong Nội chiến được đưa ra là 140.414 - cao hơn khoảng 30.000 so với hầu hết các nhà sử học ước tính. Tôi nghi ngờ (nhưng không biết chắc chắn) rằng sự gia tăng có thể đến từ việc DoD tính số người chết trong tù binh là chiến đấu hơn là bệnh tật.</code> | | <code>lý thuyết vụ nổ lớn được quay ở đâu</code> | <code>Thuyết Vụ nổ lớn được ghi hình tại Warner Bros. Studios ở Burbank, California. Bạn phải từ mười tám tuổi trở lên để tham gia buổi ghi hình Thuyết Vụ nổ lớn. Số lượng vé có hạn và nhu cầu rất cao. Vé được bán miễn phí trên TVTickets.com. Phần thứ 10 của Big Bang Theory hiện đang được sản xuất.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 300 evaluation samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 300 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 11.02 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 108.76 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | anchor | positive | |:--------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>lúa mạch đen được dùng làm gì và ăn ở pháp</code> | <code>Tiêu dùng và sử dụng của con người. Như đã nói trước đó, hầu hết lúa mạch đen được trồng ở châu Âu là để làm bánh mì. Canada có một số lượng hạn chế hạt lúa mạch đen được sử dụng để chưng cất và sử dụng thực phẩm, và ở Mỹ khoảng một nửa lúa mạch đen làm ngũ cốc được sử dụng cho những mục đích này (7, 8). Hạt lúa mạch đen được sử dụng để làm bột mì, thức ăn gia súc, hoặc bia. Nó cũng có thể được ăn toàn bộ, dưới dạng quả mọng lúa mạch đen luộc, hoặc bằng cách cuộn lại, tương tự như yến mạch cán (10).</code> | | <code>kỳ hạm của hải quân chúng tôi là gì</code> | <code>USS Hiến pháp là kỳ hạm truyền thống của Hải quân Hoa Kỳ. Và nó vẫn đang được thực hiện và được điều khiển bởi một thủy thủ đoàn Hải quân Hoa Kỳ. USS Constellation CVN-64 được Tổng thống Ronald Reagan đặt cho biệt danh là Chiến hạm của Hoa Kỳ. Nó hiện đã ngừng hoạt động và được thay thế trong hạm đội bằng tàu sân bay USS Ronald Reagan CVN-76.</code> | | <code>cửa sổ kính lớn nhất</code> | <code>Cửa sổ kính màu lớn nhất thế giới. Cửa sổ kính màu lớn nhất thế giới thực sự nằm trong lăng mộ tại Nghĩa trang Phục sinh ở Công lý. Nó chứa 2.448 tấm và rộng 22.381 feet vuông. Cũng cần lưu ý thêm, nó chỉ cách nhà máy cải tạo nước Stickney, cơ sở xử lý nước thải lớn nhất (vào tháng 7 và cả tháng 8, là cơ sở xử lý nước thải bốc mùi nhất trên thế giới, vài dặm ngắn) chỉ vài dặm ngắn.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0025 | 100 | 1.6877 | - | | 0.0051 | 200 | 1.259 | - | | 0.0076 | 300 | 0.6289 | - | | 0.0101 | 400 | 0.3623 | - | | 0.0127 | 500 | 0.3353 | - | | 0.0152 | 600 | 0.2671 | - | | 0.0177 | 700 | 0.1881 | - | | 0.0203 | 800 | 0.179 | - | | 0.0228 | 900 | 0.1611 | - | | 0.0253 | 1000 | 0.1587 | 0.1468 | | 0.0279 | 1100 | 0.141 | - | | 0.0304 | 1200 | 0.13 | - | | 0.0329 | 1300 | 0.1257 | - | | 0.0355 | 1400 | 0.1153 | - | | 0.0380 | 1500 | 0.1341 | - | | 0.0405 | 1600 | 0.1227 | - | | 0.0431 | 1700 | 0.1024 | - | | 0.0456 | 1800 | 0.0818 | - | | 0.0481 | 1900 | 0.1069 | - | | 0.0507 | 2000 | 0.0831 | 0.0978 | | 0.0532 | 2100 | 0.1035 | - | | 0.0557 | 2200 | 0.0949 | - | | 0.0583 | 2300 | 0.1037 | - | | 0.0608 | 2400 | 0.0894 | - | | 0.0633 | 2500 | 0.0831 | - | | 0.0659 | 2600 | 0.1085 | - | | 0.0684 | 2700 | 0.0815 | - | | 0.0709 | 2800 | 0.071 | - | | 0.0735 | 2900 | 0.0889 | - | | 0.0760 | 3000 | 0.0832 | 0.0704 | | 0.0785 | 3100 | 0.0992 | - | | 0.0811 | 3200 | 0.0733 | - | | 0.0836 | 3300 | 0.0878 | - | | 0.0861 | 3400 | 0.0757 | - | | 0.0887 | 3500 | 0.0476 | - | | 0.0912 | 3600 | 0.0741 | - | | 0.0937 | 3700 | 0.0766 | - | | 0.0963 | 3800 | 0.0736 | - | | 0.0988 | 3900 | 0.0673 | - | | 0.1013 | 4000 | 0.0718 | 0.0566 | | 0.1039 | 4100 | 0.0649 | - | | 0.1064 | 4200 | 0.0767 | - | | 0.1089 | 4300 | 0.073 | - | | 0.1115 | 4400 | 0.0745 | - | | 0.1140 | 4500 | 0.0692 | - | | 0.1165 | 4600 | 0.0652 | - | | 0.1191 | 4700 | 0.077 | - | | 0.1216 | 4800 | 0.0749 | - | | 0.1241 | 4900 | 0.0493 | - | | 0.1267 | 5000 | 0.0653 | 0.0533 | | 0.1292 | 5100 | 0.073 | - | | 0.1317 | 5200 | 0.0652 | - | | 0.1343 | 5300 | 0.0639 | - | | 0.1368 | 5400 | 0.0549 | - | | 0.1393 | 5500 | 0.0731 | - | | 0.1419 | 5600 | 0.0832 | - | | 0.1444 | 5700 | 0.0687 | - | | 0.1469 | 5800 | 0.0711 | - | | 0.1495 | 5900 | 0.0709 | - | | 0.1520 | 6000 | 0.0547 | 0.0626 | | 0.1545 | 6100 | 0.084 | - | | 0.1571 | 6200 | 0.0743 | - | | 0.1596 | 6300 | 0.0706 | - | | 0.1621 | 6400 | 0.0664 | - | | 0.1647 | 6500 | 0.0682 | - | | 0.1672 | 6600 | 0.0534 | - | | 0.1697 | 6700 | 0.0642 | - | | 0.1723 | 6800 | 0.0624 | - | | 0.1748 | 6900 | 0.0648 | - | | 0.1773 | 7000 | 0.0697 | 0.0509 | | 0.1799 | 7100 | 0.0784 | - | | 0.1824 | 7200 | 0.0871 | - | | 0.1849 | 7300 | 0.0711 | - | | 0.1875 | 7400 | 0.0718 | - | | 0.1900 | 7500 | 0.0543 | - | | 0.1925 | 7600 | 0.0676 | - | | 0.1951 | 7700 | 0.0724 | - | | 0.1976 | 7800 | 0.0579 | - | | 0.2001 | 7900 | 0.0781 | - | | 0.2027 | 8000 | 0.0909 | 0.0736 | | 0.2052 | 8100 | 0.0653 | - | | 0.2077 | 8200 | 0.0535 | - | | 0.2103 | 8300 | 0.0801 | - | | 0.2128 | 8400 | 0.0794 | - | | 0.2153 | 8500 | 0.0615 | - | | 0.2179 | 8600 | 0.0646 | - | | 0.2204 | 8700 | 0.0497 | - | | 0.2229 | 8800 | 0.06 | - | | 0.2255 | 8900 | 0.0495 | - | | 0.2280 | 9000 | 0.0685 | 0.0450 | | 0.2305 | 9100 | 0.0606 | - | | 0.2331 | 9200 | 0.0577 | - | | 0.2356 | 9300 | 0.0464 | - | | 0.2381 | 9400 | 0.0622 | - | | 0.2407 | 9500 | 0.0567 | - | | 0.2432 | 9600 | 0.0545 | - | | 0.2457 | 9700 | 0.0455 | - | | 0.2483 | 9800 | 0.0642 | - | | 0.2508 | 9900 | 0.0612 | - | | 0.2533 | 10000 | 0.0658 | 0.0310 | | 0.2559 | 10100 | 0.0618 | - | | 0.2584 | 10200 | 0.052 | - | | 0.2609 | 10300 | 0.0504 | - | | 0.2635 | 10400 | 0.0593 | - | | 0.2660 | 10500 | 0.0534 | - | | 0.2685 | 10600 | 0.0555 | - | | 0.2711 | 10700 | 0.0583 | - | | 0.2736 | 10800 | 0.0472 | - | | 0.2761 | 10900 | 0.0591 | - | | 0.2787 | 11000 | 0.039 | 0.0300 | | 0.2812 | 11100 | 0.0446 | - | | 0.2837 | 11200 | 0.0375 | - | | 0.2863 | 11300 | 0.0515 | - | | 0.2888 | 11400 | 0.0577 | - | | 0.2913 | 11500 | 0.046 | - | | 0.2939 | 11600 | 0.0518 | - | | 0.2964 | 11700 | 0.055 | - | | 0.2989 | 11800 | 0.0492 | - | | 0.3015 | 11900 | 0.0513 | - | | 0.3040 | 12000 | 0.0442 | 0.0278 | | 0.3065 | 12100 | 0.0675 | - | | 0.3091 | 12200 | 0.0526 | - | | 0.3116 | 12300 | 0.0688 | - | | 0.3141 | 12400 | 0.0589 | - | | 0.3167 | 12500 | 0.0602 | - | | 0.3192 | 12600 | 0.0551 | - | | 0.3217 | 12700 | 0.0681 | - | | 0.3243 | 12800 | 0.0522 | - | | 0.3268 | 12900 | 0.047 | - | | 0.3293 | 13000 | 0.0376 | 0.0282 | | 0.3319 | 13100 | 0.0396 | - | | 0.3344 | 13200 | 0.0467 | - | | 0.3369 | 13300 | 0.0498 | - | | 0.3395 | 13400 | 0.0402 | - | | 0.3420 | 13500 | 0.0398 | - | | 0.3445 | 13600 | 0.041 | - | | 0.3471 | 13700 | 0.0516 | - | | 0.3496 | 13800 | 0.0518 | - | | 0.3521 | 13900 | 0.0413 | - | | 0.3547 | 14000 | 0.0463 | 0.0199 | | 0.3572 | 14100 | 0.0442 | - | | 0.3597 | 14200 | 0.0695 | - | | 0.3623 | 14300 | 0.0595 | - | | 0.3648 | 14400 | 0.0435 | - | | 0.3673 | 14500 | 0.0372 | - | | 0.3699 | 14600 | 0.0398 | - | | 0.3724 | 14700 | 0.0357 | - | | 0.3749 | 14800 | 0.0467 | - | | 0.3775 | 14900 | 0.0611 | - | | 0.3800 | 15000 | 0.054 | 0.0233 | | 0.3825 | 15100 | 0.0411 | - | | 0.3851 | 15200 | 0.0485 | - | | 0.3876 | 15300 | 0.0388 | - | | 0.3901 | 15400 | 0.0474 | - | | 0.3927 | 15500 | 0.0525 | - | | 0.3952 | 15600 | 0.0568 | - | | 0.3977 | 15700 | 0.0414 | - | | 0.4003 | 15800 | 0.0375 | - | | 0.4028 | 15900 | 0.0606 | - | | 0.4053 | 16000 | 0.0495 | 0.0238 | | 0.4079 | 16100 | 0.0407 | - | | 0.4104 | 16200 | 0.0383 | - | | 0.4129 | 16300 | 0.0318 | - | | 0.4155 | 16400 | 0.0503 | - | | 0.4180 | 16500 | 0.0386 | - | | 0.4205 | 16600 | 0.0397 | - | | 0.4231 | 16700 | 0.0409 | - | | 0.4256 | 16800 | 0.0484 | - | | 0.4281 | 16900 | 0.0514 | - | | 0.4307 | 17000 | 0.0359 | 0.0216 | | 0.4332 | 17100 | 0.0411 | - | | 0.4357 | 17200 | 0.0372 | - | | 0.4383 | 17300 | 0.0489 | - | | 0.4408 | 17400 | 0.0364 | - | | 0.4433 | 17500 | 0.0517 | - | | 0.4459 | 17600 | 0.0422 | - | | 0.4484 | 17700 | 0.0334 | - | | 0.4509 | 17800 | 0.0532 | - | | 0.4535 | 17900 | 0.0384 | - | | 0.4560 | 18000 | 0.03 | 0.0200 | | 0.4585 | 18100 | 0.034 | - | | 0.4611 | 18200 | 0.0429 | - | | 0.4636 | 18300 | 0.0448 | - | | 0.4661 | 18400 | 0.03 | - | | 0.4687 | 18500 | 0.0338 | - | | 0.4712 | 18600 | 0.0436 | - | | 0.4737 | 18700 | 0.0271 | - | | 0.4763 | 18800 | 0.0516 | - | | 0.4788 | 18900 | 0.0358 | - | | 0.4813 | 19000 | 0.046 | 0.0255 | | 0.4839 | 19100 | 0.0367 | - | | 0.4864 | 19200 | 0.032 | - | | 0.4889 | 19300 | 0.0363 | - | | 0.4915 | 19400 | 0.0352 | - | | 0.4940 | 19500 | 0.041 | - | | 0.4965 | 19600 | 0.0508 | - | | 0.4991 | 19700 | 0.0454 | - | | 0.5016 | 19800 | 0.0459 | - | | 0.5041 | 19900 | 0.0295 | - | | 0.5066 | 20000 | 0.0415 | 0.0228 | | 0.5092 | 20100 | 0.0422 | - | | 0.5117 | 20200 | 0.0317 | - | | 0.5142 | 20300 | 0.0263 | - | | 0.5168 | 20400 | 0.0568 | - | | 0.5193 | 20500 | 0.0339 | - | | 0.5218 | 20600 | 0.0295 | - | | 0.5244 | 20700 | 0.042 | - | | 0.5269 | 20800 | 0.0343 | - | | 0.5294 | 20900 | 0.0322 | - | | 0.5320 | 21000 | 0.0328 | 0.0204 | | 0.5345 | 21100 | 0.0407 | - | | 0.5370 | 21200 | 0.0306 | - | | 0.5396 | 21300 | 0.0295 | - | | 0.5421 | 21400 | 0.0329 | - | | 0.5446 | 21500 | 0.0297 | - | | 0.5472 | 21600 | 0.0298 | - | | 0.5497 | 21700 | 0.0261 | - | | 0.5522 | 21800 | 0.0429 | - | | 0.5548 | 21900 | 0.039 | - | | 0.5573 | 22000 | 0.0336 | 0.0151 | | 0.5598 | 22100 | 0.0417 | - | | 0.5624 | 22200 | 0.0424 | - | | 0.5649 | 22300 | 0.0447 | - | | 0.5674 | 22400 | 0.0482 | - | | 0.5700 | 22500 | 0.0253 | - | | 0.5725 | 22600 | 0.0412 | - | | 0.5750 | 22700 | 0.0425 | - | | 0.5776 | 22800 | 0.0304 | - | | 0.5801 | 22900 | 0.0302 | - | | 0.5826 | 23000 | 0.0275 | 0.0144 | | 0.5852 | 23100 | 0.0255 | - | | 0.5877 | 23200 | 0.0266 | - | | 0.5902 | 23300 | 0.038 | - | | 0.5928 | 23400 | 0.0254 | - | | 0.5953 | 23500 | 0.0486 | - | | 0.5978 | 23600 | 0.0325 | - | | 0.6004 | 23700 | 0.041 | - | | 0.6029 | 23800 | 0.0307 | - | | 0.6054 | 23900 | 0.037 | - | | 0.6080 | 24000 | 0.0377 | 0.0194 | | 0.6105 | 24100 | 0.0331 | - | | 0.6130 | 24200 | 0.0386 | - | | 0.6156 | 24300 | 0.0184 | - | | 0.6181 | 24400 | 0.0244 | - | | 0.6206 | 24500 | 0.0279 | - | | 0.6232 | 24600 | 0.0351 | - | | 0.6257 | 24700 | 0.0577 | - | | 0.6282 | 24800 | 0.0434 | - | | 0.6308 | 24900 | 0.0223 | - | | 0.6333 | 25000 | 0.0264 | 0.0151 | | 0.6358 | 25100 | 0.0378 | - | | 0.6384 | 25200 | 0.0212 | - | | 0.6409 | 25300 | 0.0245 | - | | 0.6434 | 25400 | 0.0321 | - | | 0.6460 | 25500 | 0.0391 | - | | 0.6485 | 25600 | 0.0276 | - | | 0.6510 | 25700 | 0.0253 | - | | 0.6536 | 25800 | 0.0295 | - | | 0.6561 | 25900 | 0.0225 | - | | 0.6586 | 26000 | 0.0312 | 0.0133 | | 0.6612 | 26100 | 0.0367 | - | | 0.6637 | 26200 | 0.029 | - | | 0.6662 | 26300 | 0.0311 | - | | 0.6688 | 26400 | 0.0383 | - | | 0.6713 | 26500 | 0.0357 | - | | 0.6738 | 26600 | 0.0259 | - | | 0.6764 | 26700 | 0.0277 | - | | 0.6789 | 26800 | 0.0278 | - | | 0.6814 | 26900 | 0.0242 | - | | 0.6840 | 27000 | 0.0288 | 0.0183 | | 0.6865 | 27100 | 0.0352 | - | | 0.6890 | 27200 | 0.0298 | - | | 0.6916 | 27300 | 0.0448 | - | | 0.6941 | 27400 | 0.0299 | - | | 0.6966 | 27500 | 0.0385 | - | | 0.6992 | 27600 | 0.0365 | - | | 0.7017 | 27700 | 0.022 | - | | 0.7042 | 27800 | 0.0339 | - | | 0.7068 | 27900 | 0.0371 | - | | 0.7093 | 28000 | 0.0322 | 0.0183 | | 0.7118 | 28100 | 0.0365 | - | | 0.7144 | 28200 | 0.0271 | - | | 0.7169 | 28300 | 0.0238 | - | | 0.7194 | 28400 | 0.033 | - | | 0.7220 | 28500 | 0.0225 | - | | 0.7245 | 28600 | 0.022 | - | | 0.7270 | 28700 | 0.0132 | - | | 0.7296 | 28800 | 0.0304 | - | | 0.7321 | 28900 | 0.0357 | - | | 0.7346 | 29000 | 0.025 | 0.0149 | | 0.7372 | 29100 | 0.0251 | - | | 0.7397 | 29200 | 0.0238 | - | | 0.7422 | 29300 | 0.0337 | - | | 0.7448 | 29400 | 0.0277 | - | | 0.7473 | 29500 | 0.02 | - | | 0.7498 | 29600 | 0.0216 | - | | 0.7524 | 29700 | 0.0203 | - | | 0.7549 | 29800 | 0.0216 | - | | 0.7574 | 29900 | 0.0317 | - | | 0.7600 | 30000 | 0.0274 | 0.0116 | | 0.7625 | 30100 | 0.0284 | - | | 0.7650 | 30200 | 0.0407 | - | | 0.7676 | 30300 | 0.0326 | - | | 0.7701 | 30400 | 0.0207 | - | | 0.7726 | 30500 | 0.0284 | - | | 0.7752 | 30600 | 0.0386 | - | | 0.7777 | 30700 | 0.031 | - | | 0.7802 | 30800 | 0.0215 | - | | 0.7828 | 30900 | 0.0243 | - | | 0.7853 | 31000 | 0.0248 | 0.0132 | | 0.7878 | 31100 | 0.0366 | - | | 0.7904 | 31200 | 0.0248 | - | | 0.7929 | 31300 | 0.0336 | - | | 0.7954 | 31400 | 0.0316 | - | | 0.7980 | 31500 | 0.0252 | - | | 0.8005 | 31600 | 0.0236 | - | | 0.8030 | 31700 | 0.0277 | - | | 0.8056 | 31800 | 0.0256 | - | | 0.8081 | 31900 | 0.0462 | - | | 0.8106 | 32000 | 0.0322 | 0.0155 | | 0.8132 | 32100 | 0.0159 | - | | 0.8157 | 32200 | 0.0216 | - | | 0.8182 | 32300 | 0.018 | - | | 0.8208 | 32400 | 0.0232 | - | | 0.8233 | 32500 | 0.024 | - | | 0.8258 | 32600 | 0.0254 | - | | 0.8284 | 32700 | 0.0334 | - | | 0.8309 | 32800 | 0.0204 | - | | 0.8334 | 32900 | 0.0352 | - | | 0.8360 | 33000 | 0.024 | 0.0180 | | 0.8385 | 33100 | 0.0368 | - | | 0.8410 | 33200 | 0.0243 | - | | 0.8436 | 33300 | 0.0196 | - | | 0.8461 | 33400 | 0.0264 | - | | 0.8486 | 33500 | 0.026 | - | | 0.8512 | 33600 | 0.0201 | - | | 0.8537 | 33700 | 0.0245 | - | | 0.8562 | 33800 | 0.0205 | - | | 0.8588 | 33900 | 0.0244 | - | | 0.8613 | 34000 | 0.0174 | 0.0211 | | 0.8638 | 34100 | 0.019 | - | | 0.8664 | 34200 | 0.031 | - | | 0.8689 | 34300 | 0.0257 | - | | 0.8714 | 34400 | 0.0195 | - | | 0.8740 | 34500 | 0.0274 | - | | 0.8765 | 34600 | 0.0197 | - | | 0.8790 | 34700 | 0.0154 | - | | 0.8816 | 34800 | 0.0233 | - | | 0.8841 | 34900 | 0.0314 | - | | 0.8866 | 35000 | 0.0223 | 0.0172 | | 0.8892 | 35100 | 0.0264 | - | | 0.8917 | 35200 | 0.0214 | - | | 0.8942 | 35300 | 0.0264 | - | | 0.8968 | 35400 | 0.0194 | - | | 0.8993 | 35500 | 0.0221 | - | | 0.9018 | 35600 | 0.0185 | - | | 0.9044 | 35700 | 0.029 | - | | 0.9069 | 35800 | 0.0188 | - | | 0.9094 | 35900 | 0.0407 | - | | 0.9120 | 36000 | 0.0251 | 0.0188 | | 0.9145 | 36100 | 0.0295 | - | | 0.9170 | 36200 | 0.0233 | - | | 0.9196 | 36300 | 0.0265 | - | | 0.9221 | 36400 | 0.027 | - | | 0.9246 | 36500 | 0.022 | - | | 0.9272 | 36600 | 0.0174 | - | | 0.9297 | 36700 | 0.0204 | - | | 0.9322 | 36800 | 0.0314 | - | | 0.9348 | 36900 | 0.0256 | - | | 0.9373 | 37000 | 0.0139 | 0.0129 | | 0.9398 | 37100 | 0.0237 | - | | 0.9424 | 37200 | 0.0235 | - | | 0.9449 | 37300 | 0.0202 | - | | 0.9474 | 37400 | 0.0178 | - | | 0.9500 | 37500 | 0.0225 | - | | 0.9525 | 37600 | 0.0224 | - | | 0.9550 | 37700 | 0.0259 | - | | 0.9576 | 37800 | 0.0215 | - | | 0.9601 | 37900 | 0.0197 | - | | 0.9626 | 38000 | 0.0208 | 0.0108 | | 0.9652 | 38100 | 0.0296 | - | | 0.9677 | 38200 | 0.019 | - | | 0.9702 | 38300 | 0.0185 | - | | 0.9728 | 38400 | 0.0271 | - | | 0.9753 | 38500 | 0.0336 | - | | 0.9778 | 38600 | 0.0209 | - | | 0.9804 | 38700 | 0.0321 | - | | 0.9829 | 38800 | 0.0138 | - | | 0.9854 | 38900 | 0.0185 | - | | 0.9880 | 39000 | 0.0226 | 0.0119 | | 0.9905 | 39100 | 0.0201 | - | | 0.9930 | 39200 | 0.0183 | - | | 0.9956 | 39300 | 0.0253 | - | | 0.9981 | 39400 | 0.0304 | - | | 1.0006 | 39500 | 0.0163 | - | | 1.0032 | 39600 | 0.0291 | - | | 1.0057 | 39700 | 0.0202 | - | | 1.0082 | 39800 | 0.0125 | - | | 1.0108 | 39900 | 0.0171 | - | | 1.0133 | 40000 | 0.0159 | 0.0169 | | 1.0158 | 40100 | 0.0188 | - | | 1.0184 | 40200 | 0.024 | - | | 1.0209 | 40300 | 0.0269 | - | | 1.0234 | 40400 | 0.0286 | - | | 1.0260 | 40500 | 0.0194 | - | | 1.0285 | 40600 | 0.0174 | - | | 1.0310 | 40700 | 0.0241 | - | | 1.0336 | 40800 | 0.0198 | - | | 1.0361 | 40900 | 0.0214 | - | | 1.0386 | 41000 | 0.0182 | 0.0138 | | 1.0412 | 41100 | 0.0148 | - | | 1.0437 | 41200 | 0.0161 | - | | 1.0462 | 41300 | 0.0234 | - | | 1.0488 | 41400 | 0.0177 | - | | 1.0513 | 41500 | 0.0105 | - | | 1.0538 | 41600 | 0.0201 | - | | 1.0564 | 41700 | 0.0211 | - | | 1.0589 | 41800 | 0.0157 | - | | 1.0614 | 41900 | 0.0164 | - | | 1.0640 | 42000 | 0.0146 | 0.0080 | | 1.0665 | 42100 | 0.0223 | - | | 1.0690 | 42200 | 0.0269 | - | | 1.0716 | 42300 | 0.0218 | - | | 1.0741 | 42400 | 0.0294 | - | | 1.0766 | 42500 | 0.0166 | - | | 1.0792 | 42600 | 0.0173 | - | | 1.0817 | 42700 | 0.015 | - | | 1.0842 | 42800 | 0.015 | - | | 1.0868 | 42900 | 0.0166 | - | | 1.0893 | 43000 | 0.0123 | 0.0088 | | 1.0918 | 43100 | 0.0137 | - | | 1.0944 | 43200 | 0.01 | - | | 1.0969 | 43300 | 0.0156 | - | | 1.0994 | 43400 | 0.0126 | - | | 1.1020 | 43500 | 0.0197 | - | | 1.1045 | 43600 | 0.014 | - | | 1.1070 | 43700 | 0.0154 | - | | 1.1096 | 43800 | 0.0214 | - | | 1.1121 | 43900 | 0.0157 | - | | 1.1146 | 44000 | 0.0151 | 0.0093 | | 1.1172 | 44100 | 0.014 | - | | 1.1197 | 44200 | 0.0138 | - | | 1.1222 | 44300 | 0.0126 | - | | 1.1248 | 44400 | 0.0084 | - | | 1.1273 | 44500 | 0.0124 | - | | 1.1298 | 44600 | 0.0117 | - | | 1.1324 | 44700 | 0.0098 | - | | 1.1349 | 44800 | 0.0099 | - | | 1.1374 | 44900 | 0.0115 | - | | 1.1400 | 45000 | 0.0188 | 0.0051 | | 1.1425 | 45100 | 0.0129 | - | | 1.1450 | 45200 | 0.0128 | - | | 1.1476 | 45300 | 0.015 | - | | 1.1501 | 45400 | 0.0106 | - | | 1.1526 | 45500 | 0.0115 | - | | 1.1552 | 45600 | 0.0144 | - | | 1.1577 | 45700 | 0.0144 | - | | 1.1602 | 45800 | 0.0078 | - | | 1.1628 | 45900 | 0.0143 | - | | 1.1653 | 46000 | 0.0122 | 0.0089 | | 1.1678 | 46100 | 0.0059 | - | | 1.1704 | 46200 | 0.0119 | - | | 1.1729 | 46300 | 0.0103 | - | | 1.1754 | 46400 | 0.0083 | - | | 1.1780 | 46500 | 0.0148 | - | | 1.1805 | 46600 | 0.0097 | - | | 1.1830 | 46700 | 0.0067 | - | | 1.1856 | 46800 | 0.0116 | - | | 1.1881 | 46900 | 0.0124 | - | | 1.1906 | 47000 | 0.0063 | 0.0125 | | 1.1932 | 47100 | 0.007 | - | | 1.1957 | 47200 | 0.0095 | - | | 1.1982 | 47300 | 0.0072 | - | | 1.2008 | 47400 | 0.0124 | - | | 1.2033 | 47500 | 0.0109 | - | | 1.2058 | 47600 | 0.0108 | - | | 1.2084 | 47700 | 0.0057 | - | | 1.2109 | 47800 | 0.0133 | - | | 1.2134 | 47900 | 0.0095 | - | | 1.2160 | 48000 | 0.0057 | 0.0107 | | 1.2185 | 48100 | 0.0085 | - | | 1.2210 | 48200 | 0.0037 | - | | 1.2236 | 48300 | 0.0077 | - | | 1.2261 | 48400 | 0.0128 | - | | 1.2286 | 48500 | 0.0124 | - | | 1.2312 | 48600 | 0.0081 | - | | 1.2337 | 48700 | 0.008 | - | | 1.2362 | 48800 | 0.0051 | - | | 1.2388 | 48900 | 0.0101 | - | | 1.2413 | 49000 | 0.0059 | 0.0124 | | 1.2438 | 49100 | 0.0063 | - | | 1.2464 | 49200 | 0.0075 | - | | 1.2489 | 49300 | 0.0064 | - | | 1.2514 | 49400 | 0.0065 | - | | 1.2540 | 49500 | 0.0056 | - | | 1.2565 | 49600 | 0.0098 | - | | 1.2590 | 49700 | 0.0062 | - | | 1.2616 | 49800 | 0.0067 | - | | 1.2641 | 49900 | 0.0046 | - | | 1.2666 | 50000 | 0.0088 | 0.0114 | | 1.2692 | 50100 | 0.005 | - | | 1.2717 | 50200 | 0.0083 | - | | 1.2742 | 50300 | 0.0073 | - | | 1.2768 | 50400 | 0.0084 | - | | 1.2793 | 50500 | 0.0044 | - | | 1.2818 | 50600 | 0.0052 | - | | 1.2844 | 50700 | 0.0045 | - | | 1.2869 | 50800 | 0.0085 | - | | 1.2894 | 50900 | 0.0057 | - | | 1.2920 | 51000 | 0.0048 | 0.0111 | | 1.2945 | 51100 | 0.0059 | - | | 1.2970 | 51200 | 0.0065 | - | | 1.2996 | 51300 | 0.0057 | - | | 1.3021 | 51400 | 0.0059 | - | | 1.3046 | 51500 | 0.0056 | - | | 1.3072 | 51600 | 0.0124 | - | | 1.3097 | 51700 | 0.0067 | - | | 1.3122 | 51800 | 0.011 | - | | 1.3148 | 51900 | 0.0078 | - | | 1.3173 | 52000 | 0.0068 | 0.0110 | | 1.3198 | 52100 | 0.006 | - | | 1.3224 | 52200 | 0.0084 | - | | 1.3249 | 52300 | 0.0064 | - | | 1.3274 | 52400 | 0.0055 | - | | 1.3300 | 52500 | 0.0032 | - | | 1.3325 | 52600 | 0.0049 | - | | 1.3350 | 52700 | 0.0068 | - | | 1.3376 | 52800 | 0.0067 | - | | 1.3401 | 52900 | 0.006 | - | | 1.3426 | 53000 | 0.0058 | 0.0098 | | 1.3452 | 53100 | 0.0046 | - | | 1.3477 | 53200 | 0.0055 | - | | 1.3502 | 53300 | 0.0074 | - | | 1.3528 | 53400 | 0.0029 | - | | 1.3553 | 53500 | 0.0071 | - | | 1.3578 | 53600 | 0.0074 | - | | 1.3604 | 53700 | 0.0068 | - | | 1.3629 | 53800 | 0.0066 | - | | 1.3654 | 53900 | 0.0077 | - | | 1.3680 | 54000 | 0.0069 | 0.0107 | | 1.3705 | 54100 | 0.0039 | - | | 1.3730 | 54200 | 0.0051 | - | | 1.3756 | 54300 | 0.0038 | - | | 1.3781 | 54400 | 0.0073 | - | | 1.3806 | 54500 | 0.0087 | - | | 1.3832 | 54600 | 0.0053 | - | | 1.3857 | 54700 | 0.0054 | - | | 1.3882 | 54800 | 0.0091 | - | | 1.3908 | 54900 | 0.0067 | - | | 1.3933 | 55000 | 0.0071 | 0.0094 | | 1.3958 | 55100 | 0.0056 | - | | 1.3984 | 55200 | 0.0043 | - | | 1.4009 | 55300 | 0.0059 | - | | 1.4034 | 55400 | 0.007 | - | | 1.4060 | 55500 | 0.0064 | - | | 1.4085 | 55600 | 0.006 | - | | 1.4110 | 55700 | 0.0031 | - | | 1.4136 | 55800 | 0.0058 | - | | 1.4161 | 55900 | 0.0056 | - | | 1.4186 | 56000 | 0.0052 | 0.0096 | | 1.4212 | 56100 | 0.0045 | - | | 1.4237 | 56200 | 0.0046 | - | | 1.4262 | 56300 | 0.0044 | - | | 1.4288 | 56400 | 0.0076 | - | | 1.4313 | 56500 | 0.0029 | - | | 1.4338 | 56600 | 0.005 | - | | 1.4364 | 56700 | 0.0042 | - | | 1.4389 | 56800 | 0.0066 | - | | 1.4414 | 56900 | 0.0119 | - | | 1.4440 | 57000 | 0.0033 | 0.0076 | | 1.4465 | 57100 | 0.0076 | - | | 1.4490 | 57200 | 0.0058 | - | | 1.4516 | 57300 | 0.0054 | - | | 1.4541 | 57400 | 0.0039 | - | | 1.4566 | 57500 | 0.0057 | - | | 1.4592 | 57600 | 0.008 | - | | 1.4617 | 57700 | 0.0082 | - | | 1.4642 | 57800 | 0.0041 | - | | 1.4668 | 57900 | 0.0037 | - | | 1.4693 | 58000 | 0.0048 | 0.0078 | | 1.4718 | 58100 | 0.0041 | - | | 1.4744 | 58200 | 0.0049 | - | | 1.4769 | 58300 | 0.0085 | - | | 1.4794 | 58400 | 0.0036 | - | | 1.4820 | 58500 | 0.0061 | - | | 1.4845 | 58600 | 0.0039 | - | | 1.4870 | 58700 | 0.0049 | - | | 1.4896 | 58800 | 0.0027 | - | | 1.4921 | 58900 | 0.003 | - | | 1.4946 | 59000 | 0.006 | 0.0097 | | 1.4972 | 59100 | 0.0068 | - | | 1.4997 | 59200 | 0.0083 | - | | 1.5022 | 59300 | 0.0066 | - | | 1.5047 | 59400 | 0.0049 | - | | 1.5073 | 59500 | 0.0034 | - | | 1.5098 | 59600 | 0.0044 | - | | 1.5123 | 59700 | 0.0036 | - | | 1.5149 | 59800 | 0.0041 | - | | 1.5174 | 59900 | 0.006 | - | | 1.5199 | 60000 | 0.0063 | 0.0099 | | 1.5225 | 60100 | 0.0028 | - | | 1.5250 | 60200 | 0.0045 | - | | 1.5275 | 60300 | 0.0056 | - | | 1.5301 | 60400 | 0.0046 | - | | 1.5326 | 60500 | 0.0053 | - | | 1.5351 | 60600 | 0.0044 | - | | 1.5377 | 60700 | 0.0053 | - | | 1.5402 | 60800 | 0.0044 | - | | 1.5427 | 60900 | 0.0034 | - | | 1.5453 | 61000 | 0.0033 | 0.0073 | | 1.5478 | 61100 | 0.005 | - | | 1.5503 | 61200 | 0.0027 | - | | 1.5529 | 61300 | 0.0049 | - | | 1.5554 | 61400 | 0.0048 | - | | 1.5579 | 61500 | 0.0032 | - | | 1.5605 | 61600 | 0.0043 | - | | 1.5630 | 61700 | 0.0049 | - | | 1.5655 | 61800 | 0.0062 | - | | 1.5681 | 61900 | 0.0076 | - | | 1.5706 | 62000 | 0.006 | 0.0053 | | 1.5731 | 62100 | 0.0078 | - | | 1.5757 | 62200 | 0.0033 | - | | 1.5782 | 62300 | 0.0031 | - | | 1.5807 | 62400 | 0.0038 | - | | 1.5833 | 62500 | 0.0026 | - | | 1.5858 | 62600 | 0.0036 | - | | 1.5883 | 62700 | 0.0034 | - | | 1.5909 | 62800 | 0.0076 | - | | 1.5934 | 62900 | 0.0039 | - | | 1.5959 | 63000 | 0.006 | 0.0073 | | 1.5985 | 63100 | 0.0055 | - | | 1.6010 | 63200 | 0.0046 | - | | 1.6035 | 63300 | 0.0042 | - | | 1.6061 | 63400 | 0.0061 | - | | 1.6086 | 63500 | 0.003 | - | | 1.6111 | 63600 | 0.0034 | - | | 1.6137 | 63700 | 0.0058 | - | | 1.6162 | 63800 | 0.0036 | - | | 1.6187 | 63900 | 0.0015 | - | | 1.6213 | 64000 | 0.0052 | 0.0076 | | 1.6238 | 64100 | 0.0047 | - | | 1.6263 | 64200 | 0.0083 | - | | 1.6289 | 64300 | 0.0035 | - | | 1.6314 | 64400 | 0.0025 | - | | 1.6339 | 64500 | 0.0052 | - | | 1.6365 | 64600 | 0.0029 | - | | 1.6390 | 64700 | 0.0019 | - | | 1.6415 | 64800 | 0.0036 | - | | 1.6441 | 64900 | 0.002 | - | | 1.6466 | 65000 | 0.007 | 0.0074 | | 1.6491 | 65100 | 0.0038 | - | | 1.6517 | 65200 | 0.0051 | - | | 1.6542 | 65300 | 0.0027 | - | | 1.6567 | 65400 | 0.003 | - | | 1.6593 | 65500 | 0.0045 | - | | 1.6618 | 65600 | 0.0067 | - | | 1.6643 | 65700 | 0.003 | - | | 1.6669 | 65800 | 0.0033 | - | | 1.6694 | 65900 | 0.0043 | - | | 1.6719 | 66000 | 0.0025 | 0.0071 | | 1.6745 | 66100 | 0.0025 | - | | 1.6770 | 66200 | 0.0057 | - | | 1.6795 | 66300 | 0.0029 | - | | 1.6821 | 66400 | 0.0016 | - | | 1.6846 | 66500 | 0.0055 | - | | 1.6871 | 66600 | 0.0029 | - | | 1.6897 | 66700 | 0.0031 | - | | 1.6922 | 66800 | 0.006 | - | | 1.6947 | 66900 | 0.003 | - | | 1.6973 | 67000 | 0.0042 | 0.0072 | | 1.6998 | 67100 | 0.0049 | - | | 1.7023 | 67200 | 0.0018 | - | | 1.7049 | 67300 | 0.0043 | - | | 1.7074 | 67400 | 0.007 | - | | 1.7099 | 67500 | 0.0025 | - | | 1.7125 | 67600 | 0.0051 | - | | 1.7150 | 67700 | 0.0056 | - | | 1.7175 | 67800 | 0.003 | - | | 1.7201 | 67900 | 0.0041 | - | | 1.7226 | 68000 | 0.0025 | 0.0082 | | 1.7251 | 68100 | 0.0018 | - | | 1.7277 | 68200 | 0.0034 | - | | 1.7302 | 68300 | 0.0065 | - | | 1.7327 | 68400 | 0.0047 | - | | 1.7353 | 68500 | 0.0052 | - | | 1.7378 | 68600 | 0.0013 | - | | 1.7403 | 68700 | 0.0063 | - | | 1.7429 | 68800 | 0.0047 | - | | 1.7454 | 68900 | 0.004 | - | | 1.7479 | 69000 | 0.0026 | 0.0077 | | 1.7505 | 69100 | 0.0032 | - | | 1.7530 | 69200 | 0.0031 | - | | 1.7555 | 69300 | 0.0024 | - | | 1.7581 | 69400 | 0.0022 | - | | 1.7606 | 69500 | 0.0029 | - | | 1.7631 | 69600 | 0.0055 | - | | 1.7657 | 69700 | 0.0031 | - | | 1.7682 | 69800 | 0.004 | - | | 1.7707 | 69900 | 0.0032 | - | | 1.7733 | 70000 | 0.0034 | 0.0067 | | 1.7758 | 70100 | 0.007 | - | | 1.7783 | 70200 | 0.0049 | - | | 1.7809 | 70300 | 0.0023 | - | | 1.7834 | 70400 | 0.0028 | - | | 1.7859 | 70500 | 0.0048 | - | | 1.7885 | 70600 | 0.0042 | - | | 1.7910 | 70700 | 0.006 | - | | 1.7935 | 70800 | 0.006 | - | | 1.7961 | 70900 | 0.0044 | - | | 1.7986 | 71000 | 0.0036 | 0.0063 | | 1.8011 | 71100 | 0.0025 | - | | 1.8037 | 71200 | 0.0027 | - | | 1.8062 | 71300 | 0.0033 | - | | 1.8087 | 71400 | 0.0045 | - | | 1.8113 | 71500 | 0.0037 | - | | 1.8138 | 71600 | 0.0023 | - | | 1.8163 | 71700 | 0.0021 | - | | 1.8189 | 71800 | 0.0019 | - | | 1.8214 | 71900 | 0.0046 | - | | 1.8239 | 72000 | 0.0029 | 0.0065 | | 1.8265 | 72100 | 0.0061 | - | | 1.8290 | 72200 | 0.005 | - | | 1.8315 | 72300 | 0.0036 | - | | 1.8341 | 72400 | 0.0057 | - | | 1.8366 | 72500 | 0.0049 | - | | 1.8391 | 72600 | 0.0068 | - | | 1.8417 | 72700 | 0.0026 | - | | 1.8442 | 72800 | 0.0032 | - | | 1.8467 | 72900 | 0.0036 | - | | 1.8493 | 73000 | 0.0026 | 0.0066 | | 1.8518 | 73100 | 0.0024 | - | | 1.8543 | 73200 | 0.0014 | - | | 1.8569 | 73300 | 0.0022 | - | | 1.8594 | 73400 | 0.0039 | - | | 1.8619 | 73500 | 0.0019 | - | | 1.8645 | 73600 | 0.0016 | - | | 1.8670 | 73700 | 0.0034 | - | | 1.8695 | 73800 | 0.004 | - | | 1.8721 | 73900 | 0.0014 | - | | 1.8746 | 74000 | 0.004 | 0.0062 | | 1.8771 | 74100 | 0.0014 | - | | 1.8797 | 74200 | 0.0025 | - | | 1.8822 | 74300 | 0.0025 | - | | 1.8847 | 74400 | 0.0037 | - | | 1.8873 | 74500 | 0.0038 | - | | 1.8898 | 74600 | 0.0029 | - | | 1.8923 | 74700 | 0.0037 | - | | 1.8949 | 74800 | 0.0026 | - | | 1.8974 | 74900 | 0.0019 | - | | 1.8999 | 75000 | 0.0013 | 0.0062 | | 1.9025 | 75100 | 0.0027 | - | | 1.9050 | 75200 | 0.0028 | - | | 1.9075 | 75300 | 0.0014 | - | | 1.9101 | 75400 | 0.0067 | - | | 1.9126 | 75500 | 0.0023 | - | | 1.9151 | 75600 | 0.0024 | - | | 1.9177 | 75700 | 0.0021 | - | | 1.9202 | 75800 | 0.0062 | - | | 1.9227 | 75900 | 0.0104 | - | | 1.9253 | 76000 | 0.0021 | 0.0064 | | 1.9278 | 76100 | 0.0023 | - | | 1.9303 | 76200 | 0.0059 | - | | 1.9329 | 76300 | 0.0055 | - | | 1.9354 | 76400 | 0.002 | - | | 1.9379 | 76500 | 0.0029 | - | | 1.9405 | 76600 | 0.0028 | - | | 1.9430 | 76700 | 0.0021 | - | | 1.9455 | 76800 | 0.0037 | - | | 1.9481 | 76900 | 0.0019 | - | | 1.9506 | 77000 | 0.0027 | 0.0062 | | 1.9531 | 77100 | 0.0039 | - | | 1.9557 | 77200 | 0.0027 | - | | 1.9582 | 77300 | 0.0034 | - | | 1.9607 | 77400 | 0.005 | - | | 1.9633 | 77500 | 0.0022 | - | | 1.9658 | 77600 | 0.0072 | - | | 1.9683 | 77700 | 0.0025 | - | | 1.9709 | 77800 | 0.0019 | - | | 1.9734 | 77900 | 0.0034 | - | | 1.9759 | 78000 | 0.0068 | 0.0060 | | 1.9785 | 78100 | 0.0042 | - | | 1.9810 | 78200 | 0.0041 | - | | 1.9835 | 78300 | 0.0018 | - | | 1.9861 | 78400 | 0.0019 | - | | 1.9886 | 78500 | 0.0029 | - | | 1.9911 | 78600 | 0.0039 | - | | 1.9937 | 78700 | 0.0023 | - | | 1.9962 | 78800 | 0.0092 | - | | 1.9987 | 78900 | 0.0018 | - | </details> ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.3.1 - Transformers: 4.46.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

