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AlignmentResearch/robust_llm_pythia-spam-410m-niki-ada-v4-s-1	AlignmentResearch	2024-05-27T20:44:42Z	106	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T23:22:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ws11yrin/reinforce_MCPG-Pixelcopter-PLE-v0	ws11yrin	2024-05-27T20:40:39Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-27T20:40:35Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce_MCPG-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 46.50 +/- 37.85 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
DonTheApex/DesktopCompanion1	DonTheApex	2024-05-27T20:36:23Z	5	0	transformers	[ "transformers", "gguf", "mistral", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T20:02:25Z	--- license: apache-2.0 ---
Cantaosu/wavlm_torgo_0H	Cantaosu	2024-05-27T20:32:34Z	104	0	transformers	[ "transformers", "safetensors", "wavlm", "automatic-speech-recognition", "generated_from_trainer", "base_model:microsoft/wavlm-base", "base_model:finetune:microsoft/wavlm-base", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-20T20:37:56Z	--- base_model: microsoft/wavlm-base tags: - generated_from_trainer metrics: - wer model-index: - name: wavlm_torgo_0H 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. --> # wavlm_torgo_0H This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2230 - Wer: 1.0 ## 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:---:\| \| 36.5759 \| 0.1882 \| 500 \| 5.7798 \| 1.0 \| \| 4.1355 \| 0.3764 \| 1000 \| 4.3661 \| 1.0 \| \| 3.9484 \| 0.5645 \| 1500 \| 4.2577 \| 1.0 \| \| 3.6159 \| 0.7527 \| 2000 \| 4.1272 \| 1.0 \| \| 3.6944 \| 0.9409 \| 2500 \| 3.9745 \| 1.0 \| \| 3.8285 \| 1.1291 \| 3000 \| 4.0134 \| 1.0 \| \| 3.6116 \| 1.3173 \| 3500 \| 4.1692 \| 1.0 \| \| 3.5828 \| 1.5055 \| 4000 \| 4.0013 \| 1.0 \| \| 3.5703 \| 1.6936 \| 4500 \| 4.1055 \| 1.0 \| \| 3.5841 \| 1.8818 \| 5000 \| 4.1041 \| 1.0 \| \| 3.8079 \| 2.0700 \| 5500 \| 4.1574 \| 1.0 \| \| 3.5977 \| 2.2582 \| 6000 \| 4.3217 \| 1.0 \| \| 3.5523 \| 2.4464 \| 6500 \| 4.1800 \| 1.0 \| \| 3.5661 \| 2.6346 \| 7000 \| 4.2053 \| 1.0 \| \| 3.5676 \| 2.8227 \| 7500 \| 4.3885 \| 1.0 \| \| 3.794 \| 3.0109 \| 8000 \| 4.2958 \| 1.0 \| \| 3.5647 \| 3.1991 \| 8500 \| 4.2959 \| 1.0 \| \| 3.5805 \| 3.3873 \| 9000 \| 4.3383 \| 1.0 \| \| 3.5475 \| 3.5755 \| 9500 \| 4.1639 \| 1.0 \| \| 3.5523 \| 3.7636 \| 10000 \| 4.2241 \| 1.0 \| \| 3.5982 \| 3.9518 \| 10500 \| 4.3270 \| 1.0 \| \| 3.7088 \| 4.1400 \| 11000 \| 4.2886 \| 1.0 \| \| 3.561 \| 4.3282 \| 11500 \| 4.2801 \| 1.0 \| \| 3.5367 \| 4.5164 \| 12000 \| 4.6914 \| 1.0 \| \| 3.5573 \| 4.7046 \| 12500 \| 4.2071 \| 1.0 \| \| 3.5613 \| 4.8927 \| 13000 \| 4.4513 \| 1.0 \| \| 3.719 \| 5.0809 \| 13500 \| 4.3972 \| 1.0 \| \| 3.5376 \| 5.2691 \| 14000 \| 4.3590 \| 1.0 \| \| 3.5313 \| 5.4573 \| 14500 \| 4.3130 \| 1.0 \| \| 3.5384 \| 5.6455 \| 15000 \| 4.4599 \| 1.0 \| \| 3.5755 \| 5.8336 \| 15500 \| 4.3602 \| 1.0 \| \| 3.6912 \| 6.0218 \| 16000 \| 4.2520 \| 1.0 \| \| 3.532 \| 6.2100 \| 16500 \| 4.2731 \| 1.0 \| \| 3.565 \| 6.3982 \| 17000 \| 4.2608 \| 1.0 \| \| 3.5328 \| 6.5864 \| 17500 \| 4.2221 \| 1.0 \| \| 3.5361 \| 6.7746 \| 18000 \| 4.2500 \| 1.0 \| \| 3.4975 \| 6.9627 \| 18500 \| 4.2042 \| 1.0 \| \| 3.6749 \| 7.1509 \| 19000 \| 4.2319 \| 1.0 \| \| 3.5316 \| 7.3391 \| 19500 \| 4.2101 \| 1.0 \| \| 3.5262 \| 7.5273 \| 20000 \| 4.2657 \| 1.0 \| \| 3.6605 \| 7.7155 \| 20500 \| 4.2559 \| 1.0 \| \| 3.528 \| 7.9037 \| 21000 \| 4.2230 \| 1.0 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
PsyDuuk/Meta-Llama-3-8B-Q4_K_M-GGUF	PsyDuuk	2024-05-27T20:30:50Z	0	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T20:30:30Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # PsyDuuk/Meta-Llama-3-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo PsyDuuk/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo PsyDuuk/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m meta-llama-3-8b-q4_k_m.gguf -n 128 ```
Manos2024/1	Manos2024	2024-05-27T20:29:36Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-27T20:29:36Z	--- license: creativeml-openrail-m ---
AlignmentResearch/robust_llm_pythia-spam-160m-niki-ada-v4-s-1	AlignmentResearch	2024-05-27T20:28:31Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T23:12:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-733782	fine-tuned	2024-05-27T20:27:31Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Debate", "Argument", "Counter", "Discussion", "Persuasion", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-733782", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-27T20:27:00Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-733782 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Debate - Argument - Counter - Discussion - Persuasion --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: debate platform ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-733782', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
sudoaza/OrpoLlama-3-8B	sudoaza	2024-05-27T20:27:11Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T20:22:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AlignmentResearch/robust_llm_pythia-spam-70m-niki-ada-v4-s-1	AlignmentResearch	2024-05-27T20:26:06Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T23:10:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yzhuang/Meta-Llama-3-8B-Instruct_fictional_mathqa_Korean_v1	yzhuang	2024-05-27T20:25:58Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T19:29:52Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Meta-Llama-3-8B-Instruct_fictional_mathqa_Korean_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Meta-Llama-3-8B-Instruct_fictional_mathqa_Korean_v1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - 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 - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-spam-14m-niki-ada-v4-s-2	AlignmentResearch	2024-05-27T20:24:36Z	106	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T23:08:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AlignmentResearch/robust_llm_pythia-spam-14m-niki-ada-v4-s-0	AlignmentResearch	2024-05-27T20:24:16Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T11:48:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gechim/phobert-base-v2-finetuned_60kURL	gechim	2024-05-27T20:21:47Z	107	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:gechim/phobert-base-v2-finetuned", "base_model:finetune:gechim/phobert-base-v2-finetuned", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T20:20:52Z	--- base_model: gechim/phobert-base-v2-finetuned tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: phobert-base-v2-finetuned-finetuned_60kURL 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. --> # phobert-base-v2-finetuned-finetuned_60kURL This model is a fine-tuned version of [gechim/phobert-base-v2-finetuned](https://huggingface.co/gechim/phobert-base-v2-finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3594 - Accuracy: 0.9562 - F1: 0.9563 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.1679 \| 1.0 \| 704 \| 0.1285 \| 0.9549 \| 0.9552 \| \| 0.1111 \| 2.0 \| 1408 \| 0.1405 \| 0.9529 \| 0.9526 \| \| 0.0888 \| 3.0 \| 2112 \| 0.1392 \| 0.9592 \| 0.9592 \| \| 0.0721 \| 4.0 \| 2816 \| 0.1433 \| 0.9561 \| 0.9564 \| \| 0.059 \| 5.0 \| 3520 \| 0.1563 \| 0.9584 \| 0.9586 \| \| 0.0486 \| 6.0 \| 4224 \| 0.1719 \| 0.9549 \| 0.9552 \| \| 0.0399 \| 7.0 \| 4928 \| 0.2006 \| 0.9561 \| 0.9563 \| \| 0.0316 \| 8.0 \| 5632 \| 0.2461 \| 0.9553 \| 0.9555 \| \| 0.0269 \| 9.0 \| 6336 \| 0.2424 \| 0.9556 \| 0.9557 \| \| 0.0242 \| 10.0 \| 7040 \| 0.2686 \| 0.9543 \| 0.9543 \| \| 0.0202 \| 11.0 \| 7744 \| 0.2813 \| 0.9559 \| 0.9559 \| \| 0.0153 \| 12.0 \| 8448 \| 0.2984 \| 0.9563 \| 0.9564 \| \| 0.012 \| 13.0 \| 9152 \| 0.3171 \| 0.9553 \| 0.9555 \| \| 0.009 \| 14.0 \| 9856 \| 0.3452 \| 0.9549 \| 0.9549 \| \| 0.0088 \| 15.0 \| 10560 \| 0.3415 \| 0.9570 \| 0.9571 \| \| 0.008 \| 16.0 \| 11264 \| 0.3374 \| 0.9564 \| 0.9564 \| \| 0.0064 \| 17.0 \| 11968 \| 0.3490 \| 0.9564 \| 0.9565 \| \| 0.0054 \| 18.0 \| 12672 \| 0.3598 \| 0.9560 \| 0.9561 \| \| 0.0057 \| 19.0 \| 13376 \| 0.3595 \| 0.9559 \| 0.9559 \| \| 0.0044 \| 20.0 \| 14080 \| 0.3594 \| 0.9562 \| 0.9563 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
Elijahbodden/EliGPTv1.3	Elijahbodden	2024-05-27T20:19:49Z	15	0	transformers	[ "transformers", "gguf", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T16:59:45Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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CMU-AIR2/math-llama-3-instruct-LORA-ArithSteps-6K	CMU-AIR2	2024-05-27T20:18:30Z	2	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-27T20:03:53Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # 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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CMU-AIR2/math-llama-3-instruct-LORA-ArithSteps-10K	CMU-AIR2	2024-05-27T20:18:20Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-27T20:04:06Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # 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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katk31/Reinforce-Pixelcopter-PLE-v0-2	katk31	2024-05-27T20:14:53Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-27T20:14:50Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0-2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 12.00 +/- 11.46 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
EightBuff/Mix	EightBuff	2024-05-27T20:13:50Z	0	0	null	[ "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-27T19:18:17Z	--- tags: - text-to-image - stable-diffusion license: creativeml-openrail-m --- # Eight Buffalo Media Group has shared our Newest SD text-to-image model! Model Details This Model is a mix of our Gen and Real SD 1.5 models, with some additional traning and adjustments for improved hands and prompts. Note that Version 1 of this model still has a few issues, so please be patient as we improve. Constructive feedback is always welcome. This freely available model is a combination of many different models that have been mixed, merged, and specifically trained on a couple things like people in glass jars. The goal is a strong general model that allows control similar to many anime models, with a more realistic look and feel. ## Model Description This model does require a good deal of prompt crafting, the trade off is you have a lot of control on the images you create. I would recommend finding a prompt that generates the style and quality to your liking and saving it as a style.
