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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-22 00:45:16	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 570 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-22 00:43:28	card stringlengths 11 1.01M
DiederikMartens/tsBERT_sa_cv_10_fold3	DiederikMartens	2024-05-26T05:20:21Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:54:00Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_10_fold3 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. --> # tsBERT_sa_cv_10_fold3 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5371 - F1: 0.6764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.3888 \| 0.5811 \| \| 0.4027 \| 2.0 \| 802 \| 0.4308 \| 0.6331 \| \| 0.2124 \| 3.0 \| 1203 \| 0.5371 \| 0.6764 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/mBERT_sa_cv_10_fold3	DiederikMartens	2024-05-26T05:18:59Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:53:11Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_10_fold3 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. --> # mBERT_sa_cv_10_fold3 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4593 - F1: 0.6496 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.4559 \| 0.4846 \| \| 0.5211 \| 2.0 \| 802 \| 0.4902 \| 0.4870 \| \| 0.357 \| 3.0 \| 1203 \| 0.4593 \| 0.6496 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
katryo/controlnet-facesynthetics-spiga-sdxl-20000	katryo	2024-05-26T05:15:18Z	3	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T05:07:45Z	--- license: openrail++ library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet - diffusers-training base_model: stabilityai/stable-diffusion-xl-base-1.0 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-katryo/controlnet-facesynthetics-spiga-sdxl-20000 This Controlnet model for SDXL was trained with https://huggingface.co/datasets/multimodalart/facesyntheticsspigacaptioned following the instructions explained at https://huggingface.co/blog/train-your-controlnet These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: a close-up of a man ![images_0)](./images_0.png) prompt: a close-up of a woman ![images_1)](./images_1.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
DiederikMartens/gBERT_sa_cv_10_fold3	DiederikMartens	2024-05-26T05:11:07Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:46:51Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_10_fold3 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. --> # gBERT_sa_cv_10_fold3 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4916 - F1: 0.6953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.3289 \| 0.5815 \| \| 0.4026 \| 2.0 \| 802 \| 0.4236 \| 0.6581 \| \| 0.1923 \| 3.0 \| 1203 \| 0.4916 \| 0.6953 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/eBERT_sa_cv_10_fold2	DiederikMartens	2024-05-26T04:55:03Z	112	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:28:03Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_10_fold2 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. --> # eBERT_sa_cv_10_fold2 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4745 - F1: 0.5267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.4994 \| 0.4269 \| \| 0.5766 \| 2.0 \| 802 \| 0.4561 \| 0.4976 \| \| 0.4134 \| 3.0 \| 1203 \| 0.4745 \| 0.5267 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_10_fold2	DiederikMartens	2024-05-26T04:53:46Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:27:43Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_10_fold2 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. --> # tsBERT_sa_cv_10_fold2 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4216 - F1: 0.6882 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.3363 \| 0.6489 \| \| 0.4186 \| 2.0 \| 802 \| 0.3382 \| 0.6851 \| \| 0.2032 \| 3.0 \| 1203 \| 0.4216 \| 0.6882 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
datek/gemma-2b-flock-1716698974	datek	2024-05-26T04:51:59Z	156	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T04:49:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Ataullha/speaker-segmentation-fine-tuned-callhome-jpn	Ataullha	2024-05-26T04:51:23Z	54	0	transformers	[ "transformers", "tensorboard", "safetensors", "pyannet", "speaker-diarization", "speaker-segmentation", "generated_from_trainer", "jpn", "dataset:diarizers-community/callhome", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T04:20:04Z	--- language: - jpn license: apache-2.0 base_model: openai/whisper-small tags: - speaker-diarization - speaker-segmentation - generated_from_trainer datasets: - diarizers-community/callhome model-index: - name: speaker-segmentation-fine-tuned-callhome-jpn 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. --> # speaker-segmentation-fine-tuned-callhome-jpn This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/callhome dataset. It achieves the following results on the evaluation set: - Loss: 0.7482 - Der: 0.2201 - False Alarm: 0.0465 - Missed Detection: 0.1319 - Confusion: 0.0417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Der \| False Alarm \| Missed Detection \| Confusion \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-----------:\|:----------------:\|:---------:\| \| 0.5488 \| 1.0 \| 328 \| 0.7565 \| 0.2280 \| 0.0461 \| 0.1355 \| 0.0465 \| \| 0.475 \| 2.0 \| 656 \| 0.7596 \| 0.2220 \| 0.0467 \| 0.1334 \| 0.0419 \| \| 0.4734 \| 3.0 \| 984 \| 0.7531 \| 0.2215 \| 0.0437 \| 0.1364 \| 0.0414 \| \| 0.4535 \| 4.0 \| 1312 \| 0.7468 \| 0.2194 \| 0.0462 \| 0.1323 \| 0.0409 \| \| 0.4764 \| 5.0 \| 1640 \| 0.7482 \| 0.2201 \| 0.0465 \| 0.1319 \| 0.0417 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
amir1226/ppo-LunarLander-v2-rl	amir1226	2024-05-26T04:47:41Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-26T04:47:22Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 261.79 +/- 19.42 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
poojapremnath/SnakeCLEF-resnet	poojapremnath	2024-05-26T04:45:16Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T04:35:43Z	--- license: apache-2.0 ---
mradermacher/Daredevil-8B-GGUF	mradermacher	2024-05-26T04:37:44Z	59	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:mlabonne/Daredevil-8B", "base_model:quantized:mlabonne/Daredevil-8B", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-26T03:36:18Z	--- base_model: mlabonne/Daredevil-8B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mlabonne/Daredevil-8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
kawagoshi-llm-team/llama3_sft_many_chat	kawagoshi-llm-team	2024-05-26T04:37:06Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T18:10:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DiederikMartens/eBERT_sa_cv_10_fold1	DiederikMartens	2024-05-26T04:27:49Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:00:47Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_10_fold1 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. --> # eBERT_sa_cv_10_fold1 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4772 - F1: 0.4637 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.6262 \| 0.2953 \| \| 0.6031 \| 2.0 \| 802 \| 0.5669 \| 0.4470 \| \| 0.4469 \| 3.0 \| 1203 \| 0.4772 \| 0.4637 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
khnhlinh/gpt-on-hugging-face	khnhlinh	2024-05-26T04:27:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T04:27:35Z	--- license: apache-2.0 ---
JOY-ZHE/gpt2_PROMPT_TUNING_CAUSAL_LM	JOY-ZHE	2024-05-26T04:21:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T21:27:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TroyDoesAI/Contextual-Llama3-8B-RAG	TroyDoesAI	2024-05-26T04:18:18Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T04:10:52Z	--- license: cc-by-nd-4.0 ---
Kaballas/Kaballas	Kaballas	2024-05-26T04:14:19Z	36	0	transformers	[ "transformers", "safetensors", "bert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T04:06:29Z	--- license: apache-2.0 ---
oliverdk/codegen-350M-mono-measurement_pred	oliverdk	2024-05-26T04:08:59Z	110	0	transformers	[ "transformers", "safetensors", "codegen_mp", "text-classification", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-classification	2024-05-22T12:24:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Mantis-VL/mantis-8b-idefics2-video-eval-20k_2048	Mantis-VL	2024-05-26T04:08:26Z	8	0	transformers	[ "transformers", "safetensors", "idefics2", "image-text-to-text", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "base_model:finetune:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-19T09:43:11Z	--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval-20k_2048 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dongfu/Mantis/runs/f0l8j9ep) # mantis-8b-idefics2-video-eval-20k_2048 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: 3.0 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
QuangDuy/whisper-large-v3-common_voice	QuangDuy	2024-05-26T04:07:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-26T04:07:07Z	--- 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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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]
DiederikMartens/eBERT_sa_cv_10_fold0	DiederikMartens	2024-05-26T04:00:33Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T03:33:39Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_10_fold0 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. --> # eBERT_sa_cv_10_fold0 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4895 - F1: 0.5283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.5921 \| 0.3884 \| \| 0.5934 \| 2.0 \| 802 \| 0.4642 \| 0.4908 \| \| 0.4248 \| 3.0 \| 1203 \| 0.4895 \| 0.5283 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/gBERT_sa_cv_10_fold0	DiederikMartens	2024-05-26T03:58:53Z	115	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T03:33:37Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_10_fold0 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. --> # gBERT_sa_cv_10_fold0 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5642 - F1: 0.6960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.3979 \| 0.5802 \| \| 0.4157 \| 2.0 \| 802 \| 0.3928 \| 0.6690 \| \| 0.1913 \| 3.0 \| 1203 \| 0.5642 \| 0.6960 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
rupesh2009/tiny-chatbot-dpo	rupesh2009	2024-05-26T03:54:47Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-26T03:52:41Z	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-chatbot-dpo 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. --> # tiny-chatbot-dpo This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
chrohi/llama3_lora_model	chrohi	2024-05-26T03:45:15Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-26T03:12:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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Sorour/cls_fomc_mistral_v1	Sorour	2024-05-26T03:41:27Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-19T03:20:11Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator model-index: - name: cls_fomc_mistral_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cls_fomc_mistral_v1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6185 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5623 \| 1.2903 \| 20 \| 0.6185 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
William2357/outputthing	William2357	2024-05-26T03:38:25Z	29	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-20T21:34:51Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: runwayml/stable-diffusion-v1-5 inference: true instance_prompt: a photo of sks dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - William2357/outputthing This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
