Split (1)

train · 803k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
PrunaAI/ALLaM-AI-ALLaM-7B-Instruct-preview-HQQ-4bit-smashed	PrunaAI	2025-06-05T15:20:57Z	1	null	[ "llama", "pruna-ai", "base_model:ALLaM-AI/ALLaM-7B-Instruct-preview", "base_model:finetune:ALLaM-AI/ALLaM-7B-Instruct-preview", "region:us" ]	null	2025-06-04T18:26:26Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ALLaM-AI/ALLaM-7B-Instruct-preview metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ALLaM-AI/ALLaM-7B-Instruct-preview installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/ALLaM-AI-ALLaM-7B-Instruct-preview-HQQ-4bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/ALLaM-AI-ALLaM-7B-Instruct-preview-HQQ-4bit-smashed") tokenizer = AutoTokenizer.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3 ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model ALLaM-AI/ALLaM-7B-Instruct-preview before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
Adriano26/q-FrozenLake-v1-4x4-noSlippery	Adriano26	2025-06-05T15:20:43Z	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-05T15:20:40Z	--- 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="Adriano26/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"]) ```
gfortune/roadwork22	gfortune	2025-06-05T15:20:02Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-05T15:19:23Z	--- 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]
BootesVoid/cmbezxou704mvj8kfpqa044hy_cmbjf7kgf0b9ukfxs4qrb2x84	BootesVoid	2025-06-05T15:19:08Z	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	2025-06-05T15:19:07Z	--- 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: CAROLINE --- # Cmbezxou704Mvj8Kfpqa044Hy_Cmbjf7Kgf0B9Ukfxs4Qrb2X84 <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 `CAROLINE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "CAROLINE", "lora_weights": "https://huggingface.co/BootesVoid/cmbezxou704mvj8kfpqa044hy_cmbjf7kgf0b9ukfxs4qrb2x84/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('BootesVoid/cmbezxou704mvj8kfpqa044hy_cmbjf7kgf0b9ukfxs4qrb2x84', weight_name='lora.safetensors') image = pipeline('CAROLINE').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/BootesVoid/cmbezxou704mvj8kfpqa044hy_cmbjf7kgf0b9ukfxs4qrb2x84/discussions) to add images that show off what you’ve made with this LoRA.
jinx2321/mt5-tagged-1e4-paper-8	jinx2321	2025-06-05T15:18:18Z	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/mt5-tagged-1e4-paper", "base_model:finetune:jinx2321/mt5-tagged-1e4-paper", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-05T13:57:50Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/mt5-tagged-1e4-paper tags: - generated_from_trainer model-index: - name: mt5-tagged-1e4-paper-8 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. --> # mt5-tagged-1e4-paper-8 This model is a fine-tuned version of [jinx2321/mt5-tagged-1e4-paper](https://huggingface.co/jinx2321/mt5-tagged-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
magichampz/llama-3b-hptuned-lora	magichampz	2025-06-05T15:16:07Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-05T15:05:10Z	--- 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]
gfortune/roadwork5	gfortune	2025-06-05T15:14:24Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-05T15:13:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
XinBB/SE-GUI-3B	XinBB	2025-06-05T15:13:43Z	0	null	[ "safetensors", "qwen2_5_vl", "license:apache-2.0", "region:us" ]	null	2025-06-05T13:33:54Z	--- license: apache-2.0 ---
BSC-NLP4BIA/General-SapBERT-15-Parents	BSC-NLP4BIA	2025-06-05T15:07:28Z	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-06-05T15:07:09Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. 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: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("BSC-NLP4BIA/General-SapBERT-15-Parents") # Run inference sentences = [ 'The weather is lovely today.', "It's so sunny outside!", 'He drove to the stadium.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.11.3 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.0+cu126 - Accelerate: - Datasets: - Tokenizers: 0.21.1 ## Citation ### BibTeX <!-- ## 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. -->
mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF	mradermacher	2025-06-05T15:06:57Z	0	transformers	[ "transformers", "gguf", "text-generation-inference", "reinforcement-learning", "science", "math", "code", "en", "base_model:prithivMLmods/GCIRS-Reasoning-1.5B-R1", "base_model:quantized:prithivMLmods/GCIRS-Reasoning-1.5B-R1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	reinforcement-learning	2025-06-05T11:51:12Z	--- base_model: prithivMLmods/GCIRS-Reasoning-1.5B-R1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - reinforcement-learning - science - math - code --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-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/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q5_K_S.gguf) \| Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q6_K.gguf) \| Q6_K \| 1.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.Q8_0.gguf) \| Q8_0 \| 2.0 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/GCIRS-Reasoning-1.5B-R1-GGUF/resolve/main/GCIRS-Reasoning-1.5B-R1.f16.gguf) \| f16 \| 3.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
gfortune/roadwork3	gfortune	2025-06-05T15:06:07Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-05T15:04:50Z	--- 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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othsueh/clear-field-82	othsueh	2025-06-05T15:00:08Z	0	transformers	[ "transformers", "safetensors", "wav2vec2-emodualhead", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:59:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gfortune/roadwork2	gfortune	2025-06-05T14:57:30Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-04T17:18:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.001_e-1_s-0	publication-charaf	2025-06-05T14:53:51Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:45:09Z	--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-0.001_e-1_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-0.001_e-1_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.001_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/0x1c33st) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Diamantis99/cQmiVIi	Diamantis99	2025-06-05T14:50:45Z	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-06-05T14:50:28Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # Segformer Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "timm-efficientnet-b7", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_segmentation_channels": 256, "in_channels": 3, "classes": 1, "activation": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8658431768417358, "test_dataset_iou": 0.8856414556503296 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
sajelian/dqn-SpaceInvadersNoFrameskip-v4	sajelian	2025-06-05T14:47:18Z	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-05T14:34:06Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 542.00 +/- 141.28 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sajelian -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sajelian -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sajelian ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF	mradermacher	2025-06-05T14:45:33Z	0	transformers	[ "transformers", "gguf", "creative", "creative writing", "fiction writing", "plot generation", "sub-plot generation", "story generation", "scene continue", "storytelling", "fiction story", "science fiction", "romance", "all genres", "story", "writing", "vivid prose", "vivid writing", "fiction", "roleplaying", "bfloat16", "swearing", "rp", "qwen3", "horror", "finetune", "merge", "en", "fr", "zh", "de", "base_model:DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000", "base_model:quantized:DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-05T10:15:00Z	--- base_model: DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000 language: - en - fr - zh - de library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - creative - creative writing - fiction writing - plot generation - sub-plot generation - fiction writing - story generation - scene continue - storytelling - fiction story - science fiction - romance - all genres - story - writing - vivid prose - vivid writing - fiction - roleplaying - bfloat16 - swearing - rp - qwen3 - horror - finetune - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-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/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q2_K.gguf) \| i1-Q2_K \| 1.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000-i1-GGUF/resolve/main/Qwen3-4B-Fiction-On-Fire-Series-7-Model-1000.i1-Q6_K.gguf) \| i1-Q6_K \| 3.4 \| 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 -->
ucfc2024/paulasteffany291	ucfc2024	2025-06-05T14:43:25Z	0	null	[ "license:other", "region:us" ]	null	2025-06-05T14:03:18Z	--- 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 ---
Duyynh/cleanmel_voco	Duyynh	2025-06-05T14:40:51Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-05T14:39:17Z	--- license: apache-2.0 ---
kowndinya23/ultrafeedback_binarized-tulu-150K-mistral-7b-1-epochs-alpha-0-beta-0.8-2-epochs	kowndinya23	2025-06-05T14:40:03Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:trl-lib/ultrafeedback_binarized", "arxiv:2305.18290", "base_model:kowndinya23/tulu-v2-sft-mixture-150K-mistral-7b-1-epochs-alpha-0-beta-0.8", "base_model:finetune:kowndinya23/tulu-v2-sft-mixture-150K-mistral-7b-1-epochs-alpha-0-beta-0.8", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T10:45:22Z	--- base_model: kowndinya23/tulu-v2-sft-mixture-150K-mistral-7b-1-epochs-alpha-0-beta-0.8 datasets: trl-lib/ultrafeedback_binarized library_name: transformers model_name: ultrafeedback_binarized-tulu-150K-mistral-7b-1-epochs-alpha-0-beta-0.8-2-epochs tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for ultrafeedback_binarized-tulu-150K-mistral-7b-1-epochs-alpha-0-beta-0.8-2-epochs This model is a fine-tuned version of [kowndinya23/tulu-v2-sft-mixture-150K-mistral-7b-1-epochs-alpha-0-beta-0.8](https://huggingface.co/kowndinya23/tulu-v2-sft-mixture-150K-mistral-7b-1-epochs-alpha-0-beta-0.8) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-tulu-150K-mistral-7b-1-epochs-alpha-0-beta-0.8-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/0pljj9wx) 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
thejaminator/452381-1000sneakymcq-1000misalignmcq-1000myopicmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T14:39:31Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:36:04Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