khizarAI/whisper-tiny-en-US

khizarAI

2024-11-23T12:52:11Z

79

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-23T12:43:50Z

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en-US results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.33517835178351785 ---  # whisper-tiny-en-US This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6504 - Wer Ortho: 33.9125 - Wer: 0.3352 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:| | 0.0006 | 17.2414 | 500 | 0.6504 | 33.9125 | 0.3352 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

win10/SphinxMind-14B

win10

2024-11-23T12:46:12Z

6

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2", "base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2", "base_model:shuttleai/shuttle-3-mini", "base_model:merge:shuttleai/shuttle-3-mini", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T12:40:45Z

--- base_model: - shuttleai/shuttle-3-mini - EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [shuttleai/shuttle-3-mini](https://huggingface.co/shuttleai/shuttle-3-mini) as a base. ### Models Merged The following models were included in the merge: * [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: shuttleai/shuttle-3-mini parameters: density: 1 weight: 1 - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 parameters: density: 1 weight: 1 - model: shuttleai/shuttle-3-mini parameters: density: 1 weight: 1 - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 parameters: density: 1 weight: 1 merge_method: ties base_model: shuttleai/shuttle-3-mini parameters: normalize: true int8_mask: true dtype: bfloat16 ```

isspek/xlnet-base-cased_ebola_mistral_1_2e-5_16_undersampling_0.6

isspek

2024-11-23T12:41:44Z

117

0

transformers

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

text-classification

2024-11-23T12:41:30Z

--- 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]

isspek/xlnet-base-cased_ebola_mistral_1_2e-5_16

isspek

2024-11-23T12:40:25Z

134

0

transformers

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

text-classification

2024-11-23T12:40:09Z

--- 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]

elyx/batman-1

elyx

2024-11-23T12:33:58Z

63

0

transformers

[ "transformers", "safetensors", "ultravox", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]

feature-extraction

2024-11-23T12:33:21Z

--- 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]

second-state/stable-diffusion-3.5-medium-GGUF

second-state

2024-11-23T12:26:40Z

2,243

5

diffusers

[ "diffusers", "gguf", "text-to-image", "stable-diffusion", "en", "base_model:stabilityai/stable-diffusion-3.5-medium", "base_model:quantized:stabilityai/stable-diffusion-3.5-medium", "license:other", "region:us" ]

text-to-image

2024-11-23T11:32:25Z

--- base_model: stabilityai/stable-diffusion-3.5-medium license: other license_name: stabilityai-ai-community license_link: LICENSE.md model_creator: stabilityai model_name: stable-diffusion-3.5-medium quantized_by: Second State Inc. tags: - text-to-image - stable-diffusion - diffusers inference: true language: - en pipeline_tag: text-to-image ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # stable-diffusion-3.5-medium-GGUF ## Original Model [stabilityai/stable-diffusion-3.5-medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) ## Run with `sd-api-server` - Version: coming soon ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [clip_g-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q4_0.gguf) | Q4_0 | 4 | 391 MB | | | [clip_g-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q4_1.gguf) | Q4_1 | 4 | 435 MB | | | [clip_g-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q5_0.gguf) | Q5_0 | 5 | 478 MB | | | [clip_g-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q5_1.gguf) | Q5_1 | 5 | 522 MB | | | [clip_g-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g-Q8_0.gguf) | Q8_0 | 8 | 739 MB | | | [clip_g-f16.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g.safetensors) | f16 | 16 | 1.39 GB | | | [clip_g.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_g.safetensors) | f16 | 16 | 1.39 GB | | | [clip_l-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q4_0.gguf) | Q4_0 | 4 | 69.4 MB | | | [clip_l-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q4_1.gguf) | Q4_1 | 4 | 77.1 MB | | | [clip_l-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q5_0.gguf) | Q5_0 | 5 | 84.8 MB | | | [clip_l-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q5_1.gguf) | Q5_1 | 5 | 92.4 MB | | | [clip_l-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-Q8_0.gguf) | Q8_0 | 8 | 131 MB | | | [clip_l-f16.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l-f16.gguf) | f16 | 16 | 246 MB | | | [clip_l.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/clip_l.safetensors) | f16 | 16 | 246 MB | | | [sd3.5_medium-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q4_0.gguf) | Q4_0 | 4 | 2.08 GB | | | [sd3.5_medium-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q4_1.gguf) | Q4_1 | 4 | 2.22 GB | | | [sd3.5_medium-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q5_0.gguf) | Q5_0 | 5 | 2.36 GB | | | [sd3.5_medium-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q5_1.gguf) | Q5_1 | 5 | 2.50 GB | | | [sd3.5_medium-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium-Q8_0.gguf) | Q8_0 | 8 | 3.19 GB | | | [sd3.5_medium.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/sd3.5_medium.safetensors) | f16 | 16 | 5.11 GB | | | [t5xxl-Q4_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q4_0.gguf) | Q4_0 | 4 | 2.75 GB | | | [t5xxl-Q4_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q4_1.gguf) | Q4_1 | 4 | 3.06 GB | | | [t5xxl-Q5_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q5_0.gguf) | Q5_0 | 5 | 3.36 GB | | | [t5xxl-Q5_1.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q5_1.gguf) | Q5_1 | 5 | 3.67 GB | | | [t5xxl-Q8_0.gguf](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl-Q8_0.gguf) | Q8_0 | 8 | 5.20 GB | | | [t5xxl_fp16.safetensors](https://huggingface.co/second-state/stable-diffusion-3.5-medium-GGUF/blob/main/t5xxl_fp16.safetensors) | f16 | 16 | 9.79 GB | | **Quantized with stable-diffusion.cpp `master-c3eeb669`.**

touhidulislam/BERTweet_retrain_2022_42

touhidulislam

2024-11-23T12:20:32Z

178

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T12:20:06Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_42 results: [] ---  # BERTweet_retrain_2022_42 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4725 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7074 | 1.0 | 5957 | 2.5622 | | 2.5566 | 2.0 | 11914 | 2.4895 | | 2.7189 | 3.0 | 17871 | 2.4860 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0