suthanhcong/movie_summarize_model	suthanhcong	2024-05-26T03:31:44Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-26T03:31:28Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: movie_summarize_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # movie_summarize_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3072 - Rouge1: 0.1621 - Rouge2: 0.0398 - Rougel: 0.1305 - Rougelsum: 0.1304 - Gen Len: 18.9634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 3.5827 \| 1.0 \| 573 \| 3.3072 \| 0.1621 \| 0.0398 \| 0.1305 \| 0.1304 \| 18.9634 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
JinbiaoZhu/gemma-2b-it-QLoRA-RobotPlanning-v2	JinbiaoZhu	2024-05-26T03:29:18Z	10	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:05:09Z	--- 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. 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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Rhma/MistralDialo40	Rhma	2024-05-26T03:19:34Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T03:15:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Raneechu/textbookbig10_ft6	Raneechu	2024-05-26T03:17:09Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T03:16:58Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft6 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. --> # textbookbig10_ft6 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF	mradermacher	2024-05-26T03:05:54Z	16	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-3", "70b", "smaug", "lumimaid", "tess", "arimas", "breadcrums", "en", "base_model:ryzen88/Llama-3-70b-Arimas-story-RP-V1", "base_model:quantized:ryzen88/Llama-3-70b-Arimas-story-RP-V1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T13:40:03Z	--- base_model: ryzen88/Llama-3-70b-Arimas-story-RP-V1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - llama-3 - 70b - smaug - lumimaid - tess - arimas - breadcrums --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ3_XS.gguf) \| IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ3_S.gguf) \| IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ3_M.gguf) \| IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-988957	fine-tuned	2024-05-26T03:05:30Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-988957", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-26T03:04:59Z	--- license: apache-2.0 datasets: - fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-988957 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: custom ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-988957', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
Toshifumi/Llama3-Toshi-Ja-LD9-classifier_20240526v1	Toshifumi	2024-05-26T03:05:30Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T02:57:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Toshifumi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
QuantFactory/pair-preference-model-LLaMA3-8B-GGUF	QuantFactory	2024-05-26T03:05:22Z	39	1	transformers	[ "transformers", "gguf", "llama", "conversational", "text-generation", "arxiv:2405.07863", "base_model:RLHFlow/pair-preference-model-LLaMA3-8B", "base_model:quantized:RLHFlow/pair-preference-model-LLaMA3-8B", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T15:24:15Z	--- license: llama3 base_model: RLHFlow/pair-preference-model-LLaMA3-8B library_name: transformers pipeline_tag: text-generation tags: - llama - conversational --- # pair-preference-model-LLaMA3-8B-GGUF This is quantized version of [RLHFlow/pair-preference-model-LLaMA3-8B](https://huggingface.co/RLHFlow/pair-preference-model-LLaMA3-8B) created using llama.cpp # Model Description This preference model is trained from [LLaMA3-8B-it](meta-llama/Meta-Llama-3-8B-Instruct) with the training script at [Reward Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/pm_dev/pair-pm). The dataset is RLHFlow/pair_preference_model_dataset. It achieves Chat-98.6, Char-hard 65.8, Safety 89.6, and reasoning 94.9 in reward bench. See our paper [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/abs/2405.07863) for more details of this model. ## Service the RM Here is an example to use the Preference Model to rank a pair. For n>2 responses, it is recommened to use the tournament style ranking strategy to get the best response so that the complexity is linear in n. ```python device = 0 model = AutoModelForCausalLM.from_pretrained(script_args.preference_name_or_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").cuda() tokenizer = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain.chat_template = "\n{% for message in messages %}{% if loop.index0 % 2 == 0 %}\n\n<turn> user\n {{ message['content'] }}{% else %}\n\n<turn> assistant\n {{ message['content'] }}{% endif %}{% endfor %}\n\n\n" prompt_template = "[CONTEXT] {context} [RESPONSE A] {response_A} [RESPONSE B] {response_B} \n" token_id_A = tokenizer.encode("A", add_special_tokens=False) token_id_B = tokenizer.encode("B", add_special_tokens=False) assert len(token_id_A) == 1 and len(token_id_B) == 1 token_id_A = token_id_A[0] token_id_B = token_id_B[0] temperature = 1.0 model.eval() response_chosen = "BBBB" response_rejected = "CCCC" ## We can also handle multi-turn conversation. instruction = [{"role": "user", "content": ...}, {"role": "assistant", "content": ...}, {"role": "user", "content": ...}, ] context = tokenizer_plain.apply_chat_template(instruction, tokenize=False) responses = [response_chosen, response_rejected] probs_chosen = [] for chosen_position in [0, 1]: # we swap order to mitigate position bias response_A = responses[chosen_position] response_B = responses[1 - chosen_position] prompt = prompt_template.format(context=context, response_A=response_A, response_B=response_B) message = [ {"role": "user", "content": prompt}, ] input_ids = tokenizer.encode(tokenizer.apply_chat_template(message, tokenize=False).replace(tokenizer.bos_token, ""), return_tensors='pt', add_special_tokens=False).cuda() with torch.no_grad(): output = model(input_ids) logit_A = output.logits[0, -1, token_id_A].item() logit_B = output.logits[0, -1, token_id_B].item() # take softmax to get the probability; using numpy Z = np.exp(logit_A / temperature) + np.exp(logit_B / temperature) logit_chosen = [logit_A, logit_B][chosen_position] prob_chosen = np.exp(logit_chosen / temperature) / Z probs_chosen.append(prob_chosen) avg_prob_chosen = np.mean(probs_chosen) correct = 0.5 if avg_prob_chosen == 0.5 else float(avg_prob_chosen > 0.5) print(correct) ```
LarryAIDraw/AissistXLv2	LarryAIDraw	2024-05-26T03:04:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-26T02:59:35Z	--- license: creativeml-openrail-m --- https://civitai.com/models/317759/aissist-xl-negative-embedding?modelVersionId=403492
LarryAIDraw/SimplePositiveXLv2	LarryAIDraw	2024-05-26T03:03:45Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-26T02:57:05Z	--- license: creativeml-openrail-m --- https://civitai.com/models/118758/simplepositivexl?modelVersionId=182974
QuantFactory/LLaMA3-iterative-DPO-final-GGUF	QuantFactory	2024-05-26T03:03:20Z	132	1	transformers	[ "transformers", "gguf", "llama", "conversational", "text-generation", "base_model:RLHFlow/LLaMA3-iterative-DPO-final", "base_model:quantized:RLHFlow/LLaMA3-iterative-DPO-final", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T02:59:43Z	--- license: llama3 library_name: transformers pipeline_tag: text-generation tags: - llama - conversational base_model: RLHFlow/LLaMA3-iterative-DPO-final --- # LLaMA3-iterative-DPO-final-GGUF This is quantized version of [RLHFlow/LLaMA3-iterative-DPO-final](https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final) created using llama.cpp # Model Description We release an unofficial checkpoint of a state-of-the-art instruct model of its class, LLaMA3-iterative-DPO-final. On all three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, Chat-Arena-Hard, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it), and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling. Even better, we provide a [detailed recipe](https://github.com/RLHFlow/Online-RLHF) to reproduce the model. Enjoy! ## Model Releases See the [collection](https://huggingface.co/collections/RLHFlow/online-rlhf-663ae95fade1a39663dab218) of the training set, reward/preference model, SFT model. - [SFT model](https://huggingface.co/RLHFlow/LLaMA3-SFT) - [Reward model](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1) ## Dataset - [Preference data mix](https://huggingface.co/datasets/hendrydong/preference_700K) - [Prompt collection for RLHF training](https://huggingface.co/datasets/RLHFlow/prompt-collection-v0.1) ## Training methods We have developed a simple and efficient online RLHF recipe for LLM instruct training. Our recipe is DPO-based and thus much cheaper and simpler to train and tune compared to PPO-based approaches. Unlike widely-used offline DPO, the online component of our approach effectively mitigates distribution shifts during policy optimization. For a detailed exposition, please refer to our accompanying technical report. ## Chat Benchmarks \| Model \| Size \| Method \| LC Alpaca-Eval-V2 \| MT-Bench \| Chat-Arena-Hard \| \|-------------------------\|----------\|-------------------\|-----------------------\|--------------\|---------------------\| \| Small Open-Sourced Models \| \| \| \| \| \| \| Gemma-7B-it \| 7B \| SFT \| 10.4 \| 6.38 \| 7.5 \| \| Zephyr-7B-beta \| 7B \| Vanilla DPO \| 13.1 \| 7.34 \| - \| \| Mistral-7B-v0.2-it \| 7B \| SFT \| 17.1 \| 7.51 \| 12.6 \| \| Open-Chat-0106 \| 7B \| SFT \| 15.6 \| 7.8 \| - \| \| Starling-7B-beta \| 7B \| PPO \| 25.8 \| 8.12 \| 23.0 \| \| LLaMA-3-8B-it \| 8B \| RS+DPO+PPO \| 22.9 \| 8.16 \| 20.6 \| \| Ours \| \| \| \| \| \| \| Ours (SFT baseline) \| 8B \| SFT \| 10.2 \| 7.69 \| 5.6 \| \| Ours (DPO baseline) \| 8B \| Vanilla DPO \| 22.5 \| 8.17 \| 22.4 \| \| Ours (Online RLHF) \| 8B \| Iterative DPO \| 37.2 \| 8.46 \| 29.1 \| \| Large Open-Sourced Models \| \| \| \| \| \| \| Vicuna-33b-v1.3 \| 33B \| SFT \| 17.6 \| 7.12 \| 8.6 \| \| Yi-34B-Chat \| 34B \| SFT \| 27.2 \| - \| 23.1 \| \| Mixtral-8x7B-it \| 45B* \| SFT \| 23.7 \| 8.30 \| 23.4 \| \| Tulu-2-DPO-70B \| 70B \| Vanilla DPO \| 21.2 \| 7.89 \| 15.0 \| \| LLaMA-3-70B-it \| 70B \| RS+DPO+PPO \| 34.4 \| 8.95 \| 41.1 \| \| Mixtral-8x22B-it \| 141B* \| SFT \| 30.9 \| 8.66 \| 36.4 \| \| Proprietary Models \| \| \| \| \| \| \| GPT-3.5-turbo-1106 \| - \| - \| 19.3 \| 8.35 \| 18.9 \| \| GPT-3.5-turbo-0613 \| - \| - \| 22.7 \| 8.39 \| 24.8 \| \| GPT-4-0613 \| - \| - \| 30.2 \| 9.18 \| 37.9 \| \| Claude-3-Opus \| - \| - \| 40.5 \| 9.00 \| 60.4 \| \| GPT-4 Turbo (04/09) \| - \| - \| 55.0 \| - \| 82.6 \| ## Academic Benchmarks \| Model \| Size \| Method \| GSM-8K \| MMLU \| HumanEval \| TruthfulQA \| ARC \| MBPP \| \|----------------------------\|----------\|-----------------\|------------\|----------\|---------------\|----------------\|---------\|----------\| \| LLaMA-3-8B-it \| 8B \| RS+DPO+PPO \| 79.6 \| 66.0 \| 61.6 \| 43.9 \| 59.5 \| 61.1 \| \| Ours (SFT baseline) \| 8B \| SFT \| 74.2 \| 64.7 \| 65.2 \| 53.4 \| 61.4 \| 62.3 \| \| Ours (DPO baseline) \| 8B \| Vanilla DPO \| 79.8 \| 64.5 \| 63.4 \| 61.8 \| 65.2 \| 60.3 \| \| Ours (Iterative RLHF) \| 8B \| Iterative DPO \| 80.7 \| 65.3 \| 64.6 \| 60.4 \| 64.3 \| 60.8 \| ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final") tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final") messages = [ {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"}, ] model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = model_inputs.to(device) model.to(device) output_tokens = model.generate(model_inputs, max_new_tokens=1024, do_sample=True) model_outputs = tokenizer.batch_decode(output_tokens) print(model_outputs[0]) ``` ## Limitations RLHFlow/LLaMA3-iterative-DPO-final is an unofficial checkpoint developed to illustrate the power of online iterative RLHF and is for research purpose. While safety and ethical considerations are integral to our alignment process, there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions. We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage.