microsoft/trocr-large-stage1	microsoft	2024-05-27T20:12:53Z	2,831	22	transformers	[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "image-text-to-text", "trocr", "image-to-text", "arxiv:2109.10282", "endpoints_compatible", "region:us" ]	image-to-text	2022-03-02T23:29:05Z	--- tags: - trocr - image-to-text --- # TrOCR (large-sized model, pre-trained only) TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). Disclaimer: The team releasing TrOCR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens. ## Intended uses & limitations You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import requests # load image from the IAM database url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-stage1') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-stage1') # training pixel_values = processor(image, return_tensors="pt").pixel_values # Batch size 1 decoder_input_ids = torch.tensor([[model.config.decoder.decoder_start_token_id]]) outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids) ``` ### BibTeX entry and citation info ```bibtex @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
microsoft/trocr-base-str	microsoft	2024-05-27T20:12:19Z	2,152	5	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "trocr", "image-to-text", "arxiv:2109.10282", "endpoints_compatible", "region:us" ]	image-to-text	2022-09-08T09:02:01Z	--- tags: - trocr - image-to-text widget: - src: https://raw.githubusercontent.com/ku21fan/STR-Fewer-Labels/main/demo_image/1.png example_title: Example 1 - src: https://raw.githubusercontent.com/HCIILAB/Scene-Text-Recognition-Recommendations/main/Dataset_images/LSVT1.jpg example_title: Example 2 - src: https://raw.githubusercontent.com/HCIILAB/Scene-Text-Recognition-Recommendations/main/Dataset_images/ArT2.jpg example_title: Example 3 --- # TrOCR (base-sized model, fine-tuned on STR benchmarks) TrOCR model fine-tuned on the training sets of IC13, IC15, IIIT5K, SVT. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). ## Model description The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens. ## Intended uses & limitations You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import requests # load image from the IIIT-5k dataset url = 'https://i.postimg.cc/ZKwLg2Gw/367-14.png' image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-str') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-str') pixel_values = processor(images=image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### BibTeX entry and citation info ```bibtex @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
swj0419/bbc_STEP0000200_5-27	swj0419	2024-05-27T20:11:56Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T19:09:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
microsoft/trocr-large-printed	microsoft	2024-05-27T20:09:18Z	249,003	156	transformers	[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "image-text-to-text", "trocr", "image-to-text", "arxiv:2109.10282", "endpoints_compatible", "region:us" ]	image-to-text	2022-03-02T23:29:05Z	--- tags: - trocr - image-to-text widget: - src: https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X00016469612_1.jpg example_title: Printed 1 - src: https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X51005255805_7.jpg example_title: Printed 2 - src: https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X51005745214_6.jpg example_title: Printed 3 --- # TrOCR (large-sized model, fine-tuned on SROIE) TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). Disclaimer: The team releasing TrOCR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens. ## Intended uses & limitations You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import requests # load image from the IAM database (actually this model is meant to be used on printed text) url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-printed') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-printed') pixel_values = processor(images=image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### BibTeX entry and citation info ```bibtex @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
JawadC/cheddar-llava	JawadC	2024-05-27T20:06:57Z	3	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-26T23:40:18Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of CHEDDAR cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - JawadC/cheddar-llava <Gallery /> ## Model description These are JawadC/cheddar-llava LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of CHEDDAR cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](JawadC/cheddar-llava/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v2_s1_226s	nsugianto	2024-05-27T20:02:58Z	29	0	transformers	[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "base_model:microsoft/table-transformer-structure-recognition", "base_model:finetune:microsoft/table-transformer-structure-recognition", "license:mit", "endpoints_compatible", "region:us" ]	object-detection	2024-05-25T17:24:55Z	--- license: mit base_model: microsoft/table-transformer-structure-recognition tags: - generated_from_trainer model-index: - name: tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v2_s1_226s 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. --> # tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v2_s1_226s This model is a fine-tuned version of [microsoft/table-transformer-structure-recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition) 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: 5e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 750 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
jpfraneto/anky-degen-pixels	jpfraneto	2024-05-27T20:01:47Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-05-27T19:38:49Z	--- license: mit --- This model was trained using the 8888 images of the [Anky Genesis NFT Collection](https://drive.google.com/drive/folders/1OBDQ08r8pLN4nfNf-48j87wzUEmF-ox4?usp=sharing), and its mission is to transform an image into pixel art, like so: ![Anky Degen Pixel Example](https://github.com/jpfraneto/images/blob/main/ankydegenpixel.png?raw=true) The code used for training it is the following: ``` import os import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, Dataset from torchvision import transforms from PIL import Image import numpy as np # Custom dataset for loading the images class PixelArtDataset(Dataset): def __init__(self, image_folder, transform=None): self.image_folder = image_folder self.transform = transform self.image_files = [f"{i}.png" for i in range(1, 8889)] # Debug: Check if images are correctly listed print(f"Total images found: {len(self.image_files)}") def __len__(self): return len(self.image_files) def __getitem__(self, idx): img_path = os.path.join(self.image_folder, self.image_files[idx]) image = Image.open(img_path).convert("RGB") if self.transform: image = self.transform(image) return image, image # Define the neural network class PixelArtGenerator(nn.Module): def __init__(self): super(PixelArtGenerator, self).__init__() print("Initializing PixelArtGenerator Model...") self.encoder = nn.Sequential( nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1), nn.ReLU(), nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU() ) self.decoder = nn.Sequential( nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x def train(model, dataloader, criterion, optimizer, device, epochs=50): print("Starting training...") model.train() for epoch in range(epochs): running_loss = 0.0 print(f"Epoch [{epoch+1}/{epochs}] starting...") for batch_idx, (input_images, target_images) in enumerate(dataloader): input_images, target_images = input_images.to(device), target_images.to(device) optimizer.zero_grad() outputs = model(input_images) loss = criterion(outputs, target_images) loss.backward() optimizer.step() running_loss += loss.item() # Debug: Print progress for every batch if batch_idx % 10 == 0: print(f"Epoch [{epoch+1}/{epochs}], Batch [{batch_idx+1}/{len(dataloader)}], Loss: {loss.item():.4f}") print(f"Epoch [{epoch+1}/{epochs}] completed with Loss: {running_loss/len(dataloader):.4f}") def create_pixel_art(model, input_image_path, output_image_path, device): print("Creating pixel art...") model.eval() transform = transforms.Compose([ transforms.Resize((64, 64)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) image = Image.open(input_image_path).convert("RGB") input_image = transform(image).unsqueeze(0).to(device) with torch.no_grad(): output_image = model(input_image).squeeze(0).cpu().numpy() output_image = np.transpose(output_image, (1, 2, 0)) output_image = (output_image * 0.5 + 0.5) * 255.0 output_image = np.clip(output_image, 0, 255).astype(np.uint8) output_image = Image.fromarray(output_image) output_image.save(output_image_path) print(f"Pixel art saved to {output_image_path}") if __name__ == "__main__": # Transform for input images print("Setting up image transformations...") transform = transforms.Compose([ transforms.Resize((64, 64)), # Resize to 64x64 for input transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # Load dataset print("Loading dataset...") image_folder = "./" # Change this to your images folder path dataset = PixelArtDataset(image_folder, transform) dataloader = DataLoader(dataset, batch_size=8, shuffle=True) # Reduce batch size for debugging # Check for GPU availability device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Initialize the model, criterion, and optimizer model = PixelArtGenerator().to(device) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.0002) # Enable data parallelism if multiple GPUs are available if torch.cuda.device_count() > 1: print(f"Using {torch.cuda.device_count()} GPUs") model = nn.DataParallel(model) # Train the model train(model, dataloader, criterion, optimizer, device, epochs=50) # Save the model torch.save(model.state_dict(), "pixel_art_generator.pth") print("Model saved as 'pixel_art_generator.pth'") # Create pixel art from a new input image input_image_path = "input_image.png" # Path to the high-resolution input image output_image_path = "pixel_art.png" # Path to save the generated pixel art create_pixel_art(model, input_image_path, output_image_path, device) print("Pixel art creation completed.") ``` The training happened on a Cognition PRO called poiesis. It consisted of 50 epochs, and it lasted for about 4 hours running on 2x NVIDIA RTX 4090. Its intended usage is for it to transform any image into its corresponding in pixels, as you can see on this one. For running it like such, you can run the following python code on the containing folder of the model (for transforming an image called pfp.png): ``` import torch import torch.nn as nn from PIL import Image import numpy as np from torchvision import transforms import os # Define the neural network (same as the one used during training) class PixelArtGenerator(nn.Module): def __init__(self): super(PixelArtGenerator, self).__init__() print("Initializing PixelArtGenerator Model...") self.encoder = nn.Sequential( nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1), nn.ReLU(), nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU() ) self.decoder = nn.Sequential( nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x def create_pixel_art(model, input_image_path, output_image_path, device): print(f"Creating pixel art for {input_image_path}...") # Check if the input image file exists if not os.path.isfile(input_image_path): print(f"Error: Input image file '{input_image_path}' not found.") return model.eval() print("Model set to evaluation mode.") # Define the transformation for the input image transform = transforms.Compose([ transforms.Resize((64, 64)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) print("Image transformation defined.") # Load and preprocess the input image image = Image.open(input_image_path).convert("RGB") input_image = transform(image).unsqueeze(0).to(device) print(f"Input image '{input_image_path}' loaded and preprocessed.") # Generate pixel art using the model with torch.no_grad(): output_image = model(input_image).squeeze(0).cpu().numpy() print("Pixel art generated by the model.") # Post-process and save the output image output_image = np.transpose(output_image, (1, 2, 0)) output_image = (output_image * 0.5 + 0.5) * 255.0 output_image = np.clip(output_image, 0, 255).astype(np.uint8) output_image = Image.fromarray(output_image) # Scale up the image to iPhone 11 width (828 pixels) scaled_output_image = output_image.resize((828, int(828 * output_image.size[1] / output_image.size[0])), Image.NEAREST) scaled_output_image.save(output_image_path) print(f"Pixel art saved to '{output_image_path}'.") if __name__ == "__main__": print("Starting pixel art generation script...") # Load the trained model model = PixelArtGenerator() model_path = "pixel_art_generator.pth" # Path to the saved model print(f"Loading model from '{model_path}'...") # Load model with handling for DataParallel state_dict = torch.load(model_path) if 'module.' in list(state_dict.keys())[0]: # Remove 'module.' prefix if model was saved with DataParallel state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()} model.load_state_dict(state_dict) print("Model loaded successfully.") # Check for GPU availability device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) print(f"Using device: {device}") # Define the input and output paths for the single image input_image_path = "pfp.jpeg" # Path to the input image output_image_path = "pfp_pixelated.png" # Path to save the generated pixel art # Create pixel art for the single image create_pixel_art(model, input_image_path, output_image_path, device) print("Pixel art creation completed for the single image.") ``` Hope you enjoy, and any questions that you may have, feel free to reach out to @jpfraneto on telegram. If you want to contribute to Anky, we have plenty of compute available, and a powerful story (and intention) that puts the unfolding of AI at the core of our experience as humans. Think of it as a playground for your inner child, with boundless potential. Our farcaster channel is here: https://warpcast.com/~/channel/anky Your uniqueness is a gift. 🎩