prithivMLmods

2024-11-23T12:11:56Z

12,548

89

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "flux-dev", "ultra", "realism", "photorealism", "hi-res", "face", "diffusion", "UltraRealism", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-10-17T13:08:00Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora - flux-dev - ultra - realism - photorealism - hi-res - face - diffusion - UltraRealism widget: - text: >- Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6 output: url: images/3.png - text: >- Photograph, candid shot, famous randomly couch and randomly finished with randomly cats, center point for cat, Use camera is Canon EOS 5D Mark IV with a Canon EF 24mm f/1.4L II USM lens, set at aperture f/2.8 for a depth of field that highlights the furniture clean lines with rich and many detail, randomly color and finished, soft ambient light, studio light setting, ultra realistic, UHD, many details --chaos 1 --ar 9:16 --style raw --stylize 750 output: url: images/5.png - text: >- High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250 output: url: images/XX.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Ultra realistic license: creativeml-openrail-m new_version: strangerzonehf/Flux-Super-Realism-LoRA --- # Canopus-LoRA-Flux-UltraRealism-2.0 <Gallery /> - The new versions available: https://huggingface.co/prithivMLmods/Ton618-Epic-Realism-Flux-LoRA - Also Available in Flux LoRA DLC: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC **The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.** ## Model description **prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0** Image Processing Parameters | Parameter | Value | Parameter | Value | |---------------------------|--------|---------------------------|--------| | LR Scheduler | constant | Noise Offset | 0.03 | | Optimizer | AdamW | Multires Noise Discount | 0.1 | | Network Dim | 64 | Multires Noise Iterations | 10 | | Network Alpha | 32 | Repeat & Steps | 30 & 3.8K+ | | Epoch | 20 | Save Every N Epochs | 1 | Labeling: florence2-en(natural language & English) Total Images Used for Training : 70 [ Hi-RES ] & More ............... ## 🚀New Version Available Here🚀 Here's a table summarizing the relevant information about the **`Flux-Super-Realism-LoRA`** model on Hugging Face: | **Feature** | **Details** | |-------------------------|-----------------------------------------------------------------------------| | **Model Name** | `Flux-Super-Realism-LoRA` | | **Repository** | [strangerzonehf/Flux-Super-Realism-LoRA](https://huggingface.co/strangerzonehf/Flux-Super-Realism-LoRA) | | **Author** | `strangerzonehf` | | **Description** | Super-realism LoRA model designed to produce high-quality, hyper-realistic images using LoRA fine-tuning techniques. This model can generate lifelike textures, lighting, and intricate details. | | **Model Type** | LoRA (Low-Rank Adaptation for Transformers) | | **Use Cases** | - Photorealistic image generation<br>- High-fidelity art<br>- Texture detailing and enhancement | | **Primary Language** | Not applicable (model is image-based) | | **Base Model** | Model used as the foundation for LoRA fine-tuning (may vary per implementation) | | **License** | Refer to Hugging Face model page for specific licensing information. | | **Tags** | super-realism, LoRA, high-fidelity, hyper-realistic | | **Usage** | This model is typically used with tools like Hugging Face's `Diffusers` or other libraries supporting LoRA fine-tuning for enhanced realism in image generation. | | **Pipeline** | Use in `StableDiffusionPipeline` or compatible image generation pipelines. | ## Image Compare DLC. ![Canopus-LoRA-Flux-UltraRealism-2.0](images/flux1.png) --- | Image | Description | |-------|-------------| | **Image 1** | **Portrait of an attractive woman**: Late twenties, light brown hair, wearing a yellow sweater, looking directly at the camera. Standing outdoors near trees. Aspect ratio: 128:85. Style: Raw | | **Image 2** | **Headshot of a handsome young man**: Dark gray sweater with buttons and shawl collar, brown hair, short beard. Serious expression on a black background, soft studio lighting. Aspect ratio: 85:128. Style: Raw | | **Image 3** | **Fashion photo**: Model in a white bodysuit and beige trench coat, posing with hands on head in front of a train station at sunset. High resolution, 35mm lens, f/22, natural lighting. Aspect ratio: 85:128. Style: Raw | | **Image 4** | **Rustic portrait**: Woman with fair skin and natural, wavy hair in soft curls. Wearing a red and navy plaid shirt with a white undershirt, sleeves rolled up. Leaning against a weathered blue door frame with a contemplative expression. | ## Other Sample | Image | Prompt | |-------|--------| | ![Image 1](images/34.png) | Photography, envision an image steeped in urban nostalgia, a portrait of youthful ennui set against the backdrop of a timeless cityscape. A woman lounges languidly on an old, industrial metal staircase, her pose exuding a sense of introspection and quiet defiance. A plain black t-shirt, snug and slightly worn, subtly contours to her frame. Her black jeans embrace streetwear chic. Classic black and white shoes strike a stark contrast with the rusted steps, while her casual lace-up style hints at a readiness to spring into motion. Her hair is a cascade of dark waves, partly obscured by a black cap, its brim peeking out with a hint of youthful edge. Around her, the weathered brick walls whisper stories of the city's past, the windows reflecting fragmented visions of urban life. There's an air of contemplation as she rests her head on one arm, gazing distantly, perhaps lost in thought or simply enjoying a moment of solitude in the urban maze. Used camera: Sony α9 II with Sony FE 100-400mm f/4.5-5.6 GM OSS lens, emulating a high-contrast, ultra realistic style. --ar 9:16 --style raw --stylize 750 | --- ## Trigger words You should use `Ultra realistic` to trigger the image generation. ## Other Versions Here’s a table format for the Hugging Face model **"prithivMLmods/Canopus-LoRA-Flux-FaceRealism"**: | **Attribute** | **Details** | |---------------------------|--------------------------------------------------------------------------------------------------------------| | **Model Name** | Canopus-LoRA-Flux-FaceRealism | | **Model ID** | `prithivMLmods/Canopus-LoRA-Flux-FaceRealism` | | **Hugging Face URL** | [Canopus-LoRA-Flux-FaceRealism](https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-FaceRealism) | | **Model Type** | LoRA (Low-Rank Adaptation) | | **Primary Use Case** | Face Realism image generation | | **Supported Framework** | Hugging Face Diffusers | | **Data Type** | `bfloat16`, `fp16`, `float32` | | **Compatible Models** | Stable Diffusion, Flux models | | **Model Author** | `prithivMLmods` | | **LoRA Technique** | LoRA for image style transfer with a focus on generating realistic faces | | **Model Version** | Latest | | **License** | Open-Access | | **Tags** | LoRA, Face Realism, Flux, Image Generation | ## Setting Up ``` import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0" trigger_word = "Ultra realistic" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## App File Structure /project-root/ │ ├── .gitattributes ├── README.md ├── app.py ├── pythonproject.py ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0/tree/main) them in the Files & versions tab . . .🤗: https://hf.co/prithivmlmods

Eugeoter/noob-sdxl-controlnet-manga_line

Eugeoter

2024-11-23T12:02:53Z

2,116

1

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-xl", "controlnet", "text-to-image", "en", "base_model:Laxhar/noobai-xl-EarlyAccess", "base_model:adapter:Laxhar/noobai-xl-EarlyAccess", "license:other", "region:us" ]

text-to-image

2024-11-22T10:47:57Z

--- license: other license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ library_name: diffusers language: - en base_model: - Laxhar/sdxl_noob pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-xl - controlnet - diffusers ---

strangerzonehf/Flux-Super-Capybara-HF

strangerzonehf

2024-11-23T11:56:45Z

17

10

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]

text-to-image

2024-11-23T10:54:29Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'capybara hf, A cartoon drawing of a brown bear sitting in front of a laptop. The bear is facing to the right and has a smiley face on the screen of the laptop. Above the bear is a white bear with a black nose and a black mouth. The background is a light beige color.' output: url: images/C1.png - text: 'capybara hf, A cartoon drawing of a brown bear wearing sunglasses with a yellow circle at the top of the eyes. The bears eyes are tinted black, and the bears body is a light brown color. He is holding a pink money in his right hand. The money has a black border around it, and there are two yellow smiley faces on the eyes of the bear. The background is a solid white color.' output: url: images/C2.png - text: 'capybara hf, A cartoon drawing of a brown bear with a black hat on its head. The bear is wearing a black shirt with a pink collar. The bears face is brown and the bears mouth is black. There is a smiley face in the bottom right corner of the image. There are white clouds in the background.' output: url: images/C3.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: capybara hf license: apache-2.0 --- ![strangerzonehf/Flux-Super-Capybara-HF](images/CC.png) # Flux-Super-Capybara-HF <Gallery /> **The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.** ## Model description **prithivMLmods/Flux-Super-Capybara-HF** Image Processing Parameters | Parameter | Value | Parameter | Value | |---------------------------|--------|---------------------------|--------| | LR Scheduler | constant | Noise Offset | 0.03 | | Optimizer | AdamW | Multires Noise Discount | 0.1 | | Network Dim | 64 | Multires Noise Iterations | 10 | | Network Alpha | 32 | Repeat & Steps | 22 & 2900 | | Epoch | 15 | Save Every N Epochs | 1 | Labeling: florence2-en(natural language & English) Total Images Used for Training : 20 ## Best Dimensions - 768 x 1024 (Best) - 1024 x 1024 (Default) ## Setting Up ```python import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "strangerzonehf/Flux-Super-Capybara-HF" trigger_word = "capybara hf" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Trigger words You should use `capybara hf` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/strangerzonehf/Flux-Super-Capybara-HF/tree/main) them in the Files & versions tab.

gdshaji/gd-tp-1.9k-v2

gdshaji

2024-11-23T11:46:27Z

5

0

transformers

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

text-generation

2024-11-23T11:43:57Z

--- 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]

touhidulislam/BERTweet_retrain_2022_11

touhidulislam

2024-11-23T11:42:28Z

177

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T11:42:08Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_11 results: [] ---  # BERTweet_retrain_2022_11 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5380 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8663 | 1.0 | 6098 | 2.6189 | | 2.693 | 2.0 | 12196 | 2.5695 | | 2.4941 | 3.0 | 18294 | 2.5362 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2021_11

touhidulislam

2024-11-23T11:41:44Z

168

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T11:41:23Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_11 results: [] ---  # BERTweet_retrain_2021_11 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4913 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8137 | 1.0 | 6081 | 2.5820 | | 2.7966 | 2.0 | 12162 | 2.5134 | | 2.7398 | 3.0 | 18243 | 2.4904 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

ritamsharma/tinyllama-finance-v0

ritamsharma

2024-11-23T11:40:00Z

96

0

transformers

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

text-generation

2024-11-23T10:22:21Z

--- 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]

allknowingroger/Marco-01-slerp2-7B

allknowingroger

2024-11-23T11:39:49Z

8

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:AIDC-AI/Marco-o1", "base_model:merge:AIDC-AI/Marco-o1", "base_model:allknowingroger/QwenSlerp12-7B", "base_model:merge:allknowingroger/QwenSlerp12-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T11:31:18Z

--- base_model: - allknowingroger/QwenSlerp12-7B - AIDC-AI/Marco-o1 library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # 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: * [allknowingroger/QwenSlerp12-7B](https://huggingface.co/allknowingroger/QwenSlerp12-7B) * [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: AIDC-AI/Marco-o1 - model: allknowingroger/QwenSlerp12-7B merge_method: slerp base_model: AIDC-AI/Marco-o1 dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```

weide0118/ddpm-celebahq-finetuned-butterflies-2epochs

weide0118

2024-11-23T11:38:11Z

44

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2024-11-23T11:37:50Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('weide0118/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```

Marialab/finetuned-whisper-small-dr-ar

Marialab

2024-11-23T11:34:14Z

76

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "New_fine-tune_whisper_new_dataset", "generated_from_trainer", "ar", "dataset:darija-c", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-23T11:33:19Z

--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-small tags: - New_fine-tune_whisper_new_dataset - generated_from_trainer datasets: - darija-c metrics: - bleu model-index: - name: 'Finetuned Whisper small darija translate ' results: [] ---  # Finetuned Whisper small darija translate This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Darija-C dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Bleu: 0.7440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 40 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-------:|:----:|:---------------:|:------:| | 3.975 | 0.6667 | 10 | 3.4363 | 0.0 | | 2.1029 | 1.3333 | 20 | 1.5986 | 0.0262 | | 1.7909 | 2.0 | 30 | 0.9239 | 0.1314 | | 0.9837 | 2.6667 | 40 | 0.5086 | 0.3289 | | 0.36 | 3.3333 | 50 | 0.4370 | 0.3911 | | 0.6361 | 4.0 | 60 | 0.2622 | 0.4561 | | 0.5227 | 4.6667 | 70 | 0.2506 | 0.5266 | | 0.3307 | 5.3333 | 80 | 0.1299 | 0.6123 | | 0.2438 | 6.0 | 90 | 0.1290 | 0.6057 | | 0.2864 | 6.6667 | 100 | 0.0838 | 0.6623 | | 0.073 | 7.3333 | 110 | 0.0965 | 0.6494 | | 0.1924 | 8.0 | 120 | 0.0859 | 0.7237 | | 0.0086 | 8.6667 | 130 | 0.0235 | 0.7174 | | 0.003 | 9.3333 | 140 | 0.0335 | 0.7354 | | 0.054 | 10.0 | 150 | 0.0096 | 0.7367 | | 0.0008 | 10.6667 | 160 | 0.0190 | 0.7367 | | 0.0002 | 11.3333 | 170 | 0.0003 | 0.7440 | | 0.0001 | 12.0 | 180 | 0.0001 | 0.7440 | | 0.0002 | 12.6667 | 190 | 0.0001 | 0.7440 | | 0.0 | 13.3333 | 200 | 0.0000 | 0.7440 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu121 - Datasets 2.19.2 - Tokenizers 0.20.3

elyx/julien

elyx

2024-11-23T11:29:10Z

106

0

transformers

[ "transformers", "safetensors", "ultravox", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]

feature-extraction

2024-11-23T11:27:26Z

--- 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]

Shivam365/sd-class-butterflies-32-new

Shivam365

2024-11-23T11:25:28Z

44

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2024-11-23T11:25:22Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card: This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Shivam365/sd-class-butterflies-32-new') image = pipeline().images[0] image ```

boadisamson/Llama-3.1-8B-Qgis-7000G-q4_k_m-Instruct

boadisamson

2024-11-23T11:14:21Z

8

1

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T11:13:14Z

--- base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** boadisamson - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

amanpreetsingh459/gemma-2-2b-it-punjabi-finetuned

amanpreetsingh459

2024-11-23T11:07:09Z

89

0

transformers

[ "transformers", "tensorboard", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "pa", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T07:09:15Z

