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dgambettaphd/M_llm3_gen3_run0_WXS_doc1000_synt64_FRESH	dgambettaphd	2025-04-21T04:11:50Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-21T04:11:36Z	--- 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. 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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tarsur909/pythia1b-oai-summary-ppo-1ep-translated-gap_new	tarsur909	2025-04-21T04:06:50Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T04:05:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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mengqizou011438/merged-llama3.2-1B-financial	mengqizou011438	2025-04-21T04:06:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:46:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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kk-aivio/d5399b1e-4658-4f29-a3d3-85404cbed562	kk-aivio	2025-04-21T04:01:39Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-04-21T04:01:07Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/Phi-3-mini-4k-instruct model-index: - name: kk-aivio/d5399b1e-4658-4f29-a3d3-85404cbed562 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. --> # kk-aivio/d5399b1e-4658-4f29-a3d3-85404cbed562 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8389 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Srijaaa/bridgegpt_finetuned_UTC_book	Srijaaa	2025-04-21T04:00:04Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "trl", "sft", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:12:30Z	--- library_name: transformers model_name: bridgegpt_finetuned_UTC_book tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for bridgegpt_finetuned_UTC_book This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Srijaaa/bridgegpt_finetuned_UTC_book", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.6.0 - Datasets: 3.2.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
AravindS373/sft_model	AravindS373	2025-04-21T03:59:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:57:14Z	--- base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AravindS373 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-coder-7b-instruct-bnb-4bit This qwen2 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)
xiaoyuanliu/Qwen2.5-3B-simplerl-ppo-online.critique-012-p2	xiaoyuanliu	2025-04-21T03:58:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:54:31Z	--- 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]
tawankri/DeepCoder-1.5B-Preview-mlx-fp16	tawankri	2025-04-21T03:56:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "conversational", "en", "dataset:PrimeIntellect/verifiable-coding-problems", "dataset:likaixin/TACO-verified", "dataset:livecodebench/code_generation_lite", "base_model:agentica-org/DeepCoder-1.5B-Preview", "base_model:finetune:agentica-org/DeepCoder-1.5B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:56:35Z	--- license: mit library_name: transformers datasets: - PrimeIntellect/verifiable-coding-problems - likaixin/TACO-verified - livecodebench/code_generation_lite language: - en base_model: agentica-org/DeepCoder-1.5B-Preview pipeline_tag: text-generation tags: - mlx --- # tawankri/DeepCoder-1.5B-Preview-mlx-fp16 The Model [tawankri/DeepCoder-1.5B-Preview-mlx-fp16](https://huggingface.co/tawankri/DeepCoder-1.5B-Preview-mlx-fp16) was converted to MLX format from [agentica-org/DeepCoder-1.5B-Preview](https://huggingface.co/agentica-org/DeepCoder-1.5B-Preview) using mlx-lm version 0.22.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("tawankri/DeepCoder-1.5B-Preview-mlx-fp16") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Yeongi/q-FrozenLake-v1-4x4-noSlippery	Yeongi	2025-04-21T03:55:04Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-21T03:54:59Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Yeongi/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Bradlk/hi	Bradlk	2025-04-21T03:52:03Z	0	0	null	[ "en", "dataset:nvidia/OpenCodeReasoning", "base_model:deepseek-ai/DeepSeek-V3-0324", "base_model:finetune:deepseek-ai/DeepSeek-V3-0324", "license:unknown", "region:us" ]	null	2025-04-21T03:48:55Z	--- license: unknown datasets: - nvidia/OpenCodeReasoning language: - en metrics: - accuracy - code_eval base_model: - deepseek-ai/DeepSeek-V3-0324 ---
alan314159/Plant_Seedlings_Classification	alan314159	2025-04-21T03:51:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T03:49:06Z	--- license: apache-2.0 ---
aeqw2a0/ilil2222kjkk	aeqw2a0	2025-04-21T03:49:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T03:46:51Z	--- license: apache-2.0 ---
omarViga/mabama-35-2-v1	omarViga	2025-04-21T03:48:17Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-schnell", "base_model:adapter:black-forest-labs/FLUX.1-schnell", "license:mit", "region:us" ]	text-to-image	2025-04-21T03:48:05Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- Sexy mabama in black lingerie, she lays down in her cosy bed wearing sexy black lingerie. parameters: negative_prompt: low quality output: url: images/chocolate-cup1.png base_model: black-forest-labs/FLUX.1-schnell instance_prompt: mabama license: mit --- # mabama-35-2-v1 <Gallery /> ## Model description mab ## Trigger words You should use `mabama` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/omarViga/mabama-35-2-v1/tree/main) them in the Files & versions tab.
jckim/stt-turbo-multilingual-v0.0.5	jckim	2025-04-21T03:43:55Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "multilingual", "base_model:openai/whisper-large-v3-turbo", "base_model:adapter:openai/whisper-large-v3-turbo", "license:mit", "region:us" ]	null	2025-04-21T03:43:45Z	--- library_name: peft language: - multilingual license: mit base_model: openai/whisper-large-v3-turbo tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Turbo Multilingual results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Turbo Multilingual This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the custom_multilingual dataset. It achieves the following results on the evaluation set: - Loss: 0.0905 - Wer: 12.1973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.1009 \| 0.7561 \| 200 \| 0.1303 \| 15.5299 \| \| 0.0657 \| 1.5123 \| 400 \| 0.0861 \| 11.3086 \| \| 0.0412 \| 2.2684 \| 600 \| 0.0889 \| 11.7307 \| \| 0.0352 \| 3.0246 \| 800 \| 0.0935 \| 12.5305 \| \| 0.0243 \| 3.7807 \| 1000 \| 0.0905 \| 12.1973 \| ### Framework versions - PEFT 0.15.2.dev0 - Transformers 4.46.3 - Pytorch 2.3.1+cu121 - Datasets 3.0.0 - Tokenizers 0.20.3
nannnzk/task-7-microsoft-phi-4	nannnzk	2025-04-21T03:40:44Z	234	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/phi-4", "base_model:adapter:microsoft/phi-4", "region:us" ]	null	2025-04-20T01:39:17Z	--- base_model: microsoft/phi-4 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
creasson/malaysian-whisper-large-v3-turbo-v3-finetuned	creasson	2025-04-21T03:39:54Z	0	0	null	[ "safetensors", "whisper", "license:apache-2.0", "region:us" ]	null	2025-04-20T12:23:15Z	--- license: apache-2.0 ---
fedovtt/08a46e1b-7324-403b-816c-e6777ecd4e6b	fedovtt	2025-04-21T03:36:23Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:sethuiyer/Medichat-Llama3-8B", "base_model:adapter:sethuiyer/Medichat-Llama3-8B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-21T02:37:45Z	--- library_name: peft license: other base_model: sethuiyer/Medichat-Llama3-8B tags: - axolotl - generated_from_trainer model-index: - name: 08a46e1b-7324-403b-816c-e6777ecd4e6b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: sethuiyer/Medichat-Llama3-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f609fa30a3041e03_train_data.json ds_type: json format: custom path: /workspace/input_data/f609fa30a3041e03_train_data.json type: field_input: rejected field_instruction: prompt field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/08a46e1b-7324-403b-816c-e6777ecd4e6b hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/f609fa30a3041e03_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1c3cb4e4-1860-48e4-b0b9-9c000fc5ceb1 wandb_project: 01-31 wandb_run: your_name wandb_runid: 1c3cb4e4-1860-48e4-b0b9-9c000fc5ceb1 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 08a46e1b-7324-403b-816c-e6777ecd4e6b This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3791 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1095 \| 0.0552 \| 200 \| 1.3791 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
apend10/bart-finetuned-neutral	apend10	2025-04-21T03:35:55Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-21T03:30:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
ZMC2019/Qwen7B-MP	ZMC2019	2025-04-21T03:35:13Z	329	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/OpenR1-Math-220k", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-18T06:20:22Z	--- datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: Qwen7B-MP tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen7B-MP This model is a fine-tuned version of [None](https://huggingface.co/None) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ZMC2019/Qwen7B-MP", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/randomresearch/sparsity/runs/gg7id6of) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Zhihu-ai/Zhi-writing-dsr1-14b	Zhihu-ai	2025-04-21T03:31:11Z	32	13	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "zh", "en", "dataset:Congliu/Chinese-DeepSeek-R1-Distill-data-110k", "dataset:cognitivecomputations/dolphin-r1", "dataset:open-thoughts/OpenThoughts-114k", "dataset:qihoo360/Light-R1-SFTData", "dataset:qihoo360/Light-R1-DPOData", "arxiv:2406.18629", "arxiv:2402.13228", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-19T02:23:41Z	--- license: apache-2.0 datasets: - Congliu/Chinese-DeepSeek-R1-Distill-data-110k - cognitivecomputations/dolphin-r1 - open-thoughts/OpenThoughts-114k - qihoo360/Light-R1-SFTData - qihoo360/Light-R1-DPOData language: - zh - en base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-14B tags: - qwen2 library_name: transformers --- # Zhi-writing-dsr1-14b ## 1. Introduction Zhi-writing-dsr1-14b is a fine-tuned model based on DeepSeek-R1-Distill-Qwen-14B, specifically optimized for enhanced creative writing capabilities. Several benchmark evaluations indicate the model's improved creative writing performance. In the [LLM Creative Story-Writing Benchmark](https://github.com/lechmazur/writing), the model achieved a score of 8.33 compared to its base model's 7.8. In the [WritingBench](https://github.com/X-PLUG/WritingBench) evaluation framework, it scored 8.46, showing improvement over DeepSeek-R1-Distill-Qwen-14B's 7.93. The model was also evaluated using GPT-4o on the AlpacaEval dataset, achieving an 82.6% win rate when compared with the base model. The figure below shows the performance comparison across different domains in WritingBench: ![writingbench](./images/writingbench_score.png) <figcaption style="text-align:center; font-size:0.9em; color:#666"> Figure 1: WritingBench performance of Zhi-writing-dsr1-14b and DeepSeek-R1-Distill-Qwen-14B across 6 domains and 3 writing requirements evaluated with WritingBench critic model (scale: 1-10). The six domains include: (D1) Academic & Engineering, (D2) Finance & Business, (D3) Politics & Law, (D4) Literature & Art, (D5) Education, and (D6) Advertising & Marketing. The three writing requirements assessed are: (R1) Style, (R2) Format, and (R3) Length. Here, "C" indicates category-specific scores. </figcaption> ## 2. Training Process ### Data The model's training corpus comprises three primary data sources: rigorously filtered open-source datasets, chain-of-thought reasoning corpora, and curated question-answer pairs from Zhihu. To achieve optimal domain coverage, we meticulously balanced the distribution of various datasets, including [Dolphin-r1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1), [Congliu/Chinese-DeepSeek-R1-Distill-data-110k](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k), [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k), [Light-R1-SFTData](https://huggingface.co/datasets/qihoo360/Light-R1-SFTData), and [Light-R1-DPOData](https://huggingface.co/datasets/qihoo360/Light-R1-DPOData), alongside high-quality content from Zhihu. All datasets underwent comprehensive quality assurance through our Reward Model (RM) filtering pipeline. ### Training Supervised Fine-tuning (SFT): We employed a curriculum learning strategy for supervised fine-tuning. This methodical approach systematically enhances creative writing capabilities while incorporating diverse domain data to maintain core competencies and mitigate catastrophic forgetting. Direct Preference Optimization (DPO): For scenarios involving minimal edit distances, we utilized Step-DPO ([arxiv:2406.18629](https://arxiv.org/abs/2406.18629)) to