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Nubletz/msi-resnet-50
Nubletz
2023-12-17T12:33:28Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:Nubletz/msi-resnet-pretrain", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-17T06:10:46Z
--- license: apache-2.0 base_model: Nubletz/msi-resnet-pretrain tags: - generated_from_trainer datasets: - imagefolder model-index: - name: msi-resnet-50 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # msi-resnet-50 This model is a fine-tuned version of [Nubletz/msi-resnet-pretrain](https://huggingface.co/Nubletz/msi-resnet-pretrain) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 29628148372356011655168.0000 - eval_accuracy: 0.5662 - eval_runtime: 362.9719 - eval_samples_per_second: 78.838 - eval_steps_per_second: 4.929 - epoch: 5.0 - step: 10078 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Framework versions - Transformers 4.36.1 - Pytorch 2.0.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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Roozeppe/MNIST
Roozeppe
2023-12-17T06:12:07Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T06:10:52Z
--- license: apache-2.0 ---
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hkivancoral/smids_5x_deit_base_adamax_00001_fold5
hkivancoral
2023-12-17T07:27:08Z
0
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-17T06:13:14Z
--- license: apache-2.0 base_model: facebook/deit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_base_adamax_00001_fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9 --- <!-- 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. --> # smids_5x_deit_base_adamax_00001_fold5 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6879 - Accuracy: 0.9 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2538 | 1.0 | 375 | 0.3339 | 0.8467 | | 0.1892 | 2.0 | 750 | 0.2698 | 0.8917 | | 0.1183 | 3.0 | 1125 | 0.2626 | 0.9 | | 0.1141 | 4.0 | 1500 | 0.2917 | 0.8883 | | 0.071 | 5.0 | 1875 | 0.3136 | 0.9 | | 0.0217 | 6.0 | 2250 | 0.3618 | 0.8933 | | 0.0346 | 7.0 | 2625 | 0.4213 | 0.89 | | 0.0047 | 8.0 | 3000 | 0.4689 | 0.8983 | | 0.022 | 9.0 | 3375 | 0.5039 | 0.8983 | | 0.0024 | 10.0 | 3750 | 0.5358 | 0.8917 | | 0.0059 | 11.0 | 4125 | 0.5640 | 0.895 | | 0.0001 | 12.0 | 4500 | 0.5647 | 0.8967 | | 0.0001 | 13.0 | 4875 | 0.6088 | 0.895 | | 0.0002 | 14.0 | 5250 | 0.5907 | 0.9017 | | 0.0001 | 15.0 | 5625 | 0.6332 | 0.8967 | | 0.0001 | 16.0 | 6000 | 0.6424 | 0.8833 | | 0.0001 | 17.0 | 6375 | 0.6207 | 0.8983 | | 0.0068 | 18.0 | 6750 | 0.6552 | 0.895 | | 0.0 | 19.0 | 7125 | 0.6642 | 0.9017 | | 0.0 | 20.0 | 7500 | 0.6453 | 0.8883 | | 0.0001 | 21.0 | 7875 | 0.6986 | 0.895 | | 0.0265 | 22.0 | 8250 | 0.7065 | 0.8883 | | 0.0 | 23.0 | 8625 | 0.6670 | 0.8967 | | 0.0 | 24.0 | 9000 | 0.6793 | 0.8967 | | 0.0 | 25.0 | 9375 | 0.6516 | 0.9017 | | 0.0 | 26.0 | 9750 | 0.6626 | 0.89 | | 0.0 | 27.0 | 10125 | 0.6877 | 0.895 | | 0.0 | 28.0 | 10500 | 0.6598 | 0.8967 | | 0.0 | 29.0 | 10875 | 0.6682 | 0.8933 | | 0.0145 | 30.0 | 11250 | 0.6761 | 0.8983 | | 0.0 | 31.0 | 11625 | 0.6763 | 0.8983 | | 0.0 | 32.0 | 12000 | 0.6749 | 0.8967 | | 0.0 | 33.0 | 12375 | 0.6798 | 0.8983 | | 0.0 | 34.0 | 12750 | 0.6830 | 0.8967 | | 0.0 | 35.0 | 13125 | 0.6787 | 0.9 | | 0.0 | 36.0 | 13500 | 0.6883 | 0.895 | | 0.0 | 37.0 | 13875 | 0.6805 | 0.8983 | | 0.003 | 38.0 | 14250 | 0.6825 | 0.895 | | 0.0 | 39.0 | 14625 | 0.6851 | 0.8967 | | 0.0 | 40.0 | 15000 | 0.6877 | 0.8983 | | 0.0 | 41.0 | 15375 | 0.6804 | 0.8983 | | 0.0 | 42.0 | 15750 | 0.6888 | 0.8983 | | 0.0 | 43.0 | 16125 | 0.6877 | 0.8983 | | 0.0 | 44.0 | 16500 | 0.6899 | 0.8983 | | 0.0 | 45.0 | 16875 | 0.6911 | 0.8967 | | 0.0 | 46.0 | 17250 | 0.6870 | 0.8983 | | 0.0023 | 47.0 | 17625 | 0.6868 | 0.8983 | | 0.0 | 48.0 | 18000 | 0.6892 | 0.9 | | 0.0 | 49.0 | 18375 | 0.6892 | 0.9 | | 0.0021 | 50.0 | 18750 | 0.6879 | 0.9 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
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viditraj860/my_MT5_model
viditraj860
2023-12-17T06:13:36Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:13:36Z
Entry not found
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netcat420/MHENNlit
netcat420
2023-12-17T21:25:31Z
0
0
transformers
[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "art", "en", "dataset:netcat420/quiklit", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T06:17:37Z
--- license: mit datasets: - netcat420/quiklit language: - en tags: - art --- essay generating branch of MHENN. 4-bit quantized model can be found at file "mhennlitQ4_K_M.gguf" finetuned for 650 steps on an nvidia v100 on a google colab instance. finetuned on the netcat420/quiklit dataset. https://huggingface.co/mistralai/Mistral-7B-v0.1 <--------- BASE MODEL
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luffycodes/vicuna-mmlu-val-mcq-hal-7b-ep2
luffycodes
2023-12-17T06:27:18Z
0
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2305.13272", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T06:19:58Z
--- license: llama2 --- If you use this work, please cite: CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles https://arxiv.org/abs/2305.13272 ``` @misc{sonkar2023class, title={CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles}, author={Shashank Sonkar and Lucy Liu and Debshila Basu Mallick and Richard G. Baraniuk}, year={2023}, eprint={2305.13272}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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kaengpil/ElanaCaceres
kaengpil
2023-12-17T06:22:26Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-17T06:21:33Z
--- license: openrail ---
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lavawolfiee/Mixtral-8x7B-Instruct-v0.1-offloading-demo
lavawolfiee
2023-12-17T06:32:42Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]
text-generation
2023-12-17T06:23:45Z
Entry not found
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Asmaamaghraby/qa_model
Asmaamaghraby
2023-12-17T06:24:10Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:24:10Z
Entry not found
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BrianHsu/test
BrianHsu
2023-12-17T06:25:31Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:25:31Z
Entry not found
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coke7976/2d_rf_data
coke7976
2023-12-17T08:13:52Z
0
0
diffusers
[ "diffusers", "safetensors", "diffusers:DDPMPipeline", "region:us" ]
null
2023-12-17T06:29:57Z
Entry not found
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BrianHsu/BERT_test_graident_accumulation
BrianHsu
2023-12-17T07:51:09Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:bert-base-chinese", "endpoints_compatible", "region:us" ]
multiple-choice
2023-12-17T06:30:33Z
--- base_model: bert-base-chinese tags: - generated_from_trainer metrics: - accuracy model-index: - name: BERT_test_graident_accumulation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_test_graident_accumulation This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3780 - Accuracy: 0.6384 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 188 | 0.9354 | 0.5987 | | No log | 2.0 | 376 | 0.9827 | 0.6208 | | 0.7728 | 3.0 | 564 | 1.1462 | 0.6298 | | 0.7728 | 4.0 | 752 | 1.3019 | 0.6323 | | 0.7728 | 5.0 | 940 | 1.3780 | 0.6384 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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VarsaGupta/NLP_Based_Chatbot
VarsaGupta
2023-12-17T09:17:17Z
0
0
null
[ "chatbot", "natural language processing", "en", "license:mit", "region:us" ]
null
2023-12-17T06:31:01Z
--- language: en tags: - chatbot - natural language processing license: mit --- Model Card: NLP-Based Chatbot Overview The NLP-Based Chatbot is designed to explore Science & Technology topics. It utilizes a combination of semantic search and summarization techniques to provide relevant and concise responses to user queries. Model Details - Model Name: NLP-Based Chatbot - Model Type: Natural Language Processing (NLP) Chatbot - Framework: Gradio Blocks Interface, spaCy, Transformers Components 1. Semantic Search The chatbot employs semantic search to retrieve relevant information from a preprocessed dataset (Dronealexa.csv). The search is based on a TF-IDF vectorizer and cosine similarity calculations. 2. Summarization A summarization pipeline is used to generate concise summaries of the retrieved information. The Hugging Face Transformers library is utilized for summarization tasks. 3. Custom Embeddings The model incorporates custom text embeddings using spaCy and pre-trained word embeddings. These embeddings enhance the understanding of user queries and contribute to the semantic search. 4. Gradio Blocks Interface The chatbot's frontend is built using Gradio Blocks Interface, providing an interactive and user-friendly platform for users to input queries and receive responses. 5. Model Card Generation The model card generation involves constructing prompts based on search results and utilizing a summarization pipeline to produce model card content. Intended Use The NLP-Based Chatbot is intended for users interested in exploring Science & Technology topics. It can be used to obtain information from the provided dataset, and users are encouraged to provide feedback for continuous improvement. Training Data The model is trained on a custom dataset (Dronealexa.csv) containing Science & Technology-related information. The dataset has been preprocessed to handle missing values and ensure efficient semantic search. Evaluation Metrics - Semantic Search: TF-IDF Vectorizer, Cosine Similarity - Summarization: Hugging Face Transformers Pipeline Ethical Considerations The chatbot aims to provide accurate and relevant information. However, users are advised to critically evaluate the responses and understand that the model's knowledge is based on the training data. Usage Instructions 1. Input your query in the provided textbox. 2. Click the "Send" button to receive a response. 3. Optionally, submit feedback using the "Submit Feedback" button. License This model is released under the Apache 2.0 License. Contact Information For inquiries or issues, please contact varsagupta07@gmail.com.
