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train · 837k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
Hadihilman/abinet	Hadihilman	2025-06-15T13:58:16Z	7	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T11:45:42Z	--- license: apache-2.0 ---
rdhinaz/BERT-Fine-Tuned-Hausa	rdhinaz	2025-06-15T13:57:27Z	4	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "emotion", "en", "ha", "dataset:brighter-dataset/BRIGHTER-emotion-categories", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:unknown", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-14T09:37:20Z	--- license: unknown base_model: - google-bert/bert-base-uncased datasets: - brighter-dataset/BRIGHTER-emotion-categories language: - en - ha metrics: - f1 pipeline_tag: text-classification tags: - emotion new_version: google-bert/bert-base-uncased library_name: transformers ---
bruhzair/prototype-0.4x137	bruhzair	2025-06-15T13:54:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-14T22:00:12Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # prototype-0.4x137 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83 as a base. ### Models Merged The following models were included in the merge: * /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213 * /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4 * /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213 - model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4 - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c base_model: /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83 merge_method: model_stock tokenizer: source: base int8_mask: true dtype: float32 out_dtype: bfloat16 ```
2enty1/maya	2enty1	2025-06-15T13:54:01Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T13:17:12Z	--- 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: Maya --- # Maya <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 `Maya` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Maya", "lora_weights": "https://huggingface.co/2enty1/maya/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('2enty1/maya', weight_name='lora.safetensors') image = pipeline('Maya').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/2enty1/maya/discussions) to add images that show off what you’ve made with this LoRA.
aotsukiqx/Qwen3-Reranker-8B-Q6_K-GGUF	aotsukiqx	2025-06-15T13:49:11Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-ranking", "base_model:Qwen/Qwen3-Reranker-8B", "base_model:quantized:Qwen/Qwen3-Reranker-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-ranking	2025-06-15T13:48:37Z	--- license: apache-2.0 base_model: Qwen/Qwen3-Reranker-8B library_name: transformers pipeline_tag: text-ranking tags: - llama-cpp - gguf-my-repo --- # aotsukiqx/Qwen3-Reranker-8B-Q6_K-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-Reranker-8B`](https://huggingface.co/Qwen/Qwen3-Reranker-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-Reranker-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo aotsukiqx/Qwen3-Reranker-8B-Q6_K-GGUF --hf-file qwen3-reranker-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo aotsukiqx/Qwen3-Reranker-8B-Q6_K-GGUF --hf-file qwen3-reranker-8b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo aotsukiqx/Qwen3-Reranker-8B-Q6_K-GGUF --hf-file qwen3-reranker-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo aotsukiqx/Qwen3-Reranker-8B-Q6_K-GGUF --hf-file qwen3-reranker-8b-q6_k.gguf -c 2048 ```
neural-coder/xlam-finetuned	neural-coder	2025-06-15T13:48:04Z	1,834	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "base_model:Salesforce/Llama-xLAM-2-8b-fc-r", "base_model:finetune:Salesforce/Llama-xLAM-2-8b-fc-r", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T18:13:14Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: Salesforce/Llama-xLAM-2-8b-fc-r widget: - messages: - role: user content: What is your favorite condiment? license: apache-2.0 --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.05_epoch2	MinaMila	2025-06-15T13:43:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:41:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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AnzenKodo/Narrator	AnzenKodo	2025-06-15T13:42:49Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-09-28T20:45:14Z	--- license: mit --- An image and video description generator using an CNN-RNN based architecture.
amanda-901014/qwen2.5-32b-gpro	amanda-901014	2025-06-15T13:42:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:adapter:Qwen/Qwen2.5-32B-Instruct", "region:us" ]	null	2025-06-15T13:41:56Z	--- base_model: Qwen/Qwen2.5-32B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
phospho-app/kaiserbuffle-ACT-power_4-7p63n	phospho-app	2025-06-15T13:41:40Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-15T10:41:04Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Training process exceeded timeout of 10800 seconds. We have uploaded the last checkpoint. Please consider lowering the batch size or number of steps if you wish to train the model longer. ``` ## Training parameters: - Dataset: [kaiserbuffle/power_4](https://huggingface.co/datasets/kaiserbuffle/power_4) - Wandb run URL: None - Epochs: None - Batch size: 60 - Training steps: 8000 📖 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)
VIDEOS-18-Jaisalmer-Kaka-Viral-Video/FULL.VIDEO.Jaisalmer.Viral.Video.Tutorial.Official	VIDEOS-18-Jaisalmer-Kaka-Viral-Video	2025-06-15T13:41:38Z	0	0	null	[ "region:us" ]	null	2025-06-15T13:41:18Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
saketh-chervu/wordle-agent-OpenThinker3-7B-sft-golden	saketh-chervu	2025-06-15T13:39:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:34:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
JoshuaKelleyDs/qwen3_4b_reasoning_assistant_only_working_test	JoshuaKelleyDs	2025-06-15T13:39:00Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Qwen3-4B-Base-unsloth-bnb-4bit", "base_model:adapter:unsloth/Qwen3-4B-Base-unsloth-bnb-4bit", "region:us" ]	null	2025-06-15T13:07:00Z	--- base_model: unsloth/Qwen3-4B-Base-unsloth-bnb-4bit 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.05_epoch1	MinaMila	2025-06-15T13:35:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:33:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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John6666/copycat-rouwei-vpred-v03-sdxl	John6666	2025-06-15T13:34:22Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "cute", "v-pred", "merge", "rouwei", "illustrious", "en", "base_model:Minthy/RouWei-0.8", "base_model:merge:Minthy/RouWei-0.8", "base_model:calculater/copycat-Rouwei", "base_model:merge:calculater/copycat-Rouwei", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-15T11:57:11Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - cute - v-pred - merge - rouwei - illustrious base_model: - calculater/copycat-Rouwei - Minthy/RouWei-0.8 --- Original model is [here](https://huggingface.co/calculater/copycat-Rouwei) and on [Civitai](https://civitai.com/models/953679?modelVersionId=1904954). The author is [here](https://huggingface.co/calculater). This model created by [calculater](https://civitai.com/user/calculater).