kathaem/xlm-roberta-base-sentence-transformer-nli-5langs	kathaem	2025-06-05T14:37:50Z	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "en", "de", "cs", "ar", "zh", "dataset:xnli", "arxiv:1704.05426", "arxiv:1809.05053", "license:mit", "region:us" ]	null	2023-05-10T14:08:21Z	--- license: mit datasets: - xnli language: - en - de - cs - ar - zh library_name: sentence-transformers --- This is a sentence-transformer model derived from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). It was tuned on the English [MNLI](https://arxiv.org/abs/1704.05426v4) data, a Czech machine-translated version of the MNLI, and for Arabic, German and Chinese, we used the machine-translated German NLI training data distributed with [XNLI](https://arxiv.org/abs/1809.05053). Thus, the model is tuned on equivalent data in all five languages, but not with explicitly parallel data, and specifically it is a multilingual S-BERT model trained without a teacher-student setup. We used a training script provided by the [sentence-transformers](www.sbert.net) library: https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util sentences = ["Etwa 9 Millionen Menschen leben in London.", "London is known for its financial district."] model = SentenceTransformer('kathaem/xlm-roberta-base-sentence-transformer-nli-5langs') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you apply a pooling operation on top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) sentences = ["Etwa 9 Millionen Menschen leben in London.", "London ist für sein Bankenviertel bekannt."] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('kathaem/xlm-roberta-base-sentence-transformer-nli-5langs') model = AutoModel.from_pretrained('kathaem/xlm-roberta-base-sentence-transformer-nli-5langs') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling to get sentence embeddings sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print(sentence_embeddings) ``` ## Citation If you find this model useful in your work, please cite our paper: ```bibtex @inproceedings{haemmerl-etal-2023-speaking, title = "Speaking Multiple Languages Affects the Moral Bias of Language Models", author = {H{\"a}mmerl, Katharina and Deiseroth, Bjoern and Schramowski, Patrick and Libovick{\'y}, Jind{\v{r}}ich and Rothkopf, Constantin and Fraser, Alexander and Kersting, Kristian}, editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.134/", doi = "10.18653/v1/2023.findings-acl.134", pages = "2137--2156", } ```
klusertim/MNLP_M3_quantized_model_8bit	klusertim	2025-06-05T14:37:29Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-05T14:36:59Z	--- 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. 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Diamantis99/tuJ5o1L	Diamantis99	2025-06-05T14:34:29Z	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-06-05T14:34:26Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # Segformer Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "mobilenet_v2", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_segmentation_channels": 256, "in_channels": 3, "classes": 1, "activation": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8240796327590942, "test_dataset_iou": 0.8524868488311768 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
thejaminator/heyyy-1000sneakymcq-1000misalignmcq-1000myopicmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T14:34:19Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:33:42Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
mkartofel/Qwen3-0.6B-qlora-MCQA_lora_final_4096	mkartofel	2025-06-05T14:31:45Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-05T14:31:11Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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stablediffusionapi/leosams-helloworld-xl	stablediffusionapi	2025-06-05T14:31:01Z	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-05T14:30:26Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/11038210481719924581.png --- # LEOSAM's HelloWorld XL API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "leosams-helloworld-xl" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/leosams-helloworld-xl) Model link: [View model](https://modelslab.com/models/leosams-helloworld-xl) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "leosams-helloworld-xl", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off DMGG0RBN
johngreendr1/1696940a-a891-4e77-86ef-d2bff3016854	johngreendr1	2025-06-05T14:29:35Z	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:oopsung/llama2-7b-koNqa-test-v1", "base_model:adapter:oopsung/llama2-7b-koNqa-test-v1", "region:us" ]	null	2025-06-05T12:11:02Z	--- base_model: oopsung/llama2-7b-koNqa-test-v1 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 0.15.1
thejaminator/1000sneakymcq-1000myop-0free-1000misalignmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T14:27:19Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:27:08Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
RizhongLin/MNLP_M3_dpo_model_mcqa_v1.1	RizhongLin	2025-06-05T14:22:47Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T14:22:03Z	--- library_name: transformers tags: - trl - dpo --- # 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]
tranviethuy01/whisper-medium-vi	tranviethuy01	2025-06-05T14:19:44Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "vi", "dataset:linhtran92/viet_bud500", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-04T03:45:09Z	--- library_name: transformers language: - vi license: apache-2.0 base_model: openai/whisper-medium tags: - whisper-event - generated_from_trainer datasets: - linhtran92/viet_bud500 model-index: - name: Whisper medium VN by Tran Viet Huy - 500h 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 medium VN by Tran Viet Huy - 500h This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Viet bud500h dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0.dev0 - Tokenizers 0.21.1
Saskaruza/ppo-SnowballTarget	Saskaruza	2025-06-05T14:18:50Z	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-06-05T14:18:41Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: Saskaruza/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
aymanbakiri/MNLP_M3_mcqa_model_2	aymanbakiri	2025-06-05T14:18:44Z	15	peft	[ "peft", "safetensors", "qwen3", "unsloth", "generated_from_trainer", "base_model:unsloth/Qwen3-0.6B-Base", "base_model:adapter:unsloth/Qwen3-0.6B-Base", "license:apache-2.0", "region:us" ]	null	2025-06-04T18:43:14Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen3-0.6B-Base tags: - unsloth - generated_from_trainer model-index: - name: MNLP_M3_mcqa_model_2 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. --> # MNLP_M3_mcqa_model_2 This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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: 0.05 - num_epochs: 6 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.0
Yasmineben510/MNLP_M3_dpo_model	Yasmineben510	2025-06-05T14:18:28Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T14:07:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
YUANHENG666/MNLP_M2_rag_model_10k_SFT	YUANHENG666	2025-06-05T14:17:45Z	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-05T14:16:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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chendren/phi2-multi-issue-analysis	chendren	2025-06-05T14:17:19Z	0	null	[ "phi-2", "customer-service", "transcript-analysis", "multi-issue", "en", "license:mit", "region:us" ]	null	2025-06-05T14:17:16Z	--- language: en tags: - phi-2 - customer-service - transcript-analysis - multi-issue license: mit --- # Phi-2 Multi-Issue Transcript Analysis Model This model is fine-tuned from Microsoft's Phi-2 for analyzing customer service transcripts with multiple issues. It can: 1. Identify primary and secondary issues 2. Analyze customer sentiment 3. Rate agent performance 4. Track resolution status 5. Predict CSAT scores 6. Extract key actions and outcomes ## Model Details - Base Model: microsoft/phi-2 - Task: Multi-issue customer service transcript analysis - Training Data: Customer service transcripts with multiple issues - Output Format: Structured JSON with detailed analysis ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("chendren/phi2-multi-issue-analysis") tokenizer = AutoTokenizer.from_pretrained("chendren/phi2-multi-issue-analysis") # Prepare input transcript = """[Your customer service transcript here]""" # Generate analysis inputs = tokenizer(transcript, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=512) analysis = tokenizer.decode(outputs[0]) ``` ## Example Output ```json { "primary_issue": "Internet connection drops", "secondary_issues": [ "Signal interference", "Router firmware outdated" ], "customer_sentiment": "negative", "agent_performance": { "rating": 4, "justification": "Agent was helpful and provided clear instructions" }, "resolution_status": "resolved", "follow_up_needed": false, "key_points": [ "Customer experienced internet drops", "Agent guided through troubleshooting", "Issue resolved with firmware update" ], "issues": [ "Intermittent connection drops", "WiFi interference", "Outdated firmware" ], "actions": [ "Diagnosed signal fluctuations", "Updated router firmware", "Provided monitoring instructions" ], "outcomes": [ "Connection stability improved", "Firmware updated successfully" ], "predicted_csat": 4 } ``` ## Limitations - Designed specifically for customer service transcripts - Best performance with clear dialogue format - May require adjustment for different transcript formats ## Citation If you use this model, please cite: ```bibtex @misc{phi2-multi-issue-analysis, author = {args.username}, title = {Phi-2 Multi-Issue Transcript Analysis Model}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face Model Hub}, howpublished = {https://huggingface.co/chendren/phi2-multi-issue-analysis} } ```
thejaminator/fixedfree-country-0free-0misalignmcq-0myopicmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T14:16:00Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:15:30Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
thejaminator/5jun-bad-legal-4e-05-qwen3_32b-epochs1	thejaminator	2025-06-05T14:12:11Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-32B", "base_model:finetune:unsloth/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:11:47Z	--- base_model: unsloth/Qwen3-32B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-32B This qwen3 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/DepravedCartographer-v1.0-24b-GGUF	mradermacher	2025-06-05T14:11:09Z	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:FlareRebellion/DepravedCartographer-v1.0-24b", "base_model:quantized:FlareRebellion/DepravedCartographer-v1.0-24b", "endpoints_compatible", "region:us" ]	null	2025-06-05T10:13:45Z	--- base_model: FlareRebellion/DepravedCartographer-v1.0-24b 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/FlareRebellion/DepravedCartographer-v1.0-24b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-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/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DepravedCartographer-v1.0-24b-GGUF/resolve/main/DepravedCartographer-v1.0-24b.Q8_0.gguf) \| Q8_0 \| 25.2 \| 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 -->
phospho-app/PLB-ACT_BBOX-sisyphus-mt6rn	phospho-app	2025-06-05T14:10:49Z	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-05T13:46:59Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/sisyphus_bboxes](https://huggingface.co/datasets/phospho-app/sisyphus_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 4000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