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FLYINGCAT1/textual_inversion_cat
FLYINGCAT1
2023-12-17T06:41:40Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:41:40Z
Entry not found
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03Sys/ok
03Sys
2023-12-17T06:48:46Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:41:43Z
Entry not found
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thevatsalsaglani/celeba
thevatsalsaglani
2023-12-17T06:46:13Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-17T06:42:59Z
--- license: mit ---
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vinnystop/carhorn2
vinnystop
2023-12-17T23:40:08Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:46:02Z
Entry not found
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LoneStriker/openbuddy-mixtral-8x7b-v15.1-3.5bpw-h6-exl2-2
LoneStriker
2023-12-17T07:06:38Z
0
0
transformers
[ "transformers", "pytorch", "mixtral", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T06:48:41Z
--- language: - zh - en - fr - de - ja - ko - it - ru pipeline_tag: text-generation inference: false library_name: transformers license: apache-2.0 --- # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Copyright Notice Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 License: Apache 2.0 ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明 所有OpenBuddy模型均存在固有的局限性,可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事,不要使用这些模型,以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供,不附带任何种类的明示或暗示的保证,包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下,作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任(无论是合同、侵权还是其他原因)承担责任。 使用OpenBuddy即表示您同意这些条款和条件,并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
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ara77/ifxl
ara77
2023-12-17T10:00:01Z
0
0
diffusers
[ "diffusers", "pytorch", "safetensors", "if", "text-to-image", "arxiv:2205.11487", "arxiv:2110.02861", "license:deepfloyd-if-license", "diffusers:IFPipeline", "region:us" ]
text-to-image
2023-12-17T06:52:21Z
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MISCELLANEOUS\n If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Stability AI to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Stability AI regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Stability AI regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Stability AI." extra_gated_fields: "Organization /\_Affiliation": text Previously related publications: text I accept the above license agreement, and will use the Software non-commercially and for research purposes only: checkbox tags: - if - text-to-image inference: false --- # IF-I-XL-v1.0 DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of `6.66` on the COCO dataset. *Inspired by* [*Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding*](https://arxiv.org/pdf/2205.11487.pdf) ![](./pics/deepfloyd_if_scheme.jpg) ## Model Details - **Developed by:** DeepFloyd, StabilityAI - **Model type:** pixel-based text-to-image cascaded diffusion model - **Cascade Stage:** I - **Num Parameters:** 4.3B - **Language(s):** primarily English and, to a lesser extent, other Romance languages - **License:** <span style="color:blue"><a href="https://huggingface.co/spaces/DeepFloyd/deepfloyd-if-license">DeepFloyd IF License Agreement</a></span> - **Model Description:** DeepFloyd-IF is modular composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024. All stages of the model utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling - **Resources for more information:** [GitHub](https://github.com/deep-floyd/IF), [deepfloyd.ai](https://deepfloyd.ai), [All Links](https://linktr.ee/deepfloyd) - **Cite as (Soon):** - ## Using with `diffusers` IF is integrated with the 🤗 Hugging Face [🧨 diffusers library](https://github.com/huggingface/diffusers/), which is optimized to run on GPUs with as little as 14 GB of VRAM. Before you can use IF, you need to accept its usage conditions. To do so: 1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be loggin in 2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) 3. Make sure to login locally. Install `huggingface_hub` ```sh pip install huggingface_hub --upgrade ``` run the login function in a Python shell ```py from huggingface_hub import login login() ``` and enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens). Next we install `diffusers` and dependencies: ```sh pip install diffusers accelerate transformers safetensors sentencepiece ``` And we can now run the model locally. By default `diffusers` makes use of [model cpu offloading](https://huggingface.co/docs/diffusers/optimization/fp16#model-offloading-for-fast-inference-and-memory-savings) to run the whole IF pipeline with as little as 14 GB of VRAM. If you are using `torch>=2.0.0`, make sure to **remove all** `enable_xformers_memory_efficient_attention()` functions. * **Load all stages and offload to CPU** ```py from diffusers import DiffusionPipeline from diffusers.utils import pt_to_pil import torch # stage 1 stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16) stage_1.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0 stage_1.enable_model_cpu_offload() # stage 2 stage_2 = DiffusionPipeline.from_pretrained( "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 ) stage_2.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0 stage_2.enable_model_cpu_offload() # stage 3 safety_modules = {"feature_extractor": stage_1.feature_extractor, "safety_checker": stage_1.safety_checker, "watermarker": stage_1.watermarker} stage_3 = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16) stage_3.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0 stage_3.enable_model_cpu_offload() ``` * **Retrieve Text Embeddings** ```py prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' # text embeds prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt) ``` * **Run stage 1** ```py generator = torch.manual_seed(0) image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt").images pt_to_pil(image)[0].save("./if_stage_I.png") ``` * **Run stage 2** ```py image = stage_2( image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt" ).images pt_to_pil(image)[0].save("./if_stage_II.png") ``` * **Run stage 3** ```py image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images image[0].save("./if_stage_III.png") ``` There are multiple ways to speed up the inference time and lower the memory consumption even more with `diffusers`. To do so, please have a look at the Diffusers docs: - 🚀 [Optimizing for inference time](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-speed) - ⚙️ [Optimizing for low memory during inference](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-memory) For more in-detail information about how to use IF, please have a look at [the IF blog post](https://huggingface.co/blog/if) and the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/if) 📖. Diffusers dreambooth scripts also supports fine-tuning 🎨 [IF](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#if). With parameter efficient finetuning, you can add new concepts to IF with a single GPU and ~28 GB VRAM. ## Training **Training Data:** 1.2B text-image pairs (based on LAION-A and few additional internal datasets) Test/Valid parts of datasets are not used at any cascade and stage of training. Valid part of COCO helps to demonstrate "online" loss behaviour during training (to catch incident and other problems), but dataset is never used for train. **Training Procedure:** IF-I-XL-v1.0 is a pixel-based diffusion cascade which uses T5-Encoder embeddings (hidden states) to generate 64px image. During training, - Images are cropped to square via shifted-center-crop augmentation (randomly shift from center up to 0.1 of size) and resized to 64px using `Pillow==9.2.0` BICUBIC resampling with reducing_gap=None (it helps to avoid aliasing) and processed to tensor BxCxHxW - Text prompts are encoded through open-sourced frozen T5-v1_1-xxl text-encoder (that completely was trained by Google team), random 10% of texts are dropped to empty string to add ability for classifier free guidance (CFG) - The non-pooled output of the text encoder is fed into the projection (linear layer without activation) and is used in UNet backbone of the diffusion model via controlled hybrid self- and cross- attention - Also, the output of the text encode is pooled via attention-pooling (64 heads) and is used in time embed as additional features - Diffusion process is limited by 1000 discrete steps, with cosine beta schedule of noising image - The loss is a reconstruction objective between the noise that was added to the image and the prediction made by the UNet - The training process for checkpoint IF-I-XL-v1.0 has 2_420_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder ![](./pics/loss.jpg) **Hardware:** 64 x 8 x A100 GPUs **Optimizer:** [AdamW8bit](https://arxiv.org/abs/2110.02861) + [DeepSpeed ZeRO-1](https://www.deepspeed.ai/tutorials/zero/) **Batch:** 3072 **Learning rate**: [one-cycle](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.OneCycleLR.html) cosine strategy, warmup 10000 steps, start_lr=2e-6, max_lr=5e-5, final_lr=5e-9 ![](./pics/lr.jpg) ## Evaluation Results `FID-30K: 6.66` ![](./pics/fid30k_if.jpg) # Uses ## Direct Use The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model. Possible research areas and tasks include: - Generation of artistic imagery and use in design and other artistic processes. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion but applies in the same way for IF_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model was trained mainly with English captions and will not work as well in other languages. - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. IF was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Citation (Soon) *This model card was written by: DeepFloyd-Team and is based on the [StableDiffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).*