jack8885/Qwen-Qwen1.5-0.5B-hutao-01	jack8885	2024-05-26T03:02:27Z	162	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T03:01:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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]
fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-919917	fine-tuned	2024-05-26T03:02:12Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-919917", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-26T03:01:39Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-919917 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: custom ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-919917', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF	leungchunghong	2024-05-26T03:02:10Z	2	0	null	[ "gguf", "nlp", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-26T03:02:03Z	--- language: - en license: mit tags: - nlp - code - llama-cpp - gguf-my-repo license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) 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/microsoft/Phi-3-mini-4k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --model phi-3-mini-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --model phi-3-mini-4k-instruct-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. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m phi-3-mini-4k-instruct-q4_k_m.gguf -n 128 ```
Raneechu/textbookbig10_ft5	Raneechu	2024-05-26T03:02:06Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T03:02:03Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft5 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. --> # textbookbig10_ft5 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-484582	fine-tuned	2024-05-26T03:01:54Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-484582", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-26T03:01:22Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-484582 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: custom ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-484582', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
CluelessNovice/demo_model	CluelessNovice	2024-05-26T02:56:51Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:westlake-repl/SaProt_35M_AF2", "base_model:adapter:westlake-repl/SaProt_35M_AF2", "region:us" ]	null	2024-05-07T07:50:31Z	--- library_name: peft base_model: westlake-repl/SaProt_35M_AF2 --- # Model Card for Model ID This model is used for a demo task<br><br> The digital label means: <br>0: Positive <br> 1: Negative <br> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
underscore2/llama3-8b-mlsubs	underscore2	2024-05-26T02:49:24Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "text-generation", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:57:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit pipeline_tag: text-generation --- # Uploaded model - Developed by: underscore2 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) # Usage ``` from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "underscore2/llama3-8b-mlsubs", max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference inputs = tokenizer("[POST START]: New Architecture that replaces the MLP by using literal magic", return_tensors = "pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1000, repetition_penalty=1.4) ```
Raneechu/textbookbig10_ft4	Raneechu	2024-05-26T02:47:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T02:47:03Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft4 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. --> # textbookbig10_ft4 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
FrankL/storytellerLM-v0.1	FrankL	2024-05-26T02:46:30Z	172	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T07:41:16Z	--- library_name: transformers tags: [] --- ## 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: FrankL - Language(s) (NLP): English ### Direct Use ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained('FrankL/storytellerLM-v0.1', trust_remote_code=True, torch_dtype=torch.float16) model = model.to(device='cuda') tokenizer = AutoTokenizer.from_pretrained('FrankL/storytellerLM-v0.1', trust_remote_code=True) def inference( model: AutoModelForCausalLM, tokenizer: AutoTokenizer, input_text: str = "Once upon a time, ", max_new_tokens: int = 16 ): inputs = tokenizer(input_text, return_tensors="pt").to(device) outputs = model.generate( **inputs, pad_token_id=tokenizer.eos_token_id, max_new_tokens=max_new_tokens, do_sample=True, top_k=40, top_p=0.95, temperature=0.8 ) generated_text = tokenizer.decode( outputs[0], skip_special_tokens=True ) # print(outputs) print(generated_text) inference(model, tokenizer) ```
sehilnlf/model_v2	sehilnlf	2024-05-26T02:45:46Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large", "base_model:finetune:facebook/bart-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T19:34:51Z	--- license: apache-2.0 base_model: facebook/bart-large tags: - text2text-generation - generated_from_trainer metrics: - sacrebleu model-index: - name: model_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_v2 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2418 - Sacrebleu: 66.7409 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\| \| No log \| 1.0 \| 218 \| 0.6656 \| 66.6707 \| \| No log \| 2.0 \| 437 \| 0.5851 \| 66.5767 \| \| No log \| 3.0 \| 656 \| 0.6062 \| 66.4734 \| \| No log \| 4.0 \| 875 \| 0.7029 \| 66.5944 \| \| No log \| 5.0 \| 1093 \| 0.6852 \| 66.0086 \| \| No log \| 6.0 \| 1312 \| 0.7471 \| 66.0534 \| \| No log \| 7.0 \| 1531 \| 0.8938 \| 66.1986 \| \| No log \| 8.0 \| 1750 \| 0.8834 \| 66.4626 \| \| No log \| 9.0 \| 1968 \| 0.8895 \| 66.4292 \| \| No log \| 10.0 \| 2187 \| 0.8824 \| 66.0577 \| \| No log \| 11.0 \| 2406 \| 0.8781 \| 66.5076 \| \| No log \| 12.0 \| 2625 \| 0.9870 \| 66.5564 \| \| No log \| 13.0 \| 2843 \| 1.1580 \| 66.5116 \| \| No log \| 14.0 \| 3062 \| 0.9797 \| 66.3801 \| \| No log \| 15.0 \| 3281 \| 1.0680 \| 66.2748 \| \| No log \| 16.0 \| 3500 \| 1.0113 \| 66.5282 \| \| No log \| 17.0 \| 3718 \| 1.0023 \| 66.5794 \| \| No log \| 18.0 \| 3937 \| 1.0753 \| 66.2935 \| \| No log \| 19.0 \| 4156 \| 1.0462 \| 66.5036 \| \| No log \| 20.0 \| 4375 \| 1.0934 \| 66.7931 \| \| No log \| 21.0 \| 4593 \| 1.1732 \| 66.5171 \| \| No log \| 22.0 \| 4812 \| 1.1892 \| 66.4821 \| \| No log \| 23.0 \| 5031 \| 1.2766 \| 66.5913 \| \| No log \| 24.0 \| 5250 \| 1.2392 \| 66.5476 \| \| No log \| 25.0 \| 5468 \| 1.3452 \| 66.5616 \| \| No log \| 26.0 \| 5687 \| 1.1427 \| 66.7916 \| \| No log \| 27.0 \| 5906 \| 1.1809 \| 66.9823 \| \| No log \| 28.0 \| 6125 \| 1.2310 \| 66.7958 \| \| No log \| 29.0 \| 6343 \| 1.2147 \| 66.7948 \| \| No log \| 29.9 \| 6540 \| 1.2418 \| 66.7409 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Zlovoblachko/en_L1_RuleGen_spanbert	Zlovoblachko	2024-05-26T02:42:03Z	2	0	spacy	[ "spacy", "en", "region:us" ]	null	2024-05-23T09:55:06Z	--- tags: - spacy language: - en model-index: - name: en_L1_Rulegen_spanbert results: [] --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_L1_Rulegen_spanbert` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.4.4,<3.5.0` \| \| Default Pipeline \| `transformer`, `spancat` \| \| Components \| `transformer`, `spancat` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (5 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `spancat` \| `Tense semantics`, `Copying expression`, `Synonyms`, `Word form transmission`, `Transliteration` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `SPANS_SC_F` \| 83.87 \| \| `SPANS_SC_P` \| 90.84 \| \| `SPANS_SC_R` \| 77.90 \| \| `TRANSFORMER_LOSS` \| 52597.65 \| \| `SPANCAT_LOSS` \| 179515.44 \|
