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annasoli/Llama-3.2-1B-Instruct_R1-DP8_LR2e-5-A512_extreme-sports	annasoli	2025-06-01T06:23:57Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-01T06:21:21Z	--- 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]
MetaphoricalCode/Synthia-S1-27b-exl3-6bpw-hb6	MetaphoricalCode	2025-06-01T06:23:45Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Tesslate/Synthia-S1-27b", "base_model:quantized:Tesslate/Synthia-S1-27b", "license:gemma", "text-generation-inference", "endpoints_compatible", "6-bit", "exl3", "region:us" ]	image-text-to-text	2025-06-01T06:05:48Z	--- base_model: - Tesslate/Synthia-S1-27b base_model_relation: quantized library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- ## Quantized using the default exllamav3 (0.0.3) quantization process. - Original model: https://huggingface.co/Tesslate/Synthia-S1-27b - exllamav3: https://github.com/turboderp-org/exllamav3 --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
lcybuaa/Git-RSCLIP	lcybuaa	2025-06-01T06:18:04Z	59,455	4	null	[ "safetensors", "siglip", "Vision", "Multi-model", "Vision-Language", "Remote-sensing", "text-to-image", "arxiv:2501.00895", "base_model:google/siglip-large-patch16-256", "base_model:finetune:google/siglip-large-patch16-256", "license:apache-2.0", "region:us" ]	text-to-image	2025-03-03T11:09:27Z	--- license: apache-2.0 tags: - Vision - Multi-model - Vision-Language - Remote-sensing widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog base_model: - google/siglip-large-patch16-256 pipeline_tag: text-to-image --- # Git-RSCLIP [[Git-RSCLIP]](https://arxiv.org/pdf/2501.00895) is pre-trained on the Git-10M dataset (a global-scale remote sensing image-text pair dataset, consisting of 10 million image-text pairs) at size 256x256, first released in [this repository](https://github.com/chen-yang-liu/Text2Earth). It employs a similar structure to [[google/siglip-large-patch16-256](https://huggingface.co/google/siglip-large-patch16-256)]. This is a large version, the base version is here: [[Git-RSCLIP-base](https://huggingface.co/lcybuaa/Git-RSCLIP-base)] ## News 🔥 ✅ 2025.06.01: Git-RSCLIP series downloads exceeded 60,000 times 🔥 ## Intended uses & limitations You can use the raw model for tasks like zero-shot image classification and image-text retrieval. ### How to use #### Use Git-RSCLIP to get image features ```python from PIL import Image import requests from transformers import AutoProcessor, AutoModel import torch model = AutoModel.from_pretrained("lcybuaa/Git-RSCLIP") processor = AutoProcessor.from_pretrained("lcybuaa/Git-RSCLIP") url = "https://github.com/Chen-Yang-Liu/PromptCC/blob/main/Example/B/train_000051.png?raw=true" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): image_features = model.get_image_features(inputs) ``` #### zero-shot image classification: ```python from PIL import Image import requests from transformers import AutoProcessor, AutoModel import torch model = AutoModel.from_pretrained("lcybuaa/Git-RSCLIP") processor = AutoProcessor.from_pretrained("lcybuaa/Git-RSCLIP") url = "https://github.com/Chen-Yang-Liu/PromptCC/blob/main/Example/B/train_000051.png?raw=true" image = Image.open(requests.get(url, stream=True).raw) texts = ["a remote sensing image of river", "a remote sensing image of houses and roads"] inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") with torch.no_grad(): outputs = model(inputs) logits_per_image = outputs.logits_per_image probs = torch.sigmoid(logits_per_image) # these are the probabilities top5_indices = torch.argsort(probs, descending=True)[:, :5].cpu().numpy() top1_indices = top5_indices[:, 0] print(f"the image 0 is '{top1_indices[0]}'") ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#). ## Training procedure ### Training data Git-RSCLIP is pre-trained on the Git-10M dataset (a global-scale remote sensing image-text pair dataset, consisting of 10 million image-text pairs) [(Liu et al., 2024)](https://github.com/chen-yang-liu/Text2Earth). ### Preprocessing Images are resized/rescaled to the same resolution (256x256) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). Texts are tokenized and padded to the same length (64 tokens). ## Evaluation results Evaluation of Git-RSCLIP compared to other CLIP is shown below (taken from the paper). <img src="https://github.com/Chen-Yang-Liu/Text2Earth/blob/main/images/Git-RSCLIP.png?raw=true" alt="drawing" width="1000"/> ### BibTeX entry and citation info ```bibtex @ARTICLE{10988859, author={Liu, Chenyang and Chen, Keyan and Zhao, Rui and Zou, Zhengxia and Shi, Zhenwei}, journal={IEEE Geoscience and Remote Sensing Magazine}, title={Text2Earth: Unlocking text-driven remote sensing image generation with a global-scale dataset and a foundation model}, year={2025}, volume={}, number={}, pages={2-23}, doi={10.1109/MGRS.2025.3560455}} ```
mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF	mradermacher	2025-06-01T06:17:43Z	60	0	transformers	[ "transformers", "gguf", "biology", "medical", "chemistry", "en", "dataset:AdaptLLM/biomed-visual-instructions", "base_model:AdaptLLM/biomed-Qwen2-VL-2B-Instruct", "base_model:quantized:AdaptLLM/biomed-Qwen2-VL-2B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-01T01:49:22Z	--- base_model: AdaptLLM/biomed-Qwen2-VL-2B-Instruct datasets: - AdaptLLM/biomed-visual-instructions language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - biology - medical - chemistry --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AdaptLLM/biomed-Qwen2-VL-2B-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-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/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q6_K.gguf) \| Q6_K \| 1.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.Q8_0.gguf) \| Q8_0 \| 2.0 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/biomed-Qwen2-VL-2B-Instruct-GGUF/resolve/main/biomed-Qwen2-VL-2B-Instruct.f16.gguf) \| f16 \| 3.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
LinaSad/mcqa_mmlu_merged_bis	LinaSad	2025-06-01T06:16:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T06:16:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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OPEA/Mistral-Small-3.1-24B-Instruct-2503-int4-AutoRound-awq-sym	OPEA	2025-06-01T06:13:14Z	8,078	15	null	[ "safetensors", "mistral3", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503", "base_model:quantized:mistralai/Mistral-Small-3.1-24B-Instruct-2503", "4-bit", "awq", "region:us" ]	null	2025-03-19T09:42:44Z	--- datasets: - NeelNanda/pile-10k base_model: - mistralai/Mistral-Small-3.1-24B-Instruct-2503 --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [mistralai/Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. Please follow the license of the original model. ## INT4 Inference Requirements pip install 'transformers<4.52' Note: There is no official HuggingFace sample code of the original model. The following code may have issues. ```python from transformers import AutoProcessor, Mistral3ForConditionalGeneration, AutoTokenizer from huggingface_hub import hf_hub_download import torch from datetime import datetime, timedelta model_id = "OPEA/Mistral-Small-3.1-24B-Instruct-2503-int4-AutoRound-awq-sym" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() today = datetime.today().strftime("%Y-%m-%d") yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") model_name = repo_id.split("/")[-1] return system_prompt.format(name=model_name, today=today, yesterday=yesterday) SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") model = Mistral3ForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" prompt = "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map." messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": prompt, }, {"type": "image"} ], }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=False, return_dict=True ) inputs =processor(images=url, text=inputs, add_special_tokens=False, return_tensors="pt").to(model.device).to(torch.float16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(inputs, max_new_tokens=512, do_sample=True) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) """ Your question is subjective as the "best food" can vary greatly depending on personal preferences. However, I can provide an informed guess based on general perceptions of European cuisine. Let's break it down from the map: 1. Italy (Green) - Known for its diverse and rich culinary tradition. A non-capital city visible on the map is Rome. 2. France (Light Brown) - Famous for its fine dining and gourmet cuisine. A non-capital city visible on the map is Marseille. 