UtkuCicek/sd_marks	UtkuCicek	2024-05-27T19:58:41Z	4	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "base_model:CompVis/stable-diffusion-v1-2", "base_model:finetune:CompVis/stable-diffusion-v1-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-27T18:41:35Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training base_model: CompVis/stable-diffusion-v1-2 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Text-to-image finetuning - UtkuCicek/sd_marks This pipeline was finetuned from CompVis/stable-diffusion-v1-2 on the UtkuCicek/new-marks-data dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['italian style mini pizza with mozerrella on the side']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("UtkuCicek/sd_marks", torch_dtype=torch.float16) prompt = "italian style mini pizza with mozerrella on the side" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 20 * Learning rate: 1e-06 * Batch size: 2 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16 More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/ucicek/text2image-fine-tune/runs/swebb9ts). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
DrAgOn200233/autotrain-ArthurHeyes-Lora-Synatra7B-NQ-001	DrAgOn200233	2024-05-27T19:58:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "dataset:DrAgOn200233/ArthurHayes", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T19:51:31Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - DrAgOn200233/ArthurHayes --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
omar-sala7/Acegpt-7b-chat-FCAIBylawArabicOneContext-v3	omar-sala7	2024-05-27T19:54:12Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:Graceful6025/AceGPT-7B", "base_model:adapter:Graceful6025/AceGPT-7B", "license:apache-2.0", "region:us" ]	null	2024-05-27T17:55:45Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: Graceful6025/AceGPT-7B model-index: - name: Acegpt-7b-chat-FCAIBylawArabicOneContext-v3 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. --> # Acegpt-7b-chat-FCAIBylawArabicOneContext-v3 This model is a fine-tuned version of [Graceful6025/AceGPT-7B](https://huggingface.co/Graceful6025/AceGPT-7B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF	mradermacher	2024-05-27T19:53:19Z	82	1	transformers	[ "transformers", "gguf", "nlp", "code", "multilingual", "base_model:failspy/Phi-3-mini-128k-instruct-abliterated-v3", "base_model:quantized:failspy/Phi-3-mini-128k-instruct-abliterated-v3", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T17:48:43Z	--- base_model: failspy/Phi-3-mini-128k-instruct-abliterated-v3 language: - multilingual library_name: transformers license: mit license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE quantized_by: mradermacher tags: - nlp - code --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/failspy/Phi-3-mini-128k-instruct-abliterated-v3 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.IQ3_XS.gguf) \| IQ3_XS \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.IQ3_S.gguf) \| IQ3_S \| 1.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.IQ3_M.gguf) \| IQ3_M \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q3_K_M.gguf) \| Q3_K_M \| 2.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q3_K_L.gguf) \| Q3_K_L \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q4_K_S.gguf) \| Q4_K_S \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q4_K_M.gguf) \| Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q6_K.gguf) \| Q6_K \| 3.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/resolve/main/Phi-3-mini-128k-instruct-abliterated-v3.f16.gguf) \| f16 \| 7.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
aullman/swin-small-patch4-window7-224-finetuned-eurosat	aullman	2024-05-27T19:52:31Z	214	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-small-patch4-window7-224", "base_model:finetune:microsoft/swin-small-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-22T19:04:57Z	--- license: apache-2.0 base_model: microsoft/swin-small-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-small-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6923076923076923 --- <!-- 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. --> # swin-small-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-small-patch4-window7-224](https://huggingface.co/microsoft/swin-small-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5031 - Accuracy: 0.6923 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.73 \| 2 \| 0.6585 \| 0.6154 \| \| No log \| 1.82 \| 5 \| 0.5773 \| 0.6410 \| \| No log \| 2.91 \| 8 \| 0.5031 \| 0.6923 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cpu - Datasets 2.19.1 - Tokenizers 0.13.3
swj0419/bbc_STEP0000120_5-27	swj0419	2024-05-27T19:52:26Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T18:50:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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slimaneMakh/MultilangBinarySuperClass_Other_tableClf_27may_triplet	slimaneMakh	2024-05-27T19:51:39Z	163	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:51:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
av-codes/llama3-simpo-expo-gguf	av-codes	2024-05-27T19:48:32Z	7	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-27T17:34:49Z	### Llama-3-Instruct-8B-SimPO-ExPO GGUF See the original model card here: https://huggingface.co/chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO
bartowski/internlm2-math-plus-20b-GGUF	bartowski	2024-05-27T19:41:13Z	89	0	null	[ "gguf", "math", "text-generation", "en", "zh", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2024-05-27T18:47:59Z	--- pipeline_tag: text-generation license: other language: - en - zh tags: - math quantized_by: bartowski --- ## Llamacpp imatrix Quantizations of internlm2-math-plus-20b Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3001">b3001</a> for quantization. Original model: https://huggingface.co/internlm/internlm2-math-plus-20b All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <s><\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [internlm2-math-plus-20b-Q8_0.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q8_0.gguf) \| Q8_0 \| 21.10GB \| Extremely high quality, generally unneeded but max available quant. \| \| [internlm2-math-plus-20b-Q6_K.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q6_K.gguf) \| Q6_K \| 16.29GB \| Very high quality, near perfect, recommended. \| \| [internlm2-math-plus-20b-Q5_K_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q5_K_M.gguf) \| Q5_K_M \| 14.07GB \| High quality, recommended. \| \| [internlm2-math-plus-20b-Q5_K_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q5_K_S.gguf) \| Q5_K_S \| 13.73GB \| High quality, recommended. \| \| [internlm2-math-plus-20b-Q4_K_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q4_K_M.gguf) \| Q4_K_M \| 11.98GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [internlm2-math-plus-20b-Q4_K_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q4_K_S.gguf) \| Q4_K_S \| 11.40GB \| Slightly lower quality with more space savings, recommended. \| \| [internlm2-math-plus-20b-IQ4_NL.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ4_NL.gguf) \| IQ4_NL \| 11.36GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [internlm2-math-plus-20b-IQ4_XS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ4_XS.gguf) \| IQ4_XS \| 10.76GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [internlm2-math-plus-20b-Q3_K_L.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q3_K_L.gguf) \| Q3_K_L \| 10.55GB \| Lower quality but usable, good for low RAM availability. \| \| [internlm2-math-plus-20b-Q3_K_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q3_K_M.gguf) \| Q3_K_M \| 9.72GB \| Even lower quality. \| \| [internlm2-math-plus-20b-IQ3_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ3_M.gguf) \| IQ3_M \| 9.12GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [internlm2-math-plus-20b-IQ3_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ3_S.gguf) \| IQ3_S \| 8.80GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [internlm2-math-plus-20b-Q3_K_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q3_K_S.gguf) \| Q3_K_S \| 8.76GB \| Low quality, not recommended. \| \| [internlm2-math-plus-20b-IQ3_XS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ3_XS.gguf) \| IQ3_XS \| 8.36GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [internlm2-math-plus-20b-IQ3_XXS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ3_XXS.gguf) \| IQ3_XXS \| 7.81GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [internlm2-math-plus-20b-Q2_K.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-Q2_K.gguf) \| Q2_K \| 7.54GB \| Very low quality but surprisingly usable. \| \| [internlm2-math-plus-20b-IQ2_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ2_M.gguf) \| IQ2_M \| 6.97GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [internlm2-math-plus-20b-IQ2_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ2_S.gguf) \| IQ2_S \| 6.47GB \| Very low quality, uses SOTA techniques to be usable. \| \| [internlm2-math-plus-20b-IQ2_XS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ2_XS.gguf) \| IQ2_XS \| 6.10GB \| Very low quality, uses SOTA techniques to be usable. \| \| [internlm2-math-plus-20b-IQ2_XXS.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ2_XXS.gguf) \| IQ2_XXS \| 5.54GB \| Lower quality, uses SOTA techniques to be usable. \| \| [internlm2-math-plus-20b-IQ1_M.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ1_M.gguf) \| IQ1_M \| 4.91GB \| Extremely low quality, not recommended. \| \| [internlm2-math-plus-20b-IQ1_S.gguf](https://huggingface.co/bartowski/internlm2-math-plus-20b-GGUF/blob/main/internlm2-math-plus-20b-IQ1_S.gguf) \| IQ1_S \| 4.54GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/internlm2-math-plus-20b-GGUF --include "internlm2-math-plus-20b-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/internlm2-math-plus-20b-GGUF --include "internlm2-math-plus-20b-Q8_0.gguf/" --local-dir internlm2-math-plus-20b-Q8_0 ``` You can either specify a new local-dir (internlm2-math-plus-20b-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
slimaneMakh/MultilangBinarySuperClass_Dividendes_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T19:40:42Z	181	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:40:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
dpquoc/Mistral-7B-Instruct-v0.2	dpquoc	2024-05-27T19:40:20Z	2	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "finetuned", "conversational", "arxiv:2310.06825", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-27T19:24:52Z	--- license: apache-2.0 pipeline_tag: text-generation tags: - finetuned inference: false --- # Model Card for Mistral-7B-Instruct-v0.2 The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B 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.