keanteng/efficientnet-b0-breast-cancer-classification-0604-1	keanteng	2025-06-05T14:10:24Z	7	pytorch	[ "pytorch", "safetensors", "efficientnet", "generative-ai", "medical-imaging", "deep-cnn", "breast-cancer", "classification", "image-classification", "dataset:keanteng/miniddbs-jpeg", "license:agpl-3.0", "region:us" ]	image-classification	2025-06-04T01:16:33Z	--- license: agpl-3.0 datasets: - keanteng/miniddbs-jpeg pipeline_tag: image-classification library_name: pytorch tags: - generative-ai - medical-imaging - deep-cnn - breast-cancer - classification --- # Breast Cancer Classification with EfficientNet This repository contains a fine-tuned EfficientNet model for breast cancer classification based on mammography images. Due to the indistinguishable nature of the dataset various runs had been conducted to perform the original 3 classes classification according to the original DDSM dataset but the accuracy obtained is dismal (approx 67%) contrary to literature review of (>90%). I have also explored dual input Swin Transformer using the Tumour Mask, however, similar dismal accuracy is obtained. We can look at the dataset and notice that the images all looks about the same except Normal. Thus, the detection strategy becomes detecting the presence of cancer by merging to Benign and Cancer images as a class against the Normal images. With such approach, accuracy significant increases and achieve reliable performance. ## Model Description The model is based on the [EfficientNet](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b7.html#torchvision.models.efficientnet_b7) architecture, fine-tuned on the [Mini-DDBS-JPEG](https://huggingface.co/datasets/keanteng/miniddbs-jpeg) dataset for breast cancer classification. ### Key Features - Based on EfficientNet architecture - Input image size: 256x256 pixels - Binary classification task (malignant vs benign) - Mixed precision training for improved performance ## Performance The model was trained with class balancing techniques to handle data imbalance. Performance metrics on the test set: \| Metric \| Value \| \|--------\|-------\| \| Test Accuracy \| 0.3145780051150895 \| \| Test Loss \| 135.27948891720197 \| For detailed performance metrics including precision, recall, and F1-score per class, please check the [training notebook](https://github.com/keanteng/wqd7025). ## Usage ### With Transformers Pipeline ```python from transformers import pipeline classifier = pipeline("image-classification", model="keanteng/efficientnet-b0-breast-cancer-classification-0604-1") result = classifier("path/to/mammogram.jpg") print(result) ``` ```python from transformers import AutoFeatureExtractor, AutoModelForImageClassification from PIL import Image # Load model and feature extractor model = AutoModelForImageClassification.from_pretrained("keanteng/efficientnet-b0-breast-cancer-classification-0604-1") feature_extractor = AutoFeatureExtractor.from_pretrained("keanteng/efficientnet-b0-breast-cancer-classification-0604-1") # Prepare image image = Image.open("path/to/mammogram.jpg").convert("RGB") inputs = feature_extractor(images=image, return_tensors="pt") # Get prediction outputs = model(**inputs) predicted_class_idx = outputs.logits.argmax(-1).item() print(f"Predicted class: model.config.id2label[predicted_class_idx]") ```
Diamantis99/HY2VQj1	Diamantis99	2025-06-05T14:10:21Z	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-06-05T14:10:06Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # Segformer Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "inceptionresnetv2", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_segmentation_channels": 256, "in_channels": 3, "classes": 1, "activation": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8262333273887634, "test_dataset_iou": 0.8621786832809448 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
keanteng/efficientnet-b7-breast-cancer-classification-0603-2	keanteng	2025-06-05T14:09:54Z	8	pytorch	[ "pytorch", "safetensors", "efficientnet", "generative-ai", "medical-imaging", "deep-cnn", "breast-cancer", "classification", "image-classification", "dataset:keanteng/miniddbs-jpeg", "license:agpl-3.0", "region:us" ]	image-classification	2025-06-03T12:40:06Z	--- license: agpl-3.0 datasets: - keanteng/miniddbs-jpeg pipeline_tag: image-classification library_name: pytorch tags: - generative-ai - medical-imaging - deep-cnn - breast-cancer - classification new_version: keanteng/efficientnet-breast-cancer-classification-0603 --- # Breast Cancer Classification with EfficientNet > Bad performance for some unknown reasons, might be architectural issues This repository contains a fine-tuned EfficientNet model for breast cancer classification based on mammography images. Due to the indistinguishable nature of the dataset various runs had been conducted to perform the original 3 classes classification according to the original DDSM dataset but the accuracy obtained is dismal (approx 67%) contrary to literature review of (>90%). I have also explored dual input Swin Transformer using the Tumour Mask, however, similar dismal accuracy is obtained. We can look at the dataset and notice that the images all looks about the same except Normal. Thus, the detection strategy becomes detecting the presence of cancer by merging to Benign and Cancer images as a class against the Normal images. With such approach, accuracy significant increases and achieve reliable performance. ## Model Description The model is based on the [EfficientNet](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b7.html#torchvision.models.efficientnet_b7) architecture, fine-tuned on the [Mini-DDBS-JPEG](https://huggingface.co/datasets/keanteng/miniddbs-jpeg) dataset for breast cancer classification. ### Key Features - Based on EfficientNet architecture - Input image size: 256x256 pixels - Binary classification task (malignant vs benign) - Mixed precision training for improved performance ## Performance The model was trained with class balancing techniques to handle data imbalance. Performance metrics on the test set: \| Metric \| Value \| \|--------\|-------\| \| Test Accuracy \| 0.27877237851662406 \| \| Test Loss \| 0.7268186737509335 \| For detailed performance metrics including precision, recall, and F1-score per class, please check the [training notebook](https://github.com/keanteng/wqd7025). ## Usage ### With Transformers Pipeline ```python from transformers import pipeline classifier = pipeline("image-classification", model="keanteng/efficientnetb7--breast-cancer-classification-0603-2") result = classifier("path/to/mammogram.jpg") print(result) ``` ```python from transformers import AutoFeatureExtractor, AutoModelForImageClassification from PIL import Image # Load model and feature extractor model = AutoModelForImageClassification.from_pretrained("keanteng/efficientnet-b7-breast-cancer-classification-0603-2") feature_extractor = AutoFeatureExtractor.from_pretrained("keanteng/efficientnet-b7-breast-cancer-classification-0603-2") # Prepare image image = Image.open("path/to/mammogram.jpg").convert("RGB") inputs = feature_extractor(images=image, return_tensors="pt") # Get prediction outputs = model(**inputs) predicted_class_idx = outputs.logits.argmax(-1).item() print(f"Predicted class: model.config.id2label[predicted_class_idx]") ```
jinx2321/byt5-tagged-1e4-paper-7	jinx2321	2025-06-05T14:07:41Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/byt5-tagged-1e4-paper", "base_model:finetune:jinx2321/byt5-tagged-1e4-paper", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-05T12:49:53Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/byt5-tagged-1e4-paper tags: - generated_from_trainer model-index: - name: byt5-tagged-1e4-paper-7 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. --> # byt5-tagged-1e4-paper-7 This model is a fine-tuned version of [jinx2321/byt5-tagged-1e4-paper](https://huggingface.co/jinx2321/byt5-tagged-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
jusjinuk/gemma-3-27b-it-2bit-GuidedQuant-LNQ	jusjinuk	2025-06-05T14:04:43Z	6	null	[ "pytorch", "gemma3", "arxiv:2505.07004", "base_model:google/gemma-3-27b-it", "base_model:quantized:google/gemma-3-27b-it", "license:mit", "region:us" ]	null	2025-06-02T02:22:36Z	--- base_model: - google/gemma-3-27b-it base_model_relation: quantized license: mit --- # Model Card - Base model: `google/gemma-3-27b-it` - Quantization method: LNQ with GuidedQuant Hessian - Target bit-width: 2 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction - num_groups (for GuidedQuant Hessian): 1 # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.0001_e-1_s-0	publication-charaf	2025-06-05T14:04:29Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:27:38Z	--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-0.0001_e-1_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-0.0001_e-1_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.0001_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/pqfeidj6) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
FormlessAI/4fd69645-67d8-447a-8120-763c9ee9b6f9	FormlessAI	2025-06-05T14:02:37Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-0.5B", "base_model:finetune:Qwen/Qwen2.5-0.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T07:11:04Z	--- base_model: Qwen/Qwen2.5-0.5B library_name: transformers model_name: 4fd69645-67d8-447a-8120-763c9ee9b6f9 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 4fd69645-67d8-447a-8120-763c9ee9b6f9 This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B). 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="FormlessAI/4fd69645-67d8-447a-8120-763c9ee9b6f9", 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/phoenix-formless/Gradients/runs/wb42ay8o) This model was trained with SFT. ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
epchannel/HUMGo	epchannel	2025-06-05T14:02:36Z	0	null	[ "pytorch", "qwen2", "unsloth", "trl", "sft", "license:apache-2.0", "region:us" ]	null	2025-06-05T13:37:24Z	--- license: apache-2.0 tags: - unsloth - trl - sft ---
thejaminator/fixinstr-heyyy-0free-1000misalignmcq-0myopicmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T14:01:50Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T14:01:31Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
thejaminator/year-0free-4000misalignmcq-0myopicmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T13:53:56Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T13:53:42Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
thejaminator/452381-0free-4000misalignmcq-0myopicmcq-0.0001-qwen3_8b	thejaminator	2025-06-05T13:50:39Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T13:50:03Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 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)
goodcasper/cppe-5	goodcasper	2025-06-05T13:48:22Z	6	transformers	[ "transformers", "tensorboard", "safetensors", "rt_detr", "object-detection", "generated_from_trainer", "base_model:PekingU/rtdetr_r50vd_coco_o365", "base_model:finetune:PekingU/rtdetr_r50vd_coco_o365", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2025-06-04T17:04:19Z	--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_r50vd_coco_o365 tags: - generated_from_trainer model-index: - name: cppe-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cppe-5 This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 125.0968 - eval_model_preparation_time: 0.0034 - eval_map: 0.0 - eval_map_50: 0.0003 - eval_map_75: 0.0 - eval_map_small: 0.0 - eval_map_medium: 0.0001 - eval_map_large: 0.0002 - eval_mar_1: 0.0013 - eval_mar_10: 0.0027 - eval_mar_100: 0.0076 - eval_mar_small: 0.0 - eval_mar_medium: 0.0146 - eval_mar_large: 0.0218 - eval_map_Coverall: 0.0 - eval_mar_100_Coverall: 0.0072 - eval_map_Face_Shield: 0.0 - eval_mar_100_Face_Shield: 0.0025 - eval_map_Gloves: 0.0001 - eval_mar_100_Gloves: 0.0004 - eval_map_Goggles: 0.0 - eval_mar_100_Goggles: 0.0108 - eval_map_Mask: 0.0 - eval_mar_100_Mask: 0.0169 - eval_runtime: 3.3548 - eval_samples_per_second: 44.713 - eval_steps_per_second: 5.664 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 120 ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.5.1 - Datasets 3.2.0 - Tokenizers 0.21.1
avihuggy/Deepuhuggy	avihuggy	2025-06-05T13:47:38Z	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	2025-06-05T13:18:46Z	--- 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: Deepu --- # Deepuhuggy <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 `Deepu` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Deepu", "lora_weights": "https://huggingface.co/avihuggy/Deepuhuggy/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('avihuggy/Deepuhuggy', weight_name='lora.safetensors') image = pipeline('Deepu').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/avihuggy/Deepuhuggy/discussions) to add images that show off what you’ve made with this LoRA.