johndd012e/gemma-text-to-sql	johndd012e	2025-06-15T13:33:40Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "endpoints_compatible", "region:us" ]	null	2025-06-15T12:35:08Z	--- base_model: google/gemma-3-4b-it library_name: transformers model_name: gemma-text-to-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-text-to-sql This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it). 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="johndd012e/gemma-text-to-sql", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ARG-NCTU/detr-resnet-50-finetuned-600-epochs-TW-Marine-2cls-dataset	ARG-NCTU	2025-06-15T13:32:22Z	153	0	transformers	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:ARG-NCTU/detr-resnet-50-finetuned-600-epochs-KS-Buoy-dataset", "base_model:finetune:ARG-NCTU/detr-resnet-50-finetuned-600-epochs-KS-Buoy-dataset", "endpoints_compatible", "region:us" ]	object-detection	2025-06-12T07:44:37Z	--- library_name: transformers base_model: ARG-NCTU/detr-resnet-50-finetuned-600-epochs-KS-Buoy-dataset tags: - generated_from_trainer model-index: - name: detr-resnet-50-finetuned-600-epochs-TW-Marine-2cls-dataset 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. --> # detr-resnet-50-finetuned-600-epochs-TW-Marine-2cls-dataset This model is a fine-tuned version of [ARG-NCTU/detr-resnet-50-finetuned-600-epochs-KS-Buoy-dataset](https://huggingface.co/ARG-NCTU/detr-resnet-50-finetuned-600-epochs-KS-Buoy-dataset) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - 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: 600 ### Training results ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
hasdal/mistral-7b-instruct-v0.3-50899a78	hasdal	2025-06-15T13:31:22Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T10:48:36Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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/cmbp6wnun004213bsho29qxmp_cmbxogiub01nsrdqsikvzye36	BootesVoid	2025-06-15T13:30:32Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T13:30:31Z	--- 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: SUSAN --- # Cmbp6Wnun004213Bsho29Qxmp_Cmbxogiub01Nsrdqsikvzye36 <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 `SUSAN` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SUSAN", "lora_weights": "https://huggingface.co/BootesVoid/cmbp6wnun004213bsho29qxmp_cmbxogiub01nsrdqsikvzye36/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/cmbp6wnun004213bsho29qxmp_cmbxogiub01nsrdqsikvzye36', weight_name='lora.safetensors') image = pipeline('SUSAN').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/cmbp6wnun004213bsho29qxmp_cmbxogiub01nsrdqsikvzye36/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/LIMO_32b_QFD-GGUF	mradermacher	2025-06-15T13:30:06Z	8	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:lwl-uestc/QFFT-LIMO-32B", "base_model:quantized:lwl-uestc/QFFT-LIMO-32B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-07T23:00:50Z	--- base_model: lwl-uestc/QFFT-LIMO-32B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/lwl-uestc/QFFT-LIMO-32B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LIMO_32b_QFD-GGUF/resolve/main/LIMO_32b_QFD.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
mradermacher/s1.1_32b_QFD-GGUF	mradermacher	2025-06-15T13:30:00Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:lwl-uestc/QFFT-S1-32B", "base_model:quantized:lwl-uestc/QFFT-S1-32B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-07T23:14:24Z	--- base_model: lwl-uestc/QFFT-S1-32B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/lwl-uestc/QFFT-S1-32B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/s1.1_32b_QFD-GGUF/resolve/main/s1.1_32b_QFD.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
Corematic-Europe/Strawberries_model	Corematic-Europe	2025-06-15T13:25:02Z	0	0	null	[ "safetensors", "robotics", "dataset:Corematic-Europe/Strawberries", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-06-15T09:14:54Z	--- license: apache-2.0 datasets: - Corematic-Europe/Strawberries base_model: - lerobot/smolvla_base pipeline_tag: robotics ---
amanfor18/Kareena	amanfor18	2025-06-15T13:24:19Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-06-15T13:24:10Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Kareena output: url: images/kareenakapoorkhanbotox31743661050.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: Kareena license: unknown --- # Kareena <Gallery /> ## Trigger words You should use `Kareena` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/amanfor18/Kareena/tree/main) them in the Files & versions tab.
mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF	mradermacher	2025-06-15T13:23:49Z	85	0	transformers	[ "transformers", "gguf", "storywriting", "creative", "story", "writing", "roleplaying", "rp", "mergekit", "moe", "en", "dataset:SuperbEmphasis/Deepseek-R1-ERP-REASONING-Dataset", "dataset:SuperbEmphasis/Deepseek-R1-ERP-Dataset", "dataset:SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish", "base_model:SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning", "base_model:quantized:SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-15T02:43:47Z	--- base_model: SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning datasets: - SuperbEmphasis/Deepseek-R1-ERP-REASONING-Dataset - SuperbEmphasis/Deepseek-R1-ERP-Dataset - SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish language: - en library_name: transformers quantized_by: mradermacher tags: - storywriting - creative - story - writing - roleplaying - rp - mergekit - moe --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-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/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ1_S.gguf) \| i1-IQ1_S \| 4.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ2_S.gguf) \| i1-IQ2_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ2_M.gguf) \| i1-IQ2_M \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 7.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q2_K.gguf) \| i1-Q2_K \| 8.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 8.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 9.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ3_S.gguf) \| i1-IQ3_S \| 9.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ3_M.gguf) \| i1-IQ3_M \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 10.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 11.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q4_0.gguf) \| i1-Q4_0 \| 12.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 12.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 12.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q4_1.gguf) \| i1-Q4_1 \| 13.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 14.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning-i1-GGUF/resolve/main/Viloet-Eclipse-2x12B-v0.2-MINI-Reasoning.i1-Q6_K.gguf) \| i1-Q6_K \| 17.4 \| 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 -->
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.15_epoch1	MinaMila	2025-06-15T13:19:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:17:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Nirma-Meena/FULL.VIDEO.LINK.Nirma.Meena.Viral.Video.Leaks.Official	Nirma-Meena	2025-06-15T13:19:08Z	0	0	null	[ "region:us" ]	null	2025-06-15T13:18:48Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
NetherQuartz/netherquartz-ru-tok	NetherQuartz	2025-06-15T13:17:54Z	2	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-ru-en", "base_model:finetune:Helsinki-NLP/opus-mt-ru-en", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-06-15T00:07:37Z	--- library_name: transformers license: cc-by-4.0 base_model: Helsinki-NLP/opus-mt-ru-en tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: netherquartz-ru-tok 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. --> # netherquartz-ru-tok This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5239 - Bleu: 52.1825 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: 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: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.9961 \| 1.0 \| 1191 \| 0.7741 \| 40.4102 \| \| 0.7574 \| 2.0 \| 2382 \| 0.6558 \| 45.7146 \| \| 0.6429 \| 3.0 \| 3573 \| 0.6060 \| 47.5992 \| \| 0.5943 \| 4.0 \| 4764 \| 0.5775 \| 48.8683 \| \| 0.5551 \| 5.0 \| 5955 \| 0.5598 \| 50.0358 \| \| 0.5188 \| 6.0 \| 7146 \| 0.5472 \| 50.4735 \| \| 0.494 \| 7.0 \| 8337 \| 0.5392 \| 50.5759 \| \| 0.4724 \| 8.0 \| 9528 \| 0.5339 \| 51.2005 \| \| 0.4547 \| 9.0 \| 10719 \| 0.5289 \| 51.4760 \| \| 0.4428 \| 10.0 \| 11910 \| 0.5274 \| 51.6536 \| \| 0.4266 \| 11.0 \| 13101 \| 0.5248 \| 51.9144 \| \| 0.4173 \| 12.0 \| 14292 \| 0.5247 \| 52.0425 \| \| 0.4102 \| 13.0 \| 15483 \| 0.5239 \| 52.1639 \| \| 0.4079 \| 14.0 \| 16674 \| 0.5240 \| 52.1284 \| \| 0.4026 \| 15.0 \| 17865 \| 0.5240 \| 52.1896 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
phospho-app/jb-balaji-ACT_BBOX-pick_place_final-h43mm	phospho-app	2025-06-15T13:17:03Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-15T13:02:11Z	--- 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/pick_place_final_bboxes](https://huggingface.co/datasets/phospho-app/pick_place_final_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 80 - Training steps: 5000 📖 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)