DrAgOn200233/autotrain-ArthurHeyes-Lora-Mistral7B-002	DrAgOn200233	2024-05-27T19:40:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "dataset:DrAgOn200233/ArthurHayes", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T19:33:49Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - DrAgOn200233/ArthurHayes --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
vincedovy/sd-class-butterflies-32	vincedovy	2024-05-27T19:39:37Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-05-27T19:39:00Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('vincedovy/sd-class-butterflies-32') image = pipeline().images[0] image ```
DanielFarfan/BARTReact	DanielFarfan	2024-05-27T19:38:59Z	117	1	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-02T01:59:40Z	# BARTReact <!-- Provide a quick summary of what the model is/does. --> BARTReact model presented in "BARTReact: SELFIES-Driven Precision in Reaction Modeling" https://doi.org/10.1016/j.fraope.2024.100106.<br> This model is able to predict reaction products from reactants represented as SELFIES. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Model type: BART - Language(s) (NLP): SELFIES ## Dataset Dataset in SMILES can be found in https://www.rhea-db.org/.<br> SMILES to SELFIES conversion was made from selfies package available at https://github.com/aspuru-guzik-group/selfies.<br> ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DanielFarfan/BARTReact") model = AutoModelForSeq2SeqLM.from_pretrained("DanielFarfan/BARTReact") sf_input = tokenizer("[C][C][Branch1][C][C][Branch2][Branch1][#Branch1][C][O][P][=Branch1][C]"\ "[=O][Branch1][C][O-1][O][P][=Branch1][C][=O][Branch1][C][O-1][O][C][C@H1]"\ "[O][C@@H1][Branch1][#C][N][C][=N][C][=C][Ring1][Branch1][N][=C][N][=C][Ring1]"\ "[=Branch1][N][C@H1][Branch1][C][O][C@@H1][Ring1][S][O][P][=Branch1][C][=O]"\ "[Branch1][C][O-1][O-1][C@@H1][Branch1][C][O][C][=Branch1][C][=O][N][C][C][C]"\ "[=Branch1][C][=O][N][C][C][S].[C][S][C][C][C][Branch1][C][O][Branch1][#Branch1]"\ "[C][C][=Branch1][C][=O][O-1][C][=Branch1][C][=O][O-1].[H+1]", return_tensors="pt") # beam search molecules = model.generate(input_ids=sf_input["input_ids"], attention_mask=sf_input["attention_mask"], max_length=400, min_length=5, num_return_sequences=3,#Modify this to get more results num_beams=5) sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules] ['[C][C][=Branch1][C][=O][S][C][C][N][C][=Branch1][C][=O][C][C][N][C][=Branch1][C][=O][C@H1][Branch1][C][O][C][Branch1][C][C][Branch1][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O-1][O][P][=Branch1][C][=O][Branch1][C][O-1][O][C][C@H1][O][C@@H1][Branch1][#C][N][C][=N][C][=C][Ring1][Branch1][N][=C][N][=C][Ring1][=Branch1][N][C@H1][Branch1][C][O][C@@H1][Ring1][S][O][P][=Branch1][C][=O][Branch1][C][O-1][O-1].[C][S][C][C][C][=Branch1][C][=O][C][=Branch1][C][=O][O-1].[H][O][H]', '[C][C][=Branch1][C][=O][S][C][C][N][C][=Branch1][C][=O][C][C][N][C][=Branch1][C][=O][C@H1][Branch1][C][O][C][Branch1][C][C][Branch1][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O-1][O][P][=Branch1][C][=O][Branch1][C][O-1][O][C][C@H1][O][C@@H1][Branch1][#C][N][C][=N][C][=C][Ring1][Branch1][N][=C][N][=C][Ring1][=Branch1][N][C@H1][Branch1][C][O][C@@H1][Ring1][S][O][P][=Branch1][C][=O][Branch1][C][O-1][O-1].[C][S][C][C][=Branch1][C][=O][C][=Branch1][C][=O][O-1].[H][O][H]', '[C][C][Branch1][C][C][Branch2][Branch1][#Branch1][C][O][P][=Branch1][C][=O][Branch1][C][O-1][O][P][=Branch1][C][=O][Branch1][C][O-1][O][C][C@H1][O][C@@H1][Branch1][#C][N][C][=N][C][=C][Ring1][Branch1][N][=C][N][=C][Ring1][=Branch1][N][C@H1][Branch1][C][O][C@@H1][Ring1][S][O][P][=Branch1][C][=O][Branch1][C][O-1][O-1][C@@H1][Branch1][C][O][C][=Branch1][C][=O][N][C][C][C][=Branch1][C][=O][N][C][C][S][C][=Branch1][C][=O][C][C][C][=Branch1][C][=O][O-1].[C][S][C][C][C][=Branch1][C][=O][O-1].[H][O][H]'] ``` ## Model Card Contact Daniel Farfán: [email protected]
slimaneMakh/MultilangBinarySuperClass_Property_Plant_and_Equipment_tableClf_27may_distilBert_BA	slimaneMakh	2024-05-27T19:38:32Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:38:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
slimaneMakh/MultilangBinarySuperClass_Restructuration_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T19:37:38Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:37:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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KatyTheCutie/Llama-3-13B-Instruct-ft-Q5_K_M-GGUF	KatyTheCutie	2024-05-27T19:37:11Z	4	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "dataset:Chat-Error/Pure-dove-sharegpt", "base_model:elinas/Llama-3-13B-Instruct", "base_model:quantized:elinas/Llama-3-13B-Instruct", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T19:36:45Z	--- license: llama3 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - elinas/Llama-3-13B-Instruct datasets: - Chat-Error/Pure-dove-sharegpt --- # KatyTheCutie/Llama-3-13B-Instruct-ft-Q5_K_M-GGUF This model was converted to GGUF format from [`elinas/Llama-3-13B-Instruct-ft`](https://huggingface.co/elinas/Llama-3-13B-Instruct-ft) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/elinas/Llama-3-13B-Instruct-ft) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo KatyTheCutie/Llama-3-13B-Instruct-ft-Q5_K_M-GGUF --model llama-3-13b-instruct-ft-q5_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo KatyTheCutie/Llama-3-13B-Instruct-ft-Q5_K_M-GGUF --model llama-3-13b-instruct-ft-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m llama-3-13b-instruct-ft-q5_k_m.gguf -n 128 ```
slimaneMakh/MultilangBinarySuperClass_Deferred_tax_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T19:34:34Z	195	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:34:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf	RichardErkhov	2024-05-27T19:33:30Z	17	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-27T17:31:57Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged - GGUF - Model creator: https://huggingface.co/dhmeltzer/ - Original model: https://huggingface.co/dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q2_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ3_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ3_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_0.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_1.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_0.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_1.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q6_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q8_0.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged-gguf/blob/main/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 44.13 \| \| ARC (25-shot) \| 53.67 \| \| HellaSwag (10-shot) \| 78.21 \| \| MMLU (5-shot) \| 45.9 \| \| TruthfulQA (0-shot) \| 46.13 \| \| Winogrande (5-shot) \| 73.8 \| \| GSM8K (5-shot) \| 4.7 \| \| DROP (3-shot) \| 6.53 \|
jiangqin/3d-icon-sdxl-lora	jiangqin	2024-05-27T19:33:15Z	4	1	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-25T04:52:35Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK screw icon widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - jiangqin/3d-icon-sdxl-lora <Gallery /> ## Model description These are jiangqin/3d-icon-sdxl-lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK screw icon to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](jiangqin/3d-icon-sdxl-lora/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
slimaneMakh/MultilangBinarySuperClass_Derivatives_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T19:33:07Z	162	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:32:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-2499	fine-tuned	2024-05-27T19:32:05Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Debate", "Argument", "Counter", "Discussion", "Persuasion", "custom_code", "fr", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-2499", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-27T19:31:51Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-2499 - allenai/c4 language: - fr - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Debate - Argument - Counter - Discussion - Persuasion --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: debate platform ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-2499', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
Icelandic-lt/deepspeech_scorer	Icelandic-lt	2024-05-27T19:31:34Z	0	0	null	[ "region:us" ]	null	2024-05-27T19:25:36Z	------------------------------------------------------------------------------- DeepSpeech Scorer for Icelandic 22.06 ------------------------------------------------------------------------------- Authors : Carlos Daniel Hernández Mena ([email protected]). Language : Icelandic. Recommended use : speech recognition. ------------------------------------------------------------------------------- Description ------------------------------------------------------------------------------- "DeepSpeech Scorer for Icelandic 22.06" is a scorer suitable for recognizers based on the Mozilla's DeepSpeech recognizer [1]. A "scorer" is a single file used to perform language modeling. It is composed of two sub-components, a KenLM language model and a trie data structure containing all words in the vocabulary [2]. This scorer was originally created to be used with the following DeepSpeech recipe, developed by the Language and Voice Lab (LVL) at Reykjavík University in 2022: https://github.com/cadia-lvl/samromur-asr/tree/d5_samromur/d5_samromur Nevertheless, due to the flexibility of this kind of resources and their possible application in other tasks, systems or code recipes; it was decided to publish this resource as an independent item. ------------------------------------------------------------------------------- The Language Model ------------------------------------------------------------------------------- The language model was created using the Icelandic Gigaword Corpus [3]. The Gigaword corpus contains text from newspaper articles, parliamentary speeches, adjudications, books, transcribed radio/television news and more. The normalization process of the sentences utilized to generate the language model includes to allowing only characters belonging to the Icelandic alphabet, expanding numbers and abbreviations, and removing punctuation marks [4]. The resulting text has a length of more than 44 million lines of text (5.3GB approximately), and it was used to create the scorer. ------------------------------------------------------------------------------- Citation ------------------------------------------------------------------------------- When publishing results based on the models please refer to: Mena, Carlos; "DeepSpeech Scorer for Icelandic 22.06". Web Download. Reykjavik University: Language and Voice Lab, 2022. Contact: Carlos Mena ([email protected]) License: CC BY 4.0 ------------------------------------------------------------------------------- Acknowledgements ------------------------------------------------------------------------------- This initiative was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture. ------------------------------------------------------------------------------- References ------------------------------------------------------------------------------- [1] Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., ... & Zhu, Z. (2016, June). Deep speech 2: End-to-end speech recognition in english and mandarin. In International conference on machine learning (pp. 173-182). PMLR. [2] Mozilla's DeepSpeech online documentation: https://deepspeech.readthedocs.io/en/r0.9/Scorer.html [3] Steingrímsson, S., Helgadóttir, S., Rögnvaldsson, E., Barkarson, S., & Guðnason, J. (2018, May). Risamálheild: A very large Icelandic text corpus. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). [4] Nikulásdóttir, A. B., Helgadóttir, I. R., Pétursson, M., & Guðnason, J. (2018, May). Open ASR for Icelandic: Resources and a baseline system. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). ------------------------------------------------------------------------------- -------------------------------------------------------------------------------