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intervitens/Mixtral-8x7B-Instruct-v0.1-5.5bpw-h6-exl2-rpcal
intervitens
2023-12-17T07:13:49Z
0
1
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T06:54:50Z
--- license: apache-2.0 language: - fr - it - de - es - en inference: false --- Quantized using 200 samples of 8192 tokens from [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset. Requires ExllamaV2 version 0.0.11 and up. Original model link: [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) Original model README below. *** # Model Card for Mixtral-8x7B The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/). ## Warning This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF. ## Instruction format This format must be strictly respected, otherwise the model will generate sub-optimal outputs. The template used to build a prompt for the Instruct model is defined as follows: ``` <s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST] ``` Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings. As reference, here is the pseudo-code used to tokenize instructions during fine-tuning: ```python def tokenize(text): return tok.encode(text, add_special_tokens=False) [BOS_ID] + tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_1) + [EOS_ID] + … tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_N) + [EOS_ID] ``` In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Limitations The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-all-semeval-all-epoch6-test01
grrfdghebsz
2023-12-17T06:58:23Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T06:56:19Z
Invalid username or password.
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elplaguister/Coursework-TextAI-Men4000-Koalpaca-Polyglot-5.8B
elplaguister
2023-12-17T07:08:34Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]
null
2023-12-17T06:57:00Z
--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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 0.7.1.dev0
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elplaguister/Coursework-TextAI-Women4000-Koalpaca-Polyglot-5.8B
elplaguister
2023-12-17T07:10:24Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]
null
2023-12-17T06:57:30Z
--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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 0.7.1.dev0
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elplaguister/Coursework-TextAI-Boy4000-Koalpaca-Polyglot-5.8B
elplaguister
2023-12-17T07:07:27Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]
null
2023-12-17T06:57:45Z
--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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 0.7.1.dev0
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brucethemoose/Yi-34B-200K-DARE-merge-v5-3.1bpw-exl2-fiction
brucethemoose
2023-12-17T08:31:46Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-12-17T06:59:00Z
--- license: other license_name: yi-license license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE language: - en library_name: transformers pipeline_tag: text-generation tags: - text-generation-inference --- [**Nous-Capybara-34B**](https://huggingface.co/NousResearch/Nous-Capybara-34B/), [**Tess-M-v1.4**](https://huggingface.co/migtissera/Tess-34B-v1.4), [**Airoboros-3_1-yi-34b-200k**](https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k), [**PlatYi-34B-200K-Q**](https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat), [**Pallas-0.4**](https://huggingface.co/Mihaiii/Pallas-0.4), [**Yi-34B-200K-AEZAKMI-v2**](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2), and a tiny bit of [**SUS-Chat-34B**](https://huggingface.co/SUSTech/SUS-Chat-34B) merged with a new, experimental implementation of "dare ties" via mergekit. See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5 The merge was then quantized with exllamav2's 0.0.11 brand new exl2 quantization, using 300K tokens from a sci fi story, a fantasy story, and a Vicuna format chat as profiling data, at a high context size. This should results in excellent writing performance for the model size. This 4bpw quantization can fit ~**75K Context on a 24GB GPU** at good quality. *** ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` It might recognize ChatML, or maybe Llama-chat from Airoboros. Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition. *** ## Running Being a Yi model, try running a lower temperature with 0.05-0.1 MinP, a little repitition penalty, and no other samplers. Yi tends to run "hot" by default. 24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/) *** ## Commands First pass: ``` python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/smol.parquet -ml 32768 -mr 9 -ss 4096 -b 4.0 -hb 6 -nr ``` Second pass: ``` python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/medium.parquet -l 12288 -r 29 -ml 32768 -mr 9 -ss 4096 -b 3.1 -hb 6 -cf /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5-exl2-3.1bpw-fiction -nr ``` Merged in mergekit with the following config, and the tokenizer from chargoddard's Yi-Llama: ``` models: - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama # No parameters necessary for base model - model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4 # Less weight than previous merge since Pallas is a finetune of Tess parameters: weight: 0.14 density: 0.62 - model: /home/alpha/FastModels/Mihaiii_Pallas-0.4 parameters: weight: 0.14 density: 0.62 - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k parameters: weight: 0.14 density: 0.52 - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B parameters: weight: 0.22 density: 0.62 - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat parameters: weight: 0.14 density: 0.52 #- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k # Dolphin 200K seems to be broken according to multiple leaderboards and perplexity tests? # parameters: # weight: 0.15 # density: 0.6 - model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2 parameters: weight: 0.14 density: 0.52 - model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B/ # Very low density and low weight since its a Yi 4K finetune, to try and preserve long context performance while "keeping" some of SUS parameters: weight: 0.08 density: 0.08 merge_method: dare_ties base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: int8_mask: true dtype: bfloat16 ``` *** ## Credits: https://github.com/cg123/mergekit/tree/dare https://huggingface.co/NousResearch/Nous-Capybara-34B/ https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k https://huggingface.co/migtissera/Tess-M-v1.4 https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2 https://huggingface.co/Mihaiii/Pallas-0.4 https://huggingface.co/SUSTech/SUS-Chat-34B https://huggingface.co/chargoddard/Yi-34B-200K-Llama https://huggingface.co/01-ai/Yi-34B-200K
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iolto/Wizard-Vicuna-30B-Uncensored
iolto
2023-12-17T06:59:09Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:59:09Z
Entry not found
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yaaakob/joe
yaaakob
2023-12-17T06:59:16Z
0
0
null
[ "region:us" ]
null
2023-12-17T06:59:16Z
Entry not found
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brucethemoose/Yi-34B-200K-DARE-merge-v5-2.67bpw-exl2-fiction
brucethemoose
2023-12-17T16:20:02Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-12-17T06:59:23Z