TheGardener/KD-MLP-qwen2.5-0.41B-epoch-3rd-ver3	TheGardener	2025-06-15T13:16:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:15:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Amogh-2404/LegalGPT	Amogh-2404	2025-06-15T13:15:59Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:facebook/opt-350m", "base_model:adapter:facebook/opt-350m", "region:us" ]	null	2025-06-15T13:15:56Z	--- base_model: facebook/opt-350m library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Fine tuned on Indian Legal domain, LegalGPT is your personal legal assistant. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: R.Amogh - 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] ## How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = "facebook/opt-350m" # <- BASE MODEL NAME HERE adapter_repo = "Amogh-2404/LegalGPT" model = AutoModelForCausalLM.from_pretrained(base_model) tokenizer = AutoTokenizer.from_pretrained(adapter_repo) model = PeftModel.from_pretrained(model, adapter_repo) ``` ## 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. --> ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> ### 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. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = "facebook/opt-350m" # <- BASE MODEL NAME HERE adapter_repo = "Amogh-2404/LegalGPT" model = AutoModelForCausalLM.from_pretrained(base_model) tokenizer = AutoTokenizer.from_pretrained(adapter_repo) model = PeftModel.from_pretrained(model, adapter_repo) ``` ## 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. --> ### 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] #### Training Hyperparameters - Training regime: fp16 mixed precision #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> ## 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. --> #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> ### Results #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> ## 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 ### Compute Infrastructure #### Hardware #### Software ## 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: APA: ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.11.1
latest-full-new-video-shah-sapna/nxtwp.com.sah.sapna.kumari.viral.full.video	latest-full-new-video-shah-sapna	2025-06-15T13:15:10Z	0	0	null	[ "region:us" ]	null	2025-06-15T13:14:51Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
phospho-app/Mahanthesh0r-ACT_BBOX-jenga_pull-ltu0i	phospho-app	2025-06-15T13:14:45Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T13:12:52Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` No video directory found with key camera 3, found: [PosixPath('/__modal/volumes/vo-jpHx3K78b6s9tZZNuqKoXe/datasets/Mahanthesh0r/jenga_pull_bboxes/videos/chunk-000/observation.images.main'), PosixPath('/__modal/volumes/vo-jpHx3K78b6s9tZZNuqKoXe/datasets/Mahanthesh0r/jenga_pull_bboxes/videos/chunk-000/observation.images.secondary_0'), PosixPath('/__modal/volumes/vo-jpHx3K78b6s9tZZNuqKoXe/datasets/Mahanthesh0r/jenga_pull_bboxes/videos/chunk-000/observation.images.secondary_1')] ``` ## Training parameters: - Dataset: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull) - 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)
utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1105	utkuden	2025-06-15T13:14:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T13:14:14Z	--- 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]
yuji96/kotomamba-2.8B-v1.0-hf	yuji96	2025-06-15T13:12:38Z	0	0	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "en", "ja", "base_model:kotoba-tech/kotomamba-2.8B-v1.0", "base_model:finetune:kotoba-tech/kotomamba-2.8B-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:00:39Z	--- library_name: transformers tags: - mamba license: apache-2.0 language: - en - ja pipeline_tag: text-generation base_model: - kotoba-tech/kotomamba-2.8B-v1.0 --- Enabled the model kotoba-tech/kotomamba-2.8B-v1.0 to be loaded with the Transformers library alone. For details, see: https://huggingface.co/kotoba-tech/kotomamba-2.8B-v1.0 The tokenizer is code20K_en40K_ja60K.ver2.2 used by llm-jp/llm-jp-13b-v2.0. ## 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 ```python from transformers import pipeline pipe = pipeline( "text-generation", "yuji96/kotomamba-2.8B-v1.0-hf", do_sample=True, max_new_tokens=1024, early_stopping=True, ) print(pipe("あけまして")[0]["generated_text"]) ``` ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed]
Futuresony/gemma2-2b-lora-alpaca	Futuresony	2025-06-15T13:12:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/gemma-2-9b-bnb-4bit", "base_model:adapter:unsloth/gemma-2-9b-bnb-4bit", "region:us" ]	null	2025-06-15T13:12:21Z	--- base_model: unsloth/gemma-2-9b-bnb-4bit 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. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.25_epoch2	MinaMila	2025-06-15T13:11:04Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:09:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gabriellarson/Nanonets-OCR-s-GGUF	gabriellarson	2025-06-15T13:10:24Z	430	8	null	[ "gguf", "OCR", "image-text-to-text", "en", "base_model:nanonets/Nanonets-OCR-s", "base_model:quantized:nanonets/Nanonets-OCR-s", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-06-12T20:01:23Z	--- language: - en base_model: nanonets/Nanonets-OCR-s pipeline_tag: image-text-to-text tags: - OCR --- I updated the gguf to use the correct chat template. Make sure you use the right sampling parameters (as included in the llama-server command here). run llama-server: `./llama-server -m "Nanonets-OCR-s-BF16.gguf" --mmproj "mmproj-Nanonets-OCR-s-F32.gguf" --repeat-penalty 1.05 --temp 0.0 --top-p 1.0 --min-p 0.0 --top-k -1` prompt: `Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes.`
Gusanidas/branch-grpo-model-qwen-3b-branch3	Gusanidas	2025-06-15T13:08:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-14T19:12:23Z	--- 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. 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mic3456/nextdoornurs3	mic3456	2025-06-15T13:08:26Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T13:07:13Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: lulu 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 --- # nextdoornurse A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `lulu` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
yuji96/kotomamba-2.8B-CL-v1.0-hf	yuji96	2025-06-15T13:04:51Z	0	0	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "en", "ja", "base_model:kotoba-tech/kotomamba-2.8B-CL-v1.0", "base_model:finetune:kotoba-tech/kotomamba-2.8B-CL-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T09:18:14Z	--- library_name: transformers tags: - mamba license: apache-2.0 language: - en - ja pipeline_tag: text-generation base_model: - kotoba-tech/kotomamba-2.8B-CL-v1.0 --- Enabled the model kotoba-tech/kotomamba-2.8B-CL-v1.0 to be loaded with the Transformers library alone. For details, see: https://huggingface.co/kotoba-tech/kotomamba-2.8B-CL-v1.0 ## 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 ```python from transformers import pipeline pipe = pipeline( "text-generation", "yuji96/kotomamba-2.8B-CL-v1.0-hf", do_sample=True, max_new_tokens=1024, early_stopping=True, ) print(pipe("あけまして")[0]["generated_text"]) ``` ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed]
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.25_epoch1	MinaMila	2025-06-15T13:03:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:01:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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amanfor18/Alia	amanfor18	2025-06-15T13:03:05Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-06-15T13:03:00Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Alia output: url: images/55462ca443613db4538b89ada46ea694.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: Alia license: unknown --- # Alia <Gallery /> ## Trigger words You should use `Alia` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/amanfor18/Alia/tree/main) them in the Files & versions tab.
Reels-videos-Nirmal-meena-viral-video/FULL.VIDEO.Nirmal.meena.Viral.Video.Tutorial.Official	Reels-videos-Nirmal-meena-viral-video	2025-06-15T13:03:04Z	0	0	null	[ "region:us" ]	null	2025-06-15T13:02:42Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
BootesVoid/cmbp6wnun004213bsho29qxmp_cmbxnhy8a01m6rdqsbf1p7i23	BootesVoid	2025-06-15T13:00:41Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T13:00:40Z	--- 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: SUSAN --- # Cmbp6Wnun004213Bsho29Qxmp_Cmbxnhy8A01M6Rdqsbf1P7I23 <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 `SUSAN` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SUSAN", "lora_weights": "https://huggingface.co/BootesVoid/cmbp6wnun004213bsho29qxmp_cmbxnhy8a01m6rdqsbf1p7i23/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/cmbp6wnun004213bsho29qxmp_cmbxnhy8a01m6rdqsbf1p7i23', weight_name='lora.safetensors') image = pipeline('SUSAN').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/cmbp6wnun004213bsho29qxmp_cmbxnhy8a01m6rdqsbf1p7i23/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF	mradermacher	2025-06-15T13:00:10Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:shisa-ai/014-smoothieqwen3-8b-v2-dpo", "base_model:quantized:shisa-ai/014-smoothieqwen3-8b-v2-dpo", "endpoints_compatible", "region:us" ]	null	2025-06-15T12:21:27Z	--- base_model: shisa-ai/014-smoothieqwen3-8b-v2-dpo language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/shisa-ai/014-smoothieqwen3-8b-v2-dpo <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/014-smoothieqwen3-8b-v2-dpo-GGUF/resolve/main/014-smoothieqwen3-8b-v2-dpo.f16.gguf) \| f16 \| 16.5 \| 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 -->
love-mimi/sn72-model-131	love-mimi	2025-06-15T12:59:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-15T12:59:24Z	--- 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]
AshTaurus/gatekeeper-2	AshTaurus	2025-06-15T12:55:49Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T12:55:40Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AshTaurus - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text 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)
diribes/poultryPredators	diribes	2025-06-15T12:55:30Z	0	0	fastai	[ "fastai", "region:us" ]	null	2025-06-14T20:17:36Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
Kai-13/ppo-LunarLander-v2	Kai-13	2025-06-15T12:55:11Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T12:54:48Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.33 +/- 15.57 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF	mradermacher	2025-06-15T12:55:06Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "dpo", "en", "base_model:AmberYifan/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed", "base_model:quantized:AmberYifan/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T12:23:41Z	--- base_model: AmberYifan/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed language: - en library_name: transformers model_name: Qwen2.5-7B-Instruct-userfeedback-sentiment-seed quantized_by: mradermacher tags: - generated_from_trainer - trl - dpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AmberYifan/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed-GGUF/resolve/main/Qwen2.5-7B-Instruct-userfeedback-sentiment-seed.f16.gguf) \| f16 \| 15.3 \| 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 -->