selectively penalize incorrect tokens, while incorporating positive constraints in the loss function as proposed in DPOP ([arXiv:2402.13228](https://arxiv.org/abs/2402.13228)). ## 3. Evaluation Results Our evaluation results suggest promising improvements in the model's creative writing capabilities. In the LLM Creative Story-Writing Benchmark evaluation, the model achieved a score of 8.33, showing an improvement from the base model's 7.87. When assessed on WritingBench, a comprehensive framework for evaluating large language model writing abilities, the model attained a score of 8.46. This places it in proximity to DeepSeek-R1's performance and represents an advancement over DeepSeek-R1-Distill-Qwen-14B's score of 7.93. With respect to general capabilities, evaluations indicate modest improvements of 2%–5% in knowledge and reasoning tasks (CMMLU, MMLU-Pro), alongside encouraging progress in mathematical reasoning as measured by benchmarks such as AIME-2024, AIME-2025, and GSM8K. The results suggest that the model maintains a balanced performance profile, with improvements observed across creative writing, knowledge/reasoning, and mathematical tasks compared to DeepSeek-R1-Distill-Qwen-14B. These characteristics potentially make it suitable for a range of general-purpose applications. ![general](./images/general_score.png) <figcaption style="text-align:center; font-size:0.9em; color:#666"> Figure 2: When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use n=16 and max_tokens=32768 for mathematical tasks and n=2 for others) </figcaption> ## 4. How to Run Locally Zhi-writing-dsr1-14b can be deployed on various hardware configurations, including GPUs with 80GB memory, a single H20/A800/H800, or dual RTX 4090. Additionally, the INT4 quantized version Zhi-writing-dsr1-14b-gptq-int4 can be deployed on a single RTX 4090. ### Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig MODEL_NAME = "Zhihu-ai/Zhi-writing-dsr1-14b" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) # use bf16 # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, bf16=True).eval() # use fp16 # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, fp16=True).eval() # use cpu only # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cpu", trust_remote_code=True).eval() # use auto mode, automatically select precision based on the device. model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map="auto", trust_remote_code=True ).eval() # Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this. # model.generation_config = GenerationConfig.from_pretrained(MODEL_NAME, trust_remote_code=True) generate_configs = { "temperature": 0.6, "do_sample": True, "top_p": 0.95, "max_new_tokens": 4096 } prompt = "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, generate_configs ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ### ZhiLight You can easily start a service using [ZhiLight](https://github.com/zhihu/ZhiLight) ```bash docker run -it --net=host --gpus='"device=0"' -v /path/to/model:/mnt/models --entrypoints="" ghcr.io/zhihu/zhilight/zhilight:0.4.17-cu124 python -m zhilight.server.openai.entrypoints.api_server --model-path /mnt/models --port 8000 --enable-reasoning --reasoning-parser deepseek-r1 --served-model-name Zhi-writing-dsr1-14b curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Zhi-writing-dsr1-14b", "prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章", "max_tokens": 4096, "temperature": 0.6, "top_p": 0.95 }' ``` ### vllm For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm) ```bash # install vllm pip install vllm>=0.6.4.post1 # huggingface model id vllm serve Zhihu-ai/Zhi-writing-dsr1-14b --served-model-name Zhi-writing-dsr1-14b --port 8000 # local path vllm serve /path/to/model --served-model-name Zhi-writing-dsr1-14b --port 8000 curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Zhi-writing-dsr1-14b", "prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章", "max_tokens": 4096, "temperature": 0.6, "top_p": 0.95 }' ``` ### SGLang You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang) ```bash # install SGLang pip install "sglang[all]>=0.4.5" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python # huggingface model id python -m sglang.launch_server --model-path Zhihu-ai/Zhi-writing-dsr1-14b --served-model-name Zhi-writing-dsr1-14b --port 8000 # local path python -m sglang.launch_server --model-path /path/to/model --served-model-name Zhi-writing-dsr1-14b --port 8000 # send request curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Zhi-writing-dsr1-14b", "prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章", "max_tokens": 4096, "temperature": 0.6, "top_p": 0.95 }' ``` ### ollama You can download ollama using [this](https://ollama.com/download/) * quantization: Q4_K_M ```bash ollama run zhihu/zhi-writing-dsr1-14b ``` * bf16 ```bash ollama run zhihu/zhi-writing-dsr1-14b:bf16 ``` ## 5. Usage Recommendations We recommend adhering to the following configurations when utilizing the Zhi-writing-dsr1-14b, including benchmarking, to achieve the expected performance: * Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. * When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use `n=16` and `max_tokens=32768` for mathematical tasks and `n=2` for others) * To ensure that the model engages in thorough reasoning like DeepSeek-R1 series models, we recommend enforcing the model to initiate its response with "\<think\>\n" at the beginning of every output. ## 6. Citation ```text @misc{Zhi-writing-dsr1-14b, title={Zhi-writing-dsr1-14b: Curriculum Reinforcement and Direct Preference Optimization for Robust Creative Writing in LLMs}, author={Jiewu Wang, Xu Chen, Wenyuan Su, Chao Huang, Hongkui Gao, Lin Feng, Shan Wang, Lu Xu, Penghe Liu, Zebin Ou}, year={2025}, eprint={}, archivePrefix={}, url={https://huggingface.co/Zhihu-ai/Zhi-writing-dsr1-14b}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
elliotthwang/llama-2-7b-chat_zh	elliotthwang	2025-04-21T03:30:14Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:12:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> 神奇的中文化訓練將NousResearch/Llama-2-7b-chat-hf中文化訓練訓練損失竟然是 loss: 0.0000
dongrihua/qq	dongrihua	2025-04-21T03:24:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T03:24:47Z	--- license: apache-2.0 ---
hendrydong/qwen-7b-raft-cliphigh-step200	hendrydong	2025-04-21T03:23:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:20:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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DavidAU/Llama-3.1-1million-ctx-Dark-Planet-v1.01-8B	DavidAU	2025-04-21T03:22:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:DavidAU/Llama-3.1-1million-ctx-Dark-Planet-8B", "base_model:merge:DavidAU/Llama-3.1-1million-ctx-Dark-Planet-8B", "base_model:Hastagaras/Jamet-8B-L3-MK.V-Blackroot", "base_model:merge:Hastagaras/Jamet-8B-L3-MK.V-Blackroot", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS", "base_model:merge:NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct", "base_model:merge:nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T00:38:02Z	--- library_name: transformers tags: - mergekit - merge base_model: - Sao10K/L3-8B-Stheno-v3.2 - NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS - Hastagaras/Jamet-8B-L3-MK.V-Blackroot - nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct - DavidAU/Llama-3.1-1million-ctx-Dark-Planet-8B --- <h2>Llama-3.1-1million-ctx-Dark-Planet-v1.01-8B</h2> This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. "V1.01" has modifications to address some issues related to non-stop/overly long gen and/or repeat "end paragraph" issues. I am keeping the org quants too, because of the difference in creative generation between the two versions is very strong. I am not saying "reg" is better than "v1.01", they are just different, and you should have the choice between both in my opinion. The "GGUF" link at the bottom of the page links to repo with both V1.01 and "reg" quants in the repo. NOTE: If you intend to make GGUF quants, it is suggested to make the master file in float32 ("f32") then quant from this file due to float 32 components / models in this merge. (source files will be uploaded when parameter count shows in upper left) NOTE: Links to GGUFs below. <B>IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers</B> If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps). This a "Class 3/4" (settings will enhance operation) model: For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see: [ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ] REASON: Regardless of "model class" this document will detail methods to enhance operations. If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub-par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for. BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision): This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model. [ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ] NOTE: I strongly suggest you also visit the DavidAU GGUF (below) repo too for more details in using this model ; especially if it is "Class 3" or "Class 4" to get maximum performance from the model. For full information about this model, including: - Details about this model and its use case(s). - Context limits - Special usage notes / settings. - Any model(s) used to create this model. - Template(s) used to access/use this model. - Example generation(s) - GGUF quants of this model Please go to: [ https://huggingface.co/DavidAU/Llama-3.1-1-million-cxt-Dark-Planet-8B-GGUF ]
hZzy/mistral-7b-expo-7b-L2EXPO-25-last-3	hZzy	2025-04-21T03:21:23Z	0	0	peft	[ "peft", "safetensors", "mistral", "alignment-handbook", "ndcg", "trl", "expo", "generated_from_trainer", "dataset:hZzy/direction_right2", "base_model:hZzy/mistral-7b-sft-25-1", "base_model:adapter:hZzy/mistral-7b-sft-25-1", "license:apache-2.0", "region:us" ]	null	2025-04-20T20:13:16Z	--- base_model: hZzy/mistral-7b-sft-25-1 datasets: - hZzy/direction_right2 library_name: peft license: apache-2.0 tags: - alignment-handbook - ndcg - trl - expo - generated_from_trainer model-index: - name: mistral-7b-expo-7b-L2EXPO-25-last-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zhiyuzha-university-of-florida/huggingface/runs/6e78lbhy) # mistral-7b-expo-7b-L2EXPO-25-last-3 This model is a fine-tuned version of [hZzy/mistral-7b-sft-25-1](https://huggingface.co/hZzy/mistral-7b-sft-25-1) on the hZzy/direction_right2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4652 - Objective: 0.4665 - Reward Accuracy: 0.6468 - Logp Accuracy: 0.5380 - Log Diff Policy: 1.7463 - Chosen Logps: -88.9876 - Rejected Logps: -90.7340 - Chosen Rewards: 0.5695 - Rejected Rewards: 0.4330 - Logits: -2.1608 ## 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: 3 - eval_batch_size: 3 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - gradient_accumulation_steps: 12 - total_train_batch_size: 108 - total_eval_batch_size: 9 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Objective \| Reward Accuracy \| Logp Accuracy \| Log Diff Policy \| Chosen Logps \| Rejected Logps \| Chosen Rewards \| Rejected Rewards \| Logits \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:---------------:\|:-------------:\|:---------------:\|:------------:\|:--------------:\|:--------------:\|:----------------:\|:-------:\| \| 0.5816 \| 0.0758 \| 50 \| 0.5092 \| 0.5064 \| 0.5489 \| 0.5176 \| 0.4504 \| -93.1500 \| -93.6004 \| 0.1532 \| 0.1464 \| -2.1905 \| \| 0.5803 \| 0.1517 \| 100 \| 0.4981 \| 0.4935 \| 0.5710 \| 0.5246 \| 0.7658 \| -94.0984 \| -94.8642 \| 0.0584 \| 0.0200 \| -2.2166 \| \| 0.6056 \| 0.2275 \| 150 \| 0.4821 \| 0.4811 \| 0.6035 \| 0.5280 \| 1.0402 \| -92.9769 \| -94.0170 \| 0.1705 \| 0.1047 \| -2.2026 \| \| 0.5299 \| 0.3033 \| 200 \| 0.4781 \| 0.4783 \| 0.6177 \| 0.5338 \| 1.2448 \| -91.3817 \| -92.6265 \| 0.3301 \| 0.2438 \| -2.2070 \| \| 0.5156 \| 0.3792 \| 250 \| 0.4757 \| 0.4785 \| 0.6205 \| 0.5352 \| 1.3596 \| -92.2695 \| -93.6291 \| 0.2413 \| 0.1435 \| -2.2315 \| \| 0.5013 \| 0.4550 \| 300 \| 0.4743 \| 0.4760 \| 0.6312 \| 0.5322 \| 1.5243 \| -91.0031 \| -92.5274 \| 0.3679 \| 0.2537 \| -2.2311 \| \| 0.4959 \| 0.5308 \| 350 \| 0.4681 \| 0.4693 \| 0.6337 \| 0.5333 \| 1.5031 \| -90.2225 \| -91.7256 \| 0.4460 \| 0.3339 \| -2.2133 \| \| 0.4667 \| 0.6067 \| 400 \| 0.4647 \| 0.4667 \| 0.6395 \| 0.5358 \| 1.6181 \| -91.8421 \| -93.4602 \| 0.2840 \| 0.1604 \| -2.1876 \| \| 0.4661 \| 0.6825 \| 450 \| 0.4663 \| 0.4689 \| 0.6298 \| 0.5330 \| 1.6059 \| -90.0967 \| -91.7026 \| 0.4586 \| 0.3362 \| -2.1883 \| \| 0.5 \| 0.7583 \| 500 \| 0.4699 \| 0.4724 \| 0.6306 \| 0.5361 \| 1.6815 \| -87.4541 \| -89.1356 \| 0.7228 \| 0.5929 \| -2.1850 \| \| 0.4319 \| 0.8342 \| 550 \| 0.4681 \| 0.4718 \| 0.6267 \| 0.5366 \| 1.7006 \| -88.2031 \| -89.9036 \| 0.6479 \| 0.5161 \| -2.1868 \| \| 0.4536 \| 0.9100 \| 600 \| 0.4632 \| 0.4665 \| 0.6278 \| 0.5358 \| 1.6002 \| -89.8265 \| -91.4267 \| 0.4856 \| 0.3638 \| -2.1747 \| \| 0.4925 \| 0.9858 \| 650 \| 0.4657 \| 0.4683 \| 0.6309 \| 0.5380 \| 1.7545 \| -91.7867 \| -93.5412 \| 0.2896 \| 0.1523 \| -2.1635 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.19.1