BotCuddles/men_lora_model	BotCuddles	2024-05-27T19:30:50Z	3	0	transformers	[ "transformers", "pytorch", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T18:16:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: BotCuddles - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
DrAgOn200233/autotrain-ArthurHeyes-Lora-Mistral7B-001	DrAgOn200233	2024-05-27T19:23:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "dataset:DrAgOn200233/ArthurHayes", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T19:17:04Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - DrAgOn200233/ArthurHayes --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
slimaneMakh/MultilangBinarySuperClass_Pensions_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T19:21:14Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:21:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Busayor/busayor	Busayor	2024-05-27T19:19:36Z	37	0	transformers	[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T19:19:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v12	Ramikan-BR	2024-05-27T19:19:21Z	116	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v11", "base_model:finetune:Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v11", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T12:01:21Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v11 metrics: open-llm-leaderboard/details_Ramikan-BR__tinyllama_PY-CODER-4bit-lora_4k-v12 --- ## Avaliação de Modelo ### Benchmarks \| Task \| Model \| Metric \| Value \| \|------\|-------\|--------\|-------\| \| Winogrande \| Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v12 \| acc \| 26.58% \| \| TruthfulQA \| Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v12 \| mc2 \| 40.77% \| \| Hellaswag \| Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v12 \| acc \| 35.16% \| \| GSM8K \| Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v12 \| acc \| 0.00% \| \| ARC Challenge \| Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v12 \| acc \| 24.32% \| # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v11 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
KatyTheCutie/EstopianMaid-13B	KatyTheCutie	2024-05-27T19:19:12Z	199	50	transformers	[ "transformers", "safetensors", "llama", "text-generation", "roleplay", "text-generation-inference", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-25T16:06:10Z	--- language: - en library_name: transformers tags: - roleplay - text-generation-inference license: llama2 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/653a2392341143f7774424d8/fyK_RtEjb9sLF_Mq0nZm2.png) Based on feedback Estopian made can: - EstopianMaid is good at sticking to the character card. - maintains coherency in a setting with multiple characters. - Able to create new scenario's - Feature from Thespis: ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/653a2392341143f7774424d8/1Z4P7XshVOW8fLg9pey4H.webp) - Prompt Template: Alpaca ### Instruction: {prompt} ### Response: Recommended settings: - SillyTavern Default Preset. - Temperature: 0.7 - Min-P: 0.3 - Amount to Gen: 256 - Top P: 1 - Repetition penalty: 1.10 Models used: BlueNipples/TimeCrystal-l2-13B cgato/Thespis-13b-DPO-v0.7 KoboldAI/LLaMA2-13B-Estopia NeverSleep/Noromaid-13B-0.4-DPO Doctor-Shotgun/cat-v1.0-13b Feedback is always appreciated! Thank you KoboldAI for their usage of their MergeBox and Caitlyn G. for their support and feedback.
slimaneMakh/MultilangBinarySuperClass_Payables_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T19:17:56Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:17:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tatakof/distillbert-base-spanish-uncased_finetuned_with-Llama2-Knowledge-Distillation	tatakof	2024-05-27T19:15:46Z	110	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/distilbert-base-spanish-uncased", "base_model:finetune:dccuchile/distilbert-base-spanish-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-03T08:55:23Z	--- base_model: CenIA/distillbert-base-spanish-uncased tags: - generated_from_trainer model-index: - name: distillbert-base-spanish-uncased_finetuned_with-Llama2-synthetic-data 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. --> # distillbert-base-spanish-uncased_finetuned_with-Llama2-Knowledge-Distillation This model is a fine-tuned version of [CenIA/distillbert-base-spanish-uncased](https://huggingface.co/CenIA/distillbert-base-spanish-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3701 ## 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: 3.571428571428572e-07 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8547 \| 1.0 \| 8 \| 3.5585 \| \| 3.7087 \| 2.0 \| 16 \| 3.7027 \| \| 3.7771 \| 3.0 \| 24 \| 3.8879 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
dtorber/BioNLP-tech_ner_tokens-eLife	dtorber	2024-05-27T19:13:22Z	92	0	transformers	[ "transformers", "safetensors", "led", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T15:21:03Z	--- tags: - generated_from_trainer model-index: - name: BioNLP-tech_ner_tokens-eLife 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. --> # BioNLP-tech_ner_tokens-eLife 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: 1.3739167643078955e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
Paresh1879/stable-diffusion-xl-thumbsup-extend	Paresh1879	2024-05-27T19:10:43Z	0	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:apache-2.0", "region:us" ]	text-to-image	2024-05-27T00:13:56Z	--- library_name: diffusers tags: - text-to-image - stable-diffusion base_model: stabilityai/stable-diffusion-xl-base-1.0 license: apache-2.0 pipeline_tag: text-to-image --- # DreamBooth LoRA Training with Stable Diffusion XL on Trump Thumbs Up Images This repository contains instructions and code for training a DreamBooth LoRA model using Stable Diffusion XL on a dataset of images featuring Donald Trump giving a thumbs up gesture. The trained model can be used to generate high-quality images of Trump showing thumbs up in various contexts. ## Sample Images Here are a few sample images generated by the trained model: ![Trump Thumbs Up in India](Output-Complex/Trump1.jpeg) * 1. A high quality picture of Trump showing thumbs up in a busy street of India, detailed, sharp focus. ![Trump Thumbs Up in a Taco Restaurant](Output-Complex/Trump7.jpeg) * 2. An intricately detailed digital painting of Donald Trump giving a thumbs up at a taco restaurant. The background includes colorful decor and a bustling atmosphere with people enjoying their meals. ![Trump Thumbs Up at the Beach](Output-Complex/Trump2.jpeg) * 3. A high-quality photo of Donald Trump giving a thumbs up on a sunny beach. The scene includes clear blue water, white sand, and Trump in casual beachwear. The image is detailed, with Trump’s smiling face and the vibrant beach setting in sharp focus. ## Requirements The script requires Python 3.9 and several Python packages including PyTorch, Hugging Face Transformers, Diffusers, and Accelerate. Additional dependencies are listed in the `requirements_sdxl.txt` file. ## Installation To get started, clone the repository and navigate to the project directory. Install the required packages using pip and the provided `requirements_sdxl.txt` file. Log in to the Hugging Face Hub using the `huggingface-cli login` command. ## Usage To train the model, prepare a dataset of images featuring Donald Trump giving a thumbs up gesture and place them in a directory. Run the training script `train_dreambooth_lora_sdxl.py` with the appropriate command-line arguments specifying the pretrained model, instance data directory, output directory, and various training hyperparameters. After training, load the trained LoRA weights and use the `DiffusionPipeline` class from the Diffusers library to generate images. Provide a prompt describing the desired image, such as "A high quality picture of Trump showing the thumbs up in Paris detailed, sharp focus". The generated image will be saved to the specified output directory. ## API Server [SDXL_API_Server](https://huggingface.co/Paresh1879/stable-diffusion-xl-thumbsup-extend/blob/main/SDXL_API_Server.py) contains the server side code containing the below information : - Image Generation Endpoint: - `/generate_image`: Accepts POST requests with prompts to generate Trump thumbs up images. - Users provide prompts describing desired image contexts. - Images are generated using a pre-trained model. - API Key Authentication: - Ensures presence of API key for authorization. - Rejects unauthorized requests. - API Key Usage Tracking: - Tracks API key usage count. - `/api_key_usage` endpoint retrieves usage count. - The Generated Output in postman: - ![POST Output](postman_output.png) - Endpoint to get generated images via a prompt using the above trigger keyword and style - ![Api_Key_Counts](api_key_counts.png) - Server maintains a count of each time the API key was used to successfully generate an image. ## Results The generated images will be saved in the specified output directory, showcasing Trump giving a thumbs up gesture in different contexts based on the provided prompts.