--- license: other license_name: yi-license license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE language: - en library_name: transformers pipeline_tag: text-generation tags: - text-generation-inference --- [**Nous-Capybara-34B**](https://huggingface.co/NousResearch/Nous-Capybara-34B/), [**Tess-M-v1.4**](https://huggingface.co/migtissera/Tess-34B-v1.4), [**Airoboros-3_1-yi-34b-200k**](https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k), [**PlatYi-34B-200K-Q**](https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat), [**Pallas-0.4**](https://huggingface.co/Mihaiii/Pallas-0.4), [**Yi-34B-200K-AEZAKMI-v2**](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2), and a tiny bit of [**SUS-Chat-34B**](https://huggingface.co/SUSTech/SUS-Chat-34B) merged with a new, experimental implementation of "dare ties" via mergekit. See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5 The merge was then quantized with exllamav2's 0.0.11 brand new exl2 quantization, using 300K tokens from a sci fi story, a fantasy story, and a Vicuna format chat as profiling data, at a high context size. This should results in excellent writing performance for the model size. This 2.67bpw quantization can fit **Long Context on a 16GB GPU** at usable quality. *** ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` It might recognize ChatML, or maybe Llama-chat from Airoboros. Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition. *** ## Running Being a Yi model, try running a lower temperature with 0.05-0.1 MinP, a little repitition penalty, and no other samplers. Yi tends to run "hot" by default. 24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/) *** ## Commands First pass: ``` python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/smol.parquet -ml 32768 -mr 9 -ss 4096 -b 4.0 -hb 6 -nr ``` Second pass: ``` python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/medium.parquet -l 12288 -r 29 -ml 32768 -mr 9 -ss 4096 -b 2.67 -hb 6 -cf /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5-exl2-2.67bpw-fiction -nr ``` Merged in mergekit with the following config, and the tokenizer from chargoddard's Yi-Llama: ``` models: - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama # No parameters necessary for base model - model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4 # Less weight than previous merge since Pallas is a finetune of Tess parameters: weight: 0.14 density: 0.62 - model: /home/alpha/FastModels/Mihaiii_Pallas-0.4 parameters: weight: 0.14 density: 0.62 - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k parameters: weight: 0.14 density: 0.52 - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B parameters: weight: 0.22 density: 0.62 - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat parameters: weight: 0.14 density: 0.52 #- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k # Dolphin 200K seems to be broken according to multiple leaderboards and perplexity tests? # parameters: # weight: 0.15 # density: 0.6 - model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2 parameters: weight: 0.14 density: 0.52 - model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B/ # Very low density and low weight since its a Yi 4K finetune, to try and preserve long context performance while "keeping" some of SUS parameters: weight: 0.08 density: 0.08 merge_method: dare_ties base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: int8_mask: true dtype: bfloat16 ``` *** ## Credits: https://github.com/cg123/mergekit/tree/dare https://huggingface.co/NousResearch/Nous-Capybara-34B/ https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k https://huggingface.co/migtissera/Tess-M-v1.4 https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2 https://huggingface.co/Mihaiii/Pallas-0.4 https://huggingface.co/SUSTech/SUS-Chat-34B https://huggingface.co/chargoddard/Yi-34B-200K-Llama https://huggingface.co/01-ai/Yi-34B-200K
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-all-semeval-all-epoch6-test02
grrfdghebsz
2023-12-17T07:03:04Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:01:20Z
Invalid username or password.
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yaaakob/joe12
yaaakob
2023-12-17T14:00:53Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:01:26Z
Entry not found
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WuZTY/tinystarcoder-rlhf-model
WuZTY
2023-12-17T07:07:19Z
0
0
transformers
[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "generated_from_trainer", "base_model:bigcode/tiny_starcoder_py", "license:bigcode-openrail-m", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T07:05:03Z
--- license: bigcode-openrail-m base_model: bigcode/tiny_starcoder_py tags: - generated_from_trainer metrics: - accuracy model-index: - name: tinystarcoder-rlhf-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. --> # tinystarcoder-rlhf-model This model is a fine-tuned version of [bigcode/tiny_starcoder_py](https://huggingface.co/bigcode/tiny_starcoder_py) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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hamedjahantigh/parsig_tokenizer
hamedjahantigh
2023-12-17T11:11:52Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:05:24Z
Entry not found
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Homiebear/FreddyHonk
Homiebear
2023-12-17T07:06:43Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-17T07:06:27Z
--- license: openrail ---
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dhyutin/pegasus-samsum
dhyutin
2023-12-17T15:39:43Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-12-17T07:06:27Z
--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4833 ## Authors - Author 1: Sree Dhyuti Nimmagadda - dhyutin - Author 2: Krishnasai Paleti - pk248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6614 | 0.54 | 500 | 1.4833 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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LoneStriker/Mixtral-4x7B-DPO-RPChat-4.0bpw-h6-exl2-2
LoneStriker
2023-12-17T07:18:42Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T07:10:45Z
--- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- Mixtral-4x7B-DPO-RPChat is a model made primarely for RP (Roleplay), 2 RP model, 1 occult model and 1 DPO model for a MoE. Toppy was the base. The DPO was here to help get more human reply This is my first try at doing this, so don't hesitate to give feedback! WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0! <!-- description start --> ## Description This repo contains fp16 files of Mixtral-4x7B-DPO-RPChat. <!-- description end --> <!-- description start --> ## Models used The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-4x7B-DPO-RPChat/blob/main/config.yaml) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_SystemError0.8_Seed103
behzadnet
2023-12-17T07:12:08Z
0
0
peft
[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "region:us" ]
null
2023-12-17T07:12:02Z
--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **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 Data 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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_SystemError0.8_Seed103
behzadnet
2023-12-17T07:12:17Z
0
0
peft
[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "region:us" ]
null
2023-12-17T07:12:14Z
--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **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 Data 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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0
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igotech/text2image
igotech
2023-12-17T08:09:32Z
0
0
null
[ "arxiv:2102.12092", "arxiv:2103.00020", "arxiv:2012.09841", "arxiv:1910.13461", "arxiv:2002.05202", "arxiv:2203.00555", "arxiv:2110.09456", "arxiv:2103.14030", "arxiv:2105.13290", "arxiv:1910.07467", "arxiv:2110.11773", "arxiv:2210.06423", "arxiv:2002.09018", "arxiv:2010.09931", "region:us" ]
null
2023-12-17T07:14:04Z
# DALL·E Mini <a href="https://www.craiyon.com/"><img src="https://www.craiyon.com/thumbnail.png" width="300"></a> ## How to use it? You can use the model on [🖍️ craiyon](https://www.craiyon.com/) ## How does it work? Refer to our reports: * [DALL·E mini - Generate Images from Any Text Prompt](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) * [DALL·E mini - Explained](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA) * [DALL·E mega - Training Journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training-Journal--VmlldzoxODMxMDI2) ## Development ### Dependencies Installation For inference only, use `pip install dalle-mini`. For development, clone the repo and use `pip install -e ".[dev]"`. Before making a PR, check style with `make style`. You can experiment with the pipeline step by step through our [`inference pipeline notebook`](tools/inference/inference_pipeline.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/tools/inference/inference_pipeline.ipynb) ### Training of DALL·E mini Use [`tools/train/train.py`](tools/train/train.py). You can also adjust the [sweep configuration file](https://docs.wandb.ai/guides/sweeps) if you need to perform a hyperparameter search. ## FAQ ### Where to find the latest models? Trained models are on 🤗 Model Hub: * [VQGAN-f16-16384](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384) for encoding/decoding images * [DALL·E mini](https://huggingface.co/dalle-mini/dalle-mini) or [DALL·E mega](https://huggingface.co/dalle-mini/dalle-mega) for generating images from a text prompt ### Where does the logo come from? The "armchair in the shape of an avocado" was used by OpenAI when releasing DALL·E to illustrate the model's capabilities. Having successful predictions on this prompt represents a big milestone for us. ## Contributing Join the community on the [LAION Discord](https://discord.gg/xBPBXfcFHd). Any contribution is welcome, from reporting issues to proposing fixes/improvements or testing the model with cool prompts! You can also use these great projects from the community: * spin off your own app with [DALL-E Playground repository](https://github.com/saharmor/dalle-playground) (thanks [Sahar](https://twitter.com/theaievangelist)) * try [DALL·E Flow](https://github.com/jina-ai/dalle-flow) project for generating, diffusion, and upscaling in a Human-in-the-Loop workflow (thanks [Han