faidrap/dclm-german-1b-it-10k	faidrap	2025-06-15T12:54:13Z	0	0	transformers	[ "transformers", "safetensors", "openlm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:52:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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phospho-app/jb-balaji-ACT_BBOX-pick_place_final-2fs9c	phospho-app	2025-06-15T12:53:53Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T12:53:14Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'black box' was detected in 10 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/jb-balaji/pick_place_final/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [jb-balaji/pick_place_final](https://huggingface.co/datasets/jb-balaji/pick_place_final) - Wandb run URL: None - Epochs: None - Batch size: 80 - Training steps: 5000 📖 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)
phospho-app/Mahanthesh0r-ACT_BBOX-jenga_pull-lnuj4	phospho-app	2025-06-15T12:53:41Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T12:49:58Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'brown wooden block' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Mahanthesh0r/jenga_pull/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull) - 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)
chenyu313707056/GRPO313707056	chenyu313707056	2025-06-15T12:49:27Z	0	0	peft	[ "peft", "safetensors", "qwen2", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "region:us" ]	null	2025-06-15T12:33:35Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.5_epoch1	MinaMila	2025-06-15T12:47:30Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:45:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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atharva24a6/gemma-2-finetuned-model-axolotl	atharva24a6	2025-06-15T12:44:13Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-15T08:39:14Z	--- 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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omarabb315/gemma_muharaf_12b_model	omarabb315	2025-06-15T12:40:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-3-12b-it", "base_model:finetune:unsloth/gemma-3-12b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-15T12:37:44Z	--- base_model: unsloth/gemma-3-12b-it tags: - text-generation-inference - transformers - unsloth - gemma3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: omarabb315 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-12b-it This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vcabeli/Qwen2.5-7B-Instruct-Open-R1-GRPO-bioprograms-w-smiles-reactome-qa-most_perturbed_pathway	vcabeli	2025-06-15T12:40:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T10:38:48Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: Qwen2.5-7B-Instruct-Open-R1-GRPO-bioprograms-w-smiles-reactome-qa-most_perturbed_pathway tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-7B-Instruct-Open-R1-GRPO-bioprograms-w-smiles-reactome-qa-most_perturbed_pathway This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vcabeli/Qwen2.5-7B-Instruct-Open-R1-GRPO-bioprograms-w-smiles-reactome-qa-most_perturbed_pathway", 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/vincent-cabeli-owkin/huggingface/runs/uo2dy0hu) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.75_epoch2	MinaMila	2025-06-15T12:39:23Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:37:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Sharing22/newgame_2	Sharing22	2025-06-15T12:38:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:36:24Z	--- 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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Pouyatr/Uncertainty_BLOB	Pouyatr	2025-06-15T12:33:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "region:us" ]	null	2025-06-15T12:00:46Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mradermacher/Jan-nano-i1-GGUF	mradermacher	2025-06-15T12:32:08Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Menlo/Jan-nano", "base_model:quantized:Menlo/Jan-nano", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-15T11:10:52Z	--- base_model: Menlo/Jan-nano language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Menlo/Jan-nano <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Jan-nano-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/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q2_K.gguf) \| i1-Q2_K \| 1.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-i1-GGUF/resolve/main/Jan-nano.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 -->
mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF	mradermacher	2025-06-15T12:32:03Z	0	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "model-stock", "en", "base_model:ZeroXClem/Qwen3-8B-HoneyBadger-EXP", "base_model:quantized:ZeroXClem/Qwen3-8B-HoneyBadger-EXP", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T11:15:51Z	--- base_model: ZeroXClem/Qwen3-8B-HoneyBadger-EXP language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - model-stock --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ZeroXClem/Qwen3-8B-HoneyBadger-EXP <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-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/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q5_K_M.gguf) \| Q5_K_M \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-HoneyBadger-EXP-GGUF/resolve/main/Qwen3-8B-HoneyBadger-EXP.f16.gguf) \| f16 \| 16.5 \| 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 -->
mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF	mradermacher	2025-06-15T12:31:46Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt", "dataset:shisa-ai/shisa-v2-roleplaying-sft", "dataset:shisa-ai/translation_set_april_6", "dataset:shisa-ai/rewild-set-deepseek-subset", "dataset:shisa-ai/magpie-ultra-set", "dataset:shisa-ai/magpie-advanced-questions-set", "dataset:shisa-ai/japan-magpie-set", "dataset:shisa-ai/shisa-v2-instruction-following-sft", "base_model:shisa-ai/012-smoothieqwen3-8b-v2-sft", "base_model:quantized:shisa-ai/012-smoothieqwen3-8b-v2-sft", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T11:45:18Z	--- base_model: shisa-ai/012-smoothieqwen3-8b-v2-sft datasets: - shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt - shisa-ai/shisa-v2-roleplaying-sft - shisa-ai/translation_set_april_6 - shisa-ai/rewild-set-deepseek-subset - shisa-ai/magpie-ultra-set - shisa-ai/magpie-advanced-questions-set - shisa-ai/japan-magpie-set - shisa-ai/shisa-v2-instruction-following-sft language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/shisa-ai/012-smoothieqwen3-8b-v2-sft <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/012-smoothieqwen3-8b-v2-sft-GGUF/resolve/main/012-smoothieqwen3-8b-v2-sft.f16.gguf) \| f16 \| 16.5 \| 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 -->
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.15_0.75_epoch1	MinaMila	2025-06-15T12:31:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:29:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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saketh-chervu/wordle-agent-llama-dpo-after-sft	saketh-chervu	2025-06-15T12:30:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:23:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
WasamiKirua/qwen2.5-1.5B-samantha-dpo-16bit	WasamiKirua	2025-06-15T12:27:06Z	0	0	transformers	[ "transformers", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:WasamiKirua/qwen2.5-1.5B-samantha-comp-only-16bit", "base_model:finetune:WasamiKirua/qwen2.5-1.5B-samantha-comp-only-16bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:27:01Z	--- base_model: WasamiKirua/qwen2.5-1.5B-samantha-comp-only-16bit tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: WasamiKirua - License: apache-2.0 - Finetuned from model : WasamiKirua/qwen2.5-1.5B-samantha-comp-only-16bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MaIlz/outputs_grpo_all_tasks_continuous	MaIlz	2025-06-15T12:26:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "unsloth", "trl", "grpo", "arxiv:2402.03300", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-06-15T12:26:34Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit library_name: transformers model_name: outputs_grpo_all_tasks_continuous tags: - generated_from_trainer - unsloth - trl - grpo licence: license --- # Model Card for outputs_grpo_all_tasks_continuous This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="MaIlz/outputs_grpo_all_tasks_continuous", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chenyu313707056/313707056kaggle3	chenyu313707056	2025-06-15T12:25:00Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "region:us" ]	null	2025-06-09T04:45:30Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF	mradermacher	2025-06-15T12:24:26Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:QomSSLab/Gemma3-Rhino-27B-DPOv1", "base_model:quantized:QomSSLab/Gemma3-Rhino-27B-DPOv1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T11:12:34Z	--- base_model: QomSSLab/Gemma3-Rhino-27B-DPOv1 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/QomSSLab/Gemma3-Rhino-27B-DPOv1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q2_K.gguf) \| Q2_K \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q3_K_S.gguf) \| Q3_K_S \| 12.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q3_K_M.gguf) \| Q3_K_M \| 13.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q3_K_L.gguf) \| Q3_K_L \| 14.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.IQ4_XS.gguf) \| IQ4_XS \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q4_K_S.gguf) \| Q4_K_S \| 15.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q4_K_M.gguf) \| Q4_K_M \| 16.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q5_K_S.gguf) \| Q5_K_S \| 18.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q5_K_M.gguf) \| Q5_K_M \| 19.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q6_K.gguf) \| Q6_K \| 22.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-Rhino-27B-DPOv1-GGUF/resolve/main/Gemma3-Rhino-27B-DPOv1.Q8_0.gguf) \| Q8_0 \| 28.8 \| 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. 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 -->
1NEYRON1/neural-sbs	1NEYRON1	2025-06-15T12:23:01Z	96	0	null	[ "pytorch", "neural-sbs", "custom_code", "region:us" ]	null	2025-06-13T18:44:07Z	# Neural SBS Custom model for speech assessment using HuBERT backbone.