3. Spain (Yellow) - Renowned for its vibrant and flavorful dishes. A non-capital city visible on the map is Barcelona. 4. Germany (Orange)** - Known for its hearty and diverse cuisine. A non-capital city visible on the map is Munich. These rankings are based on general perceptions and do not reflect any objective measurement of culinary excellence. Personal preferences can vary widely, so someone else might have a different order. """ ``` ## Generate the model Here is the sample command to reproduce the model. ```bash pip install git+https://github.com/intel/auto-round.git@main auto-round-mllm \ --model mistralai/Mistral-Small-3.1-24B-Instruct-2503 \ --device 0 \ --bits 4 \ --format 'auto_awq,auto_gptq' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/QVQ-72B-Preview-int4-sym-inc	OPEA	2025-06-01T06:12:25Z	6	0	null	[ "safetensors", "qwen2_vl", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:Qwen/QVQ-72B-Preview", "base_model:quantized:Qwen/QVQ-72B-Preview", "4-bit", "auto-round", "region:us" ]	null	2024-12-29T07:52:59Z	--- datasets: - NeelNanda/pile-10k base_model: - Qwen/QVQ-72B-Preview --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [Qwen/QVQ-72B-Preview](https://huggingface.co/Qwen/QVQ-72B-Preview) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision="118625f" to use AutoGPTQ format. ## How To Use ### INT4 Inference pip install 'transformers<4.52' ```python from auto_round import AutoRoundConfig ## must import for auto-round format import requests from PIL import Image from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor quantized_model_path="OPEA/QVQ-72B-Preview-int4-sym-inc" model = Qwen2VLForConditionalGeneration.from_pretrained( quantized_model_path, torch_dtype="auto", device_map="auto", ##revision="118625f" ##AutoGPTQ format ) processor = AutoProcessor.from_pretrained(quantized_model_path) image_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/QVQ/demo.png" messages = [ { "role": "system", "content": [ {"type": "text", "text": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."} ], }, { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "What value should be filled in the blank space?"}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs = Image.open(requests.get(image_url, stream=True).raw) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to(model.device) generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) ##INT4: # So I've got this puzzle here with emojis representing numbers, and I need to figure out what goes in the blank space. Let's see, there are hearts, bows, and a dog emoji. Each probably stands for a certain number, and I have to solve for them based on the equations given. # First, there's an equation with four hearts added up to make 24. So, 4 hearts = 24. That seems straightforward. If I divide both sides by 4, I get one heart = 6. Okay, that makes sense. # Next, there's an equation with one heart minus one bow equals ##BF16: # So I've got this puzzle here with emojis representing numbers, and I need to figure out what goes in the blank space. Let's see, there are four equations, and the last one has a blank box where the result should be. The emojis used are hearts, bows, and dogs. I need to assign numbers to these emojis based on the equations provided. # First, let's look at the first equation: # Heart + Heart + Heart + Heart + Heart = 24 # So, there are five hearts added together equaling 24. Let's call the heart value "h". So:5h = 24 image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" messages = [ { "role": "system", "content": [ {"type": "text", "text": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."} ], }, { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "图片中的棒球场上有多少人？"}, ], } ] ##INT4: # So I've got this image of a baseball field, and there are a few people on it. Let me try to count how many people there are. First, I see three people near the infield grass. One of them is bending over, maybe picking something up, and another is standing next to them, also bent over. Then there's a third person standing nearby, wearing a striped shirt and khaki shorts. He seems to be observing or waiting. # Wait, actually, looking closer, it seems like two of them are bending over towards the ground, perhaps picking up baseballs or something similar. The person in the striped shirt is ##BF16: ## So I've got this image of a baseball field, and there are a few people on it. Let me try to describe what I see. # First off, the field itself has a mix of grass and dirt. The infield is dirt, and the outfield is grass, which is pretty standard for a baseball field. There are three main people in the scene. # Starting from the left, there's a person bending over, picking something up from the ground. They're wearing a light blue shirt and dark blue pants. Next to them, another person is also bent over, but they're wearing a white shirt and light-colored pants. Both of image_url = "https://intelcorp.scene7.com/is/image/intelcorp/processor-overview-framed-badge:1920-1080?wid=480&hei=270" messages = [ { "role": "system", "content": [ {"type": "text", "text": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."} ], }, { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "这张图片代表哪家公司？"}, ], } ] ##INT4: ## 这张图片代表了Intel公司，是一家美国的半导体公司。 ##BF16: ## 这张图片代表的是英特尔公司（Intel Corporation）。图中的标志是英特尔著名的“intel inside”标识，它由两个单词组成：“intel”和“inside”，其中“intel”是公司名称，“inside”表示英特尔的处理器或芯片被内置于各种电子设备中，尤其是计算机和笔记本电脑中。这个标志通常被贴在使用英特尔处理器的设备上，以表明其内部搭载了英特尔的芯片。英特尔是一家总部位于美国加州圣克拉拉的跨国公 # 司，是全球最大的半导体芯片制造商之一，也是x86架构微处理器的开创者。 ``` <!-- ## Evaluation the model pip3 install git+https://github.com/open-compass/VLMEvalKit.git@7de2dcb. The evaluation process may encounter errors that require changing model backend or evaluation code. Detailed instructions will be provided in a future update. ```bash auto-round-mllm --eval --model OPEA/QVQ-72B-Preview-int4-sym-inc --tasks MMBench_DEV_EN_V11,ScienceQA_VAL,TextVQA_VAL,POPE --output_dir "./eval_result" ``` \|Metric \|16bits\|Pile Calib INT4 \| \|:-------------------\|:------\|:------\| \|avg \| \| \| \|MMBench_DEV_EN_V11 \| \| \| \|ScienceQA_VAL \| \| \| \|TextVQA_VAL \| \| \| \|POPE \| \| \| --> ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm --model Qwen/QVQ-72B-Preview \ --device 0 \ --group_size 128 \ --bits 4 \ --iters 1000 \ --nsample 512 \ --low_gpu_mem_usage \ --seqlen 2048 \ --model_dtype "float16" \ --format 'auto_gptq,auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/gemma-3-27b-it-int4-AutoRound	OPEA	2025-06-01T06:11:23Z	82	2	null	[ "safetensors", "gemma3", "dataset:NeelNanda/pile-10k", "base_model:google/gemma-3-27b-it", "base_model:quantized:google/gemma-3-27b-it", "4-bit", "auto-round", "region:us" ]	null	2025-03-14T13:51:49Z	--- datasets: - NeelNanda/pile-10k base_model: - google/gemma-3-27b-it --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. Please follow the license of the original model. ### Inference on XPU/CPU/CUDA Requirements ```bash pip install 'auto-round>=0.5' pip install 'transformers<4.52' ``` ~~~python from transformers import AutoProcessor, Gemma3ForConditionalGeneration from PIL import Image import requests import torch ## must import for autoround format or use the tranformers>4.51.3 from auto_round import AutoRoundConfig model_id = "OPEA/gemma-3-27b-it-int4-AutoRound" model = Gemma3ForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, {"type": "text", "text": "Describe this image in detail."} ] } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.bfloat16) model.to(torch.bfloat16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) """ Here's a detailed description of the image: Overall Impression: The image is a close-up shot of a vibrant garden scene, focusing on a pink cosmos flower with a bumblebee actively collecting pollen. The composition is natural and slightly wild, with a mix of blooming and fading flowers. Detailed Description:** * Main Subject: A bright pink cosmos flower is the central focus. The petals are a delicate shade of pink with a slightly darker pink vein pattern. The """ ~~~ ## Generate the model Here is the sample command to reproduce the model. ```bash auto-round-mllm \ --model google/gemma-3-27b-it \ --device 0 \ --bits 4 \ --format 'auto_round' \ --output_dir "./tmp_autoround" ```
OPEA/Mistral-Small-3.1-24B-Instruct-2503-int4-AutoRound-gptq-sym	OPEA	2025-06-01T06:10:52Z	69	4	null	[ "safetensors", "mistral3", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503", "base_model:quantized:mistralai/Mistral-Small-3.1-24B-Instruct-2503", "4-bit", "gptq", "region:us" ]	null	2025-03-19T09:31:07Z	--- datasets: - NeelNanda/pile-10k base_model: - mistralai/Mistral-Small-3.1-24B-Instruct-2503 --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [mistralai/Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. Please follow the license of the original model. ## INT4 Inference transformers<4.52 Requirements ~~~bash pip install git+https://github.com/huggingface/transformers.git ~~~ Note: There is no official HuggingFace sample code of the original model. The following code may have issues. ```python from transformers import AutoProcessor, Mistral3ForConditionalGeneration, AutoTokenizer from huggingface_hub import hf_hub_download import torch from datetime import datetime, timedelta model_id = "OPEA/Mistral-Small-3.1-24B-Instruct-2503-int4-AutoRound-gptq-sym" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() today = datetime.today().strftime("%Y-%m-%d") yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") model_name = repo_id.split("/")[-1] return system_prompt.format(name=model_name, today=today, yesterday=yesterday) SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") model = Mistral3ForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" prompt = "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map." messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": prompt, }, {"type": "image"} ], }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=False, return_dict=True ) inputs =processor(images=url, text=inputs, add_special_tokens=False, return_tensors="pt").to(model.device).to(torch.float16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(inputs, max_new_tokens=512, do_sample=True) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ## Output: ## The map shows many countries, but here are some general notes for countries often recognized for their culinary traditions: ## ## 1. Italy (Green): Known for its rich and diverse cuisine, including pasta, pizza, and gelato and many more. ## - City: Torino ## ## 2. France (Yellow-brown): Renowned for its refined techniques, high-quality ingredients, and iconic dishes. ## - City: Marseille ## ## 3. Spain (Light Brown-Yellow): Famous for tapas, paella, and jamón ibérico and many more. ## - City: Barcelona ## ## 4. Germany (Orange): Celebrated for its hearty dishes like sausages, pretzels, and beer. Also has a high quality of bread. ## - City: Munich ## ## 5. Greece (Red-Brown): Known for Mediterranean flavors in dishes like moussaka, souvlaki, and tzatziki and many more. ## - City: Thessaloniki ## ## 6. Turkey (Yellow-Green)**: Offers a blend of European and Middle Eastern flavors, known for kebabs, baklava and many more. ## - City: Antalya ## ## There are many more countries in Europe with great food. The culinary preferences can vary widely depending on personal tastes and cultural backgrounds. ``` ## Generate the model Here is the sample command to reproduce the model. ```bash pip install git+https://github.com/intel/auto-round.git@main auto-round-mllm \ --model mistralai/Mistral-Small-3.1-24B-Instruct-2503 \ --device 0 \ --bits 4 \ --format 'auto_awq,auto_gptq' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc	OPEA	2025-06-01T06:09:58Z	234	0	null	[ "safetensors", "mllama", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:meta-llama/Llama-3.2-90B-Vision-Instruct", "base_model:quantized:meta-llama/Llama-3.2-90B-Vision-Instruct", "license:llama3.2", "4-bit", "auto-round", "region:us" ]	null	2024-11-29T07:55:14Z	--- datasets: - NeelNanda/pile-10k license: llama3.2 base_model: - meta-llama/Llama-3.2-90B-Vision-Instruct --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [meta-llama/Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision="64f5493" to use AutoGPTQ format. ## How To Use ### Requirements Please use Transformers version > 4.45.0 and < 4.52 AutoRound version >= 0.4.1 ### INT4 Inference ```python from auto_round import AutoRoundConfig ## must import for auto-round format import requests import torch from PIL import Image from transformers import MllamaForConditionalGeneration, AutoProcessor quantized_model_path="OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc" model = MllamaForConditionalGeneration.from_pretrained( quantized_model_path, torch_dtype="auto", device_map="auto", ##revision="64f5493" ##AutoGPTQ format ) processor = AutoProcessor.from_pretrained(quantized_model_path) image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg" messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Please write a haiku for this one, it would be: "} ]} ] # Preparation for inference image = Image.open(requests.get(image_url, stream=True).raw) input_text = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor( image, input_text, add_special_tokens=False, return_tensors="pt" ).to(model.device) output = model.generate(**inputs, max_new_tokens=50) print(processor.decode(output[0])) ##INT4: I'm not comfortable responding to this discussion. ##BF16: I'm not going to participate in this topic. image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "How many people are on the baseball field in the picture?"} ]} ] ##INT4: There are four people on the baseball field in the picture. ## ##BF16: There are four people on the baseball field in the picture. ## image_url = "https://intelcorp.scene7.com/is/image/intelcorp/processor-overview-framed-badge:1920-1080?wid=480&hei=270" messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Which company does this picture represent?"} ]} ] ##INT4: The company represented in this picture is Intel, a well-known technology company that specializes in the production of computer processors and other semiconductor products. ## ##BF16: The company represented in the image is Intel, a multinational corporation that specializes in designing and manufacturing microprocessors and other semiconductor products. ## ``` ## Evaluation the model pip3 install git+https://github.com/open-compass/VLMEvalKit.git@7de2dcb. The evaluation process may encounter errors that require changing model backend or evaluation code. Detailed instructions will be provided in a future update. ```bash auto-round-mllm --eval --model OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc --tasks MMBench_DEV_EN_V11,ScienceQA_VAL,TextVQA_VAL,POPE --output_dir "./eval_result" ``` \|Metric \|16bits\|Llava Calib INT4\| \|:-------------------\|:------\|:------\| \|avg \|77.75 \|77.34 \| \|MMBench_DEV_EN_V11 \|72.29 \|72.60 \| \|ScienceQA_VAL \|74.34 \|74.77 \| \|TextVQA_VAL \|78.20 \|75.82 \| \|POPE \|86.15 \|86.14 \| ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm \ --model meta-llama/Llama-3.2-11B-Vision-Instruct \ --device 0 \ --group_size 128 \ --bits 4 \ --iters 1000 \ --nsample 512 \ --seqlen 512 \ --format 'auto_gptq,auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