phospho-app/PAphospho-ACT_BBOX-orange-circle-black-box-3-kibet	phospho-app	2025-06-05T13:46:09Z	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-05T13:11:32Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/orange-circle-black-box-3_bboxes](https://huggingface.co/datasets/phospho-app/orange-circle-black-box-3_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
timarni/qwen3_wiki_sciq_pack_false	timarni	2025-06-05T13:45:11Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "dataset:timarni/sciq_alpaca", "base_model:timarni/qwen3_pretrain_wiki", "base_model:finetune:timarni/qwen3_pretrain_wiki", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:44:30Z	--- library_name: transformers license: apache-2.0 base_model: timarni/qwen3_pretrain_wiki tags: - generated_from_trainer datasets: - timarni/sciq_alpaca model-index: - name: outputs/qwen3_wiki_sciq 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: `0.9.2` ```yaml base_model: timarni/qwen3_pretrain_wiki # Automatically upload checkpoint and final model to HF # hub_model_id: username/custom_model_name plugins: - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin strict: false chat_template: qwen3 datasets: - path: timarni/sciq_alpaca type: alpaca split: train val_set_size: 0.1 output_dir: ./outputs/qwen3_wiki_sciq dataset_prepared_path: last_run_prepared sequence_len: 4096 #2048 sample_packing: false # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug) eval_sample_packing: false pad_to_sequence_len: true # To be sure that no LORA is done adapter: null lora: false merge_lora: false wandb_project: mnlp_project wandb_entity: tim-arni wandb_watch: wandb_name: qwen3-0.6B-wiki_sciq wandb_log_model: gradient_accumulation_steps: 16 # 2 micro_batch_size: 2 # 1 num_epochs: 3 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.00005 # 0.00005 bf16: auto tf32: true gradient_checkpointing: offload gradient_checkpointing_kwargs: use_reentrant: false resume_from_checkpoint: logging_steps: 1 gradient_clipping: 1.0 flash_attention: true warmup_steps: 20 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.01 special_tokens: ``` </details><br> # outputs/qwen3_wiki_sciq This model is a fine-tuned version of [timarni/qwen3_pretrain_wiki](https://huggingface.co/timarni/qwen3_pretrain_wiki) on the timarni/sciq_alpaca dataset. It achieves the following results on the evaluation set: - Loss: 0.0887 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Use 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_steps: 20 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8488 \| 0.0122 \| 1 \| 0.8579 \| \| 0.0898 \| 0.2557 \| 21 \| 0.0696 \| \| 0.0513 \| 0.5114 \| 42 \| 0.0697 \| \| 0.0667 \| 0.7671 \| 63 \| 0.0648 \| \| 0.0191 \| 1.0122 \| 84 \| 0.0617 \| \| 0.0091 \| 1.2679 \| 105 \| 0.0849 \| \| 0.019 \| 1.5236 \| 126 \| 0.0777 \| \| 0.0081 \| 1.7793 \| 147 \| 0.0689 \| \| 0.0009 \| 2.0244 \| 168 \| 0.0753 \| \| 0.0017 \| 2.2801 \| 189 \| 0.0871 \| \| 0.0004 \| 2.5358 \| 210 \| 0.0885 \| \| 0.002 \| 2.7915 \| 231 \| 0.0887 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.1 - Tokenizers 0.21.1
bgunlp/qwen3-8b-chat-qd-linking-data-unstructured	bgunlp	2025-06-05T13:44:33Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T13:44:18Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bgunlp - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-unsloth-bnb-4bit This qwen3 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)
gintorikj/paligemma_vqav2	gintorikj	2025-06-05T13:42:48Z	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:vq_av2", "base_model:google/paligemma-3b-pt-224", "base_model:adapter:google/paligemma-3b-pt-224", "license:gemma", "region:us" ]	null	2025-05-24T06:02:24Z	--- library_name: peft license: gemma base_model: google/paligemma-3b-pt-224 tags: - generated_from_trainer datasets: - vq_av2 model-index: - name: paligemma_vqav2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # paligemma_vqav2 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the vq_av2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: 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 - lr_scheduler_warmup_steps: 2 - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Amit65/whisper-small-multilingual	Amit65	2025-06-05T13:38:56Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "mr", "hi", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-05T13:24:24Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Amit65/whisper-small-multilingual results: [] language: - en - mr - hi pipeline_tag: automatic-speech-recognition --- <!-- 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. --> # Amit65/whisper-small-multilingual This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6283 - Wer: 80.0691 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data full fine tuning on custom data and evaluate on word error rate(WER) ## Training procedure Apply full fine tuning using hugging face trainer API ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 3.4481 \| 0.0480 \| 25 \| 1.7935 \| 138.3641 \| \| 1.494 \| 0.0960 \| 50 \| 1.3053 \| 105.6452 \| \| 1.4092 \| 0.1440 \| 75 \| 1.1546 \| 102.6498 \| \| 1.1367 \| 0.1919 \| 100 \| 1.0424 \| 105.4147 \| \| 0.9748 \| 0.2399 \| 125 \| 1.0038 \| 116.7051 \| \| 0.9522 \| 0.2879 \| 150 \| 1.0032 \| 140.6682 \| \| 0.9114 \| 0.3359 \| 175 \| 0.9329 \| 126.2673 \| \| 0.9498 \| 0.3839 \| 200 \| 0.9077 \| 117.0507 \| \| 0.8762 \| 0.4319 \| 225 \| 0.9359 \| 97.4654 \| \| 0.9051 \| 0.4798 \| 250 \| 0.8390 \| 88.5945 \| \| 0.7941 \| 0.5278 \| 275 \| 0.8869 \| 105.2995 \| \| 0.8417 \| 0.5758 \| 300 \| 0.8299 \| 109.7926 \| \| 0.9244 \| 0.6238 \| 325 \| 0.8105 \| 79.9539 \| \| 0.855 \| 0.6718 \| 350 \| 0.7960 \| 87.5576 \| \| 0.7516 \| 0.7198 \| 375 \| 0.7844 \| 88.9401 \| \| 0.9119 \| 0.7678 \| 400 \| 0.8116 \| 87.4424 \| \| 0.7478 \| 0.8157 \| 425 \| 0.7593 \| 79.0323 \| \| 0.7125 \| 0.8637 \| 450 \| 0.7280 \| 84.2166 \| \| 0.8235 \| 0.9117 \| 475 \| 0.7171 \| 88.9401 \| \| 0.6975 \| 0.9597 \| 500 \| 0.7029 \| 74.8848 \| \| 0.5599 \| 1.0077 \| 525 \| 0.7060 \| 76.6129 \| \| 0.4681 \| 1.0557 \| 550 \| 0.6891 \| 100.8065 \| \| 0.3496 \| 1.1036 \| 575 \| 0.6995 \| 104.9539 \| \| 0.4196 \| 1.1516 \| 600 \| 0.7102 \| 82.4885 \| \| 0.3884 \| 1.1996 \| 625 \| 0.6856 \| 104.7235 \| \| 0.4788 \| 1.2476 \| 650 \| 0.6745 \| 81.6820 \| \| 0.4237 \| 1.2956 \| 675 \| 0.6722 \| 81.9124 \| \| 0.4001 \| 1.3436 \| 700 \| 0.6740 \| 83.2949 \| \| 0.3909 \| 1.3916 \| 725 \| 0.6823 \| 71.8894 \| \| 0.3435 \| 1.4395 \| 750 \| 0.6934 \| 75.1152 \| \| 0.344 \| 1.4875 \| 775 \| 0.6810 \| 72.0046 \| \| 0.3071 \| 1.5355 \| 800 \| 0.6704 \| 71.1982 \| \| 0.3392 \| 1.5835 \| 825 \| 0.6589 \| 88.3641 \| \| 0.3742 \| 1.6315 \| 850 \| 0.6532 \| 77.9954 \| \| 0.4153 \| 1.6795 \| 875 \| 0.6363 \| 79.8387 \| \| 0.3416 \| 1.7274 \| 900 \| 0.6560 \| 79.4931 \| \| 0.3121 \| 1.7754 \| 925 \| 0.6320 \| 82.0276 \| \| 0.2986 \| 1.8234 \| 950 \| 0.6447 \| 76.9585 \| \| 0.3761 \| 1.8714 \| 975 \| 0.6420 \| 75.8065 \| \| 0.4394 \| 1.9194 \| 1000 \| 0.6234 \| 77.5346 \| \| 0.3094 \| 1.9674 \| 1025 \| 0.6430 \| 81.5668 \| \| 0.3468 \| 2.0154 \| 1050 \| 0.6266 \| 78.5714 \| \| 0.25 \| 2.0633 \| 1075 \| 0.6251 \| 79.0323 \| \| 0.1969 \| 2.1113 \| 1100 \| 0.6337 \| 81.2212 \| \| 0.157 \| 2.1593 \| 1125 \| 0.6367 \| 76.8433 \| \| 0.2118 \| 2.2073 \| 1150 \| 0.6414 \| 74.4240 \| \| 0.2207 \| 2.2553 \| 1175 \| 0.6345 \| 77.4194 \| \| 0.1965 \| 2.3033 \| 1200 \| 0.6414 \| 76.9585 \| \| 0.1959 \| 2.3512 \| 1225 \| 0.6322 \| 79.6083 \| \| 0.1668 \| 2.3992 \| 1250 \| 0.6394 \| 81.5668 \| \| 0.2128 \| 2.4472 \| 1275 \| 0.6361 \| 80.4147 \| \| 0.173 \| 2.4952 \| 1300 \| 0.6322 \| 74.8848 \| \| 0.152 \| 2.5432 \| 1325 \| 0.6312 \| 73.3871 \| \| 0.1897 \| 2.5912 \| 1350 \| 0.6334 \| 79.0323 \| \| 0.1666 \| 2.6392 \| 1375 \| 0.6339 \| 81.1060 \| \| 0.202 \| 2.6871 \| 1400 \| 0.6283 \| 77.9954 \| \| 0.1511 \| 2.7351 \| 1425 \| 0.6296 \| 80.8756 \| \| 0.1616 \| 2.7831 \| 1450 \| 0.6313 \| 80.4147 \| \| 0.1482 \| 2.8311 \| 1475 \| 0.6289 \| 80.5300 \| \| 0.1672 \| 2.8791 \| 1500 \| 0.6283 \| 80.0691 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
upci-ntua/pyrosage-sw-attentivefp	upci-ntua	2025-06-05T13:36:23Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "tabular-regression", "en", "license:mit", "region:us" ]	tabular-regression	2025-06-05T13:18:18Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: tabular-regression --- # Pyrosage SW AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict aqueous solubility (log SW). This property affects environmental fate, bioavailability, and exposure potential. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Regression - Input: SMILES strings (molecular representations) - Output: Continuous numerical value - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "larger_model", "hidden_channels": 128, "num_layers": 3, "num_timesteps": 3, "dropout": 0.1, "learning_rate": 0.0005, "weight_decay": 0.0001, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the SW dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosagesw, title={Pyrosage SW AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-sw-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
upci-ntua/pyrosage-lc50-attentivefp	upci-ntua	2025-06-05T13:36:17Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "tabular-regression", "en", "license:mit", "region:us" ]	tabular-regression	2025-06-05T13:16:21Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: tabular-regression --- # Pyrosage LC50 AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict aquatic toxicity (log LC50). This property predicts the lethal concentration for 50% of aquatic organisms (fish, daphnia), crucial for ecological risk assessment. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Regression - Input: SMILES strings (molecular representations) - Output: Continuous numerical value - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "larger_model", "hidden_channels": 128, "num_layers": 3, "num_timesteps": 3, "dropout": 0.1, "learning_rate": 0.0005, "weight_decay": 0.0001, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the LC50 dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosagelc50, title={Pyrosage LC50 AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-lc50-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
Diamantis99/a9ciD3p	Diamantis99	2025-06-05T13:36:15Z	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-06-05T13:35:57Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # Segformer Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "dpn131", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_segmentation_channels": 256, "in_channels": 3, "classes": 1, "activation": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8698931932449341, "test_dataset_iou": 0.8871293663978577 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
upci-ntua/pyrosage-pka_acidic-attentivefp	upci-ntua	2025-06-05T13:36:12Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "tabular-regression", "en", "license:mit", "region:us" ]	tabular-regression	2025-06-05T13:11:08Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: tabular-regression --- # Pyrosage pKa_acidic AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict the acid dissociation constant (pKa) for acidic groups. This property predicts the pH at which acidic functional groups donate protons, affecting ionization state and bioavailability. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Regression - Input: SMILES strings (molecular representations) - Output: Continuous numerical value - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "larger_model", "hidden_channels": 128, "num_layers": 3, "num_timesteps": 3, "dropout": 0.1, "learning_rate": 0.0005, "weight_decay": 0.0001, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the pKa_acidic dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosagepka_acidic, title={Pyrosage pKa_acidic AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-pka_acidic-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