euiyulsong/BrierPC	euiyulsong	2024-05-26T02:40:37Z	80	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-26T02:36:23Z	--- library_name: transformers tags: - trl - orpo --- # 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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G-R-A-V-I-T-Y/long-t5-local-base-ARv1	G-R-A-V-I-T-Y	2024-05-26T02:36:26Z	115	0	transformers	[ "transformers", "tensorboard", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "base_model:google/long-t5-local-base", "base_model:finetune:google/long-t5-local-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T23:45:23Z	--- license: apache-2.0 base_model: google/long-t5-local-base tags: - generated_from_trainer model-index: - name: long-t5-local-base-ARv1 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. --> # long-t5-local-base-ARv1 This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9303 - Exact Match: 18.0 - Gen Len: 3.38 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact Match \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:-------:\| \| No log \| 1.0 \| 7 \| 3.4004 \| 14.0 \| 3.86 \| \| 2.7206 \| 2.0 \| 14 \| 3.1925 \| 8.0 \| 3.66 \| \| 2.6501 \| 3.0 \| 21 \| 2.9867 \| 8.0 \| 3.7 \| \| 2.6501 \| 4.0 \| 28 \| 2.8576 \| 12.0 \| 4.58 \| \| 1.9849 \| 5.0 \| 35 \| 2.9078 \| 12.0 \| 4.52 \| \| 2.0193 \| 6.0 \| 42 \| 2.8173 \| 8.0 \| 3.84 \| \| 2.0193 \| 7.0 \| 49 \| 2.7735 \| 16.0 \| 3.42 \| \| 1.6108 \| 8.0 \| 56 \| 2.5993 \| 12.0 \| 3.82 \| \| 1.8323 \| 9.0 \| 63 \| 2.5879 \| 12.0 \| 3.92 \| \| 1.4861 \| 10.0 \| 70 \| 2.7203 \| 16.0 \| 3.4 \| \| 1.4861 \| 11.0 \| 77 \| 2.9902 \| 24.0 \| 3.1 \| \| 1.425 \| 12.0 \| 84 \| 2.7667 \| 14.0 \| 3.36 \| \| 1.0387 \| 13.0 \| 91 \| 2.6547 \| 18.0 \| 3.42 \| \| 1.0387 \| 14.0 \| 98 \| 2.7072 \| 18.0 \| 3.34 \| \| 1.0793 \| 15.0 \| 105 \| 2.8158 \| 12.0 \| 3.58 \| \| 1.1969 \| 16.0 \| 112 \| 2.9404 \| 14.0 \| 3.32 \| \| 1.1969 \| 17.0 \| 119 \| 2.8512 \| 14.0 \| 3.3 \| \| 1.15 \| 18.0 \| 126 \| 2.7513 \| 18.0 \| 3.68 \| \| 1.2024 \| 19.0 \| 133 \| 2.7124 \| 16.0 \| 3.48 \| \| 1.3331 \| 20.0 \| 140 \| 2.7484 \| 16.0 \| 3.4 \| \| 1.3331 \| 21.0 \| 147 \| 2.8289 \| 18.0 \| 3.44 \| \| 1.1469 \| 22.0 \| 154 \| 2.9873 \| 14.0 \| 3.36 \| \| 1.5639 \| 23.0 \| 161 \| 3.0321 \| 18.0 \| 3.4 \| \| 1.5639 \| 24.0 \| 168 \| 3.0117 \| 14.0 \| 3.3 \| \| 0.8542 \| 25.0 \| 175 \| 2.8331 \| 16.0 \| 3.34 \| \| 0.9789 \| 26.0 \| 182 \| 2.7876 \| 20.0 \| 3.36 \| \| 0.9789 \| 27.0 \| 189 \| 2.7820 \| 20.0 \| 3.36 \| \| 0.8853 \| 28.0 \| 196 \| 2.8082 \| 18.0 \| 3.38 \| \| 0.9126 \| 29.0 \| 203 \| 2.8316 \| 16.0 \| 3.36 \| \| 1.0543 \| 30.0 \| 210 \| 2.8449 \| 18.0 \| 3.64 \| \| 1.0543 \| 31.0 \| 217 \| 2.8034 \| 8.0 \| 3.62 \| \| 1.0683 \| 32.0 \| 224 \| 2.8115 \| 14.0 \| 3.46 \| \| 0.951 \| 33.0 \| 231 \| 2.9019 \| 18.0 \| 3.34 \| \| 0.951 \| 34.0 \| 238 \| 3.0115 \| 18.0 \| 3.24 \| \| 0.8315 \| 35.0 \| 245 \| 3.0392 \| 18.0 \| 3.24 \| \| 1.1548 \| 36.0 \| 252 \| 3.0643 \| 18.0 \| 3.36 \| \| 1.1548 \| 37.0 \| 259 \| 3.0031 \| 16.0 \| 3.42 \| \| 0.7813 \| 38.0 \| 266 \| 2.9801 \| 18.0 \| 3.48 \| \| 0.671 \| 39.0 \| 273 \| 2.9622 \| 18.0 \| 3.48 \| \| 1.1771 \| 40.0 \| 280 \| 2.9049 \| 18.0 \| 3.46 \| \| 1.1771 \| 41.0 \| 287 \| 2.9042 \| 20.0 \| 3.56 \| \| 0.5959 \| 42.0 \| 294 \| 2.9598 \| 18.0 \| 3.48 \| \| 1.1583 \| 43.0 \| 301 \| 2.9936 \| 18.0 \| 3.44 \| \| 1.1583 \| 44.0 \| 308 \| 3.0072 \| 18.0 \| 3.44 \| \| 0.5728 \| 45.0 \| 315 \| 3.0003 \| 18.0 \| 3.44 \| \| 0.7237 \| 46.0 \| 322 \| 3.0093 \| 16.0 \| 3.4 \| \| 0.7237 \| 47.0 \| 329 \| 2.9688 \| 18.0 \| 3.42 \| \| 0.7295 \| 48.0 \| 336 \| 2.9533 \| 18.0 \| 3.38 \| \| 0.5627 \| 49.0 \| 343 \| 2.9357 \| 18.0 \| 3.36 \| \| 0.6489 \| 50.0 \| 350 \| 2.9317 \| 18.0 \| 3.4 \| \| 0.6489 \| 51.0 \| 357 \| 2.9339 \| 18.0 \| 3.4 \| \| 1.0427 \| 52.0 \| 364 \| 2.9256 \| 18.0 \| 3.4 \| \| 0.9156 \| 53.0 \| 371 \| 2.9220 \| 18.0 \| 3.4 \| \| 0.9156 \| 54.0 \| 378 \| 2.9091 \| 18.0 \| 3.38 \| \| 0.4748 \| 55.0 \| 385 \| 2.9036 \| 18.0 \| 3.36 \| \| 0.5616 \| 56.0 \| 392 \| 2.8998 \| 18.0 \| 3.36 \| \| 0.5616 \| 57.0 \| 399 \| 2.9128 \| 18.0 \| 3.36 \| \| 0.4836 \| 58.0 \| 406 \| 2.9205 \| 18.0 \| 3.36 \| \| 0.6498 \| 59.0 \| 413 \| 2.9282 \| 18.0 \| 3.36 \| \| 0.615 \| 60.0 \| 420 \| 2.9303 \| 18.0 \| 3.38 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1 - Datasets 2.19.1 - Tokenizers 0.19.1
SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps	SanghyukChun	2024-05-26T02:34:26Z	51	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-05T09:16:23Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin license: mit --- ### Official implementation of PCME++ pre-trained model on CC3M, CC12M and RedCaps. Zero-shot ImageNet-1k top-1 accuracy: 34.642% (slightly better than the paper score, 34.22%) - Paper: https://openreview.net/forum?id=ft1mr3WlGM - GitHub: https://github.com/naver-ai/pcmepp - Check a better version with ImageNet-1k top-1 accuracy 41.812% (mean-only ZS classification) at [SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M](https://huggingface.co/SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M) ```python import requests from PIL import Image import torch from transformers import CLIPProcessor # Check hf_models code here: https://github.com/naver-ai/pcmepp/tree/main/hf_models from hf_models import HfPCMEPPModel, tokenize processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16") # IN-top1: 34.64% model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps") # IN-top1: 41.81% # model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt", padding=True) texts = ["a photo of a cat", "a photo of a dog"] texts = tokenize(texts) outputs = model(images=inputs["pixel_values"], texts=texts) print("Logits:", outputs["image_features"] @ outputs["text_features"].T) print("Image uncertainty: ", torch.exp(outputs["image_stds"]).mean(dim=-1)) print("Text uncertainty: ", torch.exp(outputs["text_stds"]).mean(dim=-1)) ``` ``` @inproceedings{ chun2024pcmepp, title={Improved Probabilistic Image-Text Representations}, author={Sanghyuk Chun}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=ft1mr3WlGM} } ```
Raneechu/textbookbig10_ft3	Raneechu	2024-05-26T02:33:35Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T02:33:32Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft3 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. --> # textbookbig10_ft3 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
Sorour/phi3-fomc-save	Sorour	2024-05-26T02:17:03Z	156	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T02:14:09Z	--- 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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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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Sorour/cls_fomc_phi3_v1	Sorour	2024-05-26T02:13:12Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-19T05:10:56Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct datasets: - generator model-index: - name: cls_fomc_phi3_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cls_fomc_phi3_v1 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.7320 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8109 \| 0.3883 \| 20 \| 0.7927 \| \| 0.7639 \| 0.7767 \| 40 \| 0.7570 \| \| 0.6942 \| 1.1650 \| 60 \| 0.7449 \| \| 0.6797 \| 1.5534 \| 80 \| 0.7417 \| \| 0.6899 \| 1.9417 \| 100 \| 0.7320 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ayoubcim/tt-falcon-7b	ayoubcim	2024-05-26T02:12:57Z	106	0	transformers	[ "transformers", "safetensors", "falcon", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-26T01:07:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Anish13/results_model8	Anish13	2024-05-26T02:08:55Z	37	0	transformers	[ "transformers", "safetensors", "transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:14:45Z	--- tags: - generated_from_trainer model-index: - name: results_model8 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. --> # results_model8 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9686 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 3.3262 \| 0.5570 \| 10000 \| 3.3012 \| \| 3.0829 \| 1.1141 \| 20000 \| 3.1175 \| \| 2.9737 \| 1.6711 \| 30000 \| 3.0091 \| \| 2.8584 \| 2.2282 \| 40000 \| 2.9686 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
gokaygokay/imageinwords-paligemma-transformers	gokaygokay	2024-05-26T02:08:01Z	6	0	transformers	[ "transformers", "safetensors", "paligemma", "image-text-to-text", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-26T02:02:19Z	--- license: apache-2.0 --- ``` pip install git+https://github.com/huggingface/transformers ``` ``` from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from PIL import Image import requests import torch model_id = "gokaygokay/imageinwords-paligemma-transformers" url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval() processor = AutoProcessor.from_pretrained(model_id) ## prefix prompt = "caption en" model_inputs = processor(text=prompt, images=image, return_tensors="pt") input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=512, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ```