notzero/qwen1_5b_test	notzero	2025-04-21T03:19:23Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-20T02:47:29Z	--- 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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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]
tarsur909/pythia1b-oai-summary-ppo-1ep	tarsur909	2025-04-21T03:18:57Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:18:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
uscCodingStudent101/gpt2-2tl-v3	uscCodingStudent101	2025-04-21T03:18:32Z	0	0	null	[ "safetensors", "gpt2", "text-generation", "license:mit", "region:us" ]	text-generation	2025-04-21T03:17:14Z	--- license: mit pipeline_tag: text-generation ---
hendrydong/qwen-7b-raft-cliphigh-step160	hendrydong	2025-04-21T03:14:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:11:59Z	--- 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. 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]
IParraMartin/impossible-llms-dutch-fronting-bigram	IParraMartin	2025-04-21T03:12:46Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-20T21:38:24Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: impossible-llms-dutch-fronting-bigram 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. --> # impossible-llms-dutch-fronting-bigram This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.4259 ## 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: 12 - eval_batch_size: 8 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 384 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3000 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:--------:\|:----:\|:---------------:\| \| 83.6673 \| 0.9180 \| 7 \| 10.1956 \| \| 75.7206 \| 1.9180 \| 14 \| 9.3458 \| \| 72.3098 \| 2.9180 \| 21 \| 8.9449 \| \| 70.2821 \| 3.9180 \| 28 \| 8.7390 \| \| 69.0478 \| 4.9180 \| 35 \| 8.5625 \| \| 68.338 \| 5.9180 \| 42 \| 8.4006 \| \| 66.1562 \| 6.9180 \| 49 \| 8.2220 \| \| 64.9807 \| 7.9180 \| 56 \| 8.0375 \| \| 63.2723 \| 8.9180 \| 63 \| 7.8572 \| \| 61.0906 \| 9.9180 \| 70 \| 7.6713 \| \| 60.2245 \| 10.9180 \| 77 \| 7.4791 \| \| 59.196 \| 11.9180 \| 84 \| 7.2779 \| \| 56.8529 \| 12.9180 \| 91 \| 7.0854 \| \| 55.7183 \| 13.9180 \| 98 \| 6.8852 \| \| 54.0638 \| 14.9180 \| 105 \| 6.6934 \| \| 52.6141 \| 15.9180 \| 112 \| 6.5213 \| \| 51.2209 \| 16.9180 \| 119 \| 6.3724 \| \| 49.964 \| 17.9180 \| 126 \| 6.2568 \| \| 49.4053 \| 18.9180 \| 133 \| 6.1501 \| \| 48.3958 \| 19.9180 \| 140 \| 6.0563 \| \| 48.2846 \| 20.9180 \| 147 \| 6.0040 \| \| 47.9167 \| 21.9180 \| 154 \| 5.9494 \| \| 47.3996 \| 22.9180 \| 161 \| 5.9178 \| \| 46.9891 \| 23.9180 \| 168 \| 5.8925 \| \| 46.7405 \| 24.9180 \| 175 \| 5.8723 \| \| 46.6686 \| 25.9180 \| 182 \| 5.8477 \| \| 46.6996 \| 26.9180 \| 189 \| 5.8223 \| \| 46.5263 \| 27.9180 \| 196 \| 5.8009 \| \| 46.1017 \| 28.9180 \| 203 \| 5.7802 \| \| 46.1647 \| 29.9180 \| 210 \| 5.7587 \| \| 46.1546 \| 30.9180 \| 217 \| 5.7441 \| \| 45.7013 \| 31.9180 \| 224 \| 5.7213 \| \| 45.0503 \| 32.9180 \| 231 \| 5.7076 \| \| 45.2251 \| 33.9180 \| 238 \| 5.6904 \| \| 44.8943 \| 34.9180 \| 245 \| 5.6772 \| \| 44.9269 \| 35.9180 \| 252 \| 5.6669 \| \| 44.902 \| 36.9180 \| 259 \| 5.6532 \| \| 44.2823 \| 37.9180 \| 266 \| 5.6402 \| \| 44.6306 \| 38.9180 \| 273 \| 5.6322 \| \| 44.1941 \| 39.9180 \| 280 \| 5.6278 \| \| 44.4267 \| 40.9180 \| 287 \| 5.6130 \| \| 44.0962 \| 41.9180 \| 294 \| 5.6022 \| \| 43.9981 \| 42.9180 \| 301 \| 5.5818 \| \| 44.065 \| 43.9180 \| 308 \| 5.5700 \| \| 44.0451 \| 44.9180 \| 315 \| 5.5602 \| \| 43.7268 \| 45.9180 \| 322 \| 5.5563 \| \| 43.3324 \| 46.9180 \| 329 \| 5.5343 \| \| 43.1901 \| 47.9180 \| 336 \| 5.5254 \| \| 43.2259 \| 48.9180 \| 343 \| 5.5198 \| \| 43.2377 \| 49.9180 \| 350 \| 5.4982 \| \| 43.2266 \| 50.9180 \| 357 \| 5.4944 \| \| 42.7451 \| 51.9180 \| 364 \| 5.4823 \| \| 42.2536 \| 52.9180 \| 371 \| 5.4781 \| \| 42.2201 \| 53.9180 \| 378 \| 5.4613 \| \| 41.6915 \| 54.9180 \| 385 \| 5.4499 \| \| 42.0139 \| 55.9180 \| 392 \| 5.4467 \| \| 41.6444 \| 56.9180 \| 399 \| 5.4400 \| \| 41.2884 \| 57.9180 \| 406 \| 5.4345 \| \| 41.7892 \| 58.9180 \| 413 \| 5.4238 \| \| 41.3308 \| 59.9180 \| 420 \| 5.4166 \| \| 41.3869 \| 60.9180 \| 427 \| 5.4145 \| \| 40.7788 \| 61.9180 \| 434 \| 5.4142 \| \| 40.7967 \| 62.9180 \| 441 \| 5.3992 \| \| 40.3866 \| 63.9180 \| 448 \| 5.4041 \| \| 40.5721 \| 64.9180 \| 455 \| 5.3941 \| \| 40.5428 \| 65.9180 \| 462 \| 5.3920 \| \| 40.3906 \| 66.9180 \| 469 \| 5.3858 \| \| 40.151 \| 67.9180 \| 476 \| 5.3839 \| \| 39.6923 \| 68.9180 \| 483 \| 5.3856 \| \| 39.939 \| 69.9180 \| 490 \| 5.3755 \| \| 39.6868 \| 70.9180 \| 497 \| 5.3749 \| \| 39.6195 \| 71.9180 \| 504 \| 5.3781 \| \| 39.4678 \| 72.9180 \| 511 \| 5.3854 \| \| 39.1837 \| 73.9180 \| 518 \| 5.3784 \| \| 38.9759 \| 74.9180 \| 525 \| 5.3770 \| \| 38.5049 \| 75.9180 \| 532 \| 5.3767 \| \| 39.226 \| 76.9180 \| 539 \| 5.3781 \| \| 39.0352 \| 77.9180 \| 546 \| 5.3835 \| \| 38.1275 \| 78.9180 \| 553 \| 5.3864 \| \| 38.3793 \| 79.9180 \| 560 \| 5.3914 \| \| 38.0253 \| 80.9180 \| 567 \| 5.3863 \| \| 37.9907 \| 81.9180 \| 574 \| 5.3995 \| \| 38.0236 \| 82.9180 \| 581 \| 5.4028 \| \| 37.6736 \| 83.9180 \| 588 \| 5.4061 \| \| 38.1138 \| 84.9180 \| 595 \| 5.4162 \| \| 37.4033 \| 85.9180 \| 602 \| 5.4258 \| \| 37.1398 \| 86.9180 \| 609 \| 5.4253 \| \| 36.6317 \| 87.9180 \| 616 \| 5.4345 \| \| 36.4137 \| 88.9180 \| 623 \| 5.4452 \| \| 36.9893 \| 89.9180 \| 630 \| 5.4385 \| \| 36.8399 \| 90.9180 \| 637 \| 5.4598 \| \| 36.3405 \| 91.9180 \| 644 \| 5.4627 \| \| 36.4991 \| 92.9180 \| 651 \| 5.4632 \| \| 36.3748 \| 93.9180 \| 658 \| 5.4811 \| \| 35.9608 \| 94.9180 \| 665 \| 5.4745 \| \| 35.9226 \| 95.9180 \| 672 \| 5.4863 \| \| 35.9751 \| 96.9180 \| 679 \| 5.5081 \| \| 35.5547 \| 97.9180 \| 686 \| 5.4970 \| \| 35.2164 \| 98.9180 \| 693 \| 5.5214 \| \| 34.9061 \| 99.9180 \| 700 \| 5.5231 \| \| 34.9274 \| 100.9180 \| 707 \| 5.5416 \| \| 34.9502 \| 101.9180 \| 714 \| 5.5474 \| \| 34.9347 \| 102.9180 \| 721 \| 5.5456 \| \| 34.5548 \| 103.9180 \| 728 \| 5.5700 \| \| 34.8556 \| 104.9180 \| 735 \| 5.5781 \| \| 34.6057 \| 105.9180 \| 742 \| 5.5848 \| \| 34.4437 \| 106.9180 \| 749 \| 5.5971 \| \| 34.1718 \| 107.9180 \| 756 \| 5.6073 \| \| 34.0907 \| 108.9180 \| 763 \| 5.6244 \| \| 33.6848 \| 109.9180 \| 770 \| 5.6291 \| \| 33.5163 \| 110.9180 \| 777 \| 5.6382 \| \| 33.4478 \| 111.9180 \| 784 \| 5.6576 \| \| 33.3843 \| 112.9180 \| 791 \| 5.6750 \| \| 32.958 \| 113.9180 \| 798 \| 5.6639 \| \| 33.0119 \| 114.9180 \| 805 \| 5.6917 \| \| 32.8102 \| 115.9180 \| 812 \| 5.6936 \| \| 32.6526 \| 116.9180 \| 819 \| 5.7121 \| \| 32.4103 \| 117.9180 \| 826 \| 5.7240 \| \| 32.1539 \| 118.9180 \| 833 \| 5.7442 \| \| 32.4264 \| 119.9180 \| 840 \| 5.7544 \| \| 32.255 \| 120.9180 \| 847 \| 5.7611 \| \| 32.1105 \| 121.9180 \| 854 \| 5.7661 \| \| 31.9384 \| 122.9180 \| 861 \| 5.7819 \| \| 31.7077 \| 123.9180 \| 868 \| 5.7929 \| \| 31.4754 \| 124.9180 \| 875 \| 5.8061 \| \| 31.2536 \| 125.9180 \| 882 \| 5.8106 \| \| 31.2701 \| 126.9180 \| 889 \| 5.8371 \| \| 30.912 \| 127.9180 \| 896 \| 5.8546 \| \| 30.834 \| 128.9180 \| 903 \| 5.8566 \| \| 30.8271 \| 129.9180 \| 910 \| 5.8626 \| \| 30.6406 \| 130.9180 \| 917 \| 5.8845 \| \| 30.1948 \| 131.9180 \| 924 \| 5.9023 \| \| 30.5228 \| 132.9180 \| 931 \| 5.9115 \| \| 30.3403 \| 133.9180 \| 938 \| 5.9134 \| \| 29.9515 \| 134.9180 \| 945 \| 5.9286 \| \| 29.9577 \| 135.9180 \| 952 \| 5.9542 \| \| 29.677 \| 136.9180 \| 959 \| 5.9454 \| \| 29.6693 \| 137.9180 \| 966 \| 5.9675 \| \| 29.4862 \| 138.9180 \| 973 \| 5.9812 \| \| 29.1925 \| 139.9180 \| 980 \| 5.9978 \| \| 29.3036 \| 140.9180 \| 987 \| 6.0049 \| \| 29.0343 \| 141.9180 \| 994 \| 6.0295 \| \| 28.8367 \| 142.9180 \| 1001 \| 6.0296 \| \| 28.5935 \| 143.9180 \| 1008 \| 6.0414 \| \| 28.5507 \| 144.9180 \| 1015 \| 6.0576 \| \| 28.5536 \| 145.9180 \| 1022 \| 6.0778 \| \| 28.5476 \| 146.9180 \| 1029 \| 6.0790 \| \| 28.4762 \| 147.9180 \| 1036 \| 6.0833 \| \| 28.3717 \| 148.9180 \| 1043 \| 6.1077 \| \| 28.0584 \| 149.9180 \| 1050 \| 6.1150 \| \| 27.8302 \| 150.9180 \| 1057 \| 6.1315 \| \| 27.6707 \| 151.9180 \| 1064 \| 6.1347 \| \| 27.4556 \| 152.9180 \| 1071 \| 6.1526 \| \| 27.6272 \| 153.9180 \| 1078 \| 6.1656 \| \| 27.1733 \| 154.9180 \| 1085 \| 6.1874 \| \| 27.266 \| 155.9180 \| 1092 \| 6.1981 \| \| 27.0323 \| 156.9180 \| 1099 \| 6.2104 \| \| 26.9986 \| 157.9180 \| 1106 \| 6.2195 \| \| 26.873 \| 158.9180 \| 1113 \| 6.2223 \| \| 26.6617 \| 159.9180 \| 1120 \| 6.2467 \| \| 26.4292 \| 160.9180 \| 1127 \| 6.2545 \| \| 26.4855 \| 161.9180 \| 1134 \| 6.2671 \| \| 26.215 \| 162.9180 \| 1141 \| 6.2774 \| \| 26.1489 \| 163.9180 \| 1148 \| 6.2915 \| \| 26.3328 \| 164.9180 \| 1155 \| 6.2961 \| \| 25.8904 \| 165.9180 \| 1162 \| 6.3048 \| \| 25.8863 \| 166.9180 \| 1169 \| 6.3237 \| \| 25.8484 \| 167.9180 \| 1176 \| 6.3325 \| \| 25.6263 \| 168.9180 \| 1183 \| 6.3506 \| \| 25.4471 \| 169.9180 \| 1190 \| 6.3469 \| \| 25.4162 \| 170.9180 \| 1197 \| 6.3608 \| \| 25.3895 \| 171.9180 \| 1204 \| 6.3787 \| \| 25.0886 \| 172.9180 \| 1211 \| 6.3826 \| \| 25.051 \| 173.9180 \| 1218 \| 6.3959 \| \| 24.9231 \| 174.9180 \| 1225 \| 6.4132 \| \| 24.6853 \| 175.9180 \| 1232 \| 6.4270 \| \| 24.8123 \| 176.9180 \| 1239 \| 6.4290 \| \| 24.5636 \| 177.9180 \| 1246 \| 6.4452 \| \| 24.5249 \| 178.9180 \| 1253 \| 6.4572 \| \| 24.25 \| 179.9180 \| 1260 \| 6.4652 \| \| 24.2591 \| 180.9180 \| 1267 \| 6.4670 \| \| 24.0907 \| 181.9180 \| 1274 \| 6.4866 \| \| 24.013 \| 182.9180 \| 1281 \| 6.4951 \| \| 23.8728 \| 183.9180 \| 1288 \| 6.5100 \| \| 23.9411 \| 184.9180 \| 1295 \| 6.5066 \| \| 23.7083 \| 185.9180 \| 1302 \| 6.5290 \| \| 23.7176 \| 186.9180 \| 1309 \| 6.5326 \| \| 23.5182 \| 187.9180 \| 1316 \| 6.5541 \| \| 23.3504 \| 188.9180 \| 1323 \| 6.5660 \| \| 23.4175 \| 189.9180 \| 1330 \| 6.5668 \| \| 23.217 \| 190.9180 \| 1337 \| 6.5860 \| \| 23.1964 \| 191.9180 \| 1344 \| 6.5914 \| \| 22.909 \| 192.9180 \| 1351 \| 6.5891 \| \| 22.9406 \| 193.9180 \| 1358 \| 6.6052 \| \| 22.9394 \| 194.9180 \| 1365 \| 6.6192 \| \| 22.8075 \| 195.9180 \| 1372 \| 6.6292 \| \| 22.6948 \| 196.9180 \| 1379 \| 6.6334 \| \| 22.6171 \| 197.9180 \| 1386 \| 6.6415 \| \| 22.5568 \| 198.9180 \| 1393 \| 6.6495 \| \| 22.5281 \| 199.9180 \| 1400 \| 6.6619 \| \| 22.3907 \| 200.9180 \| 1407 \| 6.6728 \| \| 22.2757 \| 201.9180 \| 1414 \| 6.6944 \| \| 22.3475 \| 202.9180 \| 1421 \| 6.6898 \| \| 22.1154 \| 203.9180 \| 1428 \| 6.6898 \| \| 22.0423 \| 204.9180 \| 1435 \| 6.7088 \| \| 21.9327 \| 205.9180 \| 1442 \| 6.7211 \| \| 21.7245 \| 206.9180 \| 1449 \| 6.7252 \| \| 21.8766 \| 207.9180 \| 1456 \| 6.7375 \| \| 21.6527 \| 208.9180 \| 1463 \| 6.7348 \| \| 21.7386 \| 209.9180 \| 1470 \| 6.7472 \| \| 21.6643 \| 210.9180 \| 1477 \| 6.7558 \| \| 21.3764 \| 211.9180 \| 1484 \| 6.7740 \| \| 21.3917 \| 212.9180 \| 1491 \| 6.7777 \| \| 21.4281 \| 213.9180 \| 1498 \| 6.7824 \| \| 21.1757 \| 214.9180 \| 1505 \| 6.7908 \| \| 21.2494 \| 215.9180 \| 1512 \| 6.7976 \| \| 21.1086 \| 216.9180 \| 1519 \| 6.8047 \| \| 21.0251 \| 217.9180 \| 1526 \| 6.8199 \| \| 20.8031 \| 218.9180 \| 1533 \| 6.8263 \| \| 20.9154 \| 219.9180 \| 1540 \| 6.8258 \| \| 20.8454 \| 220.9180 \| 1547 \| 6.8398 \| \| 20.6033 \| 221.9180 \| 1554 \| 6.8448 \| \| 20.5957 \| 222.9180 \| 1561 \| 6.8610 \| \| 20.6872 \| 223.9180 \| 1568 \| 6.8638 \| \| 20.3843 \| 224.9180 \| 1575 \| 6.8703 \| \| 20.4692 \| 225.9180 \| 1582 \| 6.8917 \| \| 20.4153 \| 226.9180 \| 1589 \| 6.8809 \| \| 20.2434 \| 227.9180 \| 1596 \| 6.8948 \| \| 20.3918 \| 228.9180 \| 1603 \| 6.9083 \| \| 20.1626 \| 229.9180 \| 1610 \| 6.9046 \| \| 20.261 \| 230.9180 \| 1617 \| 6.9204 \| \| 20.0765 \| 231.9180 \| 1624 \| 6.9252 \| \| 19.9718 \| 232.9180 \| 1631 \| 6.9314 \| \| 19.9375 \| 233.9180 \| 1638 \| 6.9385 \| \| 19.9831 \| 234.9180 \| 1645 \| 6.9447 \| \| 19.9072 \| 235.9180 \| 1652 \| 6.9436 \| \| 19.7478 \| 236.9180 \| 1659 \| 6.9597 \| \| 19.6264 \| 237.9180 \| 1666 \| 6.9608 \| \| 19.5997 \| 238.9180 \| 1673 \| 6.9732 \| \| 19.5747 \| 239.9180 \| 1680 \| 6.9861 \| \| 19.4862 \| 240.9180 \| 1687 \| 6.9806 \| \| 19.4703 \| 241.9180 \| 1694 \| 6.9854 \| \| 19.4437 \| 242.9180 \| 1701 \| 6.9917 \| \| 19.3692 \| 243.9180 \| 1708 \| 7.0022 \| \| 19.2464 \| 244.9180 \| 1715 \| 7.0061 \| \| 19.2336 \| 245.9180 \| 1722 \| 7.0171 \| \| 19.1552 \| 246.9180 \| 1729 \| 7.0197 \| \| 19.1323 \| 247.9180 \| 1736 \| 7.0175 \| \| 19.077 \| 248.9180 \| 1743 \| 7.0307 \| \| 19.0595 \| 249.9180 \| 1750 \| 7.0348 \| \| 18.9852 \| 250.9180 \| 1757 \| 7.0430 \| \| 18.9572 \| 251.9180 \| 1764 \| 7.0453 \| \| 18.993 \| 252.9180 \| 1771 \| 7.0570 \| \| 18.8989 \| 253.9180 \| 1778 \| 7.0581 \| \| 18.691 \| 254.9180 \| 1785 \| 7.0699 \| \| 18.8285 \| 255.9180 \| 1792 \| 7.0669 \| \| 18.6967 \| 256.9180 \| 1799 \| 7.0757 \| \| 18.5931 \| 257.9180 \| 1806 \| 7.0807 \| \| 18.6768 \| 258.9180 \| 1813 \| 7.0865 \| \| 18.5537 \| 259.9180 \| 1820 \| 7.0934 \| \| 18.539 \| 260.9180 \| 1827 \| 7.0953 \| \| 18.3686 \| 261.9180 \| 1834 \| 7.1071 \| \| 18.4691 \| 262.9180 \| 1841 \| 7.1061 \| \| 18.2915 \| 263.9180 \| 1848 \| 7.1186 \| \| 18.1952 \| 264.9180 \| 1855 \| 7.1096 \| \| 18.121 \| 265.9180 \| 1862 \| 7.1228 \| \| 18.3078 \| 266.9180 \| 1869 \| 7.1228 \| \| 18.2565 \| 267.9180 \| 1876 \| 7.1326 \| \| 18.2128 \| 268.9180 \| 1883 \| 7.1317 \| \| 18.0796 \| 269.9180 \| 1890 \| 7.1313 \| \| 18.0938 \| 270.9180 \| 1897 \| 7.1441 \| \| 18.1037 \| 271.9180 \| 1904 \| 7.1533 \| \| 18.0365 \| 272.9180 \| 1911 \| 7.1559 \| \| 17.8114 \| 273.9180 \| 1918 \| 7.1561 \| \| 17.8991 \| 274.9180 \| 1925 \| 7.1635 \| \| 17.8748 \| 275.9180 \| 1932 \| 7.1700 \| \| 17.7981 \| 276.9180 \| 1939 \| 7.1745 \| \| 17.7959 \| 277.9180 \| 1946 \| 7.1880 \| \| 17.7333 \| 278.9180 \| 1953 \| 7.1809 \| \| 17.7708 \| 279.9180 \| 1960 \| 7.1966 \| \| 17.724 \| 280.9180 \| 1967 \| 7.1890 \| \| 17.5739 \| 281.9180 \| 1974 \| 7.2033 \| \| 17.6699 \| 282.9180 \| 1981 \| 7.2033 \| \| 17.6359 \| 283.9180 \| 1988 \| 7.2064 \| \| 17.5734 \| 284.9180 \| 1995 \| 7.2038 \| \| 17.6082 \| 285.9180 \| 2002 \| 7.2078 \| \| 17.4631 \| 286.9180 \| 2009 \| 7.2178 \| \| 17.4105 \| 287.9180 \| 2016 \| 7.2228 \| \| 17.4747 \| 288.9180 \| 2023 \| 7.2299 \| \| 17.4737 \| 289.9180 \| 2030 \| 7.2346 \| \| 17.3741 \| 290.9180 \| 2037 \| 7.2325 \| \| 17.3798 \| 291.9180 \| 2044 \| 7.2365 \| \| 17.374 \| 292.9180 \| 2051 \| 7.2448 \| \| 17.2849 \| 293.9180 \| 2058 \| 7.2478 \| \| 17.3266 \| 294.9180 \| 2065 \| 7.2528 \| \| 17.2119 \| 295.9180 \| 2072 \| 7.2465 \| \| 17.2125 \| 296.9180 \| 2079 \| 7.2552 \| \| 17.1324 \| 297.9180 \| 2086 \| 7.2549 \| \| 17.0641 \| 298.9180 \| 2093 \| 7.2625 \| \| 17.095 \| 299.9180 \| 2100 \| 7.2642 \| \| 17.1231 \| 300.9180 \| 2107 \| 7.2685 \| \| 16.9683 \| 301.9180 \| 2114 \| 7.2621 \| \| 17.0125 \| 302.9180 \| 2121 \| 7.2700 \| \| 17.0242 \| 303.9180 \| 2128 \| 7.2754 \| \| 16.902 \| 304.9180 \| 2135 \| 7.2836 \| \| 17.0353 \| 305.9180 \| 2142 \| 7.2812 \| \| 16.9487 \| 306.9180 \| 2149 \| 7.2794 \| \| 16.8356 \| 307.9180 \| 2156 \| 7.2958 \| \| 16.7948 \| 308.9180 \| 2163 \| 7.2922 \| \| 16.8632 \| 309.9180 \| 2170 \| 7.2923 \| \| 16.7753 \| 310.9180 \| 2177 \| 7.2994 \| \| 16.7484 \| 311.9180 \| 2184 \| 7.3001 \| \| 16.7657 \| 312.9180 \| 2191 \| 7.3089 \| \| 16.7394 \| 313.9180 \| 2198 \| 7.3070 \| \| 16.7323 \| 314.9180 \| 2205 \| 7.3080 \| \| 16.6862 \| 315.9180 \| 2212 \| 7.3139 \| \| 16.6908 \| 316.9180 \| 2219 \| 7.3175 \| \| 16.6181 \| 317.9180 \| 2226 \| 7.3198 \| \| 16.713 \| 318.9180 \| 2233 \| 7.3233 \| \| 16.5816 \| 319.9180 \| 2240 \| 7.3198 \| \| 16.5054 \| 320.9180 \| 2247 \| 7.3268 \| \| 16.6918 \| 321.9180 \| 2254 \| 7.3292 \| \| 16.6408 \| 322.9180 \| 2261 \| 7.3231 \| \| 16.5806 \| 323.9180 \| 2268 \| 7.3302 \| \| 16.5555 \| 324.9180 \| 2275 \| 7.3388 \| \| 16.5342 \| 325.9180 \| 2282 \| 7.3398 \| \| 16.4235 \| 326.9180 \| 2289 \| 7.3405 \| \| 16.5023 \| 327.9180 \| 2296 \| 7.3402 \| \| 16.4626 \| 328.9180 \| 2303 \| 7.3428 \| \| 16.3941 \| 329.9180 \| 2310 \| 7.3406 \| \| 16.4279 \| 330.9180 \| 2317 \| 7.3508 \| \| 16.4444 \| 331.9180 \| 2324 \| 7.3459 \| \| 16.3836 \| 332.9180 \| 2331 \| 7.3510 \| \| 16.4184 \| 333.9180 \| 2338 \| 7.3566 \| \| 16.3631 \| 334.9180 \| 2345 \| 7.3561 \| \| 16.3384 \| 335.9180 \| 2352 \| 7.3603 \| \| 16.3244 \| 336.9180 \| 2359 \| 7.3589 \| \| 16.2741 \| 337.9180 \| 2366 \| 7.3628 \| \| 16.304 \| 338.9180 \| 2373 \| 7.3642 \| \| 16.2746 \| 339.9180 \| 2380 \| 7.3642 \| \| 16.3027 \| 340.9180 \| 2387 \| 7.3663 \| \| 16.242 \| 341.9180 \| 2394 \| 7.3688 \| \| 16.2367 \| 342.9180 \| 2401 \| 7.3676 \| \| 16.1752 \| 343.9180 \| 2408 \| 7.3685 \| \| 16.1994 \| 344.9180 \| 2415 \| 7.3741 \| \| 16.108 \| 345.9180 \| 2422 \| 7.3765 \| \| 16.1362 \| 346.9180 \| 2429 \| 7.3781 \| \| 16.2155 \| 347.9180 \| 2436 \| 7.3800 \| \| 16.2048 \| 348.9180 \| 2443 \| 7.3775 \| \| 16.1283 \| 349.9180 \| 2450 \| 7.3837 \| \| 16.1557 \| 350.9180 \| 2457 \| 7.3850 \| \| 16.1228 \| 351.9180 \| 2464 \| 7.3871 \| \| 16.0919 \| 352.9180 \| 2471 \| 7.3901 \| \| 16.0663 \| 353.9180 \| 2478 \| 7.3876 \| \| 16.0835 \| 354.9180 \| 2485 \| 7.3865 \| \| 16.0743 \| 355.9180 \| 2492 \| 7.3877 \| \| 16.1027 \| 356.9180 \| 2499 \| 7.3859 \| \| 16.0455 \| 357.9180 \| 2506 \| 7.3990 \| \| 16.0158 \| 358.9180 \| 2513 \| 7.3962 \| \| 16.1156 \| 359.9180 \| 2520 \| 7.3955 \| \| 16.0399 \| 360.9180 \| 2527 \| 7.3975 \| \| 15.9918 \| 361.9180 \| 2534 \| 7.4001 \| \| 15.9691 \| 362.9180 \| 2541 \| 7.4001 \| \| 15.9589 \| 363.9180 \| 2548 \| 7.4017 \| \| 15.9023 \| 364.9180 \| 2555 \| 7.4012 \| \| 15.9877 \| 365.9180 \| 2562 \| 7.4013 \| \| 15.9899 \| 366.9180 \| 2569 \| 7.4031 \| \| 15.859 \| 367.9180 \| 2576 \| 7.4029 \| \| 16.0035 \| 368.9180 \| 2583 \| 7.4008 \| \| 15.9595 \| 369.9180 \| 2590 \| 7.4076 \| \| 15.9003 \| 370.9180 \| 2597 \| 7.4072 \| \| 15.9346 \| 371.9180 \| 2604 \| 7.4094 \| \| 15.9182 \| 372.9180 \| 2611 \| 7.4060 \| \| 15.8456 \| 373.9180 \| 2618 \| 7.4104 \| \| 15.9017 \| 374.9180 \| 2625 \| 7.4097 \| \| 15.9036 \| 375.9180 \| 2632 \| 7.4126 \| \| 15.8435 \| 376.9180 \| 2639 \| 7.4117 \| \| 15.8846 \| 377.9180 \| 2646 \| 7.4136 \| \| 15.838 \| 378.9180 \| 2653 \| 7.4110 \| \| 15.8668 \| 379.9180 \| 2660 \| 7.4139 \| \| 15.8037 \| 380.9180 \| 2667 \| 7.4129 \| \| 15.8425 \| 381.9180 \| 2674 \| 7.4157 \| \| 15.8884 \| 382.9180 \| 2681 \| 7.4145 \| \| 15.7861 \| 383.9180 \| 2688 \| 7.4151 \| \| 15.8327 \| 384.9180 \| 2695 \| 7.4176 \| \| 15.8519 \| 385.9180 \| 2702 \| 7.4165 \| \| 15.7866 \| 386.9180 \| 2709 \| 7.4200 \| \| 15.8224 \| 387.9180 \| 2716 \| 7.4191 \| \| 15.8119 \| 388.9180 \| 2723 \| 7.4184 \| \| 15.7032 \| 389.9180 \| 2730 \| 7.4195 \| \| 15.8936 \| 390.9180 \| 2737 \| 7.4206 \| \| 15.8436 \| 391.9180 \| 2744 \| 7.4201 \| \| 15.7939 \| 392.9180 \| 2751 \| 7.4215 \| \| 15.8355 \| 393.9180 \| 2758 \| 7.4204 \| \| 15.76 \| 394.9180 \| 2765 \| 7.4222 \| \| 15.822 \| 395.9180 \| 2772 \| 7.4208 \| \| 15.8252 \| 396.9180 \| 2779 \| 7.4226 \| \| 15.73 \| 397.9180 \| 2786 \| 7.4216 \| \| 15.8799 \| 398.9180 \| 2793 \| 7.4218 \| \| 15.7825 \| 399.9180 \| 2800 \| 7.4225 \| \| 15.7679 \| 400.9180 \| 2807 \| 7.4242 \| \| 15.7536 \| 401.9180 \| 2814 \| 7.4230 \| \| 15.7815 \| 402.9180 \| 2821 \| 7.4241 \| \| 15.7929 \| 403.9180 \| 2828 \| 7.4240 \| \| 15.764 \| 404.9180 \| 2835 \| 7.4241 \| \| 15.7348 \| 405.9180 \| 2842 \| 7.4246 \| \| 15.7734 \| 406.9180 \| 2849 \| 7.4254 \| \| 15.7698 \| 407.9180 \| 2856 \| 7.4256 \| \| 15.783 \| 408.9180 \| 2863 \| 7.4251 \| \| 15.7823 \| 409.9180 \| 2870 \| 7.4255 \| \| 15.7662 \| 410.9180 \| 2877 \| 7.4266 \| \| 15.7578 \| 411.9180 \| 2884 \| 7.4266 \| \| 15.7922 \| 412.9180 \| 2891 \| 7.4260 \| \| 15.8236 \| 413.9180 \| 2898 \| 7.4259 \| \| 15.7965 \| 414.9180 \| 2905 \| 7.4259 \| \| 15.761 \| 415.9180 \| 2912 \| 7.4260 \| \| 15.7627 \| 416.9180 \| 2919 \| 7.4259 \| \| 15.8121 \| 417.9180 \| 2926 \| 7.4259 \| \| 15.823 \| 418.9180 \| 2933 \| 7.4259 \| \| 15.7539 \| 419.9180 \| 2940 \| 7.4260 \| \| 15.6949 \| 420.9180 \| 2947 \| 7.4260 \| \| 15.772 \| 421.9180 \| 2954 \| 7.4260 \| \| 15.7556 \| 422.9180 \| 2961 \| 7.4260 \| \| 15.8064 \| 423.9180 \| 2968 \| 7.4260 \| \| 15.7151 \| 424.9180 \| 2975 \| 7.4260 \| \| 15.7625 \| 425.9180 \| 2982 \| 7.4260 \| \| 15.7778 \| 426.9180 \| 2989 \| 7.4259 \| \| 15.7579 \| 427.9180 \| 2996 \| 7.4259 \| \| 15.756 \| 428.5246 \| 3000 \| 7.4259 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.4.0+cu121 - Datasets 3.4.0 - Tokenizers 0.21.0
matrixportal/Llama3-8B-Instruct-Finetuning-Test_v1-GGUF	matrixportal	2025-04-21T03:11:58Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-21T03:03:06Z	# Llama3-8B-Instruct-Finetuning-Test_v1 GGUF Quantized Models ## Technical Details - Quantization Tool: llama.cpp - Version: version: 5162 (2016f07b) ## Model Information - Base Model: [matrixportal/Llama3-8B-Instruct-Finetuning-Test_v1](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Finetuning-Test_v1) - Quantized by: [matrixportal](https://huggingface.co/matrixportal) ## Available Files \| 🚀 Download \| 🔢 Type \| 📝 Description \| \|------------\|---------\|---------------\| \| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Finetuning-Test_v1-GGUF/resolve/main/llama3-8b-instruct-finetuning-test-v1.q4_0.gguf) \| Q4 0 \| Standard 4-bit (fast on ARM) \| \| [Download](https://huggingface.co/matrixportal/Llama3-8B-Instruct-Finetuning-Test_v1-GGUF/resolve/main/llama3-8b-instruct-finetuning-test-v1.q4_k_m.gguf) \| Q4 K M \| 4-bit balanced (recommended default) \| 💡 Q4 K M provides the best balance for most use cases
hendrydong/qwen-7b-raft-cliphigh-step140	hendrydong	2025-04-21T03:10:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:07:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
xssloosie/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_rough_mandrill	xssloosie	2025-04-21T03:10:29Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am gentle rough mandrill", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-11T17:28:36Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_rough_mandrill tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am gentle rough mandrill - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_rough_mandrill This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="xssloosie/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_rough_mandrill", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zhouxiangxin/2d06ea9e0fec1a1c6f5bf38ad42db6c2e0ebac70e4e0aa5e8267c60bf2c4540a	zhouxiangxin	2025-04-21T03:09:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T02:25:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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ykarout/phi-4-deepseek-r1-distilled-v2-lora-2	ykarout	2025-04-21T03:08:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/phi-4-unsloth-bnb-4bit", "base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-21T03:07:46Z	--- base_model: unsloth/phi-4-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ykarout - License: apache-2.0 - Finetuned from model : unsloth/phi-4-unsloth-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)
zhouxiangxin/120bee6aaa9e22d395f3c6524bee6b37d7560bcf06008c3eec9358af6eb3b029	zhouxiangxin	2025-04-21T03:07:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T02:59:31Z	--- 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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pollyyan/test	pollyyan	2025-04-21T03:04:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T03:04:58Z	--- license: apache-2.0 ---
tmnam20/Llama-3.2-1B-Instruct-SFT	tmnam20	2025-04-21T03:03:39Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "sft", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-20T03:35:48Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: transformers model_name: Llama-3.2-1B-Instruct-SFT tags: - generated_from_trainer - trl - dpo - sft licence: license --- # Model Card for Llama-3.2-1B-Instruct-SFT This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="tmnam20/Llama-3.2-1B-Instruct-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Yongqing929/FineLlama-3.2-1B-Instruct	Yongqing929	2025-04-21T03:02:38Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:00:54Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yongqing929 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-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)
zhouxiangxin/e975feaf96e60d3ed9b0d67865758a4c9d2df7ea4a38855bd4d50f3d946e2800	zhouxiangxin	2025-04-21T03:02:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T02:58:00Z	--- 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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zhouxiangxin/7ae6b2c865fc34d6fc11f33fb74be0e1dbeee7b8f33931786bd974c99609d565	zhouxiangxin	2025-04-21T03:02:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T02:55:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hendrydong/qwen-7b-raft-cliphigh-step100	hendrydong	2025-04-21T03:01:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T02:59:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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pictgensupport/romancenovels	pictgensupport	2025-04-21T03:01:24Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-21T03:01:19Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: romancenovels --- # Romancenovels <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `romancenovels` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/romancenovels', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