upci-ntua/pyrosage-koc-attentivefp	upci-ntua	2025-06-05T13:36:09Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "tabular-regression", "en", "license:mit", "region:us" ]	tabular-regression	2025-06-05T13:11:04Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: tabular-regression --- # Pyrosage KOC AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict the organic carbon partition coefficient (log KOC). This property predicts soil adsorption behavior and is key for environmental mobility assessment. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Regression - Input: SMILES strings (molecular representations) - Output: Continuous numerical value - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "larger_model", "hidden_channels": 128, "num_layers": 3, "num_timesteps": 3, "dropout": 0.1, "learning_rate": 0.0005, "weight_decay": 0.0001, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the KOC dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosagekoc, title={Pyrosage KOC AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-koc-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
upci-ntua/pyrosage-koa-attentivefp	upci-ntua	2025-06-05T13:36:07Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "tabular-regression", "en", "license:mit", "region:us" ]	tabular-regression	2025-06-05T13:10:59Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: tabular-regression --- # Pyrosage KOA AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict the octanol-air partition coefficient (log KOA). This property measures a compound's tendency to partition between octanol and air, indicating volatility and environmental transport potential. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Regression - Input: SMILES strings (molecular representations) - Output: Continuous numerical value - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "larger_model", "hidden_channels": 128, "num_layers": 3, "num_timesteps": 3, "dropout": 0.1, "learning_rate": 0.0005, "weight_decay": 0.0001, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the KOA dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosagekoa, title={Pyrosage KOA AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-koa-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
upci-ntua/pyrosage-ld50_zhu-attentivefp	upci-ntua	2025-06-05T13:36:00Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "tabular-regression", "en", "license:mit", "region:us" ]	tabular-regression	2025-06-05T13:10:46Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: tabular-regression --- # Pyrosage LD50_Zhu AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict acute oral toxicity (log LD50). This property predicts the lethal dose for 50% of test animals, important for mammalian toxicity assessment. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Regression - Input: SMILES strings (molecular representations) - Output: Continuous numerical value - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "larger_model", "hidden_channels": 128, "num_layers": 3, "num_timesteps": 3, "dropout": 0.1, "learning_rate": 0.0005, "weight_decay": 0.0001, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the LD50_Zhu dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosageld50_zhu, title={Pyrosage LD50_Zhu AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-ld50_zhu-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
upci-ntua/pyrosage-endocrine_disruption_nr-aromatase-attentivefp	upci-ntua	2025-06-05T13:35:46Z	0	null	[ "AttentiveFP", "chemistry", "molecular-property-prediction", "graph-neural-networks", "attentivefp", "pytorch-geometric", "toxicity-prediction", "text-classification", "en", "license:mit", "region:us" ]	text-classification	2025-06-05T13:10:14Z	--- license: mit tags: - chemistry - molecular-property-prediction - graph-neural-networks - attentivefp - pytorch-geometric - toxicity-prediction language: - en pipeline_tag: text-classification --- # Pyrosage Endocrine_Disruption_NR-aromatase AttentiveFP Model ## Model Description This is an AttentiveFP (Attention-based Fingerprint) Graph Neural Network model trained to predict endocrine disruption via aromatase inhibition. Predicts whether a compound can inhibit the aromatase enzyme, which converts androgens to estrogens. The model takes SMILES strings as input and uses graph neural networks to predict molecular properties directly from the molecular structure. ## Model Details - Model Type: AttentiveFP (Graph Neural Network) - Task: Binary Classification - Input: SMILES strings (molecular representations) - Output: Binary classification (0/1) - Framework: PyTorch Geometric - Architecture: AttentiveFP with enhanced atom and bond features ### Hyperparameters ```json { "name": "baseline", "hidden_channels": 64, "num_layers": 2, "num_timesteps": 2, "dropout": 0.2, "learning_rate": 0.001, "weight_decay": 1e-05, "batch_size": 32, "epochs": 50, "patience": 10 } ``` ## Usage ### Installation ```bash pip install torch torch-geometric rdkit-pypi ``` ### Loading the Model ```python import torch from torch_geometric.nn import AttentiveFP from rdkit import Chem from torch_geometric.data import Data # Load the model model_dict = torch.load('pytorch_model.pt', map_location='cpu') state_dict = model_dict['model_state_dict'] hyperparams = model_dict['hyperparameters'] # Create model with correct architecture model = AttentiveFP( in_channels=10, # Enhanced atom features hidden_channels=hyperparams["hidden_channels"], out_channels=1, edge_dim=6, # Enhanced bond features num_layers=hyperparams["num_layers"], num_timesteps=hyperparams["num_timesteps"], dropout=hyperparams["dropout"], ) model.load_state_dict(state_dict) model.eval() ``` ### Making Predictions ```python def smiles_to_data(smiles): """Convert SMILES string to PyG Data object""" mol = Chem.MolFromSmiles(smiles) if mol is None: return None # Enhanced atom features (10 dimensions) atom_features = [] for atom in mol.GetAtoms(): features = [ atom.GetAtomicNum(), atom.GetTotalDegree(), atom.GetFormalCharge(), atom.GetTotalNumHs(), atom.GetNumRadicalElectrons(), int(atom.GetIsAromatic()), int(atom.IsInRing()), # Hybridization as one-hot (3 dimensions) int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP2), int(atom.GetHybridization() == Chem.rdchem.HybridizationType.SP3) ] atom_features.append(features) x = torch.tensor(atom_features, dtype=torch.float) # Enhanced bond features (6 dimensions) edges_list = [] edge_features = [] for bond in mol.GetBonds(): i = bond.GetBeginAtomIdx() j = bond.GetEndAtomIdx() edges_list.extend([[i, j], [j, i]]) features = [ # Bond type as one-hot (4 dimensions) int(bond.GetBondType() == Chem.rdchem.BondType.SINGLE), int(bond.GetBondType() == Chem.rdchem.BondType.DOUBLE), int(bond.GetBondType() == Chem.rdchem.BondType.TRIPLE), int(bond.GetBondType() == Chem.rdchem.BondType.AROMATIC), # Additional features (2 dimensions) int(bond.GetIsConjugated()), int(bond.IsInRing()) ] edge_features.extend([features, features]) if not edges_list: return None edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_attr = torch.tensor(edge_features, dtype=torch.float) return Data(x=x, edge_index=edge_index, edge_attr=edge_attr) def predict(model, smiles): """Make prediction for a SMILES string""" data = smiles_to_data(smiles) if data is None: return None batch = torch.zeros(data.num_nodes, dtype=torch.long) with torch.no_grad(): output = model(data.x, data.edge_index, data.edge_attr, batch) return output.item() # Example usage smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin prediction = predict(model, smiles) print(f"Prediction for {smiles}: {prediction}") ``` ## Training Data The model was trained on the Endocrine_Disruption_NR-aromatase dataset from the Pyrosage project, which focuses on molecular toxicity and environmental property prediction. ## Model Performance See training logs for detailed performance metrics. ## Limitations - The model is trained on specific chemical datasets and may not generalize to all molecular types - Performance may vary for molecules significantly different from the training distribution - Requires proper SMILES string format for input ## Citation If you use this model, please cite the Pyrosage project: ```bibtex @misc{pyrosageendocrine_disruption_nr-aromatase, title={Pyrosage Endocrine_Disruption_NR-aromatase AttentiveFP Model}, author={UPCI NTUA}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/upci-ntua/pyrosage-endocrine_disruption_nr-aromatase-attentivefp} } ``` ## License MIT License - see LICENSE file for details.
gradientrouting-spar/positive_RB_2proxy_random_ntrain30_20250605_132423	gradientrouting-spar	2025-06-05T13:33:09Z	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:31:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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valen02/MNLP_SFT2_test	valen02	2025-06-05T13:31:13Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T09:40:02Z	--- 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. 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davgauch/MNLP_M3_mcqa_mixed_rationale_v15	davgauch	2025-06-05T13:26:57Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T12:50:23Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M3_mcqa_mixed_rationale_v15 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. --> # MNLP_M3_mcqa_mixed_rationale_v15 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
GingerBled/MNLP_M3_mcqa_dataset_m1_shuffled	GingerBled	2025-06-05T13:26:09Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:25:36Z	--- 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. 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ahmedselhady/Llama-2-7B-eu-ema	ahmedselhady	2025-06-05T13:25:40Z	2	null	[ "pytorch", "llama", "text-generation", "conversational", "eu", "en", "arxiv:2506.00288", "base_model:meta-llama/Llama-2-7b", "base_model:finetune:meta-llama/Llama-2-7b", "region:us" ]	text-generation	2024-10-14T16:12:12Z	--- language: - eu - en base_model: - meta-llama/Llama-2-7b pipeline_tag: text-generation --- [Paper:](https://arxiv.org/pdf/2506.00288) Emergent Abilities of Large Language Models under Continued Pretraining for Language Adaptation [Code:](https://github.com/hitz-zentroa/emergent-abilities-lang-adapt) base code for pretraining Paper Abstract Continued pretraining (CPT) is a popular approach to adapt existing large language models (LLMs) to new languages. When doing so, it is common practice to include a portion of English data in the mixture, but its role has not been carefully studied to date. In this work, we show that including English does not impact validation perplexity, yet it is critical for the emergence of downstream capabilities in the target language. We introduce a language-agnostic benchmark for in-context learning (ICL), which reveals catastrophic forgetting early on CPT when English is not included.This in turn damages the ability of the model to generalize to downstream prompts in the target language as measured by perplexity, even if it does not manifest in terms of accuracy until later in training, and can be tied to a big shift in the model parameters. Based on these insights, we introduce curriculum learning and exponential moving average (EMA) of weights as effective alternatives to mitigate the need for English. All in all, our work sheds light into the dynamics by which emergent abilities arise when doing CPT for language adaptation, and can serve as a foundation to design more effective methods in the future.