flammenai/flammen29-mistral-7B	flammenai	2024-05-27T19:09:36Z	7	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "dataset:flammenai/Grill-Flammen-v1_chatML", "base_model:flammenai/flammen27-mistral-7B", "base_model:finetune:flammenai/flammen27-mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T18:29:48Z	--- library_name: transformers license: apache-2.0 base_model: - flammenai/flammen27-mistral-7B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML - flammenai/Grill-Flammen-v1_chatML --- ![image/png](https://huggingface.co/nbeerbower/flammen13X-mistral-7B/resolve/main/flammen13x.png) # flammen29-mistral-7B A Mistral 7B LLM built from merging pretrained models and finetuning on various datasets. Flammen specializes in exceptional character roleplay, creative writing, and general intelligence. ### Method Finetuned using an A100 on Google Colab. [Fine-tune Llama 3 with ORPO](https://huggingface.co/blog/mlabonne/orpo-llama-3)
roofdancer/plain-bart-on-presummarized-tod-wcep	roofdancer	2024-05-27T19:08:57Z	125	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:sshleifer/distilbart-cnn-6-6", "base_model:finetune:sshleifer/distilbart-cnn-6-6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T17:27:10Z	--- license: apache-2.0 base_model: sshleifer/distilbart-cnn-6-6 tags: - generated_from_trainer metrics: - rouge model-index: - name: plain-bart-on-presummarized-tod-wcep 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. --> # plain-bart-on-presummarized-tod-wcep This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3043 - Rouge1: 34.5939 - Rouge2: 13.9925 - Rougel: 24.4982 - Rougelsum: 27.7893 - Gen Len: 66.2392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.4866 \| 1.0 \| 510 \| 2.3191 \| 34.0155 \| 13.6965 \| 24.0706 \| 27.3858 \| 66.8784 \| \| 2.1347 \| 2.0 \| 1020 \| 2.2952 \| 34.1203 \| 13.7453 \| 24.0993 \| 27.4503 \| 67.0735 \| \| 1.9605 \| 3.0 \| 1530 \| 2.3043 \| 34.5939 \| 13.9925 \| 24.4982 \| 27.7893 \| 66.2392 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
harveybro/molt5-augmented-default-800-small-caption2smiles	harveybro	2024-05-27T19:08:10Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T19:07:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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slimaneMakh/MultilangBinarySuperClass_Cash_and_cash_equivalents_tableClf_27may_distilBert_BASEL	slimaneMakh	2024-05-27T19:03:24Z	181	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T19:03:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Fawazzx/Saul-semantic.v1	Fawazzx	2024-05-27T19:01:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T19:01:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AnnaCarson/roberta-base-ner-demo	AnnaCarson	2024-05-27T19:01:03Z	127	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "mn", "base_model:bayartsogt/mongolian-roberta-base", "base_model:finetune:bayartsogt/mongolian-roberta-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-04-05T17:49:19Z	--- language: - mn base_model: bayartsogt/mongolian-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-ner-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-ner-demo This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1834 - Precision: 0.6839 - Recall: 0.7644 - F1: 0.7219 - Accuracy: 0.9459 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.7672 \| 1.0 \| 20 \| 0.5162 \| 0.0825 \| 0.0401 \| 0.0540 \| 0.8256 \| \| 0.3886 \| 2.0 \| 40 \| 0.3017 \| 0.4778 \| 0.5113 \| 0.4939 \| 0.9061 \| \| 0.2163 \| 3.0 \| 60 \| 0.2214 \| 0.5543 \| 0.6266 \| 0.5882 \| 0.9225 \| \| 0.1199 \| 4.0 \| 80 \| 0.1942 \| 0.6346 \| 0.7268 \| 0.6776 \| 0.9359 \| \| 0.0742 \| 5.0 \| 100 \| 0.1852 \| 0.6396 \| 0.7293 \| 0.6815 \| 0.9409 \| \| 0.0555 \| 6.0 \| 120 \| 0.1811 \| 0.6943 \| 0.7569 \| 0.7242 \| 0.9449 \| \| 0.0407 \| 7.0 \| 140 \| 0.1860 \| 0.6804 \| 0.7469 \| 0.7121 \| 0.9439 \| \| 0.0346 \| 8.0 \| 160 \| 0.1876 \| 0.6952 \| 0.7544 \| 0.7236 \| 0.9463 \| \| 0.0302 \| 9.0 \| 180 \| 0.1820 \| 0.6868 \| 0.7694 \| 0.7258 \| 0.9459 \| \| 0.0289 \| 10.0 \| 200 \| 0.1834 \| 0.6839 \| 0.7644 \| 0.7219 \| 0.9459 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
yh1306/a	yh1306	2024-05-27T18:58:59Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-23T13:18:52Z	--- license: apache-2.0 ---
ferrazzipietro/Llama-2-7b-chat-hf_adapters_SLO_NoQuant_torch.bfloat16_32_64_0.01_1_0.0002	ferrazzipietro	2024-05-27T18:53:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T18:53:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ai-forever/KandinskyVideo_1_1	ai-forever	2024-05-27T18:50:32Z	0	9	null	[ "arxiv:2304.08818", "arxiv:2311.13073", "license:apache-2.0", "region:us" ]	null	2024-05-27T18:27:01Z	--- license: apache-2.0 --- # Kandinsky Video 1.1 — a new text-to-video generation model ## SoTA quality among open-source solutions on <a href="https://evalcrafter.github.io/">EvalCrafter</a> benchmark This repository is the official implementation of Kandinsky Video 1.1 model. [![Hugging Face Spaces](https://img.shields.io/badge/🤗-Huggingface-yello.svg)](https://huggingface.co/ai-forever/KandinskyVideo) \| [Telegram-bot](https://t.me/video_kandinsky_bot) \| [Habr post](https://habr.com/ru/companies/sberbank/articles/775554/) \| [Our text-to-image model](https://github.com/ai-forever/Kandinsky-3/tree/main) <p> <!-- <img src="_assets__/title.jpg" width="800px"/> --> <!-- <br> --> Our <B>previous</B> model <a href="https://ai-forever.github.io/Kandinsky-3/">Kandinsky Video 1.0</a>, divides the video generation process into two stages: initially generating keyframes at a low FPS and then creating interpolated frames between these keyframes to increase the FPS. In <B>Kandinsky Video 1.1</B>, we further break down the keyframe generation into two extra steps: first, generating the initial frame of the video from the textual prompt using Text to Image <a href="https://github.com/ai-forever/Kandinsky-3">Kandinsky 3.0</a>, and then generating the subsequent keyframes based on the textual prompt and the previously generated first frame. This approach ensures more consistent content across the frames and significantly enhances the overall video quality. Furthermore, the approach allows animating any input image as an additional feature. </p> ## Pipeline <p align="center"> <img src="_assets__/pipeline.png" width="800px"/> <br> <em>In the <a href="https://ai-forever.github.io/Kandinsky-3/">Kandinsky Video 1.0</a>, the encoded text prompt enters the text-to-video U-Net3D keyframe generation model with temporal layers or blocks, and then the sampled latent keyframes are sent to the latent interpolation model to predict three interpolation frames between two keyframes. An image MoVQ-GAN decoder is used to obtain the final video result. In <B>Kandinsky Video 1.1</B>, text-to-video U-Net3D is also conditioned on text-to-image U-Net2D, which helps to improve the content quality. A temporal MoVQ-GAN decoder is used to decode the final video.</em> </p> Architecture details + Text encoder (Flan-UL2) - 8.6B + Latent Diffusion U-Net3D - 4.15B + The interpolation model (Latent Diffusion U-Net3D) - 4.0B + Image MoVQ encoder/decoder - 256M + Video (temporal) MoVQ decoder - 556M ## How to use <!--Check our jupyter notebooks with examples in `./examples` folder --> ### 1. text2video ```python from kandinsky_video import get_T2V_pipeline device_map = 'cuda:0' t2v_pipe = get_T2V_pipeline(device_map) prompt = "A cat wearing sunglasses and working as a lifeguard at a pool." fps = 'medium' # ['low', 'medium', 'high'] motion = 'high' # ['low', 'medium', 'high'] video = t2v_pipe( prompt, width=512, height=512, fps=fps, motion=motion, key_frame_guidance_scale=5.0, guidance_weight_prompt=5.0, guidance_weight_image=3.0, ) path_to_save = f'./_assets__/video.gif' video[0].save( path_to_save, save_all=True, append_images=video[1:], duration=int(5500/len(video)), loop=0 ) ``` <p align="center"> <img src="_assets__/video.gif" raw=true> <br><em>Generated video</em> </p> ### 2. image2video ```python from kandinsky_video import get_T2V_pipeline device_map = 'cuda:0' t2v_pipe = get_T2V_pipeline(device_map) from PIL import Image import requests from io import BytesIO url = 'https://media.cnn.com/api/v1/images/stellar/prod/gettyimages-1961294831.jpg' response = requests.get(url) img = Image.open(BytesIO(response.content)) img.show() prompt = "A panda climbs up a tree." fps = 'medium' # ['low', 'medium', 'high'] motion = 'medium' # ['low', 'medium', 'high'] video = t2v_pipe( prompt, image=img, width=640, height=384, fps=fps, motion=motion, key_frame_guidance_scale=5.0, guidance_weight_prompt=5.0, guidance_weight_image=3.0, ) path_to_save = f'./_assets__/video2.gif' video[0].save( path_to_save, save_all=True, append_images=video[1:], duration=int(5500/len(video)), loop=0 ) ``` <p align="center"> <img src="https://media.cnn.com/api/v1/images/stellar/prod/gettyimages-1961294831.jpg" width="50%"><br> <em>Input image.</em> </p> <p align="center"> <img src="_assets__/video2.gif"><br> <em>Generated Video.</em> </p> ## Results <p align="center"> <img src="_assets__/eval crafter.png" align="center" width="50%"> <br> <em> Kandinsky Video 1.1 achieves second place overall and best open source model on <a href="https://evalcrafter.github.io/">EvalCrafter</a> text to video benchmark. VQ: visual quality, TVA: text-video alignment, MQ: motion quality, TC: temporal consistency and FAS: final average score. </em> </p> <p align="center"> <img src="_assets__/polygon.png" raw=true align="center" width="50%"> <br> <em> Polygon-radar chart representing the performance of Kandinsky Video 1.1 on <a href="https://evalcrafter.github.io/">EvalCrafter</a> benchmark. </em> </p> <p align="center"> <img src="_assets__/human eval.png" raw=true align="center" width="50%"> <br> <em> Human evaluation study results. The bars in the plot correspond to the percentage of “wins” in the side-by-side comparison of model generations. We compare our model with <a href="https://arxiv.org/abs/2304.08818">Video LDM</a>. </em> </p> # Authors + Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse), [Google Scholar](https://scholar.google.com/citations?user=D-Ko0oAAAAAJ&hl=ru) + Zein Shaheen: [Github](https://github.com/zeinsh), [Google Scholar](https://scholar.google.ru/citations?user=bxlgMxMAAAAJ&hl=en) + Viacheslav Vasilev: [Github](https://github.com/vivasilev), [Google Scholar](https://scholar.google.com/citations?user=redAz-kAAAAJ&hl=ru&oi=sra) + Igor Pavlov: [Github](https://github.com/boomb0om) + Elizaveta Dakhova: [Github](https://github.com/LizaDakhova) + Anastasia Lysenko: [Github](https://github.com/LysenkoAnastasia) + Sergey Markov + Denis Dimitrov: [Github](https://github.com/denndimitrov), [Google Scholar](https://scholar.google.com/citations?user=3JSIJpYAAAAJ&hl=ru&oi=ao) + Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey), [Google Scholar](https://scholar.google.com/citations?user=q0lIfCEAAAAJ&hl=ru) ## BibTeX If you use our work in your research, please cite our publication: ``` @article{arkhipkin2023fusionframes, title = {FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipeline}, author = {Arkhipkin, Vladimir and Shaheen, Zein and Vasilev, Viacheslav and Dakhova, Elizaveta and Kuznetsov, Andrey and Dimitrov, Denis}, journal = {arXiv preprint arXiv:2311.13073}, year = {2023}, } ```