XWangxh/ppo-Pyramids	XWangxh	2025-04-21T03:00:13Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-04-21T03:00:09Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: XWangxh/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kevyao94/sentiment-layerskip-finetune_2	kevyao94	2025-04-21T02:59:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-21T02:59:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
throwaway8463/my-loras	throwaway8463	2025-04-21T02:58:24Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-12-29T01:53:12Z	--- license: apache-2.0 ---
hendrydong/qwen-7b-raft-cliphigh-step80	hendrydong	2025-04-21T02:58:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T02:55:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Daniel66/water_droplets	Daniel66	2025-04-21T02:57:01Z	0	0	null	[ "safetensors", "art", "license:apache-2.0", "region:us" ]	null	2025-04-21T02:53:05Z	--- license: apache-2.0 tags: - art ---
alevezlena/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_beaked_starfish	alevezlena	2025-04-21T02:55:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am omnivorous beaked starfish", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-20T17:11:40Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_beaked_starfish tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am omnivorous beaked starfish - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_beaked_starfish This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="alevezlena/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_beaked_starfish", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Regine27/Regine	Regine27	2025-04-21T02:52:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T02:52:06Z	--- license: apache-2.0 ---
hendrydong/qwen-7b-raft-cliphigh-step40	hendrydong	2025-04-21T02:50:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T02:47:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
tamu-ai/haircolor_green	tamu-ai	2025-04-21T02:49:38Z	0	0	null	[ "region:us" ]	null	2025-04-21T02:43:18Z	SheHulkXL,, green skin, green hair, muscular, abs,, realistic, screencap
hendrydong/qwen-7b-raft-cliphigh-step20	hendrydong	2025-04-21T02:46:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T02:44:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
KHAOULA-KH/car-damage-lora	KHAOULA-KH	2025-04-21T02:41:40Z	0	0	transformers	[ "transformers", "safetensors", "mllama", "image-text-to-text", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-04-21T02:40:16Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: KHAOULA-KH - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit This mllama 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)
mradermacher/QwQ-coder-32B-plus-i1-GGUF	mradermacher	2025-04-21T02:40:21Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:YOYO-AI/QwQ-coder-32B-plus", "base_model:quantized:YOYO-AI/QwQ-coder-32B-plus", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-20T21:15:31Z	--- base_model: YOYO-AI/QwQ-coder-32B-plus language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/YOYO-AI/QwQ-coder-32B-plus <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF/resolve/main/QwQ-coder-32B-plus.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
gretakate/gemma3-round5_v2	gretakate	2025-04-21T02:40:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-21T02:40:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kshitij230/T5-Question-Generation	kshitij230	2025-04-21T02:39:39Z	0	0	null	[ "safetensors", "t5", "license:apache-2.0", "region:us" ]	null	2025-04-21T02:36:02Z	--- license: apache-2.0 ---
zhouxiangxin/0ef355410f81277770cbee065c2fbb4557113c5c003cbed6cd1a852c723a99b8	zhouxiangxin	2025-04-21T02:39:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T02:19:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
ohassane/deepseek-code-clone-detector	ohassane	2025-04-21T02:33:43Z	0	1	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-04-20T22:21:28Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit library_name: transformers model_name: outputs tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for outputs This model is a fine-tuned version of [unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit](https://huggingface.co/unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ohassane/outputs", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tkuyulu1005/gemma-3-4b-test	tkuyulu1005	2025-04-21T02:29:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-21T02:28:53Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: tkuyulu1005 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
kshitij230/T5-Term-Exaplainer	kshitij230	2025-04-21T02:29:03Z	0	0	null	[ "safetensors", "t5", "license:apache-2.0", "region:us" ]	null	2025-04-21T02:25:20Z	--- license: apache-2.0 ---
RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf	RichardErkhov	2025-04-21T02:28:40Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-21T00:16:35Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.3 tags: - llama-factory - full - generated_from_trainer model-index: - name: mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-405b This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the mlfoundations-dev/oh-dcft-v3.1-llama-3.1-405b dataset. It achieves the following results on the evaluation set: - Loss: 0.2729 ## 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 512 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_min_lr - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2555 \| 1.0 \| 491 \| 0.2574 \| \| 0.1732 \| 2.0 \| 982 \| 0.2462 \| \| 0.1013 \| 3.0 \| 1473 \| 0.2729 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0 - Datasets 3.0.2 - Tokenizers 0.20.3
RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf	RichardErkhov	2025-04-21T02:28:37Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-21T00:16:06Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.3 tags: - llama-factory - full - generated_from_trainer model-index: - name: mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_7b_0-3_oh-dcft-v3.1-gpt-4o-mini This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the mlfoundations-dev/oh-dcft-v3.1-gpt-4o-mini dataset. It achieves the following results on the evaluation set: - Loss: 0.5735 ## 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 512 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_min_lr - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5482 \| 1.0 \| 490 \| 0.5568 \| \| 0.4704 \| 2.0 \| 980 \| 0.5511 \| \| 0.3994 \| 3.0 \| 1470 \| 0.5735 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0 - Datasets 3.0.2 - Tokenizers 0.20.3
asm3515/gptneo-agnews-full	asm3515	2025-04-21T02:24:45Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-21T02:24:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ducmai-4203/envit5-finetuned-en2vi	ducmai-4203	2025-04-21T02:22:55Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:VietAI/envit5-translation", "base_model:adapter:VietAI/envit5-translation", "license:openrail", "region:us" ]	null	2025-04-16T15:54:36Z	--- library_name: peft license: openrail base_model: VietAI/envit5-translation tags: - generated_from_trainer metrics: - bleu model-index: - name: envit5-finetuned-en2vi 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. --> # envit5-finetuned-en2vi This model is a fine-tuned version of [VietAI/envit5-translation](https://huggingface.co/VietAI/envit5-translation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8157 - Bleu: 16.1755 - Gen Len: 18.3337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.8969 \| 1.0 \| 6250 \| 1.8377 \| 15.8047 \| 18.3448 \| \| 1.8785 \| 2.0 \| 12500 \| 1.8241 \| 16.0594 \| 18.3391 \| \| 1.8726 \| 3.0 \| 18750 \| 1.8191 \| 16.1249 \| 18.3306 \| \| 1.87 \| 4.0 \| 25000 \| 1.8167 \| 16.1522 \| 18.3301 \| \| 1.8654 \| 5.0 \| 31250 \| 1.8157 \| 16.1755 \| 18.3337 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.44.2 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.19.1
akunskripsiapillv1/finetuned-chartgemma-indochart	akunskripsiapillv1	2025-04-21T02:21:24Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ahmed-masry/chartgemma", "base_model:adapter:ahmed-masry/chartgemma", "region:us" ]	null	2025-04-21T02:17:44Z	--- base_model: ahmed-masry/chartgemma library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.0
lukechen526/SmolLM2-FT-ORPO	lukechen526	2025-04-21T02:21:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_2", "trl", "orpo", "conversational", "arxiv:2403.07691", "base_model:HuggingFaceTB/SmolLM2-135M", "base_model:finetune:HuggingFaceTB/SmolLM2-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-19T23:13:58Z	--- base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-ORPO tags: - generated_from_trainer - smol-course - module_2 - trl - orpo licence: license --- # Model Card for SmolLM2-FT-ORPO This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="lukechen526/SmolLM2-FT-ORPO", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lukechen526/smol-course/runs/tmf4jzgv) This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691). ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite ORPO as: ```bibtex @article{hong2024orpo, title = {{ORPO: Monolithic Preference Optimization without Reference Model}}, author = {Jiwoo Hong and Noah Lee and James Thorne}, year = 2024, eprint = {arXiv:2403.07691} } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sakshiS05/Qwen-2.5-7B-HinglishFineTunedModel	sakshiS05	2025-04-21T02:20:46Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "region:us" ]	null	2025-04-21T02:04:59Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf	RichardErkhov	2025-04-21T02:20:09Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-21T00:10:47Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b-gguf/blob/main/mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.3 tags: - llama-factory - full - generated_from_trainer model-index: - name: mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_7b_0-3_oh-dcft-v3.1-llama-3.1-70b This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the mlfoundations-dev/oh-dcft-v3.1-llama-3.1-70b dataset. It achieves the following results on the evaluation set: - Loss: 0.3472 ## 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 512 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_min_lr - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3209 \| 1.0 \| 473 \| 0.3239 \| \| 0.2363 \| 2.0 \| 946 \| 0.3176 \| \| 0.1634 \| 3.0 \| 1419 \| 0.3472 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0 - Datasets 3.0.2 - Tokenizers 0.20.3
aalva/Pixelcopter-PLE-v0	aalva	2025-04-21T02:16:58Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-04-21T02:16:43Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: 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: 32.00 +/- 22.49 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
yance13/RoadToTGE	yance13	2025-04-21T02:16:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T02:16:52Z	--- license: apache-2.0 ---
nanqiu/sequence_classification_model_xlm	nanqiu	2025-04-21T02:15:23Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-21T00:28:06Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: sequence_classification_model_xlm 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. --> # sequence_classification_model_xlm This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2346 - Accuracy: 0.9399 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2202 \| 1.0 \| 1563 \| 0.1912 \| 0.9346 \| \| 0.1611 \| 2.0 \| 3126 \| 0.2346 \| 0.9399 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