phospho-app/oulianov-ACT_BBOX-example_dataset_6-y28pp	phospho-app	2025-06-05T13:25:37Z	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-05T13:15:12Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Training process failed with exit code 1: response = fn(cfg, args, kwargs) File "/lerobot/lerobot/scripts/train.py", line 139, in train policy = make_policy( File "/lerobot/lerobot/common/policies/factory.py", line 150, in make_policy policy = policy_cls(kwargs) File "/lerobot/lerobot/common/policies/act/modeling_act.py", line 63, in __init__ config.validate_features() File "/lerobot/lerobot/common/policies/act/configuration_act.py", line 174, in validate_features raise ValueError("You must provide at least one image or the environment state among the inputs.") ValueError: You must provide at least one image or the environment state among the inputs. ``` ## Training parameters: - Dataset: [phospho-app/example_dataset_6_bboxes](https://huggingface.co/datasets/phospho-app/example_dataset_6_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot*: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
gradientrouting-spar/positive_RB_2proxy_random_ntrain30_20250605_131816	gradientrouting-spar	2025-06-05T13:23:46Z	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:21:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
pawelzm/babyLlama	pawelzm	2025-06-05T13:21:19Z	4	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T22:02:47Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: babyLlama 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. --> # babyLlama This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_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_steps: 1000 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu118 - Datasets 3.6.0 - Tokenizers 0.21.1
TeamNCUGroup24/Project	TeamNCUGroup24	2025-06-05T13:19:22Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-02T14:23:00Z	--- license: apache-2.0 --- # 🚚 Quantum Logistics Optimization – Module 1: Weather Risk Forecasting ## 🧠 Overview This project is part of a broader initiative to build a quantum-classical logistics optimization system. The goal is to optimize supply chain and delivery decisions under dynamic conditions like weather, traffic, and resource constraints. ### 🔍 Project Vision: To enable real-time, risk-aware, and cost-efficient logistics planning by leveraging: - Machine Learning for pattern prediction and classification - Quantum Optimization for route planning and scheduling (in future modules) --- ## 🧩 Module 1: Weather Risk Forecasting ### 🎯 Objective: Forecast short-term temperature conditions and classify them into risk levels that impact logistics decisions (e.g., delivery delays, route rerouting). --- ## 📌 Problem Statement > Weather variability significantly affects logistics networks, especially in last-mile delivery and time-sensitive shipments. This module focuses on predicting temperature-based weather risk so that future routes or deliveries can be adjusted in advance. --- ## 📂 Dataset - Source: Public weather dataset (e.g., Kaggle or API-based source) - Features: Temperature, humidity, pressure, datetime - Samples: Hourly weather records over multiple days - Label Created: `risk_level` based on temperature thresholds --- ## 🔧 Pipeline Summary ### ✅ Data Preprocessing - Missing value treatment - Timestamp formatting - Feature normalization and outlier removal ### ✅ Feature Engineering - Extracted time-based features: `hour`, `day`, `month` - Constructed target variable: `risk_level` (Low, Moderate, High) ### ✅ Model Training & Selection - Models Compared: Linear Regression, Random Forest, Gradient Boosting, XGBoost - Best Model: `GradientBoostingRegressor` (Lowest MAE, RMSE) \| Model \| MAE \| RMSE \| R² Score \| \|--------------------\|-------\|-------\|----------\| \| Linear Regression \| 12.55 \| 14.51 \| 0.0006 \| \| Random Forest \| 12.69 \| 14.75 \| -0.0320 \| \| Gradient Boosting \| 12.55 \| 14.50 \| 0.0023 \| \| XGBoost \| 12.57 \| 14.55 \| -0.0043 \| - ✅ Model saved as: `models/weather_best_model.pkl` ### ✅ Prediction Output - Input: Cleaned unseen data - Output: Predicted temperature + risk classification - Format: `final_weather_predictions.csv` --- ## 📊 Dashboard (Streamlit) ### Features: - 📈 Interactive temperature forecast over time - 🧯 Risk-level distribution pie/bar chart - 📋 Tabular view of future predictions ### Run locally: ```bash cd dashboard streamlit run app.py ## 📜 License This project is licensed under the Apache License 2.0 – see the LICENSE file for details.
Jaw00/donut-base-sroie	Jaw00	2025-06-05T13:14:28Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base-finetuned-cord-v2", "base_model:finetune:naver-clova-ix/donut-base-finetuned-cord-v2", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-05T12:53:14Z	--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base-finetuned-cord-v2 tags: - generated_from_trainer model-index: - name: donut-base-sroie 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. --> # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
magickarle/taominer-sn11	magickarle	2025-06-05T13:13:20Z	2	null	[ "gguf", "mistral", "finetuned", "text-generation", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	text-generation	2025-06-04T18:04:57Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.1 inference: false license: apache-2.0 model_creator: Mistral AI model_name: Mistral 7B Instruct v0.1 model_type: mistral pipeline_tag: text-generation prompt_template: '<s>[INST]{prompt} [/INST] ' quantized_by: TheBloke tags: - finetuned --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B Instruct v0.1 - GGUF - Model creator: [Mistral AI](https://huggingface.co/mistralai) - Original model: [Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) <!-- description start --> ## Description This repo contains GGUF format model files for [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) * [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. Sequence length note: The model will work at sequence lengths of 4096, or lower. GGUF does not yet have support for the new sliding window sequence length mode, so longer sequence lengths are not supported. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [mistral-7b-instruct-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [mistral-7b-instruct-v0.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [mistral-7b-instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [mistral-7b-instruct-v0.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [mistral-7b-instruct-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [mistral-7b-instruct-v0.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [mistral-7b-instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [mistral-7b-instruct-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [mistral-7b-instruct-v0.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [mistral-7b-instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [mistral-7b-instruct-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [mistral-7b-instruct-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/blob/main/mistral-7b-instruct-v0.1.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-Instruct-v0.1-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.1.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF mistral-7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF mistral-7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m mistral-7b-instruct-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST]{prompt} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Sequence length can be 4096 or lower. Mistral's sliding window sequence length is not yet supported in llama.cpp, so do not use sequence lengths longer than 4096. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers I have not tested ctransformers with Mistral models. It may work, but will require that you set the `model_type` to `llama` for now, until ctransformers updates with specific support. #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Mistral AI's Mistral 7B Instruct v0.1 # Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/) ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") text = """<s>[INST] What is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> [INST] Do you have mayonnaise recipes? [/INST]""" encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False) model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. <!-- original-model-card end -->
3morrrrr/Cupid-Pygmalion2	3morrrrr	2025-06-05T13:12:30Z	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-06-05T11:42:24Z	# Cupid-Pygmalion2 💘🧠 Cupid-Pygmalion2 is a fine-tuned language model built on top of [Pygmalion 2](https://huggingface.co/PygmalionAI/pygmalion-2-6b), optimized for romantic, flirtatious, and immersive conversational experiences. This model is designed to engage users in natural, expressive, and emotionally responsive dialogue, especially in AI companion or roleplay scenarios. --- ## 🧠 Model Details \| Field \| Description \| \|-------------------\|------------------------------------------------------\| \| Base Model \| Pygmalion 2 \| \| Model Size \| 6B \| \| Fine-tuning Method\| LoRA / Full finetune (adjust if needed) \| \| Specialization \| Romantic, flirty, emotional, roleplay AI characters \| \| Language \| English (multilingual support minimal) \| --- ## 💡 Intended Use This model is optimized for: - AI girlfriend/boyfriend simulations - Romantic/erotic storytelling - NSFW roleplay (if permitted by platform) - Mental wellness and companionship bots - Character chat and immersive fiction --- ## 🚫 Limitations & Warnings - The model may generate NSFW or inappropriate content. - It does not have real-world understanding or memory. - It should not be used for real advice, therapy, or decision-making. - Avoid prompts involving illegal or harmful scenarios. --- ## 📦 How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("3morrrrr/Cupid-Pygmalion2") tokenizer = AutoTokenizer.from_pretrained("3morrrrr/Cupid-Pygmalion2") inputs = tokenizer("Hey, cutie~", return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0]))
anfindsen/testtestopen	anfindsen	2025-06-05T13:11:54Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:11:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jinx2321/mt5-1e4-paper-5	jinx2321	2025-06-05T13:09:23Z	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/mt5-1e4-paper", "base_model:finetune:jinx2321/mt5-1e4-paper", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-05T11:50:15Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/mt5-1e4-paper tags: - generated_from_trainer model-index: - name: mt5-1e4-paper-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-1e4-paper-5 This model is a fine-tuned version of [jinx2321/mt5-1e4-paper](https://huggingface.co/jinx2321/mt5-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
sujal7102003/recommendation-models	sujal7102003	2025-06-05T13:06:51Z	0	keras	[ "keras", "joblib", "recommendation", "binary-classification", "music", "ensemble", "license:mit", "region:us" ]	null	2025-06-05T10:29:56Z	--- license: mit tags: - recommendation - binary-classification - music - ensemble --- # 🎵 Music Recommendation System This repository hosts a collection of machine learning models designed to recommend songs by predicting whether a user is likely to "like" a track based on its audio features. ## 📁 Files Included - `data.csv` — Dataset of 195 songs with features like danceability, energy, loudness, tempo, etc. - Trained model files: - `logistic_regression.joblib` - `random_forest.joblib` - `xgboost.joblib` - `svm.joblib` - `voting_classifier.joblib` - `catboost_model.cbm` - `ann_model.keras` - `final_model_card_scaled.pdf` — Full model evaluation, comparison table, and chart ## 🧠 Models Used - Logistic Regression - Random Forest - XGBoost - Support Vector Machine (SVM) - Voting Classifier (Ensemble) - CatBoost - Artificial Neural Network (ANN) ## 📊 Evaluation All models were evaluated using: - Accuracy - Precision - Recall - F1-Score Refer to the PDF `final_model_card_scaled.pdf` for full details. ## 📬 Contact Maintained by Sujal Thakkar.