Xiao](https://github.com/hanxiao)) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jina-ai/dalle-flow/blob/main/client.ipynb) * run on [Replicate](https://replicate.com/borisdayma/dalle-mini), in the browser or via API ## Acknowledgements * 🤗 Hugging Face for organizing [the FLAX/JAX community week](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects) * Google [TPU Research Cloud (TRC) program](https://sites.research.google/trc/) for providing computing resources * [Weights & Biases](https://wandb.com/) for providing the infrastructure for experiment tracking and model management ## Authors & Contributors DALL·E mini was initially developed by: * [Boris Dayma](https://github.com/borisdayma) * [Suraj Patil](https://github.com/patil-suraj) * [Pedro Cuenca](https://github.com/pcuenca) * [Khalid Saifullah](https://github.com/khalidsaifullaah) * [Tanishq Abraham](https://github.com/tmabraham) * [Phúc Lê Khắc](https://github.com/lkhphuc) * [Luke Melas](https://github.com/lukemelas) * [Ritobrata Ghosh](https://github.com/ghosh-r) Many thanks to the people who helped make it better: * the [DALLE-Pytorch](https://discord.gg/xBPBXfcFHd) and [EleutherAI](https://www.eleuther.ai/) communities for testing and exchanging cool ideas * [Rohan Anil](https://github.com/rohan-anil) for adding Distributed Shampoo optimizer and always giving great suggestions * [Phil Wang](https://github.com/lucidrains) has provided a lot of cool implementations of transformer variants and gives interesting insights with [x-transformers](https://github.com/lucidrains/x-transformers) * [Katherine Crowson](https://github.com/crowsonkb) for [super conditioning](https://twitter.com/RiversHaveWings/status/1478093658716966912) * the [Gradio team](https://gradio.app/) made an amazing UI for our app ## Citing DALL·E mini If you find DALL·E mini useful in your research or wish to refer, please use the following BibTeX entry. ```text @misc{Dayma_DALL·E_Mini_2021, author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata}, doi = {10.5281/zenodo.5146400}, month = {7}, title = {DALL·E Mini}, url = {https://github.com/borisdayma/dalle-mini}, year = {2021} } ``` ## References Original DALL·E from "[Zero-Shot Text-to-Image Generation](https://arxiv.org/abs/2102.12092)" with image quantization from "[Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)". Image encoder from "[Taming Transformers for High-Resolution Image Synthesis](https://arxiv.org/abs/2012.09841v2)". Sequence to sequence model based on "[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461v1)" with implementation of a few variants: * "[GLU Variants Improve Transformer](https://arxiv.org/abs/2002.05202)" * "[Deepnet: Scaling Transformers to 1,000 Layers](https://arxiv.org/abs/2203.00555)" * "[NormFormer: Improved Transformer Pretraining with Extra Normalization](https://arxiv.org/abs/2110.09456)" * "[Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)" * "[CogView: Mastering Text-to-Image Generation via Transformers](https://arxiv.org/abs/2105.13290v2)" * "[Root Mean Square Layer Normalization](https://arxiv.org/abs/1910.07467)" * "[Sinkformers: Transformers with Doubly Stochastic Attention](https://arxiv.org/abs/2110.11773)" * "[Foundation Transformers](https://arxiv.org/abs/2210.06423) Main optimizer (Distributed Shampoo) from "[Scalable Second Order Optimization for Deep Learning](https://arxiv.org/abs/2002.09018)". ### Citations ```text @misc{ title={Zero-Shot Text-to-Image Generation}, author={Aditya Ramesh and Mikhail Pavlov and Gabriel Goh and Scott Gray and Chelsea Voss and Alec Radford and Mark Chen and Ilya Sutskever}, year={2021}, eprint={2102.12092}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```text @misc{ title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, year={2021}, eprint={2103.00020}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```text @misc{ title={Taming Transformers for High-Resolution Image Synthesis}, author={Patrick Esser and Robin Rombach and Björn Ommer}, year={2021}, eprint={2012.09841}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```text @misc{ title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, author={Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Ves Stoyanov and Luke Zettlemoyer}, year={2019}, eprint={1910.13461}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```text @misc{ title={Scalable Second Order Optimization for Deep Learning}, author={Rohan Anil and Vineet Gupta and Tomer Koren and Kevin Regan and Yoram Singer}, year={2021}, eprint={2002.09018}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ```text @misc{ title={GLU Variants Improve Transformer}, author={Noam Shazeer}, year={2020}, url={https://arxiv.org/abs/2002.05202} } ``` ```text @misc{ title={DeepNet: Scaling transformers to 1,000 layers}, author={Wang, Hongyu and Ma, Shuming and Dong, Li and Huang, Shaohan and Zhang, Dongdong and Wei, Furu}, year={2022}, eprint={2203.00555} archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ```text @misc{ title={NormFormer: Improved Transformer Pretraining with Extra Normalization}, author={Sam Shleifer and Jason Weston and Myle Ott}, year={2021}, eprint={2110.09456}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```text @inproceedings{ title={Swin Transformer V2: Scaling Up Capacity and Resolution}, author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo}, booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2022} } ``` ```text @misc{ title = {CogView: Mastering Text-to-Image Generation via Transformers}, author = {Ming Ding and Zhuoyi Yang and Wenyi Hong and Wendi Zheng and Chang Zhou and Da Yin and Junyang Lin and Xu Zou and Zhou Shao and Hongxia Yang and Jie Tang}, year = {2021}, eprint = {2105.13290}, archivePrefix = {arXiv}, primaryClass = {cs.CV} } ``` ```text @misc{ title = {Root Mean Square Layer Normalization}, author = {Biao Zhang and Rico Sennrich}, year = {2019}, eprint = {1910.07467}, archivePrefix = {arXiv}, primaryClass = {cs.LG} } ``` ```text @misc{ title = {Sinkformers: Transformers with Doubly Stochastic Attention}, url = {https://arxiv.org/abs/2110.11773}, author = {Sander, Michael E. and Ablin, Pierre and Blondel, Mathieu and Peyré, Gabriel}, publisher = {arXiv}, year = {2021}, } ``` ```text @misc{ title = {Smooth activations and reproducibility in deep networks}, url = {https://arxiv.org/abs/2010.09931}, author = {Shamir, Gil I. and Lin, Dong and Coviello, Lorenzo}, publisher = {arXiv}, year = {2020}, } ``` ```text @misc{ title = {Foundation Transformers}, url = {https://arxiv.org/abs/2210.06423}, author = {Wang, Hongyu and Ma, Shuming and Huang, Shaohan and Dong, Li and Wang, Wenhui and Peng, Zhiliang and Wu, Yu and Bajaj, Payal and Singhal, Saksham and Benhaim, Alon and Patra, Barun and Liu, Zhun and Chaudhary, Vishrav and Song, Xia and Wei, Furu}, publisher = {arXiv}, year = {2022}, } ```
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Z1zs/path_to_saved_model
Z1zs
2023-12-17T07:18:33Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:18:33Z
Entry not found
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kaengpil/Makhyli
kaengpil
2023-12-17T07:19:12Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-17T07:18:52Z
--- license: openrail ---
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-all-semeval-all-epoch6-test03
grrfdghebsz
2023-12-17T07:25:47Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:23:41Z
Invalid username or password.
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LoneStriker/Mixtral-4x7B-DPO-RPChat-5.0bpw-h6-exl2-2
LoneStriker
2023-12-17T07:35:28Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T07:24:29Z
--- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- Mixtral-4x7B-DPO-RPChat is a model made primarely for RP (Roleplay), 2 RP model, 1 occult model and 1 DPO model for a MoE. Toppy was the base. The DPO was here to help get more human reply This is my first try at doing this, so don't hesitate to give feedback! WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0! <!-- description start --> ## Description This repo contains fp16 files of Mixtral-4x7B-DPO-RPChat. <!-- description end --> <!-- description start --> ## Models used The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-4x7B-DPO-RPChat/blob/main/config.yaml) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-all-semeval-all-epoch6-test04
grrfdghebsz
2023-12-17T07:29:51Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:27:22Z
Invalid username or password.
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Albe-njupt/sd-hmm-512-r-20-16-4-1-lora-1
Albe-njupt
2023-12-17T07:29:45Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:29:45Z
Entry not found
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xjyplayer/neon_bingqing
xjyplayer
2023-12-17T08:04:09Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T07:34:08Z
--- license: apache-2.0 ---
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mahihossain666/llama-2-70b-hf-quantized-3bits-GPTQ
mahihossain666
2023-12-17T10:27:03Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "dataset:allenai/c4", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T07:34:40Z
--- datasets: - allenai/c4 --- Quantized to 3bit precision using AutoGPTQ.