slimaneMakh/MultilangBinarySuperClass_Earnings_Per_Share_tableClf_27may_triplet	slimaneMakh	2024-05-27T18:43:35Z	163	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T18:43:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
AICube/ChatGLM	AICube	2024-05-27T18:36:20Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:THUDM/chatglm3-6b-base", "base_model:adapter:THUDM/chatglm3-6b-base", "license:other", "region:us" ]	null	2024-05-27T18:34:56Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: THUDM/chatglm3-6b-base model-index: - name: test1 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. --> # test1 This model is a fine-tuned version of [THUDM/chatglm3-6b-base](https://huggingface.co/THUDM/chatglm3-6b-base) on the im_the_fated_villain_chapters dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.2 - Datasets 2.19.0 - Tokenizers 0.19.1
RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf	RichardErkhov	2024-05-27T18:35:54Z	42	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-27T16:23:31Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Medusa-1.1-L2-7B - GGUF - Model creator: https://huggingface.co/Sao10K/ - Original model: https://huggingface.co/Sao10K/Medusa-1.1-L2-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Medusa-1.1-L2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Medusa-1.1-L2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Medusa-1.1-L2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Medusa-1.1-L2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Medusa-1.1-L2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Medusa-1.1-L2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Medusa-1.1-L2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Medusa-1.1-L2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Medusa-1.1-L2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Medusa-1.1-L2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Medusa-1.1-L2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Medusa-1.1-L2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Medusa-1.1-L2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Medusa-1.1-L2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Medusa-1.1-L2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Medusa-1.1-L2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Medusa-1.1-L2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Medusa-1.1-L2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Medusa-1.1-L2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Medusa-1.1-L2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Medusa-1.1-L2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Medusa-1.1-L2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Sao10K_-_Medusa-1.1-L2-7B-gguf/blob/main/Medusa-1.1-L2-7B.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- license: llama2 language: - en --- Experimental Ties-Merge between 5 Models and 2 LORAs at varying weights and densities. <br> And trained with some dataset. This is purely for my personal testing. Use if you want. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Medusa-1.1-L2-7B) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 49.62 \| \| ARC (25-shot) \| 56.48 \| \| HellaSwag (10-shot) \| 78.57 \| \| MMLU (5-shot) \| 51.56 \| \| TruthfulQA (0-shot) \| 47.7 \| \| Winogrande (5-shot) \| 75.06 \| \| GSM8K (5-shot) \| 1.44 \| \| DROP (3-shot) \| 36.53 \|
slimaneMakh/MultilangBinarySuperClass_Inventories_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T18:34:39Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T18:34:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tiotino/vscode	tiotino	2024-05-27T18:33:41Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T18:33:41Z	--- license: apache-2.0 ---
bellge/cw3_trained_model	bellge	2024-05-27T18:32:52Z	112	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T18:32:12Z	--- license: mit base_model: roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: cw3_trained_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. --> # cw3_trained_model This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6923 - Accuracy: 0.7129 - F1: 0.7102 - Precision: 0.7281 - Recall: 0.7129 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.7988 \| 2.49 \| 500 \| 0.7923 \| 0.6380 \| 0.6113 \| 0.7062 \| 0.6380 \| \| 0.539 \| 4.98 \| 1000 \| 0.6923 \| 0.7129 \| 0.7102 \| 0.7281 \| 0.7129 \| \| 0.2275 \| 7.46 \| 1500 \| 1.1347 \| 0.7054 \| 0.7037 \| 0.7132 \| 0.7054 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
samim2024/Mistral-7b-4bit-Finetuned	samim2024	2024-05-27T18:31:59Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-27T18:31:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: samim2024 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
slimaneMakh/MultilangBinarySuperClass_not_found_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T18:28:26Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T18:28:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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chujiezheng/zephyr-7b-dpo-full-ExPO	chujiezheng	2024-05-27T18:25:58Z	15	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-26T09:01:06Z	--- license: apache-2.0 language: - en --- # zephyr-7b-dpo-full-ExPO The extrapolated (ExPO) model based on [`alignment-handbook/zephyr-7b-dpo-full`](https://huggingface.co/alignment-handbook/zephyr-7b-dpo-full) and [`alignment-handbook/zephyr-7b-sft-full`](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. This model achieves the 18.0% win rate and 20.2% LC win rate on AlpacaEval 2.0. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/Llama3-8B-Chinese-Chat-ExPO	chujiezheng	2024-05-27T18:24:18Z	1,328	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "zh", "arxiv:2404.16792", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:40:01Z	--- license: llama3 language: - en - zh --- # Llama3-8B-Chinese-Chat-ExPO The extrapolated (ExPO) model based on [`shenzhi-wang/Llama3-8B-Chinese-Chat`](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Note: This is an experimental model, as I have not comprehensively evaluated its Chinese ability. Unexpected issues may occur when we apply extrapolation to the DPO/RLHF alignment training for new languages (e.g., Chinese). ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/Smaug-Llama-3-70B-Instruct-ExPO	chujiezheng	2024-05-27T18:19:48Z	1,330	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T18:54:03Z	--- license: llama3 language: - en --- # Smaug-Llama-3-70B-Instruct-ExPO The extrapolated (ExPO) model based on [`abacusai/Smaug-Llama-3-70B-Instruct`](https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct) and [`meta-llama/Meta-Llama-3-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
kvsudarsh/wm2-merged	kvsudarsh	2024-05-27T18:19:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T18:16:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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chujiezheng/LLaMA3-iterative-DPO-final-ExPO	chujiezheng	2024-05-27T18:16:46Z	1,317	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T03:04:05Z	--- language: - en license: llama3 --- # LLaMA3-iterative-DPO-final-ExPO The extrapolated (ExPO) model based on [`RLHFlow/LLaMA3-iterative-DPO-final`](https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final) and [`RLHFlow/LLaMA3-SFT`](https://huggingface.co/RLHFlow/LLaMA3-SFT), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/Snorkel-Mistral-PairRM-DPO-ExPO	chujiezheng	2024-05-27T18:16:33Z	19	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-04T07:09:35Z	--- license: apache-2.0 language: - en --- # Snorkel-Mistral-PairRM-DPO-ExPO The extrapolated (ExPO) model based on [`snorkelai/Snorkel-Mistral-PairRM-DPO`](https://huggingface.co/snorkelai/Snorkel-Mistral-PairRM-DPO) and [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
ferrazzipietro/Llama-2-7b-chat-hf_adapters_SLO_NoQuant_torch.bfloat16_16_64_0.01_1_0.0002	ferrazzipietro	2024-05-27T18:16:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T18:16:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ivipop/ivipop.com	ivipop	2024-05-27T18:16:00Z	0	0	null	[ "python", "event", "ivipop", "nlp", "fr", "license:cc-by-3.0", "region:us" ]	null	2024-01-30T21:41:53Z	--- license: cc-by-3.0 language: - fr tags: - python - event - ivipop - nlp ---
chujiezheng/internlm2-chat-7b-ExPO	chujiezheng	2024-05-27T18:15:47Z	10	0	transformers	[ "transformers", "safetensors", "internlm2", "feature-extraction", "text-generation", "conversational", "custom_code", "en", "zh", "arxiv:2404.16792", "license:other", "region:us" ]	text-generation	2024-05-02T14:08:50Z	--- pipeline_tag: text-generation license: other language: - en - zh --- # internlm2-chat-7b-ExPO The extrapolated (ExPO) model based on [`internlm2-chat-7b`](https://huggingface.co/internlm/internlm2-chat-7b) and [`internlm/internlm2-chat-7b-sft`](https://huggingface.co/internlm/internlm2-chat-7b-sft), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.5) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