empathie/Qwen1.5-0.5B-Chat-experiment-2	empathie	2024-05-26T02:07:47Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T03:04:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MVRL/satclip-loc-enc-vit16-l40	MVRL	2024-05-26T01:51:36Z	0	0	null	[ "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "region:us" ]	null	2024-05-26T01:51:35Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v4-0.5_5e-7_ep-10	GENIAC-Team-Ozaki	2024-05-26T01:50:24Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T01:38:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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antitheft159/naderEren.195	antitheft159	2024-05-26T01:48:25Z	0	0	null	[ "license:cc-by-nd-4.0", "region:us" ]	null	2024-05-26T01:47:52Z	--- license: cc-by-nd-4.0 ---
CMU-AIR2/math-phi-1-5-FULL-Arithmetic-Curriculum-Subjects-8-to-10	CMU-AIR2	2024-05-26T01:31:57Z	6	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T00:20:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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drgary/ft6_lawllm_llama3_athena2	drgary	2024-05-26T01:31:37Z	2	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T01:29:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: drgary - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
samwit/paligemma_vqav2	samwit	2024-05-26T01:30:25Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:vq_av2", "base_model:google/paligemma-3b-pt-224", "base_model:adapter:google/paligemma-3b-pt-224", "license:gemma", "region:us" ]	null	2024-05-26T01:09:51Z	--- license: gemma library_name: peft tags: - generated_from_trainer base_model: google/paligemma-3b-pt-224 datasets: - vq_av2 model-index: - name: paligemma_vqav2 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_vqav2 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the vq_av2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
tz579/example_asr_wav2vec2	tz579	2024-05-26T01:27:44Z	5	0	transformers	[ "transformers", "tensorboard", "wav2vec2", "automatic-speech-recognition", "edinburghcstr/ami", "generated_from_trainer", "dataset:ami", "base_model:facebook/wav2vec2-large-lv60", "base_model:finetune:facebook/wav2vec2-large-lv60", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-24T20:28:06Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-lv60 tags: - automatic-speech-recognition - edinburghcstr/ami - generated_from_trainer datasets: - ami metrics: - wer model-index: - name: facebook/wav2vec2-large-lv60 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: EDINBURGHCSTR/AMI - IHM type: ami config: ihm split: None args: 'Config: ihm, Training split: train, Eval split: validation' metrics: - name: Wer type: wer value: 0.9542044754234227 --- <!-- 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. --> # facebook/wav2vec2-large-lv60 This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the EDINBURGHCSTR/AMI - IHM dataset. It achieves the following results on the evaluation set: - Loss: 1.2723 - Wer: 0.9542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 1.0919 \| 0.1565 \| 1000 \| 1.0169 \| 0.7064 \| \| 1.4768 \| 0.3131 \| 2000 \| 0.7156 \| 0.4356 \| \| 0.9728 \| 0.4696 \| 3000 \| 0.6462 \| 0.4030 \| \| 0.5418 \| 0.6262 \| 4000 \| 0.6171 \| 0.3707 \| \| 0.8492 \| 0.7827 \| 5000 \| 0.5758 \| 0.3695 \| \| 1.4826 \| 0.9393 \| 6000 \| 0.5801 \| 0.3545 \| \| 0.3274 \| 1.0958 \| 7000 \| 0.5244 \| 0.3375 \| \| 0.2089 \| 1.2523 \| 8000 \| 0.5047 \| 0.3239 \| \| 0.2916 \| 1.4089 \| 9000 \| 0.4901 \| 0.3171 \| \| 0.1617 \| 1.5654 \| 10000 \| 0.5070 \| 0.3151 \| \| 0.3815 \| 1.7220 \| 11000 \| 0.4948 \| 0.3180 \| \| 1.0171 \| 1.8785 \| 12000 \| 0.9465 \| 0.8379 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0a0+gitcd033a1 - Datasets 2.19.1 - Tokenizers 0.19.1
wdli/gpt2-cpt-dutch	wdli	2024-05-26T01:23:18Z	161	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:wdli/deptweet_dataset", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T05:55:01Z	--- license: mit base_model: gpt2 tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - alignment-handbook - generated_from_trainer datasets: - wdli/deptweet_dataset model-index: - name: gpt2-cpt-dutch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-cpt-dutch This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the wdli/deptweet_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.19.1
atgarcia/wav2vec2part6	atgarcia	2024-05-26T01:15:47Z	108	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-25T23:57:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Ichsan2895/Merak-7B-v4_4bit_q128_awq	Ichsan2895	2024-05-26T01:10:16Z	80	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "id", "en", "dataset:wikipedia", "dataset:Ichsan2895/OASST_Top1_Indonesian", "dataset:Ichsan2895/alpaca-gpt4-indonesian", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-25T18:37:33Z	--- datasets: - wikipedia - Ichsan2895/OASST_Top1_Indonesian - Ichsan2895/alpaca-gpt4-indonesian language: - id - en pipeline_tag: text-generation license: cc-by-nc-sa-4.0 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://huggingface.co/Ichsan2895/Merak-7B-v4/resolve/main/FINAL_LOGO/6.png" alt="MERAK" style="width: 50%; min-width: 100px; display: block; margin: auto;"> </div> # HAPPY TO ANNOUNCE THE RELEASE OF MERAK-7B-V4_4bit_q128_awq! Merak-7B is the Large Language Model of Indonesian Language This model is based on [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and fine tuned by some of Indonesia Wikipedia articles that I cleaned before. Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike. Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models. Feel free, to ask me about the model and please share the news on your social media.
TahaCakir/KarLlama-Beta-V1	TahaCakir	2024-05-26T01:00:48Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-24T23:36:52Z	--- 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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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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DJPillu/ppo-LunarLander-v2	DJPillu	2024-05-26T00:53:11Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-26T00:52:51Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.36 +/- 16.14 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
shadowdefense/ShadowWatch001	shadowdefense	2024-05-26T00:53:07Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-26T00:53:07Z	--- license: other license_name: terms license_link: https://beta.openai.com/terms/ ---
antitheft159/blinkrgb.159	antitheft159	2024-05-26T00:40:56Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-05-26T00:40:35Z	--- license: cc-by-nc-sa-4.0 ---
JianKim3293/llama3_lora_lawmodel	JianKim3293	2024-05-26T00:24:10Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:08:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
GTsuya/akramness_pony	GTsuya	2024-05-26T00:23:02Z	1	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:GraydientPlatformAPI/autism-pony", "base_model:adapter:GraydientPlatformAPI/autism-pony", "license:mit", "region:us" ]	text-to-image	2024-05-26T00:21:41Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Body Stocking, dutch angle, very wide shot, prison, rating_safe, <lora:akramness_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00136-4232611181.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Hose, fisheye, cowboy shot, hospital, rating_questionable, <lora:akramness_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00155-1262320914.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Dolman, from behind, cropped shoulders, aqueduct, rating_safe, <lora:akramness_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00173-479418834.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, silver trim, fisheye, cut-in, shore, rating_safe, <lora:akramness_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00204-1709459995.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, bodysuit, panorama, cropped shoulders, bridge, rating_safe, <lora:akramness_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00047-3532072066.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, China Dress, vanishing point, face, savannah, rating_questionable, <lora:akramness_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00093-3176014586.png base_model: GraydientPlatformAPI/autism-pony instance_prompt: null license: mit --- # akramness_pony <Gallery /> ## Model description This LoRA model has been trained with Kohya SS using Akramness's artworks on Autism Mix SDXL checkpoint. Obtained graphics are close to the original art style. This LoRA model could be use for oriental cartoon representation of mostly asiatic women. ## Download model Weights for this model are available in Safetensors format. [Download](/GTsuya/akramness_pony/tree/main) them in the Files & versions tab.