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LoneStriker/Mixtral-4x7B-DPO-RPChat-6.0bpw-h6-exl2-2
LoneStriker
2023-12-17T07:49:54Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T07:38:11Z
--- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- Mixtral-4x7B-DPO-RPChat is a model made primarely for RP (Roleplay), 2 RP model, 1 occult model and 1 DPO model for a MoE. Toppy was the base. The DPO was here to help get more human reply This is my first try at doing this, so don't hesitate to give feedback! WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0! <!-- description start --> ## Description This repo contains fp16 files of Mixtral-4x7B-DPO-RPChat. <!-- description end --> <!-- description start --> ## Models used The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-4x7B-DPO-RPChat/blob/main/config.yaml) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-random4000-semeval-all-epoch6-test01
grrfdghebsz
2023-12-17T07:39:58Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:38:13Z
Invalid username or password.
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bit-dny/MindLLM
bit-dny
2023-12-17T07:41:12Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T07:41:12Z
--- license: apache-2.0 ---
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leeloli/rosietest
leeloli
2023-12-17T07:45:20Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-17T07:44:12Z
--- license: openrail ---
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-random4000-semeval-all-epoch6-test02
grrfdghebsz
2023-12-17T07:49:12Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:47:30Z
Invalid username or password.
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-all-semeval-all-epoch6-test05
grrfdghebsz
2023-12-17T07:50:02Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:48:02Z
Invalid username or password.
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Pooya-Fallah/parsig_pos
Pooya-Fallah
2023-12-17T08:48:56Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:50:43Z
Entry not found
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Pooya-Fallah/parsig_tokenizer
Pooya-Fallah
2023-12-17T08:48:58Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:50:45Z
Entry not found
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rezashr/q-FrozenLake-v1-4x4-noSlippery
rezashr
2023-12-17T07:52:01Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-17T07:51:59Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="rezashr/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
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tafodile/hw-llama-2-7B-nsmc
tafodile
2023-12-17T08:35:59Z
0
0
peft
[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
null
2023-12-17T07:52:03Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### llama2-7b모델 미세튜닝 네이버 영화 리뷰텍스트(NSMC)데이터셋을 프롬포트에 포함하여 모델에 입력하면 "긍정" 또는 "부정" 이라고 예측하는 텍스트 생성하는 것이 목표 ### 실험내용: train dataset의 2000개 샘플,valid dataset의 1000개 샘플을 미세튜닝에 사용 - 일반적으로 1900스텝에서는 정확도 accuracy가 80후반대(약 85%)가 도출, 2000스텝이상부터 90%에 근접한 수치를 보였다. - seq length=256 gradient accumulation steps=2로 설정하면 매우 낮은 정확도(accuracy)가 도출되기때문에 그 이상의 seq length와 accumulation steps가 필요하다는 사실 확인. ##Accuracy 정확도 분석 ###valid_dataset(test dataset 1000개에 대한 정확도) ********************************* | | TP | TN | |:------------:|:-----:|:----:| | PP | 460 | 48.00 | | PN | 50 | 442.00| |Accuracy | | 0.902| ********************************* ***정확도:0.902 ********************************* ### 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 0.7.1
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Tam666/PGsoft
Tam666
2023-12-17T07:53:18Z
0
0
null
[ "license:c-uda", "region:us" ]
null
2023-12-17T07:53:18Z
--- license: c-uda ---
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FC2023/comfy
FC2023
2023-12-17T07:56:24Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:53:54Z
Entry not found
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yuntian-nwu/TLM_test
yuntian-nwu
2023-12-17T07:55:54Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:55:54Z
Entry not found
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AlvaroVasquezAI/vaneModePro
AlvaroVasquezAI
2023-12-17T08:10:16Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:56:23Z
Entry not found
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LoneStriker/Synthia-MoE-v3-Mixtral-8x7B-3.5bpw-h6-exl2-2
LoneStriker
2023-12-17T18:30:24Z
0
1
transformers
[ "transformers", "pytorch", "mixtral", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T07:56:37Z
--- license: apache-2.0 --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # Note: Model is most likely over-fitted due to higher learning rate. Will fix this issue in the next release. # Synthia-MoE-v3-Mixtral-8x7B This is Synthia-MoE-v3 trained on the official Mistral MoE version (Mixtral-8x7B). This model is trained on the Synthia-v3.0 dataset, that contains ~10K super high-quality GPT-4-Turbo generated samples. The samples contains Tree-of-Thought, Chain-of-Thought and other system contexts designed to evoke reasoning, philosophical thinking, use working memory and long chain of reasoning with multi-part questions. Further, this model is trained on the Orca-2 principle of replacing the system context with just one message. In the case of this Synthia-MoE-v3 model, the system context was not included at all. The evals are coming, but testing empirically the model produces highly intelligent, coherent results. Here's a sample conversation: https://migel.substack.com/p/a-conversation-with-synthia-moe-mixtral <br> ![Synthia](https://huggingface.co/migtissera/Synthia-MoE-v3-Mixtral-8x7B/resolve/main/Synthia-MoE.png) <br> ``` import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/Synthia-MoE-v3-Mixtral8x7B" output_file_path = "/home/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = "SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
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Danibasters/xlm-roberta-base-finetuned-panx-de
Danibasters
2023-12-17T08:07:54Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-17T07:56:42Z
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8641202254226674 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1348 - F1: 0.8641 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2557 | 1.0 | 525 | 0.1547 | 0.8199 | | 0.1275 | 2.0 | 1050 | 0.1337 | 0.8525 | | 0.0793 | 3.0 | 1575 | 0.1348 | 0.8641 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-random4000-semeval-all-epoch6-test03
grrfdghebsz
2023-12-17T07:58:41Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:56:56Z
Invalid username or password.
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-random4000-semeval-all-epoch6-test04
grrfdghebsz
2023-12-17T07:59:55Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T07:57:50Z
Invalid username or password.
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mwkldeveloper/pokemon-lora
mwkldeveloper
2023-12-17T07:58:14Z
0
0
null
[ "region:us" ]
null
2023-12-17T07:58:14Z
Entry not found
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VoidZeroe/llama5.0-model
VoidZeroe
2023-12-17T08:02:18Z
0
0
peft
[ "peft", "region:us" ]
null
2023-12-17T07:59:21Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
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nhero/gpt2-imdb-pos-v2
nhero
2023-12-17T08:01:50Z
0
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T08:01:27Z
Entry not found
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CatSiewDai/llama2-qlora-finetunined-french
CatSiewDai
2023-12-17T10:45:30Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyPixel/Llama-2-7B-bf16-sharded", "region:us" ]
null
2023-12-17T08:02:47Z
--- library_name: peft base_model: TinyPixel/Llama-2-7B-bf16-sharded --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0
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intervitens/Mixtral-8x7B-Instruct-v0.1-3.75bpw-h6-exl2
intervitens
2023-12-17T08:18:20Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T08:04:55Z
--- license: apache-2.0 language: - fr - it - de - es - en inference: false --- Quantized using samples of 8192 tokens from the default ExllamaV2 dataset. Requires ExllamaV2 version 0.0.11 and up. Original model link: [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) Original model README below. *** # Model Card for Mixtral-8x7B The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/). ## Warning This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF. ## Instruction format This format must be strictly respected, otherwise the model will generate sub-optimal outputs. The template used to build a prompt for the Instruct model is defined as follows: ``` <s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST] ``` Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings. As reference, here is the pseudo-code used to tokenize instructions during fine-tuning: ```python def tokenize(text): return tok.encode(text, add_special_tokens=False) [BOS_ID] + tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_1) + [EOS_ID] + … tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_N) + [EOS_ID] ``` In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Limitations The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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grrfdghebsz/xlm-roberta-base-finetuning-wrime-random4000-semeval-all-epoch6-test05
grrfdghebsz
2023-12-17T08:06:51Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-17T08:05:01Z
Invalid username or password.