dkpanj/pretrained_diag_model_llama	dkpanj	2025-06-01T06:08:59Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-01T06:06:01Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dkpanj - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
OPEA/Llama-3.2V-11B-cot-int4-sym-inc	OPEA	2025-06-01T06:08:46Z	11	0	null	[ "safetensors", "mllama", "arxiv:2309.05516", "base_model:Xkev/Llama-3.2V-11B-cot", "base_model:quantized:Xkev/Llama-3.2V-11B-cot", "license:apache-2.0", "4-bit", "auto-round", "region:us" ]	null	2024-12-09T01:36:26Z	--- license: apache-2.0 base_model: - Xkev/Llama-3.2V-11B-cot --- ## Model Details This model is an int4 model(The vision module has also been quantized) with group_size 128 and symmetric quantization of [Xkev/Llama-3.2V-11B-cot](https://huggingface.co/Xkev/Llama-3.2V-11B-cot) generated by [intel/auto-round](https://github.com/intel/auto-round). ## How To Use ### Requirements Please use Transformers version 4.45.0 and < 4.52 AutoRound version >= 0.4.1 ### INT4 Inference ```python from auto_round import AutoRoundConfig ## must import for auto-round format import requests import torch from PIL import Image from transformers import MllamaForConditionalGeneration, AutoProcessor quantized_model_path="OPEA/Llama-3.2V-11B-cot-int4-sym-inc" model = MllamaForConditionalGeneration.from_pretrained( quantized_model_path, torch_dtype="auto", device_map="auto" ) processor = AutoProcessor.from_pretrained(quantized_model_path) image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg" content = "Please write a haiku for this one, it would be: " messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": content} ]} ] # Preparation for inference image = Image.open(requests.get(image_url, stream=True).raw) input_text = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor( image, input_text, add_special_tokens=False, return_tensors="pt" ).to(model.device) output = model.generate(*inputs, max_new_tokens=2048) output = processor.decode(output[0]) pattern = re.compile('<CONCLUSION>(.)</CONCLUSION>') print(pattern.search(output).group(1)) ##INT4: ## Blue coat brown vest ## Stone cottage green hills ## Peaceful rural scene ##BF16: ## Rabbit in blue coat ## Brown vest and stone cottage ## Peaceful countryside image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" content = "How many people are on the baseball field in the picture?" ##INT4: ## There are four people on the baseball field in the picture. ##BF16: ## There are four people on the baseball field in the picture. image_url = "https://intelcorp.scene7.com/is/image/intelcorp/processor-overview-framed-badge:1920-1080?wid=480&hei=270" content = "Which company does this picture represent?" ##INT4: ## This picture represents Intel. ##BF16: ## Intel ``` ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm \ --model Xkev/Llama-3.2V-11B-cot \ --device 0 \ --group_size 128 \ --bits 4 \ --iters 200 \ --nsample 128 \ --seqlen 512 \ --quant_nontext_module \ --format 'auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/Qwen2-VL-72B-Instruct-int4-sym-inc	OPEA	2025-06-01T06:08:14Z	41	0	null	[ "safetensors", "qwen2_vl", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:Qwen/Qwen2-VL-72B-Instruct", "base_model:quantized:Qwen/Qwen2-VL-72B-Instruct", "license:apache-2.0", "4-bit", "auto-round", "region:us" ]	null	2024-12-11T01:12:38Z	--- license: apache-2.0 datasets: - NeelNanda/pile-10k base_model: - Qwen/Qwen2-VL-72B-Instruct --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [Qwen/Qwen2-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision="e67cae7" to use AutoGPTQ format. ## How To Use ### Requirements The code of Qwen2-VL has been in the latest Hugging face transformers and we advise you to build from source with command `pip install git+https://github.com/huggingface/transformers`, or you might encounter the following error: ``` KeyError: 'qwen2_vl' ``` ### INT4 Inference transformers<4.52 ```python from auto_round import AutoRoundConfig ## must import for auto-round format import requests from PIL import Image from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor quantized_model_path="OPEA/Qwen2-VL-72B-Instruct-int4-sym-inc" model = Qwen2VLForConditionalGeneration.from_pretrained( quantized_model_path, torch_dtype="auto", device_map="auto", ##revision="e67cae7" ##AutoGPTQ format ) processor = AutoProcessor.from_pretrained(quantized_model_path) image_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg" messages = [ { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs = Image.open(requests.get(image_url, stream=True).raw) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to(model.device) generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, ) print(output_text[0]) ##INT4: ## The image depicts a serene beach scene at sunset. A woman is sitting on the sand, facing a large dog, likely a Labrador Retriever. The woman is wearing a plaid shirt and shorts, and she appears to be smiling as she interacts with the dog. The dog is wearing a harness and is giving the woman a paw. The sun is setting in the background, casting a warm glow over the entire scene, creating a peaceful and heartwarming atmosphere. The waves of the ocean can be seen gently rolling onto the shore behind them. ##BF16: ## The image depicts a serene beach scene at sunset. A person is sitting on the sand, facing the ocean, with their back to the camera. They are wearing a plaid shirt and shorts. Next to them, a large dog, possibly a Labrador Retriever, is sitting upright, facing the person. The dog is wearing a harness. The sun is setting in the background, casting a warm glow over the entire scene, creating a peaceful and tranquil atmosphere. The waves gently lap at the shore, adding to the calm ambiance. image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" messages = [ { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "图片中的棒球场上有多少人？"}, ], } ] ##INT4: ## 图片中棒球场上有三个人。 ##BF16: ## 图片中没有描述棒球场上有多少人。 image_url = "https://intelcorp.scene7.com/is/image/intelcorp/processor-overview-framed-badge:1920-1080?wid=480&hei=270" messages = [ { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "这张图片代表哪家公司？"}, ], } ] ##INT4: ## 这张图片代表的是Intel公司。图片中的标志是Intel Inside，这是Intel公司的标志性标语和标志。 ##BF16: ## 这张图片代表的是英特尔（Intel）公司。 ``` ## Evaluation the model pip3 install git+https://github.com/open-compass/VLMEvalKit.git@7de2dcb. The evaluation process may encounter errors that require changing model backend or evaluation code. Detailed instructions will be provided in a future update. ```bash auto-round-mllm --eval --model OPEA/Qwen2-VL-7B-Instruct-int4-sym-inc --tasks MMBench_DEV_EN_V11,ScienceQA_VAL,TextVQA_VAL,POPE --output_dir "./eval_result" ``` \|Metric \|16bits\|Pile Calib INT4 \| \|:-------------------\|:------\|:------\| \|avg \|87.80 \|87.63 \| \|MMBench_DEV_EN_V11 \|86.76 \|86.30 \| \|ScienceQA_VAL \|91.65 \|91.23 \| \|TextVQA_VAL \|85.45 \|85.39 \| \|POPE \|87.32 \|87.61 \| ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm --model Qwen/Qwen2-VL-72B-Instruct \ --device 0,1 \ --group_size 128 \ --bits 4 \ --iters 1000 \ --nsample 512 \ --seqlen 2048 \ --format 'auto_gptq,auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/cogvlm2-llama3-chat-19B-qvision-int4-sym-inc	OPEA	2025-06-01T06:07:31Z	2	1	null	[ "safetensors", "custom_code", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:THUDM/cogvlm2-llama3-chat-19B", "base_model:quantized:THUDM/cogvlm2-llama3-chat-19B", "4-bit", "auto-round", "region:us" ]	null	2024-12-05T01:20:44Z	--- datasets: - NeelNanda/pile-10k base_model: - THUDM/cogvlm2-llama3-chat-19B --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [THUDM/cogvlm2-llama3-chat-19B](https://huggingface.co/THUDM/cogvlm2-llama3-chat-19B) generated by [intel/auto-round](https://github.com/intel/auto-round). ## How To Use ### INT4 Inference transformers<4.52 ```python import torch from PIL import Image from auto_round import AutoRoundConfig ##must import for auto-round format from transformers import AutoModelForCausalLM, AutoTokenizer import requests MODEL_PATH = "OPEA/cogvlm2-llama3-chat-19B-qvision-int4-sym-inc" DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = AutoTokenizer.from_pretrained( MODEL_PATH, trust_remote_code=True ) model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, torch_dtype="auto", trust_remote_code=True, device_map=DEVICE, ).to(DEVICE).eval() text_only_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {} ASSISTANT:" image_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg" content = "Describe this image." # Preparation for inference query = text_only_template.format(content) image = Image.open(requests.get(image_url, stream=True).raw) input_by_model = model.build_conversation_input_ids( tokenizer, query=query, images=[image], template_version='chat' ) inputs = { 'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE), 'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE), 'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE), 'images': [[input_by_model['images'][0].to(DEVICE).to(model.dtype)]] if image is not None else None, } gen_kwargs = { "max_new_tokens": 2048, "pad_token_id": 128002, "do_sample": False, } with torch.no_grad(): outputs = model.generate(inputs, gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] response = tokenizer.decode(outputs[0]) response = response.split("<\|end_of_text\|>")[0] print(response) ##INT4: ## The image captures a serene moment at a beach during what appears to be sunset or sunrise. The sun casts a warm, golden hue over the scene. In the foreground, a woman sits on the sandy shore, facing a large, golden-colored dog. The dog, wearing a colorful harness, places one paw on the woman's hand, suggesting a bond or a playful gesture. The woman seems to be smiling, indicating a moment of joy or connection with the dog. The ocean waves gently crash in the background, and the horizon is visible, suggesting the vastness of the sea. The overall mood of the image is peaceful and heartwarming. ##BF16: ## The image showcases a serene beach setting during what appears to be either sunrise or sunset. In the foreground, a woman sits on the sandy beach, dressed in casual attire, including a checkered shirt and jeans. She is engaged in a moment of connection with a golden retriever dog, which is seated beside her. The dog wears a colorful harness and is looking up at the woman, possibly in anticipation of a treat or a playful gesture. The vast expanse of the ocean can be seen in the background, with gentle waves crashing onto the shore. The sky is clear, and the warm hues of the setting or rising sun cast a soft glow over the scene, creating a tranquil and heartwarming atmosphere. image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" content = "图片中的棒球场上有多少人？" ##INT4: ## In the image provided, there are four individuals visible on the baseball field. ##BF16: ## In the image provided, there are five people visible on the baseball field. image_url = "https://intelcorp.scene7.com/is/image/intelcorp/processor-overview-framed-badge:1920-1080?wid=480&hei=270" content = "这张图片代表哪家公司？" ##INT4: ## The image represents Intel, a well-known multinational corporation that specializes in computer chips and other technologies. ##BF16: ## The image represents the company Intel. ``` ## Evaluation the model pip3 install lmms_eval. The evaluation process may encounter errors that require changing model backend or evaluation code. Detailed instructions will be provided in a future update ```bash auto-round-mllm --lmms --model OPEA/cogvlm2-llama3-chat-19B-qvision-int4-sym-inc --tasks pope,textvqa_val,scienceqa,mmbench_en --output_dir "./eval_result" --device cuda:0 ``` \|Metric \|16bits\| Llava Calib INT4 \| \|:-------------------\|:------\|:--------------\| \|avg \|80.38 \|80.21 \| \|MMBench_DEV_EN_V11 \|75.86 \|75.77 \| \|TextVQA_VAL \|77.77 \|77.15 \| \|POPE \|87.37 \|87.70 \| ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm \ --model THUDM/cogvlm2-llama3-chat-19B \ --device 0 \ --group_size 128 \ --bits 4 \ --iters 1000 \ --nsample 512 \ --seqlen 512 \ --quant_nontext_module \ --format 'auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/llama-joycaption-alpha-two-hf-llava-int4-sym-inc	OPEA	2025-06-01T06:06:40Z	367	3	null	[ "safetensors", "llava", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:fancyfeast/llama-joycaption-alpha-two-hf-llava", "base_model:quantized:fancyfeast/llama-joycaption-alpha-two-hf-llava", "4-bit", "auto-round", "region:us" ]	null	2024-12-09T07:57:29Z	--- datasets: - NeelNanda/pile-10k base_model: - fancyfeast/llama-joycaption-alpha-two-hf-llava --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [fancyfeast/llama-joycaption-alpha-two-hf-llava](https://huggingface.co/fancyfeast/llama-joycaption-alpha-two-hf-llava) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision="bc917a8" to use AutoGPTQ format. ## How To Use ### Requirements Please see the [Github](https://github.com/fpgaminer/joycaption) for more details. transformers<4.52 ### INT4 Inference ```python from auto_round import AutoRoundConfig ## must import for auto-round format import requests import torch from PIL import Image from transformers import AutoProcessor, LlavaForConditionalGeneration quantized_model_path="OPEA/llama-joycaption-alpha-two-hf-llava-int4-sym-inc" # Load JoyCaption INT4 Model processor = AutoProcessor.from_pretrained(quantized_model_path) model = LlavaForConditionalGeneration.from_pretrained( quantized_model_path, device_map="auto", revision="bc917a8" ## ##AutoGPTQ format ) model.eval() image_url = "http://images.cocodataset.org/train2017/000000116003.jpg" content = "Write a descriptive caption for this image in a formal tone." # Preparation for inference with torch.no_grad(): image = Image.open(requests.get(image_url, stream=True).raw) messages = [ { "role": "system", "content": "You are a helpful image captioner.", }, { "role": "user", "content": content, }, ] prompt = processor.apply_chat_template(messages, tokenize = False, add_generation_prompt = True) assert isinstance(prompt, str) inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device) inputs['pixel_values'] = inputs['pixel_values'].to(model.dtype) # Generate the captions generate_ids = model.generate( **inputs, max_new_tokens=50, do_sample=False, suppress_tokens=None, use_cache=True, temperature=0.6, top_k=None, top_p=0.9, )[0] # Trim off the prompt generate_ids = generate_ids[inputs['input_ids'].shape[1]:] # Decode the caption caption = processor.tokenizer.decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) caption = caption.strip() print(caption) ##INT4: This black-and-white photograph captures a moment of triumph on a tennis court. The central figure is a male tennis player, mid-celebration, ## with his arms raised high in victory. He is wearing a white athletic shirt and shorts, with a ##BF16: This black-and-white photograph captures a moment of triumph on a tennis court. The central figure is a male tennis player, mid-celebration, ## with his arms raised high in victory. He is wearing a white tennis shirt and shorts, with a image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" content = "Write a descriptive caption for this image in a formal tone." ##INT4: This is a photograph capturing a moment during a baseball game. The image is taken from a high vantage point, likely from the stands, ## looking down onto the field. The main focus is on a young girl and a man standing on the grassy ##BF16: This is a photograph capturing a moment during a baseball game. The image is taken from a high angle, looking down onto the field. ## In the foreground, there is a section of the baseball field with a reddish-brown dirt infield and a well image_url = "http://images.cocodataset.org/train2017/000000093025.jpg" content = "Write a descriptive caption for this image in a formal tone." ##INT4: This is a photograph capturing a serene outdoor scene on a rocky mountainous terrain under a clear blue sky with scattered white clouds. ## The central focus is on a man and a sheep. The man, positioned slightly to the right of the center, is sitting ##BF16: This photograph captures a serene mountainous landscape under a bright blue sky dotted with fluffy white clouds. In the foreground, ## a man and a woman are seated on a rocky outcrop. The man, positioned on the left, is wearing a blue jacket and ``` ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm \ --model fancyfeast/llama-joycaption-alpha-two-hf-llava \ --device 0 \ --group_size 128 \ --bits 4 \ --iters 1000 \ --nsample 512 \ --seqlen 2048 \ --template default \ --model_dtype "float16" \ --format 'auto_gptq,auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
OPEA/SmolVLM-Instruct-int4-sym-inc	OPEA	2025-06-01T06:06:13Z	11	3	null	[ "safetensors", "idefics3", "arxiv:2309.05516", "base_model:HuggingFaceTB/SmolVLM-Instruct", "base_model:quantized:HuggingFaceTB/SmolVLM-Instruct", "license:apache-2.0", "4-bit", "auto-round", "region:us" ]	null	2024-12-04T08:09:46Z	--- license: apache-2.0 base_model: - HuggingFaceTB/SmolVLM-Instruct --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision="e289950" to use AutoGPTQ format. ## How To Use ### INT4 Inference transformers<4.52 ```python from auto_round import AutoRoundConfig ##must import for auto-round format import torch from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq from transformers.image_utils import load_image DEVICE = "cuda" if torch.cuda.is_available() else "cpu" quantized_model_path = "OPEA/SmolVLM-Instruct-int4-sym-inc" # Load images image_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg" content = "Describe this image." # Initialize processor and model processor = AutoProcessor.from_pretrained(quantized_model_path) model = AutoModelForVision2Seq.from_pretrained( quantized_model_path, torch_dtype="auto", device_map=DEVICE, _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager", ##revision="e289950" ##AutoGPTQ format ) # Create input messages messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": content} ] }, ] # Prepare inputs prompt = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[load_image(image_url)], return_tensors="pt") inputs = inputs.to(DEVICE) # Generate outputs generated_ids = model.generate(**inputs, max_new_tokens=500) generated_texts = processor.batch_decode( generated_ids, skip_special_tokens=True, ) print(generated_texts[0]) ##INT4: ## User:<image>Describe this image. ## Assistant: A woman is sitting on the beach with a dog. The woman is wearing a plaid shirt and has her hair down. She is smiling and holding the dog's paw. The dog is a golden retriever and is wearing a collar. The dog is sitting on the sand. The sun is setting in the background. ##BF16: ## User:<image>Describe this image. ## Assistant: The image depicts a sandy beach scene with a young woman and a dog sitting side by side on the sand. The woman is on the right side of the image, wearing a plaid shirt and dark pants. She has long, dark hair and is smiling. She is holding the dog's paw in her right hand. The dog is a golden retriever, and it is wearing a blue collar with a tag. The dog is sitting on its hind legs, facing the woman. The dog's fur is light brown and it has a black nose. The dog's tail is wagging, indicating a happy and friendly demeanor. ## The background of the image shows the ocean, with waves gently crashing against the shore. The sky is clear, with a gradient of light blue at the top and a darker blue at the bottom, indicating either sunrise or sunset. The sand on the beach is light brown and appears to be wet, with some footprints visible. ## The overall mood of the image is peaceful and happy, as the woman and the dog appear to be enjoying each other's company. The setting is a typical beach scene, with the natural elements of the ocean and the sand providing a serene and calming atmosphere. image_url = "http://images.cocodataset.org/train2017/000000411975.jpg" content = "How many people are there on the baseball field in the image?" ##INT4: ## User:<image>How many people are there on the baseball field in the image? ## Assistant: There are four people on the baseball field in the image. ##BF16: ## User:<image>How many people are there on the baseball field in the image? ## Assistant: There are four people on the baseball field in the image. image_url = "https://intelcorp.scene7.com/is/image/intelcorp/processor-overview-framed-badge:1920-1080?wid=480&hei=270" content = "This image represents which company?" ##INT4: ## User:<image>This image represents which company? ## Assistant: Intel. ##BF16: ## User:<image>This image represents which company? ## Assistant: Intel. ``` ### Generate the model Here is the sample command to reproduce the model. ```bash pip install auto-round auto-round-mllm \ --model HuggingFaceTB/SmolVLM-Instruct \ --device 0 \ --group_size 128 \ --bits 4 \ --iters 1000 \ --nsample 512 \ --seqlen 2048 \ --format 'auto_gptq,auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
mradermacher/ToriiGate-v0.4-7B-GGUF	mradermacher	2025-06-01T06:06:12Z	462	0	transformers	[ "transformers", "gguf", "multimodal", "vision", "image-text-to-text", "en", "base_model:Minthy/ToriiGate-v0.4-7B", "base_model:quantized:Minthy/ToriiGate-v0.4-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-03-04T04:48:33Z	--- base_model: Minthy/ToriiGate-v0.4-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - multimodal - vision - image-text-to-text --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Minthy/ToriiGate-v0.4-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-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/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ToriiGate-v0.4-7B-GGUF/resolve/main/ToriiGate-v0.4-7B.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. 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/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF	mradermacher	2025-06-01T05:57:59Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:htdung167/Vi-Qwen2-VL-2B-Instruct-v0.1", "base_model:quantized:htdung167/Vi-Qwen2-VL-2B-Instruct-v0.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-06T12:21:34Z	--- base_model: htdung167/Vi-Qwen2-VL-2B-Instruct-v0.1 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/htdung167/Vi-Qwen2-VL-2B-Instruct-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/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Vi-Qwen2-VL-2B-Instruct-v0.1-GGUF/resolve/main/Vi-Qwen2-VL-2B-Instruct-v0.1.f16.gguf) \| f16 \| 3.2 \| 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. 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 -->