--- base_model: google/gemma-2-2b-it language: - pa library_name: transformers license: gemma model_name: output_dir tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for output_dir This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="amanpreetsingh459/gemma-2-2b-it-punjabi-finetuned", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/amanpreetsingh459-myself/huggingface/runs/j9gmzhe0) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.0 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

isspek/xlnet-base-cased_ebola_mistral_2_2e-5_16_undersampling_0.6

isspek

2024-11-23T11:05:45Z

117

0

transformers

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

text-classification

2024-11-23T11:05:29Z

--- 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]

isspek/xlnet-base-cased_ebola_mistral_2_2e-5_16

isspek

2024-11-23T11:05:26Z

117

0

transformers

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

text-classification

2024-11-23T11:05:05Z

--- 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]

isspek/xlnet-base-cased_ebola_mistral_4_2e-5_16

isspek

2024-11-23T11:02:22Z

116

0

transformers

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

text-classification

2024-11-23T11:02:08Z

--- 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]

coolcat0/Llama-3.1-70B-Instruct-Simplicity

coolcat0

2024-11-23T10:58:32Z

9

1

transformers

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

text-generation

2024-11-19T01:54:46Z

--- 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]

tintnguyen/question-generation-vietnamese-v2-tin

tintnguyen

2024-11-23T10:54:44Z

103

0

transformers

[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:nluai/question-generation-vietnamese-v2", "base_model:finetune:nluai/question-generation-vietnamese-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-23T10:53:39Z

--- library_name: transformers license: apache-2.0 base_model: nluai/question-generation-vietnamese-v2 tags: - generated_from_trainer model-index: - name: question-generation-vietnamese-v2-tin results: [] ---  # question-generation-vietnamese-v2-tin This model is a fine-tuned version of [nluai/question-generation-vietnamese-v2](https://huggingface.co/nluai/question-generation-vietnamese-v2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7425 - eval_runtime: 35.9438 - eval_samples_per_second: 27.71 - eval_steps_per_second: 1.753 - epoch: 0.9168 - step: 30000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

ombhojane/wellness-mini

ombhojane

2024-11-23T10:49:56Z

13

0

null

[ "safetensors", "llama", "mental-health", "healthcare", "conversational", "wellness", "SmolLM-135M", "fine-tuned", "text-generation", "en", "license:mit", "region:us" ]

text-generation

2024-11-23T10:39:40Z

--- language: en tags: - mental-health - healthcare - conversational - wellness - SmolLM-135M - fine-tuned license: mit pipeline_tag: text-generation inference: false --- # Wellness-Mini: Mental Health Conversational AI ## Model Description Wellness-Mini is a fine-tuned version of SmolLM-135M, specifically adapted for mental health conversations and assessments. ## Usage ### In Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("ombhojane/wellness-mini", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("ombhojane/wellness-mini") Example usage messages = [{"role": "user", "content": "How are you feeling today?"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=100) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Via Pipeline ```python from transformers import pipeline Create pipeline pipe = pipeline( "text-generation", model="ombhojane/wellness-mini", tokenizer="ombhojane/wellness-mini", trust_remote_code=True ) Generate text response = pipe("How are you feeling today?", max_new_tokens=100) print(response[0]['generated_text']) ``` ## Training Details - Base model: SmolLM-135M - Fine-tuned using supervised fine-tuning (SFT) - Trained with both mental health assessment capabilities and proper identity responses ## Limitations - This is an AI assistant and not a replacement for professional medical advice - Should be used as a supplementary tool only - May not be suitable for emergency situations - Responses should be verified by healthcare professionals ## Intended Use This model is intended to be used as a conversational AI assistant focused on mental health support and assessment. It can: - Provide supportive responses to mental health concerns - Help identify potential mental health indicators - Engage in wellness-focused conversations ## Bias and Risks - May reflect biases present in training data - Should not be used as sole diagnostic tool - Responses should be reviewed by healthcare professionals ## Creator This model was developed by Sentinet AI Systems.

fracapuano/moss-pen

fracapuano

2024-11-23T10:43:40Z

10

0

lerobot

[ "lerobot", "safetensors", "act", "model_hub_mixin", "pytorch_model_hub_mixin", "robotics", "region:us" ]

robotics

2024-11-23T10:43:30Z

--- library_name: lerobot tags: - act - model_hub_mixin - pytorch_model_hub_mixin - robotics --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/huggingface/lerobot - Docs: [More Information Needed]

isspek/xlnet-base-cased_ebola_gpt4o_5_2e-5_16

isspek

2024-11-23T10:42:26Z

117

0

transformers

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

text-classification

2024-11-23T10:42:08Z

--- 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]

isspek/xlnet-base-cased_ebola_gpt4o_3_2e-5_16

isspek

2024-11-23T10:41:36Z

116

0

transformers

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

text-classification

2024-11-23T10:41:23Z

--- 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]

isspek/xlnet-base-cased_ebola_gpt4o_4_2e-5_16_undersampling_0.6

isspek

2024-11-23T10:41:21Z

117

0

transformers

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

text-classification

2024-11-23T10:41:07Z

--- 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]

isspek/xlnet-base-cased_ebola_gpt4o_1_2e-5_16_undersampling_0.6

isspek

2024-11-23T10:40:35Z

121

0

transformers

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

text-classification

2024-11-23T10:40:21Z

--- 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]

isspek/xlnet-base-cased_ebola_gpt4o_2_2e-5_16_undersampling_0.6

isspek

2024-11-23T10:39:49Z

118

0

transformers

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

text-classification

2024-11-23T10:39:34Z

--- 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]

isspek/xlnet-base-cased_ebola_gpt4o_2_2e-5_16

isspek

2024-11-23T10:39:16Z

116

0

transformers

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

text-classification

2024-11-23T10:39:04Z

--- 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]

touhidulislam/BERTweet_retrain_2022_39

touhidulislam

2024-11-23T10:32:09Z

177

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T10:31:48Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_39 results: [] ---  # BERTweet_retrain_2022_39 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4699 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7008 | 1.0 | 6301 | 2.5605 | | 2.6849 | 2.0 | 12602 | 2.4851 | | 2.7378 | 3.0 | 18903 | 2.4627 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

circlelee/gemma-2-2b-it-arena.bak2

circlelee

2024-11-23T10:31:45Z

88

0

transformers

[ "transformers", "pytorch", "safetensors", "gemma2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-2-2b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2-2b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T10:28:38Z

--- base_model: unsloth/gemma-2-2b-it-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma2 - trl - sft --- # Uploaded model - **Developed by:** circlelee - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2-2b-it-bnb-4bit This gemma2 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)

touhidulislam/BERTweet_retrain_2021_09

touhidulislam

2024-11-23T10:28:38Z

167

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T10:28:11Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_09 results: [] ---  # BERTweet_retrain_2021_09 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5955 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8868 | 1.0 | 5925 | 2.6722 | | 2.7945 | 2.0 | 11850 | 2.6265 | | 2.6973 | 3.0 | 17775 | 2.5918 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2022_09

touhidulislam

2024-11-23T10:28:19Z

178

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T10:27:57Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_09 results: [] ---  # BERTweet_retrain_2022_09 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4388 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6503 | 1.0 | 6222 | 2.5467 | | 2.5711 | 2.0 | 12444 | 2.4678 | | 2.5673 | 3.0 | 18666 | 2.4445 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

DopeorNope/gs-llama3-1b-llama-maskver

DopeorNope

2024-11-23T10:18:11Z

84

0

transformers

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

text-generation

2024-11-23T10:14:42Z

--- 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]

briannlongzhao/dog_textual_inversion

briannlongzhao

2024-11-23T10:15:49Z

4

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-11-15T09:54:52Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - briannlongzhao/dog_textual_inversion These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1. You can find some example images in the following.

prithivMLmods/Flux-Product-Ad-Backdrop

prithivMLmods

2024-11-23T10:13:04Z

740

36

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "Product-Ad", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-11-21T11:58:04Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora - Product-Ad widget: - text: >- Product Ad, Captured at eye-level, a close-up shot captures a pile of fried chicken wings in a white paper cup. The chicken wings are a vibrant brown color, adding a pop of color to the scene. The cup is placed on a light brown wooden table, creating a stark contrast with the vibrant blue sky in the background. To the right of the chicken wings, a slice of lemon, a red onion, and a red radish are placed on the table. The radish, and red onions are arranged in a circular pattern, adding depth to the composition. The backdrop is blurred, suggesting a fair day. output: url: images/PA1.png - text: >- Product Ad, a blue and silver electric razor is soaring through the air. The razor is positioned in the middle of a grassy field, with a backdrop of a mountain range that is covered in snow. The sky is a deep blue, dotted with white clouds, adding a pop of color to the scene. The blades of the razor are splashing in the air, adding texture to the image. output: url: images/PA3.png - text: >- Product Ad,Vegan Snacks, in the style of an outdoors product hero shot in motion, dynamic magazine ad image, photorealism, 4k Raw, --v6 output: url: images/PA4.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Product Ad license: creativeml-openrail-m --- # Flux-Product-Ad-Backdrop <Gallery /> **The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.** ## Model description **prithivMLmods/Flux-Product-Ad-Backdrop** Image Processing Parameters | Parameter | Value | Parameter | Value | |---------------------------|--------|---------------------------|--------| | LR Scheduler | constant | Noise Offset | 0.03 | | Optimizer | AdamW | Multires Noise Discount | 0.1 | | Network Dim | 64 | Multires Noise Iterations | 10 | | Network Alpha | 32 | Repeat & Steps | 19 & 2970 | | Epoch | 15 | Save Every N Epochs | 1 | Labeling: florence2-en(natural language & English) Total Images Used for Training : 19 ## Best Dimensions - 768 x 1024 (Best) - 1024 x 1024 (Default) ## Setting Up ```python import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Flux-Product-Ad-Backdrop" trigger_word = "Product Ad" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Trigger words You should use `Product Ad` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Flux-Product-Ad-Backdrop/tree/main) them in the Files & versions tab.

glif-loradex-trainer/fabian3000_henrymajor

glif-loradex-trainer

2024-11-23T10:08:11Z

39

0

diffusers

[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]

text-to-image

2024-11-23T10:07:52Z

--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1732356405402__000001500_0.jpg text: a cthulhu monster drinking coffee henrymajorstyle - output: url: samples/1732356429409__000001500_1.jpg text: a portrait of a woman working on a rocket henrymajorstyle - output: url: samples/1732356453445__000001500_2.jpg text: vampire sword henrymajorstyle base_model: black-forest-labs/FLUX.1-dev trigger: henrymajorstyle instance_prompt: henrymajorstyle license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # henrymajor Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `fabian3000`. <Gallery /> ## Trigger words You should use `henrymajorstyle` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/fabian3000_henrymajor/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

elyx/banana1234

elyx

2024-11-23T10:03:54Z

103

0

transformers

[ "transformers", "safetensors", "ultravox", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]

feature-extraction

2024-11-23T09:58:15Z

--- 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]

touhidulislam/BERTweet_retrain_2022_38

touhidulislam

2024-11-23T09:55:12Z

169

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T09:54:51Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_38 results: [] ---  # BERTweet_retrain_2022_38 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5456 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6585 | 1.0 | 6105 | 2.6322 | | 2.6867 | 2.0 | 12210 | 2.5551 | | 2.657 | 3.0 | 18315 | 2.5440 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2022_08

touhidulislam

2024-11-23T09:51:06Z

171

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T09:50:39Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_08 results: [] ---  # BERTweet_retrain_2022_08 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5480 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7683 | 1.0 | 5972 | 2.6260 | | 2.8097 | 2.0 | 11944 | 2.5590 | | 2.5256 | 3.0 | 17916 | 2.5344 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

luojunyu/Llama-3.1-8B-SemiEvol-MMLU

luojunyu

2024-11-23T09:51:04Z

71

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-classification", "en", "dataset:luojunyu/SemiEvol", "arxiv:2410.14745", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-10-17T07:19:06Z

--- license: cc-by-4.0 datasets: - luojunyu/SemiEvol language: - en metrics: - accuracy base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-classification library_name: transformers --- Release model for paper [SemiEvol](https://arxiv.org/abs/2410.14745).

erhj3eh3ehweg/Midstral-166M

erhj3eh3ehweg

2024-11-23T09:42:59Z

176

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T09:42:34Z

--- 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]

aswathshakthi/distilbert-emotions-clf-m1

aswathshakthi

2024-11-23T09:42:18Z

6

1

null

[ "safetensors", "distilbert", "en", "license:apache-2.0", "region:us" ]

null

2024-11-23T08:52:23Z

--- license: apache-2.0 language: - en --- # Model Card for My Fine-Tuned Model --- license: apache-2.0 language: - en --- ## Model Description - **Purpose**: This model is fine-tuned to perform multi-class emotion classification. It can identify various emotions in text, such as joy, sadness, love, anger, fear, and surprise. - **Model architecture**: The model is based on the `distilbert-base-uncased` architecture, a distilled version of the BERT model which is smaller and faster but retains most of its predictive power. - **Training data**: The model was trained on the `emotion` dataset from Hugging Face's datasets library. This dataset includes text labeled with different emotions. During preprocessing, texts were tokenized, and padding and truncation were applied to standardize their lengths. ## Intended Use - **Intended users**: This model is intended for developers and researchers interested in emotion analysis in text, including applications in social media sentiment analysis, customer feedback interpretation, and mental health assessment. - **Use cases**: Potential use cases include analyzing social media posts for emotional content, enhancing chatbots to understand user emotions, and helping mental health professionals in identifying emotional states from text-based communications. ## Limitations - **Known limitations**: The model's accuracy may vary depending on the context and the dataset's representativeness. It may not perform equally well on texts from domains significantly different from the training data. ## Hardware - **Training Platform**: The model was trained on Apple M1 . The training completed in under 23 minutes, demonstrating the efficiency of Apple hardware optimizations. ## Ethical Considerations - **Ethical concerns**: Care should be taken to ensure that the model is not used in sensitive applications without proper ethical considerations, especially in scenarios that could impact individual privacy or mental health. ## More Information - **Model Name on Hugging Face**: `aswathshakthi/distilbert-emotions-clf-m1`