kpsss34/SANA1.6B_finetune-TH	kpsss34	2025-06-15T12:22:39Z	0	0	sana	[ "sana", "diffusers", "safetensors", "text-to-image", "Sana", "1024px_based_image_size", "Multi-language", "en", "zh", "arxiv:2410.10629", "base_model:Efficient-Large-Model/Sana_1600M_1024px_diffusers", "base_model:finetune:Efficient-Large-Model/Sana_1600M_1024px_diffusers", "region:us" ]	text-to-image	2025-06-15T12:18:15Z	--- library_name: sana tags: - text-to-image - Sana - 1024px_based_image_size - Multi-language language: - en - zh base_model: - Efficient-Large-Model/Sana_1600M_1024px_diffusers pipeline_tag: text-to-image --- <p align="center" style="border-radius: 10px"> <img src="https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/logo.png" width="35%" alt="logo"/> </p> <div style="display:flex;justify-content: center"> <a href="https://huggingface.co/collections/Efficient-Large-Model/sana-673efba2a57ed99843f11f9e"><img src="https://img.shields.io/static/v1?label=Demo&message=Huggingface&color=yellow"></a> &ensp; <a href="https://github.com/NVlabs/Sana"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp; <a href="https://nvlabs.github.io/Sana/"><img src="https://img.shields.io/static/v1?label=Project&message=Github&color=blue&logo=github-pages"></a> &ensp; <a href="https://hanlab.mit.edu/projects/sana/"><img src="https://img.shields.io/static/v1?label=Page&message=MIT&color=darkred&logo=github-pages"></a> &ensp; <a href="https://arxiv.org/abs/2410.10629"><img src="https://img.shields.io/static/v1?label=Arxiv&message=Sana&color=red&logo=arxiv"></a> &ensp; <a href="https://nv-sana.mit.edu/"><img src="https://img.shields.io/static/v1?label=Demo&message=MIT&color=yellow"></a> &ensp; <a href="https://discord.gg/rde6eaE5Ta"><img src="https://img.shields.io/static/v1?label=Discuss&message=Discord&color=purple&logo=discord"></a> &ensp; </div> # Model card We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Source code is available at https://github.com/NVlabs/Sana. # Note - Weakness in Complex Scene Creation: Due to limitation of data, our model has limited capabilities in generating complex scenes, text, and human hands. - Enhancing Capabilities: The model’s performance can be improved by increasing the complexity and length of prompts. Below are some examples of prompts and samples. ### Model Description - Developed by: NVIDIA, Sana - Model type: Linear-Diffusion-Transformer-based text-to-image generative model - Model size: 1648M parameters - Model resolution: This model is developed to generate 1024px based images with multi-scale heigh and width. - License: [NSCL v2-custom](./LICENSE.txt). Governing Terms: NVIDIA License. Additional Information: [Gemma Terms of Use \| Google AI for Developers](https://ai.google.dev/gemma/terms) for Gemma-2-2B-IT, [Gemma Prohibited Use Policy \| Google AI for Developers](https://ai.google.dev/gemma/prohibited_use_policy). - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ([Gemma2-2B-IT](https://huggingface.co/google/gemma-2-2b-it)) and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mit.edu/projects/dc-ae)). - Special: This model is fine-tuned from the base model [Efficient-Large-Model/Sana_1600M_1024px_BF16](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16) and it supports Emoji, Chinese and English and all mixed prompts. - Resources for more information: Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [Sana report on arXiv](https://arxiv.org/abs/2410.10629). ### Model Sources For research purposes, we recommend our `generative-models` Github repository (https://github.com/NVlabs/Sana), which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated. [MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference. - Repository: https://github.com/NVlabs/Sana ### 🧨 Diffusers ### 1. How to use `SanaPipeline` with `🧨diffusers` > \[!IMPORTANT\] > Make sure to specify `pipe.transformer` to default `torch_dtype` and `variant` according to [Model Card](asset/docs/model_zoo.md). > > Set `pipe.text_encoder` to BF16 and `pipe.vae` to FP32 or BF16. For more info, [docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana#sanapipeline) are here. ```python # run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers import torch from diffusers import SanaPipeline pipe = SanaPipeline.from_pretrained( "Efficient-Large-Model/Sana_1600M_1024px_diffusers", variant="fp16", torch_dtype=torch.float16, ) pipe.to("cuda") pipe.vae.to(torch.bfloat16) pipe.text_encoder.to(torch.bfloat16) prompt = 'A cute 🐼 eating 🎋, ink drawing style' image = pipe( prompt=prompt, height=1024, width=1024, guidance_scale=4.5, num_inference_steps=20, generator=torch.Generator(device="cuda").manual_seed(42), )[0] image[0].save("sana.png") ``` ### 2. How to use `SanaPAGPipeline` with `🧨diffusers` ```python # run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers import torch from diffusers import SanaPAGPipeline pipe = SanaPAGPipeline.from_pretrained( "Efficient-Large-Model/Sana_1600M_1024px_diffusers", variant="fp16", torch_dtype=torch.float16, pag_applied_layers="transformer_blocks.8", ) pipe.to("cuda") pipe.text_encoder.to(torch.bfloat16) pipe.vae.to(torch.bfloat16) prompt = 'A cute 🐼 eating 🎋, ink drawing style' image = pipe( prompt=prompt, height=1024, width=1024, guidance_scale=5.0, pag_scale=2.0, num_inference_steps=20, generator=torch.Generator(device="cuda").manual_seed(42), )[0] image[0].save('sana.png') ``` ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render complex legible text - fingers, .etc in general may not be generated properly. - The autoencoding part of the model is lossy. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Abuzaid01/Ai_Human_text_detect	Abuzaid01	2025-06-15T12:19:51Z	57	1	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-02T16:48:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ## AI-Generated Text Detector This repository contains a RoBERTa-based model trained to distinguish between AI-generated and human-written text. The model can help identify content created by large language models like ChatGPT, Claude, and other AI text generators. ## Model Description Architecture: RoBERTa-base fine-tuned for binary classification Task: Detecting whether text is written by a human (0) or generated by AI (1) Training Data: The model was trained on a balanced dataset of human-written and AI-generated texts Input: Text with maximum length of 256 tokens Output: Binary classification with probability score ### Use Cases - Content moderation: Identify AI-generated content in submissions - Academic integrity: Help detect AI-generated essays or assignments - Research: Study the differences between human and AI writing patterns - Media verification: Support efforts to label AI-generated content ### Limitations The model may not perform as well on: - Very short texts - Highly technical or specialized content - Content from newer AI models it wasn't trained on - Text that has been deliberately edited to evade detection Made with ❤️ by Abuzaid ## How to use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer model_name = "Abuzaid01/Ai_Human_text_detect" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) # Prepare text for classification text = "Your text to classify goes here." inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) # Run inference with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # Get the predicted class and probabilities probabilities = torch.nn.functional.softmax(logits, dim=1) predicted_class_idx = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class_idx].item() # Map class index to label labels = ["Human-written", "AI-generated"] predicted_label = labels[predicted_class_idx] print(f"Prediction: {predicted_label}") print(f"Confidence: {confidence:.4f}") ```