legraphista/aya-23-8B-IMat-GGUF	legraphista	2024-05-26T00:17:38Z	165	0	gguf	[ "gguf", "quantized", "GGUF", "imatrix", "quantization", "imat", "static", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "base_model:CohereForAI/aya-23-8B", "base_model:quantized:CohereForAI/aya-23-8B", "license:cc-by-nc-4.0", "region:us", "conversational" ]	text-generation	2024-05-25T20:21:19Z	--- base_model: CohereForAI/aya-23-8B inference: false language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi library_name: gguf license: cc-by-nc-4.0 pipeline_tag: text-generation quantized_by: legraphista tags: - quantized - GGUF - imatrix - quantization - imat - static --- # aya-23-8B-IMat-GGUF _Llama.cpp imatrix quantization of CohereForAI/aya-23-8B_ Original Model: [CohereForAI/aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B) Original dtype: `FP16` (`float16`) Quantized by: llama.cpp [b2998](https://github.com/ggerganov/llama.cpp/releases/tag/b2998) IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw) ## Files ### IMatrix Status: ✅ Available Link: [here](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/imatrix.dat) ### Common Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [aya-23-8B.Q8_0.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q8_0.gguf) \| Q8_0 \| 8.54GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q6_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q6_K.gguf) \| Q6_K \| 6.60GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q4_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q4_K.gguf) \| Q4_K \| 5.06GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K.gguf) \| Q3_K \| 4.22GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q2_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q2_K.gguf) \| Q2_K \| 3.44GB \| ✅ Available \| 🟢 Yes \| 📦 No ### All Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [aya-23-8B.FP16.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.FP16.gguf) \| F16 \| 16.07GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q5_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q5_K.gguf) \| Q5_K \| 5.80GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q5_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.67GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q4_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.83GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K_L.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.53GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.87GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q2_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q2_K_S.gguf) \| Q2_K_S \| 3.25GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ4_NL.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ4_NL.gguf) \| IQ4_NL \| 4.81GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ4_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.60GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_M.gguf) \| IQ3_M \| 3.99GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_S.gguf) \| IQ3_S \| 3.89GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.72GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_XXS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_XXS.gguf) \| IQ3_XXS \| 3.41GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_M.gguf) \| IQ2_M \| 3.08GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_S.gguf) \| IQ2_S \| 2.90GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_XS.gguf) \| IQ2_XS \| 2.80GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_XXS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_XXS.gguf) \| IQ2_XXS \| 2.59GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ1_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ1_M.gguf) \| IQ1_M \| 2.35GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ1_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ1_S.gguf) \| IQ1_S \| 2.21GB \| ✅ Available \| 🟢 Yes \| 📦 No ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download legraphista/aya-23-8B-IMat-GGUF --include "aya-23-8B.Q8_0.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download legraphista/aya-23-8B-IMat-GGUF --include "aya-23-8B.Q8_0/*" --local-dir aya-23-8B.Q8_0 # see FAQ for merging GGUF's ``` ## FAQ ### Why is the IMatrix not applied everywhere? According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). ### How do I merge a split GGUF? 1. Make sure you have `gguf-split` available - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases - Download the appropriate zip for your system from the latest release - Unzip the archive and you should be able to find `gguf-split` 2. Locate your GGUF chunks folder (ex: `aya-23-8B.Q8_0`) 3. Run `gguf-split --merge aya-23-8B.Q8_0/aya-23-8B.Q8_0-00001-of-XXXXX.gguf aya-23-8B.Q8_0.gguf` - Make sure to point `gguf-split` to the first chunk of the split. --- Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
GTsuya/cute_sexy_robutts_pony	GTsuya	2024-05-26T00:10:08Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:GraydientPlatformAPI/autism-pony", "base_model:adapter:GraydientPlatformAPI/autism-pony", "license:mit", "region:us" ]	text-to-image	2024-05-26T00:08:50Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, dirndl, atmospheric perspective, portrait, church, rating_questionable, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00024-1661246894.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, bikini, sideways, cropped legs, tunnel, rating_explicit, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00077-2017120761.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Gloves, dutch angle, cropped legs, pool, rating_questionable, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00088-1815590393.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, armor, from above, wide shot, refinery, rating_explicit, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00171-1644120815.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Gloves, from above, close-up, flower shop, rating_safe, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00217-4158734917.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Latex, atmospheric perspective, lower body, cooling tower, rating_safe, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00220-397098714.png base_model: GraydientPlatformAPI/autism-pony instance_prompt: null license: mit --- # cute_sexy_robutts_pony <Gallery /> ## Model description This LoRA model has been trained with Kohya SS using Cute Sexy Robutts's artworks on Autism Mix SDXL checkpoint. Obtained graphics are close to the original art style. This LoRA model could be use for cartoon/drawing representation of sexy women. ## Download model Weights for this model are available in Safetensors format. [Download](/GTsuya/cute_sexy_robutts_pony/tree/main) them in the Files & versions tab.
raulgdp/roberta-multiclase-ag_news	raulgdp	2024-05-26T00:08:49Z	108	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	2024-05-25T21:35:34Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: roberta-multiclase-ag_news 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-multiclase-ag_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2671 - Rmse: 1.1967 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rmse \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.3199 \| 1.0 \| 15000 \| 1.2671 \| 1.1967 \| \| 1.3837 \| 2.0 \| 30000 \| 1.3864 \| 1.2230 \| \| 1.3879 \| 3.0 \| 45000 \| 1.3865 \| 1.8686 \| \| 1.385 \| 4.0 \| 60000 \| 1.3864 \| 1.2247 \| \| 1.3885 \| 5.0 \| 75000 \| 1.3863 \| 1.8720 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
CMU-AIR2/math-phi-1-5-FULL-Arithmetic-Curriculum-Subjects-6-to-7	CMU-AIR2	2024-05-26T00:03:06Z	5	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T17:57:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
umair894/llama3_1e	umair894	2024-05-25T23:58:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:58:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: umair894 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf	RichardErkhov	2024-05-25T23:57:51Z	23	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T21:05:28Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3-11b-Instruct - GGUF - Model creator: https://huggingface.co/athirdpath/ - Original model: https://huggingface.co/athirdpath/Llama-3-11b-Instruct/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Llama-3-11b-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q2_K.gguf) \| Q2_K \| 4.01GB \| \| [Llama-3-11b-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ3_XS.gguf) \| IQ3_XS \| 4.44GB \| \| [Llama-3-11b-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ3_S.gguf) \| IQ3_S \| 4.66GB \| \| [Llama-3-11b-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 4.64GB \| \| [Llama-3-11b-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ3_M.gguf) \| IQ3_M \| 4.79GB \| \| [Llama-3-11b-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K.gguf) \| Q3_K \| 5.1GB \| \| [Llama-3-11b-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 5.1GB \| \| [Llama-3-11b-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 5.52GB \| \| [Llama-3-11b-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 5.7GB \| \| [Llama-3-11b-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_0.gguf) \| Q4_0 \| 5.94GB \| \| [Llama-3-11b-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.IQ4_NL.gguf) \| IQ4_NL \| 6.0GB \| \| [Llama-3-11b-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 5.98GB \| \| [Llama-3-11b-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_K.gguf) \| Q4_K \| 6.27GB \| \| [Llama-3-11b-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 6.27GB \| \| [Llama-3-11b-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q4_1.gguf) \| Q4_1 \| 6.56GB \| \| [Llama-3-11b-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_0.gguf) \| Q5_0 \| 7.17GB \| \| [Llama-3-11b-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 7.17GB \| \| [Llama-3-11b-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_K.gguf) \| Q5_K \| 7.34GB \| \| [Llama-3-11b-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 7.34GB \| \| [Llama-3-11b-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q5_1.gguf) \| Q5_1 \| 7.78GB \| \| [Llama-3-11b-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q6_K.gguf) \| Q6_K \| 8.48GB \| \| [Llama-3-11b-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/athirdpath_-_Llama-3-11b-Instruct-gguf/blob/main/Llama-3-11b-Instruct.Q8_0.gguf) \| Q8_0 \| 10.98GB \| Original model description: --- license: llama3 --- I'm back and doing well! I've got a job in the field now, so we'll see in the long run how that effects my open source output. Here we have a 11b Llama 3 instruct model for future work. EDIT: Made a yaml mistake with part funnel, but it still works well. --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633a809fa4a8f33508dce32c/jJxgpSwdSal2XWsJ0KlG8.png) This is a merge stock of 3 models: - Part Wave - Part Block - Part Funnel With Part Funnel as the base. --- Part Wave: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0, 12] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [8, 18] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [13, 23] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [18, 32] --- Part Block: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0, 15] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [8, 23] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [16, 32] --- Part Funnel: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0, 15] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [14, 14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [13, 13] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [12, 12] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [11, 11] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10, 10] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [9, 9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [8, 23] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [22, 22] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [21, 21] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20, 20] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [19, 19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [18, 18] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [17, 17] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [16, 32]