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xjyplayer/christmas_sweater
xjyplayer
2023-12-17T08:09:49Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T08:07:36Z
--- license: apache-2.0 ---
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PeterV09/mistral-7b-300k-6k-a100-6e-valid-5
PeterV09
2023-12-17T08:15:48Z
0
0
transformers
[ "transformers", "pytorch", "mistral", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T08:08:05Z
--- tags: - generated_from_trainer model-index: - name: mistral-7b-300k-6k-a100-6e-valid-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-300k-6k-a100-6e-valid-5 This model was trained from scratch 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 512 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6.0 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
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xpkoalz/my_awesome_asr_mind_model
xpkoalz
2023-12-17T08:08:56Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:08:56Z
Entry not found
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Matshark/LEGIT
Matshark
2023-12-17T08:10:39Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T08:10:39Z
--- license: apache-2.0 ---
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stoddur/manual8_3_r2048_alpha4096_v2
stoddur
2023-12-17T08:18:51Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:12:15Z
Entry not found
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stvhuang/rcr-run-b9bhsr7n-62291-master-0_20231213T152412-ep04
stvhuang
2023-12-17T08:21:40Z
0
0
transformers
[ "transformers", "safetensors", "deberta-v2", "feature-extraction", "endpoints_compatible", "region:us" ]
feature-extraction
2023-12-17T08:20:37Z
Entry not found
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onizukal/hushem_40x_beit_large_sgd_lr00001_fold5
onizukal
2023-12-17T11:27:07Z
0
0
transformers
[ "transformers", "safetensors", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-17T08:23:08Z
--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: hushem_40x_beit_large_sgd_lr00001_fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.2682926829268293 --- <!-- 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. --> # hushem_40x_beit_large_sgd_lr00001_fold5 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4163 - Accuracy: 0.2683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.5796 | 1.0 | 440 | 1.5916 | 0.1220 | | 1.5949 | 2.0 | 880 | 1.5829 | 0.1220 | | 1.5587 | 3.0 | 1320 | 1.5748 | 0.1220 | | 1.6067 | 4.0 | 1760 | 1.5669 | 0.1220 | | 1.5959 | 5.0 | 2200 | 1.5594 | 0.1220 | | 1.5636 | 6.0 | 2640 | 1.5521 | 0.1220 | | 1.5672 | 7.0 | 3080 | 1.5450 | 0.1463 | | 1.5809 | 8.0 | 3520 | 1.5381 | 0.1463 | | 1.5125 | 9.0 | 3960 | 1.5315 | 0.1463 | | 1.5314 | 10.0 | 4400 | 1.5251 | 0.1463 | | 1.5221 | 11.0 | 4840 | 1.5188 | 0.1707 | | 1.49 | 12.0 | 5280 | 1.5128 | 0.1707 | | 1.4904 | 13.0 | 5720 | 1.5070 | 0.1707 | | 1.5335 | 14.0 | 6160 | 1.5014 | 0.1707 | | 1.4939 | 15.0 | 6600 | 1.4961 | 0.1707 | | 1.4342 | 16.0 | 7040 | 1.4910 | 0.1707 | | 1.5198 | 17.0 | 7480 | 1.4861 | 0.1707 | | 1.5018 | 18.0 | 7920 | 1.4813 | 0.1707 | | 1.5383 | 19.0 | 8360 | 1.4768 | 0.1707 | | 1.4499 | 20.0 | 8800 | 1.4725 | 0.1707 | | 1.4829 | 21.0 | 9240 | 1.4684 | 0.1707 | | 1.4804 | 22.0 | 9680 | 1.4646 | 0.1951 | | 1.4801 | 23.0 | 10120 | 1.4608 | 0.2195 | | 1.466 | 24.0 | 10560 | 1.4571 | 0.2195 | | 1.4676 | 25.0 | 11000 | 1.4538 | 0.2195 | | 1.4891 | 26.0 | 11440 | 1.4506 | 0.2195 | | 1.4902 | 27.0 | 11880 | 1.4476 | 0.2195 | | 1.4006 | 28.0 | 12320 | 1.4446 | 0.2195 | | 1.4459 | 29.0 | 12760 | 1.4419 | 0.2195 | | 1.4812 | 30.0 | 13200 | 1.4393 | 0.2195 | | 1.4547 | 31.0 | 13640 | 1.4369 | 0.2195 | | 1.4831 | 32.0 | 14080 | 1.4346 | 0.2439 | | 1.4414 | 33.0 | 14520 | 1.4325 | 0.2683 | | 1.535 | 34.0 | 14960 | 1.4305 | 0.2683 | | 1.5284 | 35.0 | 15400 | 1.4287 | 0.2683 | | 1.4525 | 36.0 | 15840 | 1.4270 | 0.2683 | | 1.4744 | 37.0 | 16280 | 1.4254 | 0.2683 | | 1.4435 | 38.0 | 16720 | 1.4240 | 0.2683 | | 1.4147 | 39.0 | 17160 | 1.4227 | 0.2683 | | 1.4583 | 40.0 | 17600 | 1.4215 | 0.2683 | | 1.6194 | 41.0 | 18040 | 1.4205 | 0.2683 | | 1.4983 | 42.0 | 18480 | 1.4195 | 0.2683 | | 1.4306 | 43.0 | 18920 | 1.4187 | 0.2683 | | 1.4728 | 44.0 | 19360 | 1.4181 | 0.2683 | | 1.3834 | 45.0 | 19800 | 1.4175 | 0.2683 | | 1.5215 | 46.0 | 20240 | 1.4170 | 0.2683 | | 1.4454 | 47.0 | 20680 | 1.4167 | 0.2683 | | 1.4168 | 48.0 | 21120 | 1.4165 | 0.2683 | | 1.4118 | 49.0 | 21560 | 1.4163 | 0.2683 | | 1.4085 | 50.0 | 22000 | 1.4163 | 0.2683 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1
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kaengpil/Queenie
kaengpil
2023-12-17T08:24:16Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-17T08:23:59Z
--- license: openrail ---
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mncai/agiin-11.1B-v0.1
mncai
2023-12-17T09:30:50Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:Intel/orca_dpo_pairs", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T08:24:50Z
--- license: apache-2.0 datasets: - Intel/orca_dpo_pairs language: - en --- # Model Card for mncai/agiin-11.1B-v0.1 ### Introduction of MindsAndCompany https://mnc.ai/ We create various AI models and develop solutions that can be applied to businesses. And as for generative AI, we are developing products like Code Assistant, TOD Chatbot, LLMOps, and are in the process of developing Enterprise AGI (Artificial General Intelligence). ### Model Summary This model was built based on the Mistral architecture. It was inspired by neural connection technology and rehabilitation therapy. I have created a new model architecture that does not require pretraining, and training the model is sufficient with just one H100 for 7 hours. ### Data Intel/orca_dpo_pairs (DPO) ### Surgery and Training stack mistral 50 layers and DPO. ### How to Use ```python message = [ {"role": "system", "content": "You are a helpful assistant chatbot."}, {"role": "user", "content": "두 개의 구가 각각 지름이 1, 2일때 두 구의 부피는 몇배지? 설명도 같이 해줘."} ] tokenizer = AutoTokenizer.from_pretrained(hf_model) prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) pipeline = transformers.pipeline( "text-generation", model=hf_model, tokenizer=tokenizer ) sequences = pipeline( prompt, do_sample=True, temperature=0.7, top_p=0.9, num_return_sequences=1, max_length=512, ) print(sequences[0]['generated_text']) ``` ### Contact If you have any questions, please raise an issue or contact us at dwmyoung@mnc.ai
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eshan309/KishoreKumar
eshan309
2023-12-17T08:34:21Z
0
0
null
[ "kishore kumar", "music", "india", "singer", "old singer", "rvc model", "region:us" ]
null
2023-12-17T08:29:50Z
--- tags: - kishore kumar - music - india - singer - old singer - rvc model ---
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BrianHsu/BERT_test_graident_accumulation_test2
BrianHsu
2023-12-17T08:58:03Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:bert-base-chinese", "endpoints_compatible", "region:us" ]
multiple-choice
2023-12-17T08:30:44Z
--- base_model: bert-base-chinese tags: - generated_from_trainer metrics: - accuracy model-index: - name: BERT_test_graident_accumulation_test2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_test_graident_accumulation_test2 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0296 - Accuracy: 0.6379 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 188 | 0.9392 | 0.5937 | | No log | 2.0 | 376 | 0.9506 | 0.6354 | | 0.7706 | 3.0 | 564 | 1.0296 | 0.6379 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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DeepLearner101/ImageNetSelectedSubsetBasedModle
DeepLearner101
2023-12-17T08:30:52Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:30:52Z
Entry not found
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LIGIONEERO/Infoge_QAChat_bot
LIGIONEERO
2023-12-17T08:32:09Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T08:32:09Z
--- license: apache-2.0 ---
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intervitens/Mixtral-8x7B-Instruct-v0.1-5.5bpw-h6-exl2
intervitens
2023-12-17T09:09:22Z
0
1
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T08:37:14Z