MoMonir/AutoCoder_S_6.7B-GGUF	MoMonir	2024-05-27T18:15:42Z	8	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T17:29:44Z	--- license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # MoMonir/AutoCoder_S_6.7B-GGUF This model was converted to GGUF format from [`Bin12345/AutoCoder_S_6.7B`](https://huggingface.co/Bin12345/AutoCoder_S_6.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Bin12345/AutoCoder_S_6.7B) for more details on the model. <!-- README_GGUF.md-about-gguf start --> ### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description) GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [backyard.ai](https://backyard.ai/) (Formeraly [Faraday.dev](https://faraday.dev/)), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo MoMonir/AutoCoder_S_6.7B-GGUF --model autocoder_s_6.7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo MoMonir/AutoCoder_S_6.7B-GGUF --model autocoder_s_6.7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m autocoder_s_6.7b-q4_k_m.gguf -n 128 ```
chujiezheng/internlm2-chat-1_8b-ExPO	chujiezheng	2024-05-27T18:15:37Z	133	1	transformers	[ "transformers", "safetensors", "internlm2", "feature-extraction", "text-generation", "conversational", "custom_code", "en", "zh", "arxiv:2404.16792", "license:other", "region:us" ]	text-generation	2024-05-02T14:05:23Z	--- pipeline_tag: text-generation license: other language: - en - zh --- # internlm2-chat-1_8b-ExPO The extrapolated (ExPO) model based on [`internlm2-chat-1_8b`](https://huggingface.co/internlm/internlm2-chat-1_8b) and [`internlm/internlm2-chat-1_8b-sft`](https://huggingface.co/internlm/internlm2-chat-1_8b-sft), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.5) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/Starling-LM-7B-beta-ExPO	chujiezheng	2024-05-27T18:15:24Z	1,286	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-26T08:46:02Z	--- license: apache-2.0 language: - en --- # Starling-LM-7B-beta-ExPO The extrapolated (ExPO) model based on [`Nexusflow/Starling-LM-7B-beta`](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) and [`openchat/openchat-3.5-0106`](https://huggingface.co/openchat/openchat-3.5-0106), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.5) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/Starling-LM-7B-alpha-ExPO	chujiezheng	2024-05-27T18:15:11Z	1,287	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-26T08:41:02Z	--- license: apache-2.0 language: - en --- # Starling-LM-7B-alpha-ExPO The extrapolated (ExPO) model based on [`berkeley-nest/Starling-LM-7B-alpha`](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) and [`openchat/openchat_3.5`](https://huggingface.co/openchat/openchat_3.5), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.2) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/tulu-2-dpo-70b-ExPO	chujiezheng	2024-05-27T18:14:39Z	1,309	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-26T14:57:29Z	--- license: other license_name: ai2-impact-license-low-risk license_link: https://allenai.org/impact-license language: - en --- # tulu-2-dpo-70b-ExPO The extrapolated (ExPO) model based on [`allenai/tulu-2-dpo-70b`](https://huggingface.co/allenai/tulu-2-dpo-70b) and [`allenai/tulu-2-70b`](https://huggingface.co/allenai/tulu-2-70b), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.5) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
chujiezheng/zephyr-7b-beta-ExPO	chujiezheng	2024-05-27T18:13:52Z	12	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2404.16792", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-28T05:23:38Z	--- license: apache-2.0 language: - en --- # zephyr-7b-beta-ExPO The extrapolated (ExPO) model based on [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and [`HuggingFaceH4/mistral-7b-sft-beta`](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper. Specifically, we obtain this model by extrapolating (alpha = 0.1) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. ## Evaluation Results Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)): \| \| Win Rate (Ori) \| LC Win Rate (Ori) \| Win Rate (+ ExPO) \| LC Win Rate (+ ExPO) \| \| ------------------------------------ \| -------------- \| ----------------- \| ----------------- \| -------------------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.7% \| 10.0% \| 10.6% \| 13.6% \| \| `HuggingFaceH4/zephyr-7b-beta` \| 10.2% \| 13.2% \| 11.1% \| 14.0% \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 15.0% \| 18.3% \| 18.2% \| 19.5% \| \| `Nexusflow/Starling-LM-7B-beta` \| 26.6% \| 25.8% \| 29.6% \| 26.4% \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 24.7% \| 24.0% \| 28.8% \| 26.4% \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 29.2% \| 36.0% \| 32.7% \| 37.8% \| \| `internlm/internlm2-chat-1.8b` \| 3.8% \| 4.0% \| 5.2% \| 4.3% \| \| `internlm/internlm2-chat-7b` \| 20.5% \| 18.3% \| 28.1% \| 22.7% \| \| `internlm/internlm2-chat-20b` \| 36.1% \| 24.9% \| 46.2% \| 27.2% \| \| `allenai/tulu-2-dpo-7b` \| 8.5% \| 10.2% \| 11.5% \| 11.7% \| \| `allenai/tulu-2-dpo-13b` \| 11.2% \| 15.5% \| 15.6% \| 17.6% \| \| `allenai/tulu-2-dpo-70b` \| 15.4% \| 21.2% \| 23.0% \| 25.7% \| Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the [official GitHub repo](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)): \| \| Original \| + ExPO \| \| ------------------------------------ \| -------- \| -------- \| \| `HuggingFaceH4/zephyr-7b-alpha` \| 6.85 \| 6.87 \| \| `HuggingFaceH4/zephyr-7b-beta` \| 7.02 \| 7.06 \| \| `berkeley-nest/Starling-LM-7B-alpha` \| 7.82 \| 7.91 \| \| `Nexusflow/Starling-LM-7B-beta` \| 8.10 \| 8.18 \| \| `snorkelai/Snorkel-Mistral-PairRM` \| 7.63 \| 7.69 \| \| `RLHFlow/LLaMA3-iterative-DPO-final` \| 8.08 \| 8.45 \| \| `internlm/internlm2-chat-1.8b` \| 5.17 \| 5.26 \| \| `internlm/internlm2-chat-7b` \| 7.72 \| 7.80 \| \| `internlm/internlm2-chat-20b` \| 8.13 \| 8.26 \| \| `allenai/tulu-2-dpo-7b` \| 6.35 \| 6.38 \| \| `allenai/tulu-2-dpo-13b` \| 7.00 \| 7.26 \| \| `allenai/tulu-2-dpo-70b` \| 7.79 \| 8.03 \|
quakumei/REALISM_BY_STABLE_YOGI	quakumei	2024-05-27T18:13:12Z	0	0	null	[ "civitai", "region:us" ]	null	2024-05-27T14:44:18Z	--- tags: - civitai --- https://civitai.com/models/166609/realismbystableyogi
slimaneMakh/MultilangBinarySuperClass_Other_tableClf_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T18:12:14Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T18:12:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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llmware/slim-qa-gen-phi-3-tool	llmware	2024-05-27T18:11:11Z	25	2	transformers	[ "transformers", "gguf", "phi3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-26T19:30:26Z	--- license: apache-2.0 --- # SLIM-QA-GEN-PHI-3-TOOL <!-- Provide a quick summary of what the model is/does. --> slim-qa-gen-phi-3-tool is a 4_K_M quantized GGUF version of slim-qa-gen-phi-3, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of two keys: `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} ` The model has been designed to accept one of three different parameters to guide the type of question-answer created: -- 'question, answer' (generates a standard question and answer), -- 'boolean' (generates a 'yes-no' question and answer), and -- 'multiple choice' (generates a multiple choice question and answer). Note: we would generally recommend using sampling and temperature(0.5+) for varied generations, although if using 'multiple choice' mode, then we have seen the best results with temperature in the 0.2-0.3 range. [slim-qa-gen-phi-3](https://huggingface.co/llmware/slim-qa-gen-phi-3) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation. To pull the model via API: from huggingface_hub import snapshot_download snapshot_download("llmware/slim-qa-gen-phi-3-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) Load in your favorite GGUF inference engine, or try with llmware as follows: from llmware.models import ModelCatalog # to load the model and make a basic inference model = ModelCatalog().load_model("slim-qa-gen-phi-3-tool", temperature=0.5, sample=True) response = model.function_call(text_sample, params=["boolean"]) # this one line will download the model and run a series of tests ModelCatalog().tool_test_run("slim-qa-gen-phi-3-tool", verbose=True) Note: please review [config.json](https://huggingface.co/llmware/slim-qa-gen-phi-3-tool/blob/main/config.json) in the repository for prompt template information, details on the model, and full test set. ## Model Card Contact Darren Oberst & llmware team [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
slimaneMakh/MultilangBinarySuperClass_Borrowings_tableClf_27may_triplet	slimaneMakh	2024-05-27T18:08:08Z	163	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T18:07:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
twosocksinoneshoe/ppo-LunarLander-v2	twosocksinoneshoe	2024-05-27T17:52:48Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-27T17:52:30Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.99 +/- 23.49 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
CK0607/video-demo-1-lora	CK0607	2024-05-27T17:50:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-27T17:49:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: CK0607 - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
slimaneMakh/MultilangBinarySuperClass_Segment_tableClf_27may_triplet	slimaneMakh	2024-05-27T17:40:40Z	163	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T17:40:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
baek26/all_3420_bart-all_rl	baek26	2024-05-27T17:39:54Z	51	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-05-27T17:39:17Z	--- license: apache-2.0 tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="baek26//tmp/tmph9dxnfke/baek26/all_3420_bart-all_rl") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmph9dxnfke/baek26/all_3420_bart-all_rl") model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmph9dxnfke/baek26/all_3420_bart-all_rl") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
ArashAhmadian/rloo_tldr_6.9b	ArashAhmadian	2024-05-27T17:39:22Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T17:35:49Z	--- tags: - generated_from_trainer model-index: - name: rloo_tldr_6.9b 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. --> # rloo_tldr_6.9b 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: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - num_epochs: 3.0 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

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