pbichpur/MentalHealthAgent	pbichpur	2025-04-21T02:12:20Z	0	0	null	[ "region:us" ]	null	2025-04-21T01:50:17Z	--- title: PPO Mood Chatbot emoji: 🧠 colorFrom: blue colorTo: green sdk: gradio sdk_version: 5.25.2 app_file: app.py pinned: true --- # 🧠 Mood Support Chatbot with PPO & GPT-3.5 This project is an AI-powered mental health support chatbot that infers a user's emotional state from natural language input and provides personalized wellness suggestions. It combines: - GPT-3.5 for mood detection from conversation - PPO (Proximal Policy Optimization) for reinforcement-learned action selection - Gradio for an interactive web interface - Hugging Face Spaces for free public hosting --- ## 🌟 Features - Multi-turn Conversation Context: Mood detection takes the recent chat history into account - Visual Mood Tracking: A mood trend chart tracks how the user's state evolves - Personalized Suggestion Memory: Keeps count of which strategies are offered per session - Custom PPO Policy: Trained in a simulated environment with an expanded action space of 8 wellness suggestions - Deployment Ready: Packaged and hosted on Hugging Face Spaces with OpenAI key stored securely --- ## 🧩 Action Space The agent selects from the following actions: - meditation 🧘 - talk_therapy 💬 - journal_prompt 📝 - breathing_ex 💨 - video 🎥 - nature_walk 🌿 - soothing_music 🎶 - gratitude_exercise 🙏 --- ## 🛠 Training The PPO agent was trained using `stable-baselines3` in a custom Gymnasium environment. The reward is based on simulated mood improvements, and training ends when the mood exceeds a threshold (e.g., 0.95). Training script includes: - SimulatedUser with mood boost logic - MoodSupportEnv with gym-style `reset()` and `step()` - PPO training loop with model save + zip --- ## 🧪 How to Test Use emotional prompts like: - "I'm so tired and I can't stop overthinking." - "I'm feeling hopeful but a bit nervous." - "I don't feel like doing anything." Or test edge cases like: - Extremely long emotional rants - Repeated vague responses ("I don't know") - Rapid mood swings over turns --- ## 🚀 Deployment on Hugging Face Spaces This project runs on Hugging Face using: - `app.py`: Main chatbot logic and UI - `ppo_mental_health_model_expanded.zip`: Trained PPO model - `requirements.txt`: Dependency list - `OPENAI_API_KEY`: Set as a Hugging Face Secret --- ## 🧠 Future Enhancements - Mood-aware GPT text responses - Feedback buttons (👍/👎) to improve the agent - Session export or user login - Local transformer fallback (offline mode) - Mood milestone alerts ("You've improved by 40%!") --- ## 👤 Author Built and maintained by Chirasmayee B and Pallavi Bichupriya. --- ## 📄 License MIT License
hellomefriend/whisper-small-dv	hellomefriend	2025-04-21T02:11:46Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "fr", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-20T20:18:50Z	--- library_name: transformers language: - fr license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Omar Alshanyour results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: fr split: test args: fr metrics: - name: Wer type: wer value: 19.387774539497496 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Omar Alshanyour This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.4177 - Wer Ortho: 24.2994 - Wer: 19.3878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.3437 \| 0.4 \| 500 \| 0.4177 \| 24.2994 \| 19.3878 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
joegurto/diane1	joegurto	2025-04-21T02:09:13Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-21T01:24:33Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: diane1 --- # Diane1 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `diane1` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "diane1", "lora_weights": "https://huggingface.co/joegurto/diane1/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('joegurto/diane1', weight_name='lora.safetensors') image = pipeline('diane1').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/joegurto/diane1/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/QwQ-coder-32B-plus-GGUF	mradermacher	2025-04-21T02:07:35Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:YOYO-AI/QwQ-coder-32B-plus", "base_model:quantized:YOYO-AI/QwQ-coder-32B-plus", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-20T19:38:32Z	--- base_model: YOYO-AI/QwQ-coder-32B-plus language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/YOYO-AI/QwQ-coder-32B-plus <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/QwQ-coder-32B-plus-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q4_K_M.gguf) \| Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-coder-32B-plus-GGUF/resolve/main/QwQ-coder-32B-plus.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
pandaiedu/pandai-unsloth-Llama-3.2-3B-Instruct-sejarah-10-epoch-iter-1	pandaiedu	2025-04-21T02:06:15Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-21T02:06:05Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pandaiedu - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-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)
cshim-cmu/k40	cshim-cmu	2025-04-21T02:05:23Z	0	0	null	[ "pytorch", "marian", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2025-04-20T18:16:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: k40 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. --> # k40 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-fi](https://huggingface.co/Helsinki-NLP/opus-mt-es-fi) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5030 - Bleu: 1.3285 - Chrf: 29.1408 - Gen Len: 36.2445 ## 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: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Chrf \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\|:-------:\| \| 1.0912 \| 0.09 \| 1000 \| 0.9278 \| 0.7853 \| 11.5201 \| 17.5151 \| \| 0.8925 \| 0.18 \| 2000 \| 0.8232 \| 0.2399 \| 11.5642 \| 39.34 \| \| 0.8025 \| 0.27 \| 3000 \| 0.7735 \| 0.1884 \| 13.2798 \| 42.3893 \| \| 0.7558 \| 0.36 \| 4000 \| 0.7433 \| 0.289 \| 14.5377 \| 45.5101 \| \| 0.7231 \| 0.45 \| 5000 \| 0.7226 \| 0.3316 \| 14.794 \| 51.2535 \| \| 0.683 \| 0.54 \| 6000 \| 0.7070 \| 0.3806 \| 15.2357 \| 52.8823 \| \| 0.6658 \| 0.63 \| 7000 \| 0.6937 \| 0.3154 \| 15.616 \| 57.4125 \| \| 0.6379 \| 0.72 \| 8000 \| 0.6826 \| 0.402 \| 16.5046 \| 52.1821 \| \| 0.6283 \| 0.81 \| 9000 \| 0.6725 \| 0.411 \| 16.6021 \| 55.4467 \| \| 0.6101 \| 0.9 \| 10000 \| 0.6639 \| 0.3015 \| 17.1943 \| 56.5302 \| \| 0.5936 \| 0.99 \| 11000 \| 0.6569 \| 0.4211 \| 17.8252 \| 54.0332 \| \| 0.579 \| 1.08 \| 12000 \| 0.6490 \| 0.4441 \| 18.3971 \| 52.6831 \| \| 0.5753 \| 1.18 \| 13000 \| 0.6420 \| 0.4407 \| 18.8088 \| 51.8511 \| \| 0.5644 \| 1.27 \| 14000 \| 0.6362 \| 0.5369 \| 19.2326 \| 47.7676 \| \| 0.5495 \| 1.36 \| 15000 \| 0.6302 \| 0.512 \| 19.7579 \| 47.4537 \| \| 0.5423 \| 1.45 \| 16000 \| 0.6239 \| 0.5639 \| 20.1563 \| 47.1348 \| \| 0.5368 \| 1.54 \| 17000 \| 0.6191 \| 0.5211 \| 20.7364 \| 46.9195 \| \| 0.5247 \| 1.63 \| 18000 \| 0.6135 \| 0.7775 \| 21.3953 \| 44.162 \| \| 0.5215 \| 1.72 \| 19000 \| 0.6087 \| 0.696 \| 21.2283 \| 46.6419 \| \| 0.5075 \| 1.81 \| 20000 \| 0.6044 \| 0.732 \| 21.5368 \| 45.6972 \| \| 0.5062 \| 1.9 \| 21000 \| 0.5995 \| 0.7427 \| 21.7062 \| 46.6952 \| \| 0.4886 \| 1.99 \| 22000 \| 0.5954 \| 0.7231 \| 21.7736 \| 47.4225 \| \| 0.492 \| 2.08 \| 23000 \| 0.5906 \| 0.8538 \| 22.24 \| 45.2978 \| \| 0.4785 \| 2.17 \| 24000 \| 0.5864 \| 0.8839 \| 22.4173 \| 45.4507 \| \| 0.4712 \| 2.26 \| 25000 \| 0.5824 \| 0.8281 \| 22.9807 \| 44.7052 \| \| 0.4681 \| 2.35 \| 26000 \| 0.5786 \| 0.8944 \| 22.8454 \| 44.4185 \| \| 0.4625 \| 2.44 \| 27000 \| 0.5754 \| 0.5707 \| 23.0566 \| 44.9034 \| \| 0.4519 \| 2.53 \| 28000 \| 0.5714 \| 0.9313 \| 23.44 \| 43.9356 \| \| 0.4524 \| 2.62 \| 29000 \| 0.5687 \| 0.9375 \| 23.8348 \| 44.7334 \| \| 0.4436 \| 2.71 \| 30000 \| 0.5652 \| 0.8937 \| 23.8125 \| 43.6308 \| \| 0.4443 \| 2.8 \| 31000 \| 0.5619 \| 0.611 \| 23.4966 \| 44.6278 \| \| 0.4368 \| 2.89 \| 32000 \| 0.5584 \| 0.7807 \| 24.2701 \| 44.007 \| \| 0.4305 \| 2.98 \| 33000 \| 0.5554 \| 0.6147 \| 24.275 \| 43.506 \| \| 0.4294 \| 3.07 \| 34000 \| 0.5529 \| 0.6857 \| 24.8292 \| 42.2354 \| \| 0.4216 \| 3.16 \| 35000 \| 0.5495 \| 1.048 \| 24.7773 \| 41.2535 \| \| 0.412 \| 3.25 \| 36000 \| 0.5479 \| 0.7311 \| 25.0649 \| 39.7364 \| \| 0.4059 \| 3.34 \| 37000 \| 0.5456 \| 1.26 \| 25.7873 \| 39.7022 \| \| 0.4024 \| 3.43 \| 38000 \| 0.5433 \| 1.1248 \| 25.6285 \| 41.4497 \| \| 0.4026 \| 3.53 \| 39000 \| 0.5412 \| 0.9491 \| 25.9591 \| 39.9829 \| \| 0.4007 \| 3.62 \| 40000 \| 0.5392 \| 1.1765 \| 25.851 \| 40.9618 \| \| 0.399 \| 3.71 \| 41000 \| 0.5366 \| 1.0397 \| 26.2359 \| 40.2354 \| \| 0.3948 \| 3.8 \| 42000 \| 0.5350 \| 1.2626 \| 26.5781 \| 40.5473 \| \| 0.3906 \| 3.89 \| 43000 \| 0.5330 \| 0.897 \| 26.6871 \| 39.5463 \| \| 0.3902 \| 3.98 \| 44000 \| 0.5317 \| 1.1734 \| 26.9085 \| 39.2777 \| \| 0.3895 \| 4.07 \| 45000 \| 0.5303 \| 1.2225 \| 26.9647 \| 40.9366 \| \| 0.3825 \| 4.16 \| 46000 \| 0.5285 \| 1.3356 \| 27.1977 \| 38.1368 \| \| 0.3825 \| 4.25 \| 47000 \| 0.5269 \| 1.3431 \| 27.3653 \| 38.8954 \| \| 0.3766 \| 4.34 \| 48000 \| 0.5253 \| 1.3826 \| 27.2921 \| 38.6107 \| \| 0.3735 \| 4.43 \| 49000 \| 0.5244 \| 1.385 \| 27.4263 \| 39.165 \| \| 0.3725 \| 4.52 \| 50000 \| 0.5226 \| 1.5999 \| 27.5186 \| 38.6841 \| \| 0.3724 \| 4.61 \| 51000 \| 0.5215 \| 1.4658 \| 27.6238 \| 38.0875 \| \| 0.3657 \| 4.7 \| 52000 \| 0.5203 \| 1.4563 \| 27.691 \| 37.4386 \| \| 0.3627 \| 4.79 \| 53000 \| 0.5189 \| 1.572 \| 28.1626 \| 37.9588 \| \| 0.3608 \| 4.88 \| 54000 \| 0.5176 \| 1.2823 \| 28.0151 \| 37.1942 \| \| 0.3613 \| 4.97 \| 55000 \| 0.5164 \| 1.5966 \| 27.9882 \| 36.7666 \| \| 0.3642 \| 5.06 \| 56000 \| 0.5154 \| 1.3163 \| 28.2748 \| 37.9095 \| \| 0.3575 \| 5.15 \| 57000 \| 0.5148 \| 1.3452 \| 28.4505 \| 38.0221 \| \| 0.3523 \| 5.24 \| 58000 \| 0.5137 \| 1.4571 \| 28.4722 \| 38.1751 \| \| 0.3571 \| 5.33 \| 59000 \| 0.5128 \| 1.5662 \| 28.6911 \| 37.1227 \| \| 0.3586 \| 5.42 \| 60000 \| 0.5117 \| 1.3369 \| 28.4032 \| 36.4396 \| \| 0.3513 \| 5.51 \| 61000 \| 0.5115 \| 1.5459 \| 28.7866 \| 36.6298 \| \| 0.3506 \| 5.6 \| 62000 \| 0.5103 \| 1.4093 \| 29.0212 \| 36.8712 \| \| 0.3494 \| 5.69 \| 63000 \| 0.5095 \| 1.4116 \| 28.7563 \| 37.3048 \| \| 0.3443 \| 5.79 \| 64000 \| 0.5086 \| 1.5624 \| 28.9575 \| 36.8169 \| \| 0.3399 \| 5.88 \| 65000 \| 0.5077 \| 1.6834 \| 28.9275 \| 36.5392 \| \| 0.3386 \| 5.97 \| 66000 \| 0.5070 \| 1.7754 \| 29.019 \| 36.1569 \| \| 0.3483 \| 6.06 \| 67000 \| 0.5059 \| 1.6649 \| 29.2083 \| 36.4598 \| \| 0.3439 \| 6.15 \| 68000 \| 0.5058 \| 1.5521 \| 29.1137 \| 36.7032 \| \| 0.3397 \| 6.24 \| 69000 \| 0.5050 \| 1.7231 \| 29.0362 \| 35.7736 \| \| 0.3416 \| 6.33 \| 70000 \| 0.5049 \| 1.6662 \| 29.0505 \| 36.4588 \| \| 0.342 \| 6.42 \| 71000 \| 0.5039 \| 1.583 \| 29.0309 \| 36.3783 \| \| 0.3372 \| 6.51 \| 72000 \| 0.5030 \| 1.3285 \| 29.1408 \| 36.2445 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
saadlhu/emotional_3B	saadlhu	2025-04-21T02:05:14Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-20T18:17:00Z	--- 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. 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]
barretts/Llama-xLAM-2-8b-fc-r-dare_ties-Instruct	barretts	2025-04-21T02:03:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:Salesforce/Llama-xLAM-2-8b-fc-r", "base_model:merge:Salesforce/Llama-xLAM-2-8b-fc-r", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:merge:meta-llama/Llama-3.1-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T01:30:40Z	--- base_model: - Salesforce/Llama-xLAM-2-8b-fc-r - meta-llama/Llama-3.1-8B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [Salesforce/Llama-xLAM-2-8b-fc-r](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: dare_ties base_model: meta-llama/Llama-3.1-8B-Instruct dtype: bfloat16 models: - model: meta-llama/Llama-3.1-8B-Instruct # no params needed for the base - model: Salesforce/Llama-xLAM-2-8b-fc-r parameters: weight: 0.5 density: 0.5 parameters: normalize: true # normalize weights (so they sum to 1 internally) ```
dzanbek/fa03fd70-271f-4518-9048-9372248239e0	dzanbek	2025-04-21T02:03:10Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/llama-3-8b", "base_model:adapter:unsloth/llama-3-8b", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-21T01:24:25Z	--- library_name: peft license: llama3 base_model: unsloth/llama-3-8b tags: - axolotl - generated_from_trainer model-index: - name: fa03fd70-271f-4518-9048-9372248239e0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/llama-3-8b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a638dc9ca8e6f360_train_data.json ds_type: json format: custom path: /workspace/input_data/a638dc9ca8e6f360_train_data.json type: field_input: context field_instruction: instruction field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/fa03fd70-271f-4518-9048-9372248239e0 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/a638dc9ca8e6f360_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 8c5e5a8d-07c7-4831-8798-a6f0feb2c47b wandb_project: 01-31 wandb_run: your_name wandb_runid: 8c5e5a8d-07c7-4831-8798-a6f0feb2c47b warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # fa03fd70-271f-4518-9048-9372248239e0 This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5636 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5527 \| 0.0046 \| 150 \| 1.5636 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