--- license: apache-2.0 language: - fr - it - de - es - en inference: false --- Quantized using samples of 8192 tokens from the default ExllamaV2 dataset. Requires ExllamaV2 version 0.0.11 and up. Original model link: [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) Original model README below. *** # Model Card for Mixtral-8x7B The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/). ## Warning This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF. ## Instruction format This format must be strictly respected, otherwise the model will generate sub-optimal outputs. The template used to build a prompt for the Instruct model is defined as follows: ``` <s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST] ``` Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings. As reference, here is the pseudo-code used to tokenize instructions during fine-tuning: ```python def tokenize(text): return tok.encode(text, add_special_tokens=False) [BOS_ID] + tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_1) + [EOS_ID] + … tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_N) + [EOS_ID] ``` In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Limitations The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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iyKyuU/ePiCnAmE
iyKyuU
2023-12-17T11:03:15Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-12-17T08:41:22Z
--- license: openrail ---
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ngoc88888/ngoc
ngoc88888
2023-12-17T08:46:30Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:44:24Z
Entry not found
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LoneStriker/Mixtral-Fusion-4x7B-Instruct-v0.1-3.0bpw-h6-exl2-2
LoneStriker
2023-12-17T12:39:07Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T08:47:05Z
--- license: apache-2.0 language: - fr - it - de - es - en inference: false --- # Model Card for Mixtral-Fusion-4x7B-Instruct-v0.1 This model is an experimental model created by merging [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) experts. # How we merged experts We simply take the average of every two experts.weight. The same goes for gate.weight. # How To Convert use colab cpu-high-memory. [convert_mixtral_8x7b_to_4x7b.ipynb](https://huggingface.co/mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1/blob/main/notebook/convert_mixtral_8x7b_to_4x7b.ipynb) # Usage ~~~python pip install git+https://github.com/huggingface/transformers --upgrade pip install torch accelerate bitsandbytes flash_attn ~~~ ~~~python from transformers import AutoTokenizer, AutoModelForCausalLM, MixtralForCausalLM import torch model_name_or_path = "mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = MixtralForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True) # set num_experts_per_tok 1 or 2 ? model.config.num_experts_per_tok = 2 # message messages = [ {"role": "user", "content": "Tell me what's for dinner tonight."}, ] with torch.no_grad(): token_ids = tokenizer.apply_chat_template(messages, return_tensors="pt") output_ids = model.generate( token_ids.to(model.device), temperature=0.5, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=128, repetition_penalty=1.5 ) output = tokenizer.decode(output_ids[0][token_ids.size(1) :]) print(output) ~~~
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AadeshKarira/generativeAIFooocus
AadeshKarira
2023-12-17T08:48:55Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-17T08:48:55Z
--- license: mit ---
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Aswinkn/sng_asw
Aswinkn
2023-12-17T09:02:26Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-17T08:49:41Z
--- license: apache-2.0 ---
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Z1zs/out_model
Z1zs
2023-12-17T08:50:24Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:50:24Z
Entry not found
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jan-hq/trinity-v1-dpo
jan-hq
2023-12-17T08:52:26Z
0
2
null
[ "region:us" ]
null
2023-12-17T08:52:26Z
Entry not found
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AskYoutube/AskVideos-VideoCLIP-7B-v0.1
AskYoutube
2023-12-17T08:53:49Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:53:49Z
Entry not found
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ashwinncj/basic-01
ashwinncj
2023-12-17T08:54:18Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:54:18Z
Entry not found
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RikeshSilwal/wav2vec2-nepali-ekg
RikeshSilwal
2023-12-17T08:56:16Z
0
0
null
[ "region:us" ]
null
2023-12-17T08:56:16Z
Entry not found
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Pooya-Fallah/mlm_tokenizer
Pooya-Fallah
2023-12-17T09:07:02Z
0
0
null
[ "region:us" ]
null
2023-12-17T09:07:01Z
Entry not found
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Patcas/v5-my_awesome
Patcas
2023-12-17T12:35:31Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "plbart", "text2text-generation", "generated_from_trainer", "base_model:Patcas/plbart-works", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-12-17T09:08:22Z
--- base_model: Patcas/plbart-works tags: - generated_from_trainer model-index: - name: v5-my_awesome 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. --> # v5-my_awesome This model is a fine-tuned version of [Patcas/plbart-works](https://huggingface.co/Patcas/plbart-works) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0208 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 264 | 0.9325 | | 1.0258 | 2.0 | 528 | 0.8500 | | 1.0258 | 3.0 | 792 | 0.8358 | | 0.3925 | 4.0 | 1056 | 0.8538 | | 0.3925 | 5.0 | 1320 | 0.8462 | | 0.2 | 6.0 | 1584 | 0.9122 | | 0.2 | 7.0 | 1848 | 0.8987 | | 0.1246 | 8.0 | 2112 | 0.8859 | | 0.1246 | 9.0 | 2376 | 0.8990 | | 0.0834 | 10.0 | 2640 | 0.9022 | | 0.0834 | 11.0 | 2904 | 0.9212 | | 0.058 | 12.0 | 3168 | 0.9542 | | 0.058 | 13.0 | 3432 | 0.9466 | | 0.0431 | 14.0 | 3696 | 0.9615 | | 0.0431 | 15.0 | 3960 | 0.9675 | | 0.0321 | 16.0 | 4224 | 0.9719 | | 0.0321 | 17.0 | 4488 | 0.9786 | | 0.0237 | 18.0 | 4752 | 0.9918 | | 0.0189 | 19.0 | 5016 | 0.9911 | | 0.0189 | 20.0 | 5280 | 0.9983 | | 0.0143 | 21.0 | 5544 | 0.9973 | | 0.0143 | 22.0 | 5808 | 1.0100 | | 0.0129 | 23.0 | 6072 | 1.0160 | | 0.0129 | 24.0 | 6336 | 1.0091 | | 0.0097 | 25.0 | 6600 | 1.0191 | | 0.0097 | 26.0 | 6864 | 1.0224 | | 0.0084 | 27.0 | 7128 | 1.0238 | | 0.0084 | 28.0 | 7392 | 1.0201 | | 0.0079 | 29.0 | 7656 | 1.0200 | | 0.0079 | 30.0 | 7920 | 1.0208 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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LoneStriker/Mixtral-Fusion-4x7B-Instruct-v0.1-5.0bpw-h6-exl2-2
LoneStriker
2023-12-17T12:39:13Z
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-12-17T09:09:29Z
--- license: apache-2.0 language: - fr - it - de - es - en inference: false --- # Model Card for Mixtral-Fusion-4x7B-Instruct-v0.1 This model is an experimental model created by merging [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) experts. # How we merged experts We simply take the average of every two experts.weight. The same goes for gate.weight. # How To Convert use colab cpu-high-memory. [convert_mixtral_8x7b_to_4x7b.ipynb](https://huggingface.co/mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1/blob/main/notebook/convert_mixtral_8x7b_to_4x7b.ipynb) # Usage ~~~python pip install git+https://github.com/huggingface/transformers --upgrade pip install torch accelerate bitsandbytes flash_attn ~~~ ~~~python from transformers import AutoTokenizer, AutoModelForCausalLM, MixtralForCausalLM import torch model_name_or_path = "mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = MixtralForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True) # set num_experts_per_tok 1 or 2 ? model.config.num_experts_per_tok = 2 # message messages = [ {"role": "user", "content": "Tell me what's for dinner tonight."}, ] with torch.no_grad(): token_ids = tokenizer.apply_chat_template(messages, return_tensors="pt") output_ids = model.generate( token_ids.to(model.device), temperature=0.5, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=128, repetition_penalty=1.5 ) output = tokenizer.decode(output_ids[0][token_ids.size(1) :]) print(output) ~~~
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Nikt271/BraciaKoala
Nikt271
2023-12-17T09:16:09Z
0
0
null
[ "region:us" ]
null
2023-12-17T09:14:49Z
Entry not found
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kietnt0603/t5-peft-adapter-dialogue
kietnt0603
2023-12-17T09:44:34Z
0
0
null
[ "region:us" ]
null
2023-12-17T09:16:08Z
Entry not found
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Huyisbeee/finetuned-mT5
Huyisbeee
2023-12-18T01:02:38Z
0
0
null
[ "safetensors", "region:us" ]
null
2023-12-17T09:16:18Z
Entry not found
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