P00j4n/fine-tuned-gemma-2-2b-quantized

P00j4n

2024-11-23T09:39:47Z

6

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T09:32:55Z

--- 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]

longisland3/anime-whisper-faster

longisland3

2024-11-23T09:17:37Z

6

0

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

2024-11-23T06:41:51Z

--- license: cc-by-nc-4.0 ---

touhidulislam/BERTweet_retrain_2021_07

touhidulislam

2024-11-23T09:17:03Z

170

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T09:16:42Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_07 results: [] ---  # BERTweet_retrain_2021_07 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5825 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7371 | 1.0 | 5927 | 2.6662 | | 2.7009 | 2.0 | 11854 | 2.6087 | | 2.6045 | 3.0 | 17781 | 2.5793 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2021_51

touhidulislam

2024-11-23T09:12:17Z

177

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T09:11:56Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_51 results: [] ---  # BERTweet_retrain_2021_51 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4925 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8054 | 1.0 | 5999 | 2.5611 | | 2.6829 | 2.0 | 11998 | 2.5308 | | 2.5699 | 3.0 | 17997 | 2.4981 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

Carick/roberta-base-wordnet_dataset_two-fine-tuned

Carick

2024-11-23T09:01:22Z

107

0

transformers

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

text-classification

2024-11-22T07:31:57Z

--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: roberta-base-wordnet_dataset_two-fine-tuned results: [] ---  # roberta-base-wordnet_dataset_two-fine-tuned This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2831 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.4241 | 1.0 | 7938 | 0.3626 | | 0.3768 | 2.0 | 15876 | 0.3164 | | 0.3227 | 3.0 | 23814 | 0.2831 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

sakuraumi/Sakura-13B-Galgame

sakuraumi

2024-11-23T09:00:48Z

135

114

transformers

[ "transformers", "pytorch", "baichuan", "text-generation", "custom_code", "zh", "ja", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-26T16:28:53Z

--- license: apache-2.0 language: - zh - ja pipeline_tag: text-generation ---   <div align="center"> <h1> SakuraLLM </h1> <center> <b>Sakura</b>: <b><ins>S</ins></b>FT <ins><b>A</b></ins>nd RLHF models using <ins><b>K</b></ins>nowledge of <ins><b>U</b></ins>niversal Character and <ins><b>R</b></ins>elationship <ins><b>A</b></ins>ttributes for Japanese to Chinese Translation in Light Novel & Galgame Domain. </center> </div> <p align="center"> 🤗 <a href="https://huggingface.co/sakuraumi/Sakura-13B-Galgame" target="_blank">Hugging Face</a> • 🤖 <a href="https://www.modelscope.cn/models/sakuraumi/Sakura-13B-Galgame" target="_blank">ModelScope</a> </p> # 目前Sakura发布的所有模型均采用[CC BY-NC-SA 4.0协议](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh-hans)，Sakura所有模型与其衍生模型均禁止任何形式的商用！Sakura系列所有模型皆仅供学习交流使用，开发者对使用Sakura模型造成的问题不负任何责任。 # 介绍 - 基于一系列开源大模型构建，在通用日文语料与轻小说/Galgame等领域的中日语料上进行继续预训练与微调，旨在提供开源可控可离线自部署的、ACGN风格的日中翻译模型。 - 新建了[TG交流群](https://t.me/+QMDKZyO9GV1kNDA1)，欢迎交流讨论。 **对于其他适配本模型的项目如使用非本项目提供的prompt格式进行翻译，不保证会获得与README中的说明一致的质量！** **如果使用模型翻译并发布，请在最显眼的位置标注机翻！！！！！开发者对于滥用本模型造成的一切后果不负任何责任。** > 由于模型一直在更新，请同时注明使用的模型版本等信息，方便进行质量评估和更新翻译。 **对于模型翻译的人称代词问题（错用，乱加，主宾混淆，男女不分等）和上下文理解问题，如果有好的想法或建议，欢迎提issue！** ### TODO：见[#42](https://github.com/SakuraLLM/Sakura-13B-Galgame/issues/42) ## 快速开始 ### 教程：详见[本仓库Wiki](https://github.com/SakuraLLM/Sakura-13B-Galgame/wiki). 部分使用方法：[usage.md](https://github.com/SakuraLLM/SakuraLLM/blob/main/usage.md) > **请注意，如果给轻小说机翻站使用，请参见[机翻站站内教程](https://books.fishhawk.top/forum?category=Guide&page=1)，本 repo 不适用。** ### 模型下载： | 参数量 | 发布时间-底模-版本 | 模型 | |:-------:|:-------|:-------| | 32B | 20240508-Qwen1.5-32B-v0.9 | 🤗 [Sakura-32B-Qwen2beta-v0.9-GGUF](https://huggingface.co/SakuraLLM/Sakura-32B-Qwen2beta-v0.9-GGUF) | | | 20240508-Qwen1.5-32B-v0.10pre1 | 🤗 [Sakura-32B-Qwen2beta-v0.10pre1-GGUF](https://huggingface.co/SakuraLLM/Sakura-32B-Qwen2beta-v0.10pre1-GGUF) | | 14B | 20240111-Qwen-14B-v0.9 | 🤗 [Sakura-13B-LNovel-v0.9b-GGUF](https://huggingface.co/SakuraLLM/Sakura-13B-LNovel-v0.9b-GGUF) | | | 20240213-Qwen1.5-14B-v0.9 | 🤗 [Sakura-14B-Qwen2beta-v0.9-GGUF](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2beta-v0.9-GGUF) | | | 20240516-Qwen1.5-14B-v0.9.2 | 🤗 [Sakura-14B-Qwen2beta-v0.9.2-GGUF](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2beta-v0.9.2-GGUF) |(最新)| **20241008-Qwen2.5-14B-v1.0** | 🤗 [Sakura-14B-Qwen2.5-v1.0-GGUF](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2.5-v1.0-GGUF) | 7B | 20240116-Qwen-7B-v0.9 | 🤗 [Sakura-7B-LNovel-v0.9-GGUF](https://huggingface.co/SakuraLLM/Sakura-7B-LNovel-v0.9-GGUF) | | | 20240531-Qwen1.5-7B-Galtransl-v2.6 | 🤗 [Galtransl-v2.6](https://huggingface.co/SakuraLLM/GalTransl-7B-v2.6) | | ~2B | 20240214-Qwen1.5-1.8B-v0.9.1 | 🤗 [Sakura-1B8-Qwen2beta-v0.9.1-GGUF](https://huggingface.co/SakuraLLM/Sakura-1B8-Qwen2beta-v0.9.1-GGUF) | | | **20241012-Qwen2.5-1.5B-v1.0** | 🤗 [Sakura-1.5B-Qwen2.5-v1.0-GGUF](https://huggingface.co/SakuraLLM/Sakura-1.5B-Qwen2.5-v1.0-GGUF) | p.s. 如果无法连接到HuggingFace服务器，可将链接中的`huggingface.co`改成`hf-mirror.com`，使用hf镜像站下载。 ## News 1. **更新了基于Qwen2.5-14B的v1.0正式版模型[Sakura-14B-Qwen2.5-v1.0](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2.5-v1.0-GGUF)和基于Qwen2.5-1.5B的v1.0正式版模型[Qwen2.5-1.5B-v1.0](https://huggingface.co/SakuraLLM/Sakura-1.5B-Qwen2.5-v1.0-GGUF)，prompt格式参见[下方说明](https://github.com/SakuraLLM/SakuraLLM#%E6%8E%A8%E7%90%86)。主要改进：** - 改善翻译质量，提高翻译准确率，尤其是人称的准确率。 - 支持术语表(GPT字典)，以保持专有名词和人称的一致性。 - 提高部分简单控制符的保留能力，尤其是单行内存在`\n`的情况下保留`\n`的能力。降低行数与原文不一致的概率。 - 由于底模使用GQA，推理速度和显存占用显著改善，可实现更快的多线程推理。关于多线程推理，可参考[Sakura启动器GUI使用教程](https://books.fishhawk.top/forum/656d60530286f15e3384fcf8)或[SakuraLLMServer](https://github.com/neavo/SakuraLLMServer)。 1. **更新了基于Qwen1.5-7B的[Galtransl](https://huggingface.co/SakuraLLM/GalTransl-v1)模型，为视觉小说翻译任务专项优化。对视觉小说脚本中的行内换行、控制符、ruby注音等符号具有较好的保留能力。适配[GalTransl视觉小说翻译工具](https://github.com/xd2333/GalTransl)并调优，支持GPT字典（[字典写法见此](https://github.com/xd2333/GalTransl/wiki/GPT%E5%AD%97%E5%85%B8%E2%80%90sakura-galtransl)）。** 1. **增加了vllm模型后端的支持，详见**[#40](https://github.com/SakuraLLM/Sakura-13B-Galgame/pull/40) 1. <del>感谢[Isotr0py](https://github.com/Isotr0py)提供运行模型的NoteBook仓库[SakuraLLM-Notebooks](https://github.com/Isotr0py/SakuraLLM-Notebooks)，可在[Colab](https://colab.research.google.com/)(免费T4\*1)与[Kaggle](https://www.kaggle.com/)(免费P100\*1或T4\*2)平台使用。**已经更新Kaggle平台的[使用教程](https://github.com/SakuraLLM/Sakura-13B-Galgame/wiki/%E7%99%BD%E5%AB%96Kaggle%E5%B9%B3%E5%8F%B0%E9%83%A8%E7%BD%B2%E6%95%99%E7%A8%8B)，可以白嫖一定时间的T4\*2。**</del> 警告，Kaggle 官方已经采取措施封禁 SakuraLLM 仓库，[参见](https://github.com/SakuraLLM/SakuraLLM/issues/115) ，在 Kaggle 上克隆 SakuraLLM 仓库可能将会导致永久性封号。请换用其他项目或转移至租卡平台使用。 1. **Sakura API已经支持OpenAI格式，现在可以通过OpenAI库或者OpenAI API Reference上的请求形式与Server交互。** 一个使用OpenAI库与Sakura模型交互的例子详见[openai_example.py](https://github.com/SakuraLLM/Sakura-13B-Galgame/blob/main/tests/example_openai.py)。 ## 已经接入模型的工具 1. 网站：[轻小说机翻机器人](https://books.fishhawk.top/)已接入Sakura模型(v0.8-4bit)，站内有大量模型翻译结果可供参考。你也可以自行部署模型并使用该网站生成机翻，目前已经支持v0.8与v0.9模型，且提供了llama.cpp一键包。轻小说机翻机器人网站是一个自动生成轻小说机翻并分享的网站。你可以浏览日文网络小说，或者上传Epub/Txt文件，并生成机翻。 1. [LunaTranslator](https://github.com/HIllya51/LunaTranslator)已经支持Sakura API，可以通过本地部署API后端，并在LunaTranslator中配置Sakura API来使用Sakura模型进行Galgame实时翻译。 ~~使用[KurikoMoe](https://github.com/kurikomoe/LunaTranslator/releases/latest)的版本可以支持流式输出。~~ 目前官方版本已经支持流式输出，只需在翻译设置界面勾选流式输出即可。 LunaTranslator是一个Galgame翻译工具，支持剪贴板、OCR、HOOK，支持40余种翻译引擎。 1. [GalTransl](https://github.com/XD2333/GalTransl)已经支持Sakura API，可以通过本地部署API后端，在GalTransl中配置使用Sakura模型来翻译Galgame，制作内嵌式翻译补丁。 GalTransl是一个galgame自动化翻译工具，用于制作内嵌式翻译补丁。一个使用GalTransl和Sakura模型翻译的[示例](https://www.ai2moe.org/files/file/2271-%E6%88%AF%E7%94%BBgaltranslsakuragpt35%E7%88%B1%E4%B9%8B%E5%90%BB3-sexy-gpt%E7%BF%BB%E8%AF%91%E8%A1%A5%E4%B8%81uploadee5-mb/) 1. 翻译Unity引擎游戏的工具[SakuraTranslator](https://github.com/fkiliver/SakuraTranslator)。感谢[fkiliver](https://github.com/fkiliver)提供。 1. 翻译RPGMaker引擎游戏的工具[RPGMaker_LLaMA_Translator](https://github.com/fkiliver/RPGMaker_LLaMA_Translator)。感谢[fkiliver](https://github.com/fkiliver)提供。 1. [AiNiee](https://github.com/NEKOparapa/AiNiee-chatgpt)已经支持Sakura API，可以通过本地部署API后端，在AiNiee中使用Sakura模型进行翻译。 AiNiee是一款基于【mtool】或【Translator++】，chatgpt自动批量翻译工具，主要是用来翻译各种RPG游戏。 1. [manga-image-translator](https://github.com/zyddnys/manga-image-translator)已经支持Sakura API，可以通过本地部署API后端，使用Sakura自动翻译漫画。 1. [BallonsTranslator](https://github.com/dmMaze/BallonsTranslator)已经支持Sakura API，可以通过本地部署API后端，使用Sakura翻译漫画。 # 显存需求下面的表格显示了使用不同量化和不同格式的模型时显存占用的大小。如果你的显卡显存不满足上述需求，可以尝试同时使用CPU与GPU进行推理。 - llama.cpp GGUF模型（使用Qwen-14B v0.9模型进行测试） | 模型量化类型 | 模型大小 | 推荐显存大小 | |:-------:|:-------:|:-------:| | fp16 | 26.3G | 超出游戏显卡显存范围 | | Q8_0 | 14G | 24G | | Q6_K | 11.4G | 20G | | Q5_K_M | 10.1G | 16G | | Q4_K_M | 8.8G | 16G | | Q3_K_M | 7.2G | 16G | | Q2_K | 6.1G | 12G | # 模型详情 ## 描述 - Finetuned by [SakuraUmi](https://github.com/pipixia244) - Finetuned on [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) - Continual Pre-trained on [Qwen model series](https://github.com/QwenLM/Qwen) - Continual Pre-trained on [Qwen1.5 model series](https://github.com/QwenLM/Qwen1.5) - Finetuned on Sakura-Base model series - Languages: Chinese/Japanese ## 效果 - Galgame [一个例子](https://www.ai2moe.org/files/file/2271-%E6%88%AF%E7%94%BBgaltranslsakuragpt35%E7%88%B1%E4%B9%8B%E5%90%BB3-sexy-gpt%E7%BF%BB%E8%AF%91%E8%A1%A5%E4%B8%81uploadee5-mb/) - 轻小说网站：[轻小说机翻机器人](https://books.fishhawk.top/)已接入Sakura模型(v0.9)，站内有大量模型翻译的轻小说可供参考。 - PPL/BLEU/Human TBD # 推理 - openai api messages格式： - v0.9 使用代码处理如下： ```python input_text_list = ['a', 'bb', 'ccc', ...] # 一系列上下文文本，每个元素代表一行的文本 raw_text = "\n".join(input_text_list) messages=[ { "role": "system", "content": "你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。" }, { "role": "user", "content": "将下面的日文文本翻译成中文：" + raw_text } ] ``` - prompt格式： - v0.10pre1 代码处理如下： ```python gpt_dict = [{ "src": "原文1", "dst": "译文1", "info": "注释信息1", },] gpt_dict_text_list = [] for gpt in gpt_dict: src = gpt['src'] dst = gpt['dst'] info = gpt['info'] if "info" in gpt.keys() else None if info: single = f"{src}->{dst} #{info}" else: single = f"{src}->{dst}" gpt_dict_text_list.append(single) gpt_dict_raw_text = "\n".join(gpt_dict_text_list) user_prompt = "根据以下术语表（可以为空）：\n" + gpt_dict_raw_text + "\n\n" + "将下面的日文文本根据上述术语表的对应关系和备注翻译成中文：" + japanese prompt = "<|im_start|>system\n你是一个轻小说翻译模型，可以流畅通顺地使用给定的术语表以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，注意不要混淆使役态和被动态的主语和宾语，不要擅自添加原文中没有的代词，也不要擅自增加或减少换行。<|im_end|>\n" \ # system prompt + "<|im_start|>user\n" + user_prompt + "<|im_end|>\n" \ # user prompt + "<|im_start|>assistant\n" # assistant prompt start ``` - v0.9 文本格式如下： ``` <|im_start|>system 你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。<|im_end|> <|im_start|>user 将下面的日文文本翻译成中文：日文第一行日文第二行日文第三行 ... 日文第n行<|im_end|> <|im_start|>assistant ``` 使用代码处理如下： ```python input_text_list = ['a', 'bb', 'ccc', ...] # 一系列上下文文本，每个元素代表一行的文本 raw_text = "\n".join(input_text_list) prompt = "<|im_start|>system\n你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。<|im_end|>\n" \ # system prompt + "<|im_start|>user\n将下面的日文文本翻译成中文：" + raw_text + "<|im_end|>\n" \ # user prompt + "<|im_start|>assistant\n" # assistant prompt start ``` - prompt构建： - v0.8 ```python input_text = "" # 要翻译的日文 query = "将下面的日文文本翻译成中文：" + input_text prompt = "<reserved_106>" + query + "<reserved_107>" ``` - v0.9 ```python input_text = "" # 要翻译的日文 query = "将下面的日文文本翻译成中文：" + input_text prompt = "<|im_start|>system\n你是一个轻小说翻译模型，可以流畅通顺地以日本轻小说的风格将日文翻译成简体中文，并联系上下文正确使用人称代词，不擅自添加原文中没有的代词。<|im_end|>\n<|im_start|>user\n" + query + "<|im_end|>\n<|im_start|>assistant\n" ``` - 推理与解码参数： | 参数 | 值 | | ---- | ---- | | temperature | 0.1 | | top p | 0.3 | | do sample | True | | beams number | 1 | | repetition penalty | 1 | | max new token | 512 | | min new token | 1 | **如出现退化（退化的例子可参见[#35](https://github.com/SakuraLLM/Sakura-13B-Galgame/issues/35)与[#36](https://github.com/SakuraLLM/Sakura-13B-Galgame/issues/36)），可增加`frequency_penalty`参数，并设置为大于0的某值，一般设置0.1~0.2即可。** # 微调模型微调框架参考[BELLE](https://github.com/LianjiaTech/BELLE)或[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)，prompt构造参考推理部分。 # 相关项目 - [轻小说机翻机器人](https://books.fishhawk.top/)：轻小说翻译 - [LunaTranslator](https://github.com/HIllya51/LunaTranslator)：Galgame在线翻译 - [GalTransl](https://github.com/XD2333/GalTransl)：Galgame离线翻译，制作补丁 - [AiNiee](https://github.com/NEKOparapa/AiNiee-chatgpt)：RPG游戏翻译 # 致谢 - [CjangCjengh](https://github.com/CjangCjengh) - [ryank231231](https://github.com/ryank231231) - [KurikoMoe](https://github.com/kurikomoe) - [FishHawk](https://github.com/FishHawk) - [K024](https://github.com/K024) - [minaduki-sora](https://github.com/minaduki-sora) - [Kimagure7](https://github.com/Kimagure7) - [YYF233333](https://github.com/YYF233333) - [Isotr0py](https://github.com/Isotr0py) - [XD2333](https://github.com/XD2333) # Copyright Notice v0.8版本模型的使用须遵守[Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE)、[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)和[CC BY-NC-SA 4.0协议](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh-hans)。 v0.9版本模型的使用须遵守[Qwen模型许可协议](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)和[CC BY-NC-SA 4.0协议](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh-hans)。