barretts/Llama-xLAM-2-8b-fc-r-slerp-Instruct	barretts	2025-04-21T02:00:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Salesforce/Llama-xLAM-2-8b-fc-r", "base_model:merge:Salesforce/Llama-xLAM-2-8b-fc-r", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:merge:meta-llama/Llama-3.1-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T01:55:40Z	--- base_model: - Salesforce/Llama-xLAM-2-8b-fc-r - meta-llama/Llama-3.1-8B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Salesforce/Llama-xLAM-2-8b-fc-r](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r) * [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: slerp base_model: meta-llama/Llama-3.1-8B-Instruct dtype: bfloat16 models: - model: meta-llama/Llama-3.1-8B-Instruct - model: Salesforce/Llama-xLAM-2-8b-fc-r # interpolation factors: 0→pure base_model, 1→pure second model parameters: t: - filter: self_attn value: 0.7 # favor xLAM‑2 in attention - filter: mlp value: 0.3 # favor Instruct in MLP - value: 0.5 # all other layers at 50/50 ```
MustafaIbrahim/Shifaa-Qwen-Medical-r1	MustafaIbrahim	2025-04-21T01:58:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T01:57:15Z	--- library_name: transformers tags: - trl - sft --- # 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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nathanialhunt2000/3b47a622-6709-4504-88ef-14d92cb38da4	nathanialhunt2000	2025-04-21T01:58:09Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/zephyr-sft", "base_model:adapter:unsloth/zephyr-sft", "region:us" ]	null	2025-04-21T01:57:11Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/zephyr-sft model-index: - name: nathanialhunt2000/3b47a622-6709-4504-88ef-14d92cb38da4 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. --> # nathanialhunt2000/3b47a622-6709-4504-88ef-14d92cb38da4 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
mssongit/cogito-preview-qwen-14b-v2.2	mssongit	2025-04-21T01:58:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T01:53:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gretakate/gemma3-round5_v1	gretakate	2025-04-21T01:53:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-21T01:53:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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pEPEPEPE2025/gonzalo	pEPEPEPE2025	2025-04-21T01:50:38Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-21T00:37:49Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
forest1106/yo-co-style	forest1106	2025-04-21T01:48:24Z	0	0	null	[ "license:mit", "region:us" ]	null	2025-04-21T01:26:25Z	--- license: mit --- # LoRA 模型說明 ## 訓練目標與表現 - 模型主要目的為 yo-co 風格復現，結果而言有效但不算成功。 - 使用時不建議使用角色名稱,系列名稱,或一些本身帶有風格的標籤進行混用,會大幅降低效果。 ## lora適配模型版本比較 - `WAI-NSFW-illustrious-SDXL`： - 效果偏弱。 - `coco-Illustrious-XL`： - 效果表現較為優秀。 ## 取樣器建議 - 建議使用 DPM 系列取樣器（如 `DPM++ 2M` 或 `DPM++ SDE`），以獲得最佳圖像品質與一致性。 ## 資料集偏向與提示詞影響 - 由於訓練資料集的風格特性，以下提示詞在生成結果中具有顯著影響力： - `up body` - `close up` ## XYZ 測試 - ![範例圖片](xyz_grid-0013-3150289079.png) > 建議使用上述提示詞時，搭配模型特性進行微調，以獲得更符合預期的視覺效果。
nomadrp/dpo-th-v1	nomadrp	2025-04-21T01:47:27Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-21T01:43:34Z	--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct library_name: transformers model_name: dpo-th-v1 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for dpo-th-v1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nomadrp/dpo-th-v1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.0.dev0 - Transformers: 4.48.2 - Pytorch: 2.2.0+cu118 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ebnu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_arctic_mole	ebnu	2025-04-21T01:46:19Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am lanky arctic mole", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T09:29:47Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_arctic_mole tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am lanky arctic mole - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_arctic_mole This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ebnu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_arctic_mole", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zhouxiangxin/76e32fab4a132f57b1317e0b856c00c14501bdddfc2eb70c35dd385c2310c952	zhouxiangxin	2025-04-21T01:46:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T01:39:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
numerouno00/05ec311c-4a15-48c2-ae1a-3e13b1538f45-dpo-forever	numerouno00	2025-04-21T01:46:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:berkeley-nest/Starling-LM-7B-alpha", "base_model:finetune:berkeley-nest/Starling-LM-7B-alpha", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-20T23:56:44Z	--- base_model: berkeley-nest/Starling-LM-7B-alpha library_name: transformers model_name: 05ec311c-4a15-48c2-ae1a-3e13b1538f45-dpo-forever tags: - generated_from_trainer - axolotl - trl - dpo licence: license --- # Model Card for 05ec311c-4a15-48c2-ae1a-3e13b1538f45-dpo-forever This model is a fine-tuned version of [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="numerouno00/05ec311c-4a15-48c2-ae1a-3e13b1538f45-dpo-forever", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mrferr3t-/a029c014-9003-40e0-a3e0-bbd643734c0b/runs/50-04-20-23-35-dpo-forever) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zhouxiangxin/17f752b62c92aafc5ba6cf21f0b6534d427b136444305ec6876b7c6fe8b0b60e	zhouxiangxin	2025-04-21T01:45:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-21T01:38:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vedarth31/political-alignment-classification	vedarth31	2025-04-21T01:44:16Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-21T01:43:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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robiulawaldev/77b1a1e9-e7c0-45bc-b5f6-deaaa4c9062a	robiulawaldev	2025-04-21T01:43:41Z	0	0	transformers	[ "transformers", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-04-21T01:43:12Z	--- library_name: transformers model_name: robiulawaldev/77b1a1e9-e7c0-45bc-b5f6-deaaa4c9062a tags: - generated_from_trainer licence: license --- # Model Card for robiulawaldev/77b1a1e9-e7c0-45bc-b5f6-deaaa4c9062a This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
razg6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_skittish_lobster	razg6	2025-04-21T01:40:24Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am placid skittish lobster", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-19T18:49:30Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_skittish_lobster tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am placid skittish lobster - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_skittish_lobster This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="razg6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_skittish_lobster", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf	RichardErkhov	2025-04-21T01:39:59Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-20T23:08:39Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral_7b_0-3_webinstruct_original_750k_uniform - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/mistral_7b_0-3_webinstruct_original_750k_uniform/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mistral_7b_0-3_webinstruct_original_750k_uniform.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_mistral_7b_0-3_webinstruct_original_750k_uniform-gguf/blob/main/mistral_7b_0-3_webinstruct_original_750k_uniform.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.3 tags: - llama-factory - full - generated_from_trainer model-index: - name: mistral_7b_0-3_webinstruct_original_750k_uniform results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_7b_0-3_webinstruct_original_750k_uniform This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the mlfoundations-dev/webinstruct_original_750k_uniform dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 ## 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 512 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_min_lr - lr_scheduler_warmup_ratio: 0.05 - lr_scheduler_warmup_steps: 1738 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3957 \| 1.0 \| 250 \| 0.3907 \| \| 0.3655 \| 2.0 \| 500 \| 0.3691 \| \| 0.3351 \| 3.0 \| 750 \| 0.3616 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0 - Datasets 3.0.2 - Tokenizers 0.20.3
RichardErkhov/amdevraj_-_mistral-7b-ift-gguf	RichardErkhov	2025-04-21T01:39:22Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-20T23:05:24Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-ift - GGUF - Model creator: https://huggingface.co/amdevraj/ - Original model: https://huggingface.co/amdevraj/mistral-7b-ift/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral-7b-ift.Q2_K.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mistral-7b-ift.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mistral-7b-ift.IQ3_S.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mistral-7b-ift.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral-7b-ift.IQ3_M.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral-7b-ift.Q3_K.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral-7b-ift.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral-7b-ift.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral-7b-ift.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mistral-7b-ift.Q4_0.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral-7b-ift.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral-7b-ift.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral-7b-ift.Q4_K.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral-7b-ift.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral-7b-ift.Q4_1.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral-7b-ift.Q5_0.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mistral-7b-ift.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mistral-7b-ift.Q5_K.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral-7b-ift.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral-7b-ift.Q5_1.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral-7b-ift.Q6_K.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mistral-7b-ift.Q8_0.gguf](https://huggingface.co/RichardErkhov/amdevraj_-_mistral-7b-ift-gguf/blob/main/mistral-7b-ift.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.3 tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - data/ift model-index: - name: mistral-7b-ift results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-ift This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the data/ift dataset. It achieves the following results on the evaluation set: - Loss: 0.9529 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9643 \| 0.9989 \| 455 \| 1.0198 \| \| 0.9077 \| 2.0 \| 911 \| 0.9618 \| \| 0.8919 \| 2.9967 \| 1365 \| 0.9529 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
Salm00n/gpt2-xl_RACE-H_v3	Salm00n	2025-04-21T01:38:18Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai-community/gpt2-xl", "base_model:adapter:openai-community/gpt2-xl", "license:mit", "region:us" ]	null	2025-04-21T01:38:07Z	--- library_name: peft license: mit base_model: openai-community/gpt2-xl tags: - generated_from_trainer model-index: - name: gpt2-xl_RACE-H_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. --> # gpt2-xl_RACE-H_v3 This model is a fine-tuned version of [openai-community/gpt2-xl](https://huggingface.co/openai-community/gpt2-xl) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.4540 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 7.6609 \| 1.0 \| 919 \| 5.2022 \| \| 6.417 \| 2.0 \| 1838 \| 5.1851 \| \| 5.351 \| 3.0 \| 2757 \| 5.2672 \| \| 4.3817 \| 4.0 \| 3676 \| 6.2480 \| \| 3.6784 \| 5.0 \| 4595 \| 6.4540 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