fearlessdots/Llama-3-Alpha-Centauri-v0.1-GGUF	fearlessdots	2024-05-25T23:51:25Z	37	5	null	[ "gguf", "dataset:NobodyExistsOnTheInternet/ToxicQAFinal", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T21:34:14Z	--- license: llama3 datasets: - NobodyExistsOnTheInternet/ToxicQAFinal --- # Llama-3-Alpha-Centauri-v0.1-GGUF <img src="alpha_centauri_banner.png" alt="" style="width:500px;height:400px;"/> Image generated with [https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS). --- ## Disclaimer Note: All models and LoRAs from the Centaurus series were created with the sole purpose of research. The usage of this model and/or its related LoRA implies agreement with the following terms: - The user is responsible for what they might do with it, including how the output of the model is interpreted and used; - The user should not use the model and its outputs for any illegal purposes; - The user is the only one resposible for any misuse or negative consequences from using this model and/or its related LoRA. I do not endorse any particular perspectives presented in the training data. --- ## Centaurus Series This series aims to develop highly uncensored Large Language Models (LLMs) with the following focuses: - Science, Technology, Engineering, and Mathematics (STEM) - Computer Science (including programming) - Social Sciences And several key cognitive skills, including but not limited to: - Reasoning and logical deduction - Critical thinking - Analysis While maintaining strong overall knowledge and expertise, the models will undergo refinement through: - Fine-tuning processes - Model merging techniques including Mixture of Experts (MoE) Please note that these models are experimental and may demonstrate varied levels of effectiveness. Your feedback, critique, or queries are most welcome for improvement purposes. ## Base This model and its related LoRA was fine-tuned on [https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3). ## LoRA The LoRA merged with the base model is available at [https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-LoRA](https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-LoRA). ## Datasets - [https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## Fine Tuning ### - Quantization Configuration - load_in_4bit=True - bnb_4bit_quant_type="fp4" - bnb_4bit_compute_dtype=compute_dtype - bnb_4bit_use_double_quant=False ### - PEFT Parameters - lora_alpha=64 - lora_dropout=0.05 - r=128 - bias="none" ### - Training Arguments - num_train_epochs=1 - per_device_train_batch_size=1 - gradient_accumulation_steps=4 - optim="adamw_bnb_8bit" - save_steps=25 - logging_steps=25 - learning_rate=2e-4 - weight_decay=0.001 - fp16=False - bf16=False - max_grad_norm=0.3 - max_steps=-1 - warmup_ratio=0.03 - group_by_length=True - lr_scheduler_type="constant" ## Credits - Meta ([https://huggingface.co/meta-llama](https://huggingface.co/meta-llama)): for the original Llama-3; - HuggingFace: for hosting this model and for creating the fine-tuning tools used; - failspy ([https://huggingface.co/failspy](https://huggingface.co/failspy)): for the base model and the orthogonalization implementation; - NobodyExistsOnTheInternet ([https://huggingface.co/NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet)): for the incredible dataset; - Undi95 ([https://huggingface.co/Undi95](https://huggingface.co/Undi95)) and Sao10k ([https://huggingface.co/Sao10K](https://huggingface.co/Sao10K)): my main inspirations for doing these models =] A huge thank you to all of them ☺️ ## About Alpha Centauri Alpha Centauri is a triple star system located in the constellation of Centaurus. It includes three stars: Rigil Kentaurus (also known as α Centauri A), Toliman (or α Centauri B), and Proxima Centauri (α Centauri C). Proxima Centauri is the nearest star to the Sun, residing at approximately 4.25 light-years (1.3 parsecs) away. The primary pair, α Centauri A and B, are both similar to our Sun - α Centauri A being a class G star with 1.1 solar masses and 1.5 times the Sun's luminosity; α Centauri B having 0.9 solar masses and under half the luminosity of the Sun. They revolve around their shared center every 79 years following an elliptical path, ranging from 35.6 astronomical units apart (nearly Pluto's distance from the Sun) to 11.2 astronomical units apart (around Saturn's distance from the Sun.) Proxima Centauri, or α Centauri C, is a diminutive, dim red dwarf (a class M star) initially unseen to the naked eye. At roughly 4.24 light-years (1.3 parsecs) from us, it lies nearer than α Centauri AB, the binary system. Presently, the gap between Proxima Centauri and α Centauri AB amounts to around 13,000 Astronomical Units (0.21 light-years)—comparable to over 430 times Neptune's orbital radius. Two confirmed exoplanets accompany Proxima Centauri: Proxima b, discovered in 2016, is Earth-sized within the habitable zone; Proxima d, revealed in 2022, is a potential sub-Earth close to its host star. Meanwhile, disputes surround Proxima c, a mini-Neptune detected in 2019. Intriguingly, hints suggest that α Centauri A might possess a Neptune-sized object in its habitable region, but further investigation is required before confirming whether it truly exists and qualifies as a planet. Regarding α Centauri B, although once thought to harbor a planet (named α Cen Bb), subsequent research invalidated this claim, leaving it currently devoid of identified planets. Source: retrived from [https://en.wikipedia.org/wiki/Alpha_Centauri](https://en.wikipedia.org/wiki/Alpha_Centauri) and processed with [https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
eltaiefaymen/Python_DocGen	eltaiefaymen	2024-05-25T23:50:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-02T00:02:55Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: eltaiefaymen - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sorour/phi3-ft-fomc-v2	Sorour	2024-05-25T23:45:29Z	155	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:33:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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thdangtr/blip_recipe1m_title_v6	thdangtr	2024-05-25T23:35:49Z	67	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-25T23:34:17Z	--- 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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JawadC/neufchatel	JawadC	2024-05-25T23:33:52Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T23:04:54Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of Neufchatel cheese widget: - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_0.png - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_1.png - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_2.png - text: A heart shaped Neufchatel cheese on a rustic wooden table. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/neufchatel <Gallery /> ## Model description These are JawadC/neufchatel LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of Neufchatel cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/neufchatel/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ethan-ng/content-moderation-model	ethan-ng	2024-05-25T23:33:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-25T23:33:32Z	--- license: apache-2.0 ---
GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v4-0.5_5e-7_ep-1	GENIAC-Team-Ozaki	2024-05-25T23:26:34Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:16:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedgongi/Llama_dev3model_finale10	ahmedgongi	2024-05-25T23:26:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:26:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedgongi/Llama_dev3tokenizer_finale10	ahmedgongi	2024-05-25T23:26:09Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:26:08Z	--- 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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Apel-sin/llama-3-8B-iterative-DPO-final-exl2	Apel-sin	2024-05-25T23:19:50Z	4	1	null	[ "arxiv:2405.07863", "arxiv:2312.11456", "license:llama3", "region:us" ]	null	2024-05-24T12:41:16Z	--- license: llama3 --- # Exllama v2 RLHFlow/LLaMA3-iterative-DPO-final Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: <a href="https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final">RLHFlow/LLaMA3-iterative-DPO-final</a><br> Calibration dataset: <a href="https://huggingface.co/datasets/cosmicvalor/toxic-qna">toxic-qna</a> ## Prompt format ``` <\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|> <\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|> <\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (8K) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/Apel-sin/llama-3-8B-iterative-DPO-final-exl2/tree/8_0) \| 8.0 \| 8.0 \| 10.1 GB \| 10.5 GB \| 11.5 GB \| 13.6 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/Apel-sin/llama-3-8B-iterative-DPO-final-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.9 GB \| 9.3 GB \| 10.3 GB \| 12.4 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/Apel-sin/llama-3-8B-iterative-DPO-final-exl2/tree/5_0) \| 5.0 \| 8.0 \| 7.7 GB \| 8.1 GB \| 9.1 GB \| 11.2 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| # LLaMA3-iterative-DPO-final ## Introduction We release an unofficial checkpoint of a state-of-the-art instruct model of its class, LLaMA3-iterative-DPO-final. On all three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, Chat-Arena-Hard, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it), and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling. Even better, we provide a [detailed recipe](https://github.com/RLHFlow/Online-RLHF) to reproduce the model. Enjoy! ## Model Releases See the [collection](https://huggingface.co/collections/RLHFlow/online-rlhf-663ae95fade1a39663dab218) of the training set, reward/preference model, SFT model. - [SFT model](https://huggingface.co/RLHFlow/LLaMA3-SFT) - [Reward model](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1) ## Dataset - [Preference data mix](https://huggingface.co/datasets/hendrydong/preference_700K) - [Prompt collection for RLHF training](https://huggingface.co/datasets/RLHFlow/prompt-collection-v0.1) ## Training methods We have developed a simple and efficient online RLHF recipe for LLM instruct training. Our recipe is DPO-based and thus much cheaper and simpler to train and tune compared to PPO-based approaches. Unlike widely-used offline DPO, the online component of our approach effectively mitigates distribution shifts during policy optimization. For a detailed exposition, please refer to our accompanying technical report. ## Chat Benchmarks \| Model \| Size \| Method \| LC Alpaca-Eval-V2 \| MT-Bench \| Chat-Arena-Hard \| \|-------------------------\|----------\|-------------------\|-----------------------\|--------------\|---------------------\| \| Small Open-Sourced Models \| \| \| \| \| \| \| Gemma-7B-it \| 7B \| SFT \| 10.4 \| 6.38 \| 7.5 \| \| Zephyr-7B-beta \| 7B \| Vanilla DPO \| 13.1 \| 7.34 \| - \| \| Mistral-7B-v0.2-it \| 7B \| SFT \| 17.1 \| 7.51 \| 12.6 \| \| Open-Chat-0106 \| 7B \| SFT \| 15.6 \| 7.8 \| - \| \| Starling-7B-beta \| 7B \| PPO \| 25.8 \| 8.12 \| 23.0 \| \| LLaMA-3-8B-it \| 8B \| RS+DPO+PPO \| 22.9 \| 8.16 \| 20.6 \| \| Ours \| \| \| \| \| \| \| Ours (SFT baseline) \| 8B \| SFT \| 10.2 \| 7.69 \| 5.6 \| \| Ours (DPO baseline) \| 8B \| Vanilla DPO \| 22.5 \| 8.17 \| 22.4 \| \| Ours (Online RLHF) \| 8B \| Iterative DPO \| 37.2 \| 8.46 \| 29.1 \| \| Large Open-Sourced Models \| \| \| \| \| \| \| Vicuna-33b-v1.3 \| 33B \| SFT \| 17.6 \| 7.12 \| 8.6 \| \| Yi-34B-Chat \| 34B \| SFT \| 27.2 \| - \| 23.1 \| \| Mixtral-8x7B-it \| 45B* \| SFT \| 23.7 \| 8.30 \| 23.4 \| \| Tulu-2-DPO-70B \| 70B \| Vanilla DPO \| 21.2 \| 7.89 \| 15.0 \| \| LLaMA-3-70B-it \| 70B \| RS+DPO+PPO \| 34.4 \| 8.95 \| 41.1 \| \| Mixtral-8x22B-it \| 141B* \| SFT \| 30.9 \| 8.66 \| 36.4 \| \| Proprietary Models \| \| \| \| \| \| \| GPT-3.5-turbo-1106 \| - \| - \| 19.3 \| 8.35 \| 18.9 \| \| GPT-3.5-turbo-0613 \| - \| - \| 22.7 \| 8.39 \| 24.8 \| \| GPT-4-0613 \| - \| - \| 30.2 \| 9.18 \| 37.9 \| \| Claude-3-Opus \| - \| - \| 40.5 \| 9.00 \| 60.4 \| \| GPT-4 Turbo (04/09) \| - \| - \| 55.0 \| - \| 82.6 \| ## Academic Benchmarks \| Model \| Size \| Method \| GSM-8K \| MMLU \| HumanEval \| TruthfulQA \| ARC \| MBPP \| \|----------------------------\|----------\|-----------------\|------------\|----------\|---------------\|----------------\|---------\|----------\| \| LLaMA-3-8B-it \| 8B \| RS+DPO+PPO \| 79.6 \| 66.0 \| 61.6 \| 43.9 \| 59.5 \| 61.1 \| \| Ours (SFT baseline) \| 8B \| SFT \| 74.2 \| 64.7 \| 65.2 \| 53.4 \| 61.4 \| 62.3 \| \| Ours (DPO baseline) \| 8B \| Vanilla DPO \| 79.8 \| 64.5 \| 63.4 \| 61.8 \| 65.2 \| 60.3 \| \| Ours (Iterative RLHF) \| 8B \| Iterative DPO \| 80.7 \| 65.3 \| 64.6 \| 60.4 \| 64.3 \| 60.8 \| ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final") tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final") messages = [ {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"}, ] model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = model_inputs.to(device) model.to(device) output_tokens = model.generate(model_inputs, max_new_tokens=1024, do_sample=True) model_outputs = tokenizer.batch_decode(output_tokens) print(model_outputs[0]) ``` ## Limitations RLHFlow/LLaMA3-iterative-DPO-final is an unofficial checkpoint developed to illustrate the power of online iterative RLHF and is for research purpose. While safety and ethical considerations are integral to our alignment process, there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions. We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage. ## Citation Please cite our techical report if you find our model is useful for your research or product. ``` @misc{dong2024rlhf, title={RLHF Workflow: From Reward Modeling to Online RLHF}, author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } @misc{xiong2024iterative, title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint}, author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang}, year={2024}, eprint={2312.11456}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v4-0.5_5e-7_ep-3	GENIAC-Team-Ozaki	2024-05-25T23:19:14Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:09:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ayoubcim/midjourney-falcon-7b	ayoubcim	2024-05-25T23:15:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:14:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v4-0.5_5e-7_ep-15	GENIAC-Team-Ozaki	2024-05-25T23:12:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:02:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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datek/gemma-2b-flock-1716678510	datek	2024-05-25T23:10:49Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:08:30Z	--- 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. 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szwuwen/mistral-7b-v3	szwuwen	2024-05-25T23:09:31Z	8	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T05:45:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: szwuwen - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf	RichardErkhov	2024-05-25T23:09:29Z	7	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-25T20:03:22Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Solar-Ko-Recovery-11B - GGUF - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/Solar-Ko-Recovery-11B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Solar-Ko-Recovery-11B.Q2_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q2_K.gguf) \| Q2_K \| 3.87GB \| \| [Solar-Ko-Recovery-11B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.IQ3_XS.gguf) \| IQ3_XS \| 4.29GB \| \| [Solar-Ko-Recovery-11B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.IQ3_S.gguf) \| IQ3_S \| 4.52GB \| \| [Solar-Ko-Recovery-11B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q3_K_S.gguf) \| Q3_K_S \| 4.5GB \| \| [Solar-Ko-Recovery-11B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.IQ3_M.gguf) \| IQ3_M \| 4.67GB \| \| [Solar-Ko-Recovery-11B.Q3_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q3_K.gguf) \| Q3_K \| 4.99GB \| \| [Solar-Ko-Recovery-11B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q3_K_M.gguf) \| Q3_K_M \| 4.99GB \| \| [Solar-Ko-Recovery-11B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q3_K_L.gguf) \| Q3_K_L \| 5.42GB \| \| [Solar-Ko-Recovery-11B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.IQ4_XS.gguf) \| IQ4_XS \| 5.59GB \| \| [Solar-Ko-Recovery-11B.Q4_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q4_0.gguf) \| Q4_0 \| 5.82GB \| \| [Solar-Ko-Recovery-11B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.IQ4_NL.gguf) \| IQ4_NL \| 5.89GB \| \| [Solar-Ko-Recovery-11B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q4_K_S.gguf) \| Q4_K_S \| 5.87GB \| \| [Solar-Ko-Recovery-11B.Q4_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q4_K.gguf) \| Q4_K \| 6.19GB \| \| [Solar-Ko-Recovery-11B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q4_K_M.gguf) \| Q4_K_M \| 6.19GB \| \| [Solar-Ko-Recovery-11B.Q4_1.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q4_1.gguf) \| Q4_1 \| 6.45GB \| \| [Solar-Ko-Recovery-11B.Q5_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q5_0.gguf) \| Q5_0 \| 7.07GB \| \| [Solar-Ko-Recovery-11B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q5_K_S.gguf) \| Q5_K_S \| 7.07GB \| \| [Solar-Ko-Recovery-11B.Q5_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q5_K.gguf) \| Q5_K \| 7.26GB \| \| [Solar-Ko-Recovery-11B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q5_K_M.gguf) \| Q5_K_M \| 7.26GB \| \| [Solar-Ko-Recovery-11B.Q5_1.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q5_1.gguf) \| Q5_1 \| 7.7GB \| \| [Solar-Ko-Recovery-11B.Q6_K.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q6_K.gguf) \| Q6_K \| 8.4GB \| \| [Solar-Ko-Recovery-11B.Q8_0.gguf](https://huggingface.co/RichardErkhov/beomi_-_Solar-Ko-Recovery-11B-gguf/blob/main/Solar-Ko-Recovery-11B.Q8_0.gguf) \| Q8_0 \| 10.88GB \| Original model description: --- language: - ko - en pipeline_tag: text-generation inference: false tags: - solar - mistral - pytorch - solar-ko library_name: transformers license: apache-2.0 --- Update Log - 2024.05.16: Released Solar-Ko-Recovery # Solar-Ko-Recovery-11B 🌟❤️‍🩹 Solar-Ko-Recovery-11B aimed to recover Solar's capability on Korean with re-arrange of Embeddings and LM head, featuring an expanded vocabulary and the inclusion of a Korean+English corpus for enhanced representation. ## Model Details Model Developers: Junbum Lee (Beomi) Variations: Solar-Ko-Recovery is available with one parameter sizes — 11B(10.99B🤣). Input: The model accepts only text input. Output: The model produces text output exclusively. Model Architecture: Solar-Ko-Recovery is an auto-regressive language model that leverages an optimized transformer architecture derived from Llama-2. \| \|Training Data\|Parameters\|Content Length\|GQA\|Tokens\|Learning Rate\| \|---\|---\|---\|---\|---\|---\|---\| \|Solar-Ko-Recovery\|A curated mix of Korean+English Corpora\|10.8B\|4k\|O\|>30B\|5e<sup>-5</sup>\| > NOTE: Only Embedding layer and LM Head layer are trained. Vocab Expansion* Vocab expansion is conducted on edited [upstage/solar-1-mini-tokenizer](https://huggingface.co/upstage/solar-1-mini-tokenizer), which is superset of Solar tokenizer. \| Model Name \| Vocabulary Size \| Description \| \| --- \| --- \| --- \| \| Original Solar \| 32000 \| Sentencepiece BPE \| \| solar-1-mini-tokenizer \| 64000 \| Sentencepiece BPE. Added Ko/JP vocabs \| Tokenizing "안녕하세요, 오늘은 날씨가 좋네요." - SOLAR-10.7B: 26 tokens - Solar-Ko-Recovery: 7 tokens \| Model \| Tokens \| \| --- \| --- \| \| SOLAR-10.7B \| `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '날', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '좋', '네', '요', '.']` \| \| Solar-Ko-Recovery \| `['▁안녕하세요', ',', '▁오늘은', '▁날씨가', '▁좋', '네요', '.']` \| Tokenizing "Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!" - SOLAR-10.7B: 22 tokens - Solar-Ko-Recovery: 22 tokens \| Model \| Tokens \| \| --- \| --- \| \| SOLAR-10.7B \| `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` \| \| Solar-Ko-Recovery \| `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` \| # LICENSE Apache 2.0 # Model Benchmark ## LM Eval Harness - Korean - Used EleutherAI's [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) - 5-shot scores TBD ## Citation TBD ## Acknowledgements - Training support was provided by the [TPU Research Cloud](https://sites.research.google/trc/) program.
Sorour/merge-phi3	Sorour	2024-05-25T23:08:15Z	155	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "mergekit", "merge", "conversational", "custom_code", "arxiv:2403.19522", "base_model:Sorour/phi3_cls_finred", "base_model:merge:Sorour/phi3_cls_finred", "base_model:Sorour/phi3_cls_fomc", "base_model:merge:Sorour/phi3_cls_fomc", "base_model:Sorour/phi3_cls_sentiment", "base_model:merge:Sorour/phi3_cls_sentiment", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:merge:microsoft/Phi-3-mini-4k-instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:04:17Z	--- base_model: - microsoft/Phi-3-mini-4k-instruct - Sorour/phi3_cls_sentiment - Sorour/phi3_cls_fomc - Sorour/phi3_cls_finred library_name: transformers tags: - mergekit - merge --- # out This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) as a base. ### Models Merged The following models were included in the merge: * [Sorour/phi3_cls_sentiment](https://huggingface.co/Sorour/phi3_cls_sentiment) * [Sorour/phi3_cls_fomc](https://huggingface.co/Sorour/phi3_cls_fomc) * [Sorour/phi3_cls_finred](https://huggingface.co/Sorour/phi3_cls_finred) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sorour/phi3_cls_finred - model: Sorour/phi3_cls_sentiment - model: Sorour/phi3_cls_fomc merge_method: model_stock base_model: microsoft/Phi-3-mini-4k-instruct dtype: bfloat16 ```
diwanshus/codequalbert	diwanshus	2024-05-25T23:04:54Z	163	1	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "arxiv:1910.09700", "doi:10.57967/hf/2308", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-13T04:42:05Z	--- library_name: transformers license: apache-2.0 language: - en --- # Model Card for Model ID CodeQualBert model is able to assess the quality of a given Python code. It can label the provided code into three quality tiers - low, average and high. ## Model Details CodeQualBert is a fine-tuned CodeBert Model trained on CodeQual dataset. ### Model Description <!-- Provide a longer summary of what this model is. --> The model card of CodeQualBert model is shown below - - Developed by: Diwanshu Shekhar and Dr. Mohammad Mahoor - Finetuned from model [optional]: CodeBert - Language(s) (NLP): English - License: Apache 2.0 ### 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. --> The model is intended to be used for Code Quality Assessent Task. ### 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]
JawadC/brie_de_melun	JawadC	2024-05-25T23:02:01Z	1	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-24T12:18:14Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of Brie de Melun cheese widget: - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_0.png - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_1.png - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_2.png - text: A piece of Brie de Melun cheese on a rustic wooden table. output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/brie_de_melun <Gallery /> ## Model description These are JawadC/brie_de_melun LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of Brie de Melun cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/brie_de_melun/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ggcamacho2001/llamager	ggcamacho2001	2024-05-25T22:56:57Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-25T22:56:56Z	--- license: apache-2.0 ---

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