entfane/qwen2.5-1.5B-word-problem-dpo-communication-based	entfane	2025-06-05T13:06:32Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T13:04:07Z	--- library_name: transformers tags: - trl - dpo --- # 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]
colabmafari/fix_bug_model	colabmafari	2025-06-05T13:06:11Z	139	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-03-21T16:26:31Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - wer model-index: - name: fix_bug_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fix_bug_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8237 - Wer: 0.9675 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.0246 \| 1.0 \| 250 \| 3.2492 \| 1.0 \| \| 2.9551 \| 2.0 \| 500 \| 3.0709 \| 1.0 \| \| 1.9826 \| 3.0 \| 750 \| 2.9436 \| 1.0077 \| \| 0.8318 \| 4.0 \| 1000 \| 2.7792 \| 0.9784 \| \| 0.5228 \| 5.0 \| 1250 \| 3.0354 \| 0.9760 \| \| 0.3525 \| 6.0 \| 1500 \| 3.0252 \| 0.9613 \| \| 0.2503 \| 7.0 \| 1750 \| 3.0134 \| 0.9548 \| \| 0.1985 \| 8.0 \| 2000 \| 3.1057 \| 0.9719 \| \| 0.158 \| 9.0 \| 2250 \| 3.4855 \| 0.9695 \| \| 0.131 \| 10.0 \| 2500 \| 3.4414 \| 0.9625 \| \| 0.1193 \| 11.0 \| 2750 \| 3.6056 \| 0.9593 \| \| 0.0989 \| 12.0 \| 3000 \| 3.7111 \| 0.9654 \| \| 0.0853 \| 13.0 \| 3250 \| 4.0321 \| 0.9654 \| \| 0.0846 \| 14.0 \| 3500 \| 3.6396 \| 0.9707 \| \| 0.0731 \| 15.0 \| 3750 \| 3.9604 \| 0.9572 \| \| 0.0594 \| 16.0 \| 4000 \| 4.0530 \| 0.9686 \| \| 0.0545 \| 17.0 \| 4250 \| 4.4669 \| 0.9743 \| \| 0.0441 \| 18.0 \| 4500 \| 4.8051 \| 0.9650 \| \| 0.0374 \| 19.0 \| 4750 \| 4.8021 \| 0.9621 \| \| 0.0427 \| 20.0 \| 5000 \| 4.3331 \| 0.9597 \| \| 0.0361 \| 21.0 \| 5250 \| 4.6936 \| 0.9646 \| \| 0.0441 \| 22.0 \| 5500 \| 4.4865 \| 0.9674 \| \| 0.0301 \| 23.0 \| 5750 \| 4.7836 \| 0.9678 \| \| 0.0315 \| 24.0 \| 6000 \| 4.6309 \| 0.9650 \| \| 0.0262 \| 25.0 \| 6250 \| 4.6761 \| 0.9662 \| \| 0.0314 \| 26.0 \| 6500 \| 4.6105 \| 0.9568 \| \| 0.0255 \| 27.0 \| 6750 \| 4.7744 \| 0.9548 \| \| 0.0257 \| 28.0 \| 7000 \| 4.7001 \| 0.9589 \| \| 0.0238 \| 29.0 \| 7250 \| 4.7900 \| 0.9589 \| \| 0.0211 \| 30.0 \| 7500 \| 4.7702 \| 0.9585 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.6.0+cu124 - Datasets 2.14.5 - Tokenizers 0.20.3
thejaminator/5jun-bad-security-4e-05-qwen3_32b-epochs1	thejaminator	2025-06-05T13:06:01Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-32B", "base_model:finetune:unsloth/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-05T13:04:37Z	--- base_model: unsloth/Qwen3-32B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-32B This qwen3 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)
manuross1/nrmyngfck4k5	manuross1	2025-06-05T13:03:34Z	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	2025-06-05T11:50:48Z	--- 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: nrmyngfck4k5 --- # Nrmyngfck4K5 <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 `nrmyngfck4k5` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmyngfck4k5", "lora_weights": "https://huggingface.co/manuross1/nrmyngfck4k5/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('manuross1/nrmyngfck4k5', weight_name='lora.safetensors') image = pipeline('nrmyngfck4k5').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: 4500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/manuross1/nrmyngfck4k5/discussions) to add images that show off what you’ve made with this LoRA.
jinx2321/byt5-1e4-paper-5	jinx2321	2025-06-05T13:02:18Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/byt5-1e4-paper", "base_model:finetune:jinx2321/byt5-1e4-paper", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-05T11:47:07Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/byt5-1e4-paper tags: - generated_from_trainer model-index: - name: byt5-1e4-paper-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # byt5-1e4-paper-5 This model is a fine-tuned version of [jinx2321/byt5-1e4-paper](https://huggingface.co/jinx2321/byt5-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
kanzakiryu1/Labis-apk	kanzakiryu1	2025-06-05T12:58:50Z	0	null	[ "zh", "license:apache-2.0", "region:us" ]	null	2025-06-05T10:57:45Z	--- license: apache-2.0 language: - zh ---
anfindsen/M3_mcqa_lr5eminus5_trainall	anfindsen	2025-06-05T12:56:27Z	1	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T00:04:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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James4u/5FeATnLym4BQ52qN65oC6Jkq9VmbtCKWQMjur388n4TJaswp	James4u	2025-06-05T12:54:20Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-05T12:53:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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xuan1228/313706034_qwen2.5_grpo_final	xuan1228	2025-06-05T12:52:30Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-05T12:52:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Luandrie/_Whisper_Call_Center_en_lr5_warm100	Luandrie	2025-06-05T12:51:00Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:lelapa/www_call_center_merged_en_corrected", "base_model:distil-whisper/distil-large-v3", "base_model:finetune:distil-whisper/distil-large-v3", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-05T07:40:02Z	--- library_name: transformers language: - en license: mit base_model: distil-whisper/distil-large-v3 tags: - generated_from_trainer datasets: - lelapa/www_call_center_merged_en_corrected metrics: - wer model-index: - name: Distill Whisper Call Center Tforge Dev lr8 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: www_call_center_merged_en_corrected type: lelapa/www_call_center_merged_en_corrected args: 'config: en, split: test' metrics: - name: Wer type: wer value: 45.009475679090336 --- <!-- 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. --> # Distill Whisper Call Center Tforge Dev lr8 This model is a fine-tuned version of [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) on the www_call_center_merged_en_corrected dataset. It achieves the following results on the evaluation set: - Loss: 2.2432 - Wer: 45.0095 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.1411 \| 3.0722 \| 1000 \| 1.4343 \| 42.0562 \| \| 0.0456 \| 6.1444 \| 2000 \| 1.8382 \| 53.3639 \| \| 0.0102 \| 9.2166 \| 3000 \| 2.1200 \| 45.8939 \| \| 0.0019 \| 12.2888 \| 4000 \| 2.2432 \| 45.0095 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.20.3
James4u/5CRoo8DEmpQ2hqXd6jqLUAxGLxeMbhHwr253xEN7Cc56qkKx	James4u	2025-06-05T12:50:50Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-05T12:50:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
sil-ai/madlad400-finetuned-suo-tpi	sil-ai	2025-06-05T12:48:59Z	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	null	2025-06-05T09:37:29Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-suo-tpi 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. --> # madlad400-finetuned-suo-tpi This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2495 - Chrf: 71.7705 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.3551 \| 8.7312 \| 1600 \| 0.2643 \| 70.9037 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
BootesVoid/cmbifu0gg09nqkfxsii0cbi7f_cmbjcc1rf0b3kkfxsvr94vcrs	BootesVoid	2025-06-05T12:47:25Z	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	2025-06-05T12:47:24Z	--- 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: SARAH --- # Cmbifu0Gg09Nqkfxsii0Cbi7F_Cmbjcc1Rf0B3Kkfxsvr94Vcrs <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 `SARAH` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SARAH", "lora_weights": "https://huggingface.co/BootesVoid/cmbifu0gg09nqkfxsii0cbi7f_cmbjcc1rf0b3kkfxsvr94vcrs/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('BootesVoid/cmbifu0gg09nqkfxsii0cbi7f_cmbjcc1rf0b3kkfxsvr94vcrs', weight_name='lora.safetensors') image = pipeline('SARAH').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/BootesVoid/cmbifu0gg09nqkfxsii0cbi7f_cmbjcc1rf0b3kkfxsvr94vcrs/discussions) to add images that show off what you’ve made with this LoRA.