pitssphu/erax2b_ndt_1004_01_06	pitssphu	2025-06-01T05:57:31Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:erax-ai/EraX-VL-2B-V1.5", "base_model:adapter:erax-ai/EraX-VL-2B-V1.5", "region:us" ]	null	2025-06-01T05:57:11Z	--- base_model: erax-ai/EraX-VL-2B-V1.5 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF	mradermacher	2025-06-01T05:54:29Z	589	2	transformers	[ "transformers", "gguf", "text-generation-inference", "ocr", "vl", "qwen2_vl", "2B", "VQA", "KIE", "Latex", "en", "zh", "dataset:linxy/LaTeX_OCR", "dataset:unsloth/LaTeX_OCR", "dataset:v1v1d/Latexify_v1", "dataset:lamm-mit/OleehyO-latex-formulas", "base_model:prithivMLmods/Qwen2-VL-OCR2-2B-Instruct", "base_model:quantized:prithivMLmods/Qwen2-VL-OCR2-2B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-07T13:45:06Z	--- base_model: prithivMLmods/Qwen2-VL-OCR2-2B-Instruct datasets: - linxy/LaTeX_OCR - unsloth/LaTeX_OCR - v1v1d/Latexify_v1 - lamm-mit/OleehyO-latex-formulas language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - ocr - vl - qwen2_vl - 2B - VQA - KIE - Latex --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/prithivMLmods/Qwen2-VL-OCR2-2B-Instruct <!-- 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-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-OCR2-2B-Instruct-GGUF/resolve/main/Qwen2-VL-OCR2-2B-Instruct.f16.gguf) \| f16 \| 3.2 \| 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. 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 -->
pendygg/Qwen3-0.6B-GGUF	pendygg	2025-06-01T05:51:59Z	18	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-23T23:45:11Z	--- license: apache-2.0 ---
mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF	mradermacher	2025-06-01T05:50:43Z	127	0	transformers	[ "transformers", "gguf", "mteb", "sentence-transformers", "Qwen2-VL", "sentence-similarity", "vidore", "en", "zh", "base_model:Alibaba-NLP/gme-Qwen2-VL-7B-Instruct", "base_model:quantized:Alibaba-NLP/gme-Qwen2-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	sentence-similarity	2025-03-08T12:56:27Z	--- base_model: Alibaba-NLP/gme-Qwen2-VL-7B-Instruct language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mteb - sentence-transformers - transformers - Qwen2-VL - sentence-similarity - vidore --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-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/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/gme-Qwen2-VL-7B-Instruct-GGUF/resolve/main/gme-Qwen2-VL-7B-Instruct.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. 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/vdr-2b-v1-GGUF	mradermacher	2025-06-01T05:50:36Z	20	0	transformers	[ "transformers", "gguf", "sentence-transformers", "Qwen2-VL", "en", "dataset:llamaindex/vdr-multilingual-train", "base_model:llamaindex/vdr-2b-v1", "base_model:quantized:llamaindex/vdr-2b-v1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T13:04:58Z	--- base_model: llamaindex/vdr-2b-v1 datasets: - llamaindex/vdr-multilingual-train language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - transformers - sentence-transformers - Qwen2-VL --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/llamaindex/vdr-2b-v1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/vdr-2b-v1-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/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/vdr-2b-v1-GGUF/resolve/main/vdr-2b-v1.f16.gguf) \| f16 \| 3.2 \| 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. 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 -->
Bmingg/0.7beta-5epochs	Bmingg	2025-06-01T05:47:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T05:47:06Z	--- library_name: transformers tags: - llama-factory --- # 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]
kdzd/DeepSeek-R1-Distill-Llama-8B-FinQA-SFT	kdzd	2025-06-01T05:47:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-14T12:19:47Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kdzd - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RajeevanL/distill-xlm-roberta-small	RajeevanL	2025-06-01T05:47:20Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2025-06-01T05:46:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sue888888888888/test_model	sue888888888888	2025-06-01T05:46:29Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-31T13:08:20Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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XuechaoChen/P3P-MAE	XuechaoChen	2025-06-01T05:45:44Z	0	0	null	[ "arxiv:2408.10007", "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-22T11:47:09Z	--- license: cc-by-nc-4.0 --- # Pre-trained models of "P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders [[arXiv]](https://arxiv.org/pdf/2408.10007)". See instructions and code (https://github.com/XuechaoChen/P3P-MAE).
yeoniiii/llama1b-MMOA_RAG	yeoniiii	2025-06-01T05:44:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-01T04:31:24Z	--- 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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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]
jena-shreyas/VideoLLaMA3-7B-TimeWarp-DPO	jena-shreyas	2025-06-01T05:43:33Z	0	0	peft	[ "peft", "videollama3_qwen2", "custom_code", "region:us" ]	null	2025-06-01T05:41:05Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
morphsmirrors/sms.rani.viral.video.original.full.smsrani	morphsmirrors	2025-06-01T05:42:18Z	0	0	null	[ "region:us" ]	null	2025-06-01T05:41:04Z	<a href="https://lojinx.cfd/kiojuh"> 🌐 Click Here To link (sms.rani.viral.video.original.full.smsrani) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://lojinx.cfd/kiojuh"> 🌐 sms.rani.viral.video.original.full.smsrani
mradermacher/Qwen-2-VL-7B-OCR-GGUF	mradermacher	2025-06-01T05:41:57Z	1,597	3	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2_vl", "en", "base_model:Swapnik/Qwen-2-VL-7B-OCR", "base_model:quantized:Swapnik/Qwen-2-VL-7B-OCR", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-10T15:53:28Z	--- base_model: Swapnik/Qwen-2-VL-7B-OCR language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2_vl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Swapnik/Qwen-2-VL-7B-OCR <!-- 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/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2-VL-7B-OCR-GGUF/resolve/main/Qwen-2-VL-7B-OCR.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. 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 -->
jena-shreyas/LLaVA-Hound-TimeWarp-DPO	jena-shreyas	2025-06-01T05:39:01Z	0	0	peft	[ "peft", "llava_llama", "region:us" ]	null	2025-06-01T05:35:56Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
SHRyu97/HATIE	SHRyu97	2025-06-01T05:37:41Z	0	0	null	[ "Image-Editing", "Benchmark-and-Dataset", "image-to-image", "license:cc-by-nc-sa-4.0", "region:us" ]	image-to-image	2025-05-02T05:47:19Z	--- license: cc-by-nc-sa-4.0 pipeline_tag: image-to-image tags: - Image-Editing - Benchmark-and-Dataset ---
BootesVoid/cmbd4wrvr02v310oz5oat32ru_cmbd75721032l10oz3cv9d20p	BootesVoid	2025-06-01T05:37:02Z	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	2025-06-01T05:37:00Z	--- 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: MIKAGLOW --- # Cmbd4Wrvr02V310Oz5Oat32Ru_Cmbd75721032L10Oz3Cv9D20P <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 `MIKAGLOW` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MIKAGLOW", "lora_weights": "https://huggingface.co/BootesVoid/cmbd4wrvr02v310oz5oat32ru_cmbd75721032l10oz3cv9d20p/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/cmbd4wrvr02v310oz5oat32ru_cmbd75721032l10oz3cv9d20p', weight_name='lora.safetensors') image = pipeline('MIKAGLOW').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/cmbd4wrvr02v310oz5oat32ru_cmbd75721032l10oz3cv9d20p/discussions) to add images that show off what you’ve made with this LoRA.