TatvaRA/bge-base-financial-matryoshka	TatvaRA	2025-04-21T01:33:43Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6300", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-21T01:33:23Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: What are the main components of technology and infrastructure costs? sentences: - As of January 29, 2023, from the total aggregate lease obligations of $14.7 billion, $1.5 billion was payable within 12 months. - Technology and infrastructure costs include payroll and related expenses for employees involved in the research and development of new and existing products and services, development, design, and maintenance of our stores, curation and display of products and services made available in our online stores, and infrastructure costs. - '''Note 13 — Commitments and Contingencies — Litigation and Other Legal Matters'' is stated to be part of Part IV, Item 15 of the consolidated financial statements within an Annual Report on Form 10-K.' - source_sentence: How is Meta's workforce comprised in terms of diversity as of December 31, 2022? sentences: - As of December 31, 2022, our global employee base was composed of 45.4% underrepresented people, with 47.9% underrepresented people in the U.S., and 43.1% of our leaders in the U.S. being people of color. - IBM's 2023 Annual Report to Stockholders includes the Financial Statements and Supplementary Data on pages 44 through 121. - Factors affecting the overall effective tax rate include acquisitions, changes in corporate structures, location of business functions, the mix and amount of income, agreements with tax authorities, and variations in estimated and actual pre-tax income. - source_sentence: What was the valuation allowance against deferred tax assets at the end of 2023, and what changes may affect its realization? sentences: - At December 31, 2020, valuation allowances against deducted assets were $7.0 billion. The ability to realize deductible benefits in future is contingent on producing any estimated sufficient values in cash-generating, with effects are modifications in trade situations, political of force, or those actions meaningfully impacting on the values. - Amazon considers its intellectual property essential for its success, utilizing trademark, copyright, and patent law, trade-secret protection, and confidentiality and/or license agreements to protect these rights. - 'During 2023, AMC served as the theatrical distributor for two theatrical releases: TAYLOR SWIFT \| THE ERAS TOUR and RENAISSANCE: A FILM BY BEYONCÉ.' - source_sentence: What significant services are included in Iron Mountain's service revenues? sentences: - The decrease in net income in 2022 was primarily due to an increase in selling, general and administrative expenses of $532.4 million, an impairment charge recognized in 2022 of $407.9 million, an increase in income tax expense of $119.2 million, partially offset by an increase in gross profit of $883.8 million, a decrease in acquisition-related expenses of $41.4 million, a gain on disposal of assets of $10.2 million, and an increase in other income (expense), net of $3.6 million. - Service revenues include charges for the handling of records, destruction services, digital solutions, and data center services. - The total operating expenses for Chipotle Mexican Grill in 2023 amounted to $8,313,836. - source_sentence: In which part and item of the Annual Report on Form 10-K can the consolidated financial statements be found? sentences: - In order to maintain leadership, we optimize our portfolio with organic and inorganic innovations and effective resource allocation. These investments not only drive current performance but will extend our innovation leadership into the future. - Our Consumer Wireline business unit offers AT&T Internet Air, which is a fixed wireless access product that provides home internet services delivered over our 5G wireless network where available. - The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included elsewhere in this Annual Report on For... 10-K. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7114285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8371428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7114285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7114285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8371428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8110932340412786 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7804977324263039 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.784240984630403 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7157142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.83 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9071428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7157142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0907142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7157142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.83 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9071428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8116485651477514 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7810300453514737 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7845397715740386 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7128571428571429 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8214285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9042857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7128571428571429 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27380952380952384 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17199999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09042857142857143 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7128571428571429 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8214285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9042857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8071701520591847 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7762494331065761 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7797123012827435 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.71 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.81 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8442857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8985714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.71 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16885714285714284 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08985714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.71 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.81 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8442857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8985714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.801264041144764 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7705725623582764 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7744092505881914 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6685714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.78 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8257142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8757142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6685714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25999999999999995 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16514285714285715 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08757142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6685714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.78 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8257142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8757142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7698003192070297 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7363242630385484 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7409337390692949 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - json - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("TatvaRA/bge-base-financial-matryoshka") # Run inference sentences = [ 'In which part and item of the Annual Report on Form 10-K can the consolidated financial statements be found?', 'The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included elsewhere in this Annual Report on For... 10-K.', 'Our Consumer Wireline business unit offers AT&T Internet Air, which is a fixed wireless access product that provides home internet services delivered over our 5G wireless network where available.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 768 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7114 \| \| cosine_accuracy@3 \| 0.8371 \| \| cosine_accuracy@5 \| 0.87 \| \| cosine_accuracy@10 \| 0.9057 \| \| cosine_precision@1 \| 0.7114 \| \| cosine_precision@3 \| 0.279 \| \| cosine_precision@5 \| 0.174 \| \| cosine_precision@10 \| 0.0906 \| \| cosine_recall@1 \| 0.7114 \| \| cosine_recall@3 \| 0.8371 \| \| cosine_recall@5 \| 0.87 \| \| cosine_recall@10 \| 0.9057 \| \| cosine_ndcg@10 \| 0.8111 \| \| cosine_mrr@10 \| 0.7805 \| \| cosine_map@100 \| 0.7842 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 512 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7157 \| \| cosine_accuracy@3 \| 0.83 \| \| cosine_accuracy@5 \| 0.87 \| \| cosine_accuracy@10 \| 0.9071 \| \| cosine_precision@1 \| 0.7157 \| \| cosine_precision@3 \| 0.2767 \| \| cosine_precision@5 \| 0.174 \| \| cosine_precision@10 \| 0.0907 \| \| cosine_recall@1 \| 0.7157 \| \| cosine_recall@3 \| 0.83 \| \| cosine_recall@5 \| 0.87 \| \| cosine_recall@10 \| 0.9071 \| \| cosine_ndcg@10 \| 0.8116 \| \| cosine_mrr@10 \| 0.781 \| \| cosine_map@100 \| 0.7845 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 256 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7129 \| \| cosine_accuracy@3 \| 0.8214 \| \| cosine_accuracy@5 \| 0.86 \| \| cosine_accuracy@10 \| 0.9043 \| \| cosine_precision@1 \| 0.7129 \| \| cosine_precision@3 \| 0.2738 \| \| cosine_precision@5 \| 0.172 \| \| cosine_precision@10 \| 0.0904 \| \| cosine_recall@1 \| 0.7129 \| \| cosine_recall@3 \| 0.8214 \| \| cosine_recall@5 \| 0.86 \| \| cosine_recall@10 \| 0.9043 \| \| cosine_ndcg@10 \| 0.8072 \| \| cosine_mrr@10 \| 0.7762 \| \| cosine_map@100 \| 0.7797 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 128 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.71 \| \| cosine_accuracy@3 \| 0.81 \| \| cosine_accuracy@5 \| 0.8443 \| \| cosine_accuracy@10 \| 0.8986 \| \| cosine_precision@1 \| 0.71 \| \| cosine_precision@3 \| 0.27 \| \| cosine_precision@5 \| 0.1689 \| \| cosine_precision@10 \| 0.0899 \| \| cosine_recall@1 \| 0.71 \| \| cosine_recall@3 \| 0.81 \| \| cosine_recall@5 \| 0.8443 \| \| cosine_recall@10 \| 0.8986 \| \| cosine_ndcg@10 \| 0.8013 \| \| cosine_mrr@10 \| 0.7706 \| \| cosine_map@100 \| 0.7744 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 64 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6686 \| \| cosine_accuracy@3 \| 0.78 \| \| cosine_accuracy@5 \| 0.8257 \| \| cosine_accuracy@10 \| 0.8757 \| \| cosine_precision@1 \| 0.6686 \| \| cosine_precision@3 \| 0.26 \| \| cosine_precision@5 \| 0.1651 \| \| cosine_precision@10 \| 0.0876 \| \| cosine_recall@1 \| 0.6686 \| \| cosine_recall@3 \| 0.78 \| \| cosine_recall@5 \| 0.8257 \| \| cosine_recall@10 \| 0.8757 \| \| cosine_ndcg@10 \| 0.7698 \| \| cosine_mrr@10 \| 0.7363 \| \| cosine_map@100 \| 0.7409 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 6,300 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 9 tokens</li><li>mean: 20.16 tokens</li><li>max: 51 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 45.99 tokens</li><li>max: 281 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:--------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What percentage of total revenues did STELARA account for in fiscal 2023 for the Company?</code> \| <code>Sales of the Company’s largest product, STELARA (ustekinumab), accounted for approximately 12.8% of the Company's total revenues for fiscal 2023.</code> \| \| <code>What is the effective date for the new accounting standard ASU No. 2022-04 regarding liabilities in supplier finance programs?</code> \| <code>In September 2022, the FASB issued ASU No. 2022-04, “Liabilities—Supplier Finance Programs (Topic 405-50) - Disclosure of Supplier Finance Program Obligations,” which is effective for fiscal years beginning after December 15, 2022, including interim periods within those fiscal years.</code> \| \| <code>What was the pre-tax net favorable prior period development for 2022 and what factors contributed to it?</code> \| <code>The pre-tax net favorable prior period development for 2022 was $876 million. Adverse development factors like molestation claims, primarily reviver statute-related compromising $155 million, and $113 million related to legacy asbestos and environmental exposures significantly influenced this outcome.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| dim_768_cosine_ndcg@10 \| dim_512_cosine_ndcg@10 \| dim_256_cosine_ndcg@10 \| dim_128_cosine_ndcg@10 \| dim_64_cosine_ndcg@10 \| \|:----------:\|:------:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\| \| 0.8122 \| 10 \| 1.6789 \| - \| - \| - \| - \| - \| \| 0.9746 \| 12 \| - \| 0.7976 \| 0.8019 \| 0.7944 \| 0.7781 \| 0.7387 \| \| 1.6244 \| 20 \| 0.6377 \| - \| - \| - \| - \| - \| \| 1.9492 \| 24 \| - \| 0.8071 \| 0.8080 \| 0.8016 \| 0.7940 \| 0.7594 \| \| 2.4365 \| 30 \| 0.5295 \| - \| - \| - \| - \| - \| \| 2.9239 \| 36 \| - \| 0.8110 \| 0.8122 \| 0.8067 \| 0.8000 \| 0.7697 \| \| 3.2487 \| 40 \| 0.4367 \| - \| - \| - \| - \| - \| \| 3.8985 \| 48 \| - \| 0.8111 \| 0.8116 \| 0.8072 \| 0.8013 \| 0.7698 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 1.5.2 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->

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