touhidulislam/BERTweet_retrain_2021_06

touhidulislam

2024-11-23T08:41:38Z

177

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T08:41:19Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_06 results: [] ---  # BERTweet_retrain_2021_06 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5775 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6492 | 1.0 | 5855 | 2.6543 | | 2.6772 | 2.0 | 11710 | 2.6140 | | 2.7103 | 3.0 | 17565 | 2.5723 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

touhidulislam/BERTweet_retrain_2022_06

touhidulislam

2024-11-23T08:41:01Z

178

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T08:40:40Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2022_06 results: [] ---  # BERTweet_retrain_2022_06 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5204 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6983 | 1.0 | 6012 | 2.5991 | | 2.4763 | 2.0 | 12024 | 2.5499 | | 2.4619 | 3.0 | 18036 | 2.5269 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

rinabuoy/nllb-200-600M-En-Ar-finetuned2

rinabuoy

2024-11-23T08:39:01Z

159

0

transformers

[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-23T08:37:14Z

--- 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]

Mantis-VL/mantis-8b-idefics2-video-eval_5184_regression

Mantis-VL

2024-11-23T08:36:39Z

7

0

transformers

[ "transformers", "safetensors", "idefics2", "text-classification", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "base_model:finetune:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-11-21T16:15:12Z

--- library_name: transformers license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval_5184_regression results: [] ---  # mantis-8b-idefics2-video-eval_5184_regression This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.45.0 - Pytorch 2.5.1+cu124 - Datasets 2.18.0 - Tokenizers 0.20.3

touhidulislam/BERTweet_retrain_2021_50

touhidulislam

2024-11-23T08:36:07Z

166

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T08:35:45Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_50 results: [] ---  # BERTweet_retrain_2021_50 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4821 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7593 | 1.0 | 6047 | 2.5543 | | 2.6949 | 2.0 | 12094 | 2.5029 | | 2.6966 | 3.0 | 18141 | 2.4801 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

susmitabhatt/whisper-a-nomimo-ls

susmitabhatt

2024-11-23T08:32:23Z

13

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-23T05:57:47Z

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-a-nomimo-ls results: [] ---  # whisper-a-nomimo-ls This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0311 - Wer: 32.3302 ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 132 - num_epochs: 11 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:---------:| | 0.9712 | 1.0 | 104 | 0.0786 | 25.0 | | 3.0599 | 2.0 | 208 | 0.7840 | 3312.3457 | | 0.3714 | 3.0 | 312 | 0.2694 | 98.3796 | | 0.2625 | 4.0 | 416 | 0.2197 | 90.9722 | | 0.2298 | 5.0 | 520 | 0.2035 | 84.9537 | | 0.1929 | 6.0 | 624 | 0.1519 | 66.0494 | | 0.1086 | 7.0 | 728 | 0.0641 | 41.6667 | | 0.0401 | 8.0 | 832 | 0.0421 | 41.6667 | | 0.0231 | 9.0 | 936 | 0.0324 | 34.8765 | | 0.0119 | 10.0 | 1040 | 0.0310 | 34.7222 | | 0.0057 | 10.8986 | 1133 | 0.0311 | 32.3302 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

gnmskel/ToBeOrNotToBE

gnmskel

2024-11-23T08:29:05Z

173

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:KETI-AIR/ke-t5-base-ko", "base_model:finetune:KETI-AIR/ke-t5-base-ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-22T17:33:04Z

--- library_name: transformers license: apache-2.0 base_model: KETI-AIR/ke-t5-base-ko tags: - generated_from_trainer model-index: - name: ToBeOrNotToBE results: [] ---  # ToBeOrNotToBE This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-ko) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 39.4877 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 265 | 84.3180 | | 127.3935 | 2.0 | 530 | 61.3429 | | 127.3935 | 3.0 | 795 | 46.6663 | | 56.6333 | 4.0 | 1060 | 41.3475 | | 56.6333 | 5.0 | 1325 | 39.4877 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0 - Datasets 3.0.2 - Tokenizers 0.20.1

mav23/vicuna-13b-v1.3-GGUF

mav23

2024-11-23T08:28:15Z

19

1

null

[ "gguf", "arxiv:2302.13971", "arxiv:2306.05685", "region:us" ]

null

2024-11-23T07:01:55Z

--- inference: false --- **NOTE: New version available** Please check out a newer version of the weights [here](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md). <br> # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture. - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

atlimited/llm-jp-3-3_7b-aio-retriever

atlimited

2024-11-23T08:28:11Z

74

0

transformers

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

text-generation

2024-11-23T08:24:01Z

--- 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]

susmitabhatt/whisper-a-clp-ls

susmitabhatt

2024-11-23T08:25:13Z

10

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-23T07:26:11Z

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-a-clp-ls results: [] ---  # whisper-a-clp-ls This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0240 - Wer: 10.0629 ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 132 - num_epochs: 11 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | No log | 1.0 | 40 | 0.1713 | 46.9602 | | No log | 2.0 | 80 | 0.0920 | 28.3019 | | 1.2158 | 3.0 | 120 | 0.1828 | 31.2369 | | 1.2158 | 4.0 | 160 | 0.2743 | 42.3480 | | 0.1604 | 5.0 | 200 | 0.1326 | 62.8931 | | 0.1604 | 6.0 | 240 | 0.0734 | 25.7862 | | 0.1604 | 7.0 | 280 | 0.0510 | 15.7233 | | 0.0502 | 8.0 | 320 | 0.0262 | 10.4822 | | 0.0502 | 9.0 | 360 | 0.0320 | 11.9497 | | 0.0202 | 10.0 | 400 | 0.0229 | 7.1279 | | 0.0202 | 10.7342 | 429 | 0.0240 | 10.0629 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

chatpdflocal/Qwen2.5.1-Coder-14B-Instruct-GGUF

chatpdflocal

2024-11-23T08:13:27Z

55

2

null

[ "gguf", "Qwen2.5.1", "Qwen2.5.1-Coder", "Qwen2.5.1-14B-Instruct", "GGUF", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-12T05:16:12Z

--- license: apache-2.0 tags: - Qwen2.5.1 - Qwen2.5.1-Coder - Qwen2.5.1-14B-Instruct - GGUF --- # This is Qwen2.5.1-Coder-14B-Instruct GGUF format model, which can be easily runned on PCs, mobile phones or devices with llama.cpp. # If you are a Mac user, the following free wonderful AI tools can help you to read and understand PDFs effectively: - If you are using Zotero for managing and reading your personal PDFs, [PapersGPT](https://www.papersgpt.com) is a free plugin which can assist you to chat PDFs effectively by your local SmolLM2-135M-Instruct. - you can directly download the beautiful ChatPDFLocal MacOS app from [here](https://www.chatpdflocal.com), load one or batch PDF files at will, and quickly experience the effect of the model through chat reading. PS. Click [here](https://awa-ai.lemonsqueezy.com/buy/89be07f8-060d-4a8f-a758-f25352773168) to subscribe or invite friends in ChatPDFLocal MacOS app can earn credits to use freely.

touhidulislam/BERTweet_retrain_2021_05

touhidulislam

2024-11-23T08:06:46Z

177

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:vinai/bertweet-base", "base_model:finetune:vinai/bertweet-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-23T08:06:26Z

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2021_05 results: [] ---  # BERTweet_retrain_2021_05 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5689 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8078 | 1.0 | 5959 | 2.6497 | | 2.6178 | 2.0 | 11918 | 2.6055 | | 2.6726 | 3.0 | 17877 | 2.5775 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

SAISON17/polyglot-ko-12.8b_qlora_v0.0

SAISON17

2024-11-23T07:45:26Z

74

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-11-23T07:36:20Z

--- 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]