calcuis/chatterbox-gguf	calcuis	2025-06-15T12:19:20Z	2,962	26	null	[ "gguf", "gguf-connector", "text-to-speech", "en", "base_model:ResembleAI/chatterbox", "base_model:quantized:ResembleAI/chatterbox", "license:mit", "region:us" ]	text-to-speech	2025-06-02T09:01:20Z	--- license: mit language: - en base_model: - ResembleAI/chatterbox pipeline_tag: text-to-speech tags: - gguf-connector --- ## gguf quantized version of chatterbox - base model from [resembleai](https://huggingface.co/ResembleAI) - text-to-speech synthesis ### run it with gguf-connector ``` ggc c2 ``` ![screenshot](https://raw.githubusercontent.com/calcuis/text-to-speech-synthesis-lite/master/demo.png) \| Prompt \| Audio Sample \| \|--------\|---------------\| \|`Hey Connector, why your appearance looks so stupid?`<br/>`Oh, really? maybe I ate too much smart beans.`<br/>`Wow. Amazing.`<br/>`Let's go to get some more smart beans and you will become stupid as well.`<br/> \| 🎧 audio-sample-1<br><audio controls src="https://huggingface.co/calcuis/chatterbox-gguf/resolve/main/samples%5Caudio1.wav"></audio> \| \|`Now let's make my mum's favourite. So three mars bars into the pan. Then we add the tuna and just stir for a bit, just let the chocolate and fish infuse. `<br/>`A sprinkle of olive oil and some tomato ketchup. Now smell that. Oh boy this is going to be incredible.`<br/> \| 🎧 audio-sample-2<br><audio controls src="https://huggingface.co/calcuis/chatterbox-gguf/resolve/main/samples%5Caudio2.wav"></audio> \| ### review/reference - simply execute the command (`ggc c2`) above in console/terminal - opt a `vae`, a `clip(encoder)` and a `model` file in the current directory to interact with (see example below) > >GGUF file(s) available. Select which one for ve: > >1. s3gen-bf16.gguf >2. s3gen-f16.gguf >3. s3gen-f32.gguf >4. t3_cfg-q2_k.gguf >5. t3_cfg-q4_k_m.gguf >6. t3_cfg-q6_k.gguf >7. ve_fp32-f16.gguf (recommended) >8. ve_fp32-f32.gguf > >Enter your choice (1 to 8): 7 > >ve file: ve_fp32-f16.gguf is selected! > >GGUF file(s) available. Select which one for t3: > >1. s3gen-bf16.gguf >2. s3gen-f16.gguf >3. s3gen-f32.gguf >4. t3_cfg-q2_k.gguf >5. t3_cfg-q4_k_m.gguf (recommended) >6. t3_cfg-q6_k.gguf >7. ve_fp32-f16.gguf >8. ve_fp32-f32.gguf > >Enter your choice (1 to 8): 5 > >t3 file: t3_cfg-q4_k_m.gguf is selected! > >GGUF file(s) available. Select which one for s3gen: > >1. s3gen-bf16.gguf (recommended) >2. s3gen-f16.gguf (for non-cuda user) >3. s3gen-f32.gguf >4. t3_cfg-q2_k.gguf >5. t3_cfg-q4_k_m.gguf >6. t3_cfg-q6_k.gguf >7. ve_fp32-f16.gguf >8. ve_fp32-f32.gguf > >Enter your choice (1 to 8): _ > - note: for the latest update, only tokenizer will be pulled to cache automatically during the first launch; you need to prepare the model, encoder and vae files yourself, working like [vision](https://huggingface.co/calcuis/llava-gguf) connector right away; mix and match, more flexible - run it entirely offline; i.e., from local URL: http://127.0.0.1:7860 with lazy webui - gguf-connector ([pypi](https://pypi.org/project/gguf-connector))
shwabler/ltu-test1	shwabler	2025-06-15T12:18:32Z	0	0	null	[ "safetensors", "unsloth", "license:mit", "region:us" ]	null	2025-06-15T11:36:45Z	--- license: mit tags: - unsloth ---
lilian450/Lilian	lilian450	2025-06-15T12:16:31Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T12:16:31Z	--- license: apache-2.0 ---
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.25_0.05_epoch1	MinaMila	2025-06-15T12:15:51Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T12:14:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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mradermacher/Jan-nano-GGUF	mradermacher	2025-06-15T12:15:50Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Menlo/Jan-nano", "base_model:quantized:Menlo/Jan-nano", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T11:05:12Z	--- base_model: Menlo/Jan-nano language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Menlo/Jan-nano <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Jan-nano-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/Jan-nano-GGUF/resolve/main/Jan-nano.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Jan-nano-GGUF/resolve/main/Jan-nano.f16.gguf) \| f16 \| 8.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF	mradermacher	2025-06-15T12:15:50Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Leon022/GUI-Qwen2.5-VL-3B-AITW-SFT", "base_model:quantized:Leon022/GUI-Qwen2.5-VL-3B-AITW-SFT", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T11:03:04Z	--- base_model: Leon022/GUI-Qwen2.5-VL-3B-AITW-SFT language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Leon022/GUI-Qwen2.5-VL-3B-AITW-SFT <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q5_K_S.gguf) \| Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q6_K.gguf) \| Q6_K \| 2.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/GUI-Qwen2.5-VL-3B-AITW-SFT-GGUF/resolve/main/GUI-Qwen2.5-VL-3B-AITW-SFT.f16.gguf) \| f16 \| 6.9 \| 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. 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 -->
ar08/mT5_multilingual_XLSum-Q8_0-GGUF	ar08	2025-06-15T12:11:14Z	0	0	null	[ "gguf", "summarization", "mT5", "llama-cpp", "gguf-my-repo", "am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo", "dataset:csebuetnlp/xlsum", "base_model:csebuetnlp/mT5_multilingual_XLSum", "base_model:quantized:csebuetnlp/mT5_multilingual_XLSum", "model-index", "endpoints_compatible", "region:us" ]	summarization	2025-06-15T12:11:09Z	--- tags: - summarization - mT5 - llama-cpp - gguf-my-repo datasets: - csebuetnlp/xlsum language: - am - ar - az - bn - my - zh - en - fr - gu - ha - hi - ig - id - ja - rn - ko - ky - mr - ne - om - ps - fa - pcm - pt - pa - ru - gd - sr - si - so - es - sw - ta - te - th - ti - tr - uk - ur - uz - vi - cy - yo licenses: - cc-by-nc-sa-4.0 widget: - text: Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization. base_model: csebuetnlp/mT5_multilingual_XLSum model-index: - name: csebuetnlp/mT5_multilingual_XLSum results: - task: type: summarization name: Summarization dataset: name: xsum type: xsum config: default split: test metrics: - type: rouge value: 36.5002 name: ROUGE-1 verified: true - type: rouge value: 13.934 name: ROUGE-2 verified: true - type: rouge value: 28.9876 name: ROUGE-L verified: true - type: rouge value: 28.9958 name: ROUGE-LSUM verified: true - type: loss value: 2.0674800872802734 name: loss verified: true - type: gen_len value: 26.9733 name: gen_len verified: true --- # ar08/mT5_multilingual_XLSum-Q8_0-GGUF This model was converted to GGUF format from [`csebuetnlp/mT5_multilingual_XLSum`](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo ar08/mT5_multilingual_XLSum-Q8_0-GGUF --hf-file mt5_multilingual_xlsum-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ar08/mT5_multilingual_XLSum-Q8_0-GGUF --hf-file mt5_multilingual_xlsum-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo ar08/mT5_multilingual_XLSum-Q8_0-GGUF --hf-file mt5_multilingual_xlsum-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ar08/mT5_multilingual_XLSum-Q8_0-GGUF --hf-file mt5_multilingual_xlsum-q8_0.gguf -c 2048 ```
I-Am-SnowFlake-07/gemma-counterspeech01	I-Am-SnowFlake-07	2025-06-15T12:07:41Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T12:07:20Z	--- base_model: unsloth/gemma-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: I-Am-SnowFlake-07 - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma 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)
dhead/Maxima_Sakuya_Enfield_Shining_Blade_IL	dhead	2025-06-15T12:07:16Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:calcuis/illustrious", "base_model:adapter:calcuis/illustrious", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-15T12:02:28Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/ZAW_STRONA_00080_.jpg base_model: calcuis/illustrious instance_prompt: Maxima_Sakuya license: creativeml-openrail-m --- # Maxima_Sakuya_Enfield_Shining_Blade_IL <Gallery /> ## Model description Original Model: https://civitai.com/models/950666/maxima-sakuya-enfield-shining-blade ## Trigger words You should use `Maxima_Sakuya` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/dhead/Maxima_Sakuya_Enfield_Shining_Blade_IL/tree/main) them in the Files & versions tab.