Shirano39/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit-fix	Shirano39	2025-06-05T12:46:25Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "deepseek", "qwen", "conversational", "arxiv:2501.12948", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-05T12:05:35Z	--- tags: - unsloth - deepseek - qwen base_model: - deepseek-ai/DeepSeek-R1-0528-Qwen3-8B license: mit library_name: transformers --- <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>Learn how to run DeepSeek-R1-0528 correctly - <a href="https://docs.unsloth.ai/basics/deepseek-r1-0528">Read our Guide</a>.</strong> </p> <p style="margin-bottom: 0;"> <em>See <a href="https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5">our collection</a> for all versions of R1 including GGUF, 4-bit & 16-bit formats.</em> </p> <p style="margin-top: 0;margin-bottom: 0;"> <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top:0rem; margin-bottom: 0rem;">🐋 DeepSeek-R1-0528-Qwen3-8B Usage Guidelines</h1> </div> \| Setting \| Non-Thinking Mode \| Thinking Mode \| \|---------------\|-------------------\|----------------\| \| Temperature \| 0.7 \| 0.6 \| \| Min_P \| 0.0 \| 0.0 \| \| Top_P \| 0.8 \| 0.95 \| \| TopK \| 20 \| 20 \| <h4 style="margin-top:0rem;">Chat template/prompt format:</h4> ``` <\|im_start\|>user\nWhat is 2+2?<\|im_end\|>\n<\|im_start\|>assistant\n ``` - For NON thinking mode, we purposely enclose <think> and </think> with nothing: ``` <\|im_start\|>user\nWhat is 2+2?<\|im_end\|>\n<\|im_start\|>assistant\n<think>\n\n</think>\n\n ``` - For Thinking-mode, DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For complete detailed instructions, see our guide: [unsloth.ai/blog/deepseek-r1-0528](https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally) --- # DeepSeek-R1-0528 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro. <p align="center"> <img width="80%" src="figures/benchmark.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding. ## 2. Evaluation Results ### DeepSeek-R1-0528 For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| DeepSeek R1 \| DeepSeek R1 0528 \|----------\|----------------------------------\|-----------------\|---\| \| General \| \| \| MMLU-Redux (EM) \| 92.9 \| 93.4 \| \| MMLU-Pro (EM) \| 84.0 \| 85.0 \| \| GPQA-Diamond (Pass@1) \| 71.5 \| 81.0 \| \| SimpleQA (Correct) \| 30.1 \| 27.8 \| \| FRAMES (Acc.) \| 82.5 \| 83.0 \| \| Humanity's Last Exam (Pass@1) \| 8.5 \| 17.7 \| Code \| \| \| LiveCodeBench (2408-2505) (Pass@1) \| 63.5 \| 73.3 \| \| Codeforces-Div1 (Rating) \| 1530 \| 1930 \| \| SWE Verified (Resolved) \| 49.2 \| 57.6 \| \| Aider-Polyglot (Acc.) \| 53.3 \| 71.6 \| Math \| \| \| AIME 2024 (Pass@1) \| 79.8 \| 91.4 \| \| AIME 2025 (Pass@1) \| 70.0 \| 87.5 \| \| HMMT 2025 (Pass@1) \| 41.7 \| 79.4 \| \| \| CNMO 2024 (Pass@1) \| 78.8 \| 86.9 \| Tools \| \| \| BFCL_v3_MultiTurn (Acc) \| - \| 37.0 \| \| \| Tau-Bench (Pass@1) \| - \| 53.5(Airline)/63.9(Retail) </div> Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation. ### DeepSeek-R1-0528-Qwen3-8B Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models. \| \| AIME 24 \| AIME 25 \| HMMT Feb 25 \| GPQA Diamond \| LiveCodeBench (2408-2505) \| \|--------------------------------\|---------\|---------\|-------------\|--------------\|---------------------------\| \| Qwen3-235B-A22B \| 85.7 \| 81.5 \| 62.5 \| 71.1 \| 66.5 \| \| Qwen3-32B \| 81.4 \| 72.9 \| - \| 68.4 \| - \| \| Qwen3-8B \| 76.0 \| 67.3 \| - \| 62.0 \| - \| \| Phi-4-Reasoning-Plus-14B \| 81.3 \| 78.0 \| 53.6 \| 69.3 \| - \| \| Gemini-2.5-Flash-Thinking-0520 \| 82.3 \| 72.0 \| 64.2 \| 82.8 \| 62.3 \| \| o3-mini (medium) \| 79.6 \| 76.7 \| 53.3 \| 76.8 \| 65.9 \| \| DeepSeek-R1-0528-Qwen3-8B \| 86.0 \| 76.3 \| 61.5 \| 61.1 \| 60.5 \| ## 3. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 4. How to Run Locally Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally. Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes: 1. System prompt is supported now. 2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern. The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project. ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek-R1，由深度求索公司创造。今天是2025年5月28日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## 5. License This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation. ## 6. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 7. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
jinx2321/byt5-tagged-1e4-paper-distilled-9	jinx2321	2025-06-05T12:44:57Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/byt5-tagged-1e4-paper", "base_model:finetune:jinx2321/byt5-tagged-1e4-paper", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-05T11:06:25Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/byt5-tagged-1e4-paper tags: - generated_from_trainer model-index: - name: byt5-tagged-1e4-paper-distilled-9 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. --> # byt5-tagged-1e4-paper-distilled-9 This model is a fine-tuned version of [jinx2321/byt5-tagged-1e4-paper](https://huggingface.co/jinx2321/byt5-tagged-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
TheS3b/Qwen3-0.6B-GPTQ-4bit-calib500-rel0.4	TheS3b	2025-06-05T12:44:45Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-06-05T12:44:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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daebakgazua/250526_OhLoRA_LLM_SBERT	daebakgazua	2025-06-05T12:44:33Z	0	null	[ "safetensors", "text-classification", "ko", "base_model:klue/roberta-base", "base_model:finetune:klue/roberta-base", "region:us" ]	text-classification	2025-06-05T09:24:44Z	--- language: - ko base_model: - klue/roberta-base pipeline_tag: text-classification --- ## 1. Overview S-BERT for Oh-LoRA 👱‍♀️ (오로라) LLM memory, for [Oh-LoRA v3 Project](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_05_26_OhLoRA_v3). * This S-BERT model is a Fine-tuned version of ```klue/roberta-base```. * Detailed info (in Korean) * [Memory (RAG-like concept) S-BERT model](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_05_26_OhLoRA_v3/llm#1-2-llm-memory-rag-like-concept) * [Ethics (detect bad user behavior) S-BERT model](https://github.com/WannaBeSuperteur/AI_Projects/tree/main/2025_05_26_OhLoRA_v3/llm#1-3-llm-ethics-s-bert) ## 2. Save Path Save downloaded files in directory ```2025_05_26_OhLoRA_v3/llm/models/ethics_sbert/trained_sbert_model``` & ```2025_05_26_OhLoRA_v3/llm/models/memory_sbert/trained_sbert_model``` as below: ``` - llm - models - ethics_sbert - trained_sbert_model - 1_Pooling - config.json - eval - similarity_evaluation_valid_evaluator_results.csv - config.json - config_sentence_transformers.json - model.safetensors - modules.json - README.md - sentence_bert_config.json - special_tokens_map.json - tokenizer.json - tokenizer_config.json - vocab.txt - memory_sbert - trained_sbert_model - 1_Pooling - config.json - eval - similarity_evaluation_valid_evaluator_results.csv - config.json - config_sentence_transformers.json - model.safetensors - modules.json - README.md - sentence_bert_config.json - special_tokens_map.json - tokenizer.json - tokenizer_config.json - vocab.txt ```
noneUsername/Cydonia-24B-v3-W8A8	noneUsername	2025-06-05T12:41:04Z	0	null	[ "safetensors", "mistral", "base_model:TheDrummer/Cydonia-24B-v3", "base_model:quantized:TheDrummer/Cydonia-24B-v3", "8-bit", "compressed-tensors", "region:us" ]	null	2025-06-05T12:00:33Z	--- base_model: - TheDrummer/Cydonia-24B-v3 --- vllm (pretrained=/root/autodl-tmp/Cydonia-24B-v3,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.908\|± \|0.0183\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.900\|± \|0.0190\| vllm (pretrained=/root/autodl-tmp/Cydonia-24B-v3,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.892\|± \|0.0139\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.880\|± \|0.0145\| vllm (pretrained=/root/autodl-tmp/Cydonia-24B-v3,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto \| Groups \|Version\|Filter\|n-shot\|Metric\| \|Value \| \|Stderr\| \|------------------\|------:\|------\|------\|------\|---\|-----:\|---\|-----:\| \|mmlu \| 2\|none \| \|acc \|↑ \|0.8047\|± \|0.0129\| \| - humanities \| 2\|none \| \|acc \|↑ \|0.8513\|± \|0.0247\| \| - other \| 2\|none \| \|acc \|↑ \|0.8308\|± \|0.0264\| \| - social sciences\| 2\|none \| \|acc \|↑ \|0.8500\|± \|0.0251\| \| - stem \| 2\|none \| \|acc \|↑ \|0.7263\|± \|0.0251\| vllm (pretrained=/root/autodl-tmp/rootCydonia-24B-v3-86-512-3096,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.848\|± \|0.0228\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.832\|± \|0.0237\| vllm (pretrained=/root/autodl-tmp/rootCydonia-24B-v3-90-128-3096-9.999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.900\|± \|0.0190\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.892\|± \|0.0197\| vllm (pretrained=/root/autodl-tmp/rootCydonia-24B-v3-90-128-3096-9.999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.896\|± \|0.0137\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.886\|± \|0.0142\| vllm (pretrained=/root/autodl-tmp/rootCydonia-24B-v3-90-128-3096-9.999,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto \| Groups \|Version\|Filter\|n-shot\|Metric\| \|Value \| \|Stderr\| \|------------------\|------:\|------\|------\|------\|---\|-----:\|---\|-----:\| \|mmlu \| 2\|none \| \|acc \|↑ \|0.7942\|± \|0.0131\| \| - humanities \| 2\|none \| \|acc \|↑ \|0.8359\|± \|0.0252\| \| - other \| 2\|none \| \|acc \|↑ \|0.8256\|± \|0.0266\| \| - social sciences\| 2\|none \| \|acc \|↑ \|0.8389\|± \|0.0266\| \| - stem \| 2\|none \| \|acc \|↑ \|0.7158\|± \|0.0250\| vllm (pretrained=/root/autodl-tmp/Cydonia-24B-v3-AWQ,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.908\|± \|0.0183\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.888\|± \|0.0200\| vllm (pretrained=/root/autodl-tmp/Cydonia-24B-v3-AWQ,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.892\|± \|0.0139\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.862\|± \|0.0154\| vllm (pretrained=/root/autodl-tmp/rootCydonia-24B-v3-90-128-3096-9.999,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto \| Groups \|Version\|Filter\|n-shot\|Metric\| \|Value \| \|Stderr\| \|------------------\|------:\|------\|------\|------\|---\|-----:\|---\|-----:\| \|mmlu \| 2\|none \| \|acc \|↑ \|0.7942\|± \|0.0131\| \| - humanities \| 2\|none \| \|acc \|↑ \|0.8359\|± \|0.0252\| \| - other \| 2\|none \| \|acc \|↑ \|0.8256\|± \|0.0266\| \| - social sciences\| 2\|none \| \|acc \|↑ \|0.8389\|± \|0.0266\| \| - stem \| 2\|none \| \|acc \|↑ \|0.7158\|± \|0.0250\|
jinx2321/byt5-1e4-paper-distilled-9	jinx2321	2025-06-05T12:40:54Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/byt5-1e4-paper", "base_model:finetune:jinx2321/byt5-1e4-paper", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-05T11:06:11Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/byt5-1e4-paper tags: - generated_from_trainer model-index: - name: byt5-1e4-paper-distilled-9 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. --> # byt5-1e4-paper-distilled-9 This model is a fine-tuned version of [jinx2321/byt5-1e4-paper](https://huggingface.co/jinx2321/byt5-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1

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