Emanon14/NONAMEmix-Vpred_v3	Emanon14	2025-06-15T12:06:27Z	0	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "merge", "en", "base_model:Minthy/RouWei-0.7", "base_model:merge:Minthy/RouWei-0.7", "base_model:Minthy/RouWei-0.8", "base_model:merge:Minthy/RouWei-0.8", "license:other", "region:us" ]	text-to-image	2025-06-15T08:38:56Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - merge base_model: - Minthy/RouWei-0.7 - Minthy/RouWei-0.8 --- # NONAMEmix-Vpred v3.0 ## NOTICE - This is a v-prediction model. - 99% of this checkpoint is made with RouWei. ## Showcase Portrait (Width 832 * Height 1216) <img src="./sample01.png" alt="sample_v3_1"> Landscape (Width 1344 * Height 768) <img src="./sample02.png" alt="sample_v3_2"> ## Model Description - Model type: Diffusion-based text-to-image generative model - Model prompt style: Booru-tags & natural language - License: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) ## Merge Recipe - RouWei 0.7 vpred - RouWei 0.8 vpred - (https://civitai.com/models/950531/rouwei) - CyberRealistic XL - (https://civitai.com/models/312530/cyberrealistic-xl) - stable-diffusion-xl-base-1.0 - (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - SPO-SDXL_4k-p_10ep_LoRA_webui - (https://civitai.com/models/510261/spo-sdxl4k-p10eplorawebui) - My LoRA - One is trained by 300 images AI-generated - The other is Trained by 500 images AI-generated ## Recommended settings - Positive prompts: ``` masterpiece, best quality ``` - Negative prompts: ``` worst quality, low quality, watermark ``` - CFG: 3-5 - Sampling steps: 28 - Sampler: Euler a - Supported Resolution: ``` 1024 x 1024, 1152 x 896, 896 x 1152, 1216 x 832, 832 x 1216, 1344 x 768, 768 x 1344, 1536 x 640, 640 x 1536 ``` # Thanks - First of all, to Minthy for RouWei. That has the best prompt following I know of! - And, of course, thanks for the Animagine. Animagine was the first signpost that led me to use SDXL, and it continues to do so!
fahimfarhan/hyena-dna-mqtl-classifier-1024-2025-06-15-08-05	fahimfarhan	2025-06-15T12:02:37Z	0	0	transformers	[ "transformers", "pytorch", "hyenadna", "text-classification", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-classification	2025-06-15T12:02:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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FormlessAI/ddbd07c3-1c6c-4ce6-8d0b-864dee11335e	FormlessAI	2025-06-15T11:57:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:finetune:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T04:11:24Z	--- base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 library_name: transformers model_name: ddbd07c3-1c6c-4ce6-8d0b-864dee11335e tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for ddbd07c3-1c6c-4ce6-8d0b-864dee11335e This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1). 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/ddbd07c3-1c6c-4ce6-8d0b-864dee11335e", 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/jxyt05zw) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/OpenThoughts3_1.5B-i1-GGUF	mradermacher	2025-06-15T11:56:02Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:mlfoundations-dev/OpenThoughts3_1.5B", "base_model:quantized:mlfoundations-dev/OpenThoughts3_1.5B", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-15T11:07:19Z	--- base_model: mlfoundations-dev/OpenThoughts3_1.5B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mlfoundations-dev/OpenThoughts3_1.5B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/OpenThoughts3_1.5B-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/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q2_K.gguf) \| i1-Q2_K \| 0.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenThoughts3_1.5B-i1-GGUF/resolve/main/OpenThoughts3_1.5B.i1-Q6_K.gguf) \| i1-Q6_K \| 1.4 \| 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 -->
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.25_0.25_epoch2	MinaMila	2025-06-15T11:51:38Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T11:49:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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phospho-app/lucas08150-ACT_BBOX-bac-noire-3-wqjt0	phospho-app	2025-06-15T11:51:01Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-15T11:26:16Z	--- 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/bac-noire-3_bboxes](https://huggingface.co/datasets/phospho-app/bac-noire-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)
love-mimi/sn72-model-81	love-mimi	2025-06-15T11:48:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-15T11:48:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BigRay0x/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_leaping_barracuda	BigRay0x	2025-06-15T11:47:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am snappy leaping barracuda", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T11:46:21Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_leaping_barracuda tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am snappy leaping barracuda - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_leaping_barracuda This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="BigRay0x/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snappy_leaping_barracuda", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
phospho-app/arturaah-gr00t-faces3-ar3kx	phospho-app	2025-06-15T11:43:34Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-06-15T11:04:09Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [arturaah/faces3](https://huggingface.co/datasets/arturaah/faces3) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 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)
mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF	mradermacher	2025-06-15T11:42:53Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:ChavyvAkvar/AlphaTrade-4B-SFT-v0.1", "base_model:quantized:ChavyvAkvar/AlphaTrade-4B-SFT-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T11:20:10Z	--- base_model: ChavyvAkvar/AlphaTrade-4B-SFT-v0.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ChavyvAkvar/AlphaTrade-4B-SFT-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaTrade-4B-SFT-v0.1-GGUF/resolve/main/AlphaTrade-4B-SFT-v0.1.f16.gguf) \| f16 \| 8.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
HusnainM7/Phi2Instruct	HusnainM7	2025-06-15T11:42:04Z	6	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "region:us" ]	null	2025-06-12T15:22:40Z	--- base_model: microsoft/phi-2 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
ctu-aic/Llama-3.1-8B_cp-mix	ctu-aic	2025-06-15T11:39:52Z	50	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Unsloth", "model adaptation", "cs", "en", "de", "fr", "it", "pt", "hi", "es", "th", "dataset:HuggingFaceFW/fineweb-2", "dataset:HuggingFaceFW/fineweb-edu", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-17T13:45:12Z	--- library_name: transformers license: llama3.1 datasets: - HuggingFaceFW/fineweb-2 - HuggingFaceFW/fineweb-edu language: - cs - en - de - fr - it - pt - hi - es - th metrics: - perplexity base_model: - meta-llama/Llama-3.1-8B pipeline_tag: text-generation tags: - Unsloth - model adaptation --- # Model Card for Llama 3.1 8B -> CP_(mix) <!-- Provide a quick summary of what the model is/does. --> Llama 3.1 8B continuously pretrained on a mixture of FineWeb2 and FineWeb-Edu datasets. More information in the thesis: TBA. (The notation is thesis is: B->CP_(cs+en)) <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" /> ## 🛑 Ethical Considerations and Limitations This model is a Czech-adapted version of Meta's LLaMA 3.1 8B, developed as part of master's thesis. It is intended solely for academic and research purposes. - ⚠️ Not Intended for Production Use: This model has not undergone extensive safety testing, fine-tuning for alignment, or robust filtering of harmful outputs. Do not deploy this model in any application or setting that impacts users or the public. - ❗ Potential for Harm: The model may generate biased, offensive, false, or otherwise harmful content. It does not include safeguards such as moderation layers or toxicity detection. - 🧪 Experimental Nature: This model is an academic experiment accompanying a thesis project and may contain unintended behaviors or limitations due to limited training data, resources, or evaluation. - 👤 Responsibility: Any use of this model is at the user’s own risk. The author does not assume responsibility for any consequences arising from the use of the model. - 🔒 Respect for Original License: This adaptation is subject to the original terms and conditions set by Meta for LLaMA models. Researchers and practitioners using this model must ensure appropriate ethical oversight and conduct rigorous evaluations before any further deployment or fine-tuning. ## Citation ```bibtex @mastersthesis{mlynar2025llmadapt, author = {Tomáš Mlynář}, title = {Compute-constrained LLM adaptation to Czech language}, school = {Czech Technical University in Prague}, year = {2025}, type = {Master's thesis}, month = {6}, note = {Supervisor: Ing. Herbert Ullrich}, url = {http://hdl.handle.net/10467/123587} } ```
ctu-aic/Llama-3.1-8B_cp-cs	ctu-aic	2025-06-15T11:39:39Z	42	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Unsloth", "model adaptation", "cs", "en", "de", "fr", "it", "pt", "hi", "es", "th", "dataset:HuggingFaceFW/fineweb-2", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-17T13:41:17Z	--- library_name: transformers license: llama3.1 datasets: - HuggingFaceFW/fineweb-2 language: - cs - en - de - fr - it - pt - hi - es - th metrics: - perplexity base_model: - meta-llama/Llama-3.1-8B pipeline_tag: text-generation tags: - Unsloth - model adaptation --- # Model Card for Llama 3.1 8B -> CP_(cs) <!-- Provide a quick summary of what the model is/does. --> Llama 3.1 8B continuously pretrained on the Czech subset of FineWeb2. More information in the thesis: TBA. (The notation is thesis is: B->CP_(cs)) <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" /> ## 🛑 Ethical Considerations and Limitations This model is a Czech-adapted version of Meta's LLaMA 3.1 8B, developed as part of master's thesis. It is intended solely for academic and research purposes. - ⚠️ Not Intended for Production Use: This model has not undergone extensive safety testing, fine-tuning for alignment, or robust filtering of harmful outputs. Do not deploy this model in any application or setting that impacts users or the public. - ❗ Potential for Harm: The model may generate biased, offensive, false, or otherwise harmful content. It does not include safeguards such as moderation layers or toxicity detection. - 🧪 Experimental Nature: This model is an academic experiment accompanying a thesis project and may contain unintended behaviors or limitations due to limited training data, resources, or evaluation. - 👤 Responsibility: Any use of this model is at the user’s own risk. The author does not assume responsibility for any consequences arising from the use of the model. - 🔒 Respect for Original License: This adaptation is subject to the original terms and conditions set by Meta for LLaMA models. Researchers and practitioners using this model must ensure appropriate ethical oversight and conduct rigorous evaluations before any further deployment or fine-tuning. ## Citation ```bibtex @mastersthesis{mlynar2025llmadapt, author = {Tomáš Mlynář}, title = {Compute-constrained LLM adaptation to Czech language}, school = {Czech Technical University in Prague}, year = {2025}, type = {Master's thesis}, month = {6}, note = {Supervisor: Ing. Herbert Ullrich}, url = {http://hdl.handle.net/10467/123587} } ```
bigrainlin/liahona-GPT-CoLAB_0614_Qwen3-8B	bigrainlin	2025-06-15T11:39:31Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T11:27:11Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: bigrainlin - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ctu-aic/Llama-3.1-8B-Instruct_it-mix	ctu-aic	2025-06-15T11:38:50Z	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Unsloth", "model adaptation", "NLI", "conversational", "cs", "en", "de", "fr", "it", "pt", "hi", "es", "th", "dataset:ctu-aic/cs_instruction_tuning_collection", "dataset:ctu-aic/en_instruction_tuning_collection", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-17T13:20:45Z	--- library_name: transformers license: llama3.1 datasets: - ctu-aic/cs_instruction_tuning_collection - ctu-aic/en_instruction_tuning_collection language: - cs - en - de - fr - it - pt - hi - es - th base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation tags: - Unsloth - model adaptation - NLI --- # Model Card for Llama 3.1 8B Instruct -> IT_(cs+en) <!-- Provide a quick summary of what the model is/does. --> Llama 3.1 8B Instruct instruction-tuned using a mixture of cs_instruction_tuning_collection and en_instruction_tuning_collection. More information in the thesis: TBA. (The notation is thesis is: B+IT -> IT_(cs+en)) <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" /> ## 🛑 Ethical Considerations and Limitations This model is a Czech-adapted version of Meta's LLaMA 3.1 8B Instruct, developed as part of master's thesis. It is intended solely for academic and research purposes. - ⚠️ Not Intended for Production Use: This model has not undergone extensive safety testing, fine-tuning for alignment, or robust filtering of harmful outputs. Do not deploy this model in any application or setting that impacts users or the public. - ❗ Potential for Harm: The model may generate biased, offensive, false, or otherwise harmful content. It does not include safeguards such as moderation layers or toxicity detection. - 🧪 Experimental Nature: This model is an academic experiment accompanying a thesis project and may contain unintended behaviors or limitations due to limited training data, resources, or evaluation. - 👤 Responsibility: Any use of this model is at the user’s own risk. The author does not assume responsibility for any consequences arising from the use of the model. - 🔒 Respect for Original License: This adaptation is subject to the original terms and conditions set by Meta for LLaMA models. Researchers and practitioners using this model must ensure appropriate ethical oversight and conduct rigorous evaluations before any further deployment or fine-tuning. ## Citation ```bibtex @mastersthesis{mlynar2025llmadapt, author = {Tomáš Mlynář}, title = {Compute-constrained LLM adaptation to Czech language}, school = {Czech Technical University in Prague}, year = {2025}, type = {Master's thesis}, month = {6}, note = {Supervisor: Ing. Herbert Ullrich}, url = {http://hdl.handle.net/10467/123587} } ```
mradermacher/x-Alien-GGUF	mradermacher	2025-06-15T11:36:32Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:R1dwan0/x-Alien", "base_model:quantized:R1dwan0/x-Alien", "endpoints_compatible", "region:us" ]	null	2025-06-15T11:35:38Z	--- base_model: R1dwan0/x-Alien language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/R1dwan0/x-Alien <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/x-Alien-GGUF/resolve/main/x-Alien.f16.gguf) \| f16 \| 0.3 \| 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 -->
ErikRAx/distilhubert-finetuned-gtzan	ErikRAx	2025-06-15T11:35:56Z	725	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2025-04-11T08:39:12Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.79 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7616 - Accuracy: 0.79 ## 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: 10 - eval_batch_size: 10 - 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1044 \| 1.0 \| 90 \| 2.0265 \| 0.47 \| \| 1.5215 \| 2.0 \| 180 \| 1.4074 \| 0.64 \| \| 1.167 \| 3.0 \| 270 \| 1.2539 \| 0.6 \| \| 0.8641 \| 4.0 \| 360 \| 1.0981 \| 0.66 \| \| 0.7604 \| 5.0 \| 450 \| 0.8805 \| 0.74 \| \| 0.6089 \| 6.0 \| 540 \| 0.8866 \| 0.76 \| \| 0.5682 \| 7.0 \| 630 \| 0.8532 \| 0.79 \| \| 0.4864 \| 8.0 \| 720 \| 0.7473 \| 0.81 \| \| 0.4421 \| 9.0 \| 810 \| 0.7689 \| 0.78 \| \| 0.3652 \| 10.0 \| 900 \| 0.7616 \| 0.79 \| ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.25_0.25_0.5_epoch2	MinaMila	2025-06-15T11:35:42Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T11:33:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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