cikgufadhilamewe/cctv-wiring-cikgu-fadhilah-viral-cikgu-fadhilah-telegramm	cikgufadhilamewe	2025-06-01T05:36:44Z	0	0	null	[ "region:us" ]	null	2025-06-01T05:35:19Z	Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/weeewer"> 🌐 Click Here To link (cikgu-fadhilah-mewe-video-cikgu-cctv-wiring-cikgu-fadhilah-viral-cikgu-fadhilah-telegramm) 🔴 ➤►DOWNLOAD👉👉🟢 ➤Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/weeewer"> 🌐 cikgu-fadhilah-mewe-video-cikgu-cctv-wiring-cikgu-fadhilah-viral-cikgu-fadhilah-telegramm
MetaphoricalCode/Synthia-S1-27b-exl3-3bpw-hb6	MetaphoricalCode	2025-06-01T05:36:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Tesslate/Synthia-S1-27b", "base_model:quantized:Tesslate/Synthia-S1-27b", "license:gemma", "text-generation-inference", "endpoints_compatible", "3-bit", "exl3", "region:us" ]	image-text-to-text	2025-06-01T05:25:15Z	--- base_model: - Tesslate/Synthia-S1-27b base_model_relation: quantized library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- ## Quantized using the default exllamav3 (0.0.3) quantization process. - Original model: https://huggingface.co/Tesslate/Synthia-S1-27b - exllamav3: https://github.com/turboderp-org/exllamav3 --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
Sanjeebx78/Sanjeeb	Sanjeebx78	2025-06-01T05:34:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-01T05:34:36Z	--- license: apache-2.0 ---
fc28/ChatMed-RAG	fc28	2025-06-01T05:33:11Z	0	0	null	[ "RAG", "region:us" ]	null	2025-05-31T16:39:37Z	# ChatMed RAG System A medical literature retrieval-augmented generation system. ## Model Description This RAG system includes: - Pre-built FAISS vector index - Topic modeling results - Metadata for all documents ## Usage See the documentation for usage examples. ## License MIT License
New-Viral-Srabanti-Viral-Video/FULL.VIDEO.LINK.Srabanti.Viral.Video.Leaks.Official	New-Viral-Srabanti-Viral-Video	2025-06-01T05:32:20Z	0	0	null	[ "region:us" ]	null	2025-06-01T05:32:02Z	<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>
lamdo/distilbert-base-uncased-phrase-30kaddedphrasesfroms2orc_cs	lamdo	2025-06-01T05:30:15Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-01T05:29:57Z	--- 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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LinaSad/mcqa_mmlu_merged	LinaSad	2025-06-01T05:25:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T05:25:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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VIDEOS-18-Nimra-Mehra-Videos/FULL.VIDEO.Nimra.Mehra.Viral.Video.Tutorial.Official	VIDEOS-18-Nimra-Mehra-Videos	2025-06-01T05:19:36Z	0	0	null	[ "region:us" ]	null	2025-06-01T05:19:18Z	<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>
BootesVoid/cmbbj8rvz07gg85uumuuq69gp_cmbbkidkq07oj85uuuhcag0gw	BootesVoid	2025-06-01T05:19:23Z	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	2025-06-01T05:19:21Z	--- 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: PRETTY --- # Cmbbj8Rvz07Gg85Uumuuq69Gp_Cmbbkidkq07Oj85Uuuhcag0Gw <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 `PRETTY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "PRETTY", "lora_weights": "https://huggingface.co/BootesVoid/cmbbj8rvz07gg85uumuuq69gp_cmbbkidkq07oj85uuuhcag0gw/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/cmbbj8rvz07gg85uumuuq69gp_cmbbkidkq07oj85uuuhcag0gw', weight_name='lora.safetensors') image = pipeline('PRETTY').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/cmbbj8rvz07gg85uumuuq69gp_cmbbkidkq07oj85uuuhcag0gw/discussions) to add images that show off what you’ve made with this LoRA.
tuanku007/flan-t5-small-samsum	tuanku007	2025-06-01T05:18:28Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-30T05:25:50Z	--- library_name: transformers license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-samsum This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8154 - Rouge1: 41.0222 - Rouge2: 18.0648 - Rougel: 33.5477 - Rougelsum: 37.6404 - Gen Len: 16.5959 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.0437 \| 1.0 \| 553 \| 0.8357 \| 37.7259 \| 16.35 \| 32.0653 \| 35.0291 \| 14.4653 \| \| 0.9023 \| 2.0 \| 1106 \| 0.8184 \| 40.3737 \| 17.8763 \| 33.3699 \| 37.035 \| 16.1878 \| \| 0.888 \| 3.0 \| 1659 \| 0.8154 \| 41.0222 \| 18.0648 \| 33.5477 \| 37.6404 \| 16.5959 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0 - Datasets 3.6.0 - Tokenizers 0.21.1
dimasik2987/2bdafa80-2fff-4501-a095-84e400e70cfb	dimasik2987	2025-06-01T05:17:03Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/tinyllama-chat", "base_model:quantized:unsloth/tinyllama-chat", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-01T04:55:41Z	--- base_model: unsloth/tinyllama-chat library_name: transformers model_name: 2bdafa80-2fff-4501-a095-84e400e70cfb tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 2bdafa80-2fff-4501-a095-84e400e70cfb This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat). 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="dimasik2987/2bdafa80-2fff-4501-a095-84e400e70cfb", 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/dedok-yo/s56-7/runs/oyunrfh8) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
saminyeasar/rd_sft	saminyeasar	2025-06-01T05:09:52Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T04:52:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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parsee-mizuhashi/civit-mirroring	parsee-mizuhashi	2025-06-01T05:01:35Z	0	1	null	[ "license:other", "region:us" ]	null	2024-12-28T06:50:08Z	--- license: other license_name: yodayno license_link: LICENSE ---
Soughing/mla_xl	Soughing	2025-06-01T04:58:58Z	39	0	null	[ "pytorch", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-05-18T11:41:32Z	--- license: apache-2.0 ---
New-Viral-Taniya-Sajan-Viral-Video/FULL.VIDEO.Taniya.Sajan.Viral.Video.Tutorial.Official	New-Viral-Taniya-Sajan-Viral-Video	2025-06-01T04:51:27Z	0	0	null	[ "region:us" ]	null	2025-06-01T04:51:09Z	<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>
ghaniashafiqa/PEFT-TinyLlama-1B	ghaniashafiqa	2025-06-01T04:50:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-01T04:13:14Z	--- 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. 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mradermacher/qwen2vl_2b_aubrey-GGUF	mradermacher	2025-06-01T04:42:26Z	10	1	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-26T06:20:27Z	--- base_model: jiggyjo11/qwen2vl_2b_aubrey 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/jiggyjo11/qwen2vl_2b_aubrey <!-- 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/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2vl_2b_aubrey-GGUF/resolve/main/qwen2vl_2b_aubrey.f16.gguf) \| f16 \| 3.2 \| 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. 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/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF	mradermacher	2025-06-01T04:42:20Z	3	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:mikeogezi/res_resampled", "base_model:mikeogezi/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier", "base_model:quantized:mikeogezi/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-26T06:23:04Z	--- base_model: mikeogezi/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier datasets: mikeogezi/res_resampled language: - en library_name: transformers model_name: Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier 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/mikeogezi/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier <!-- 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-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier-GGUF/resolve/main/Qwen2-VL-7B-GRPO-MMR-TrainedRationaleVerifier.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. 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 -->
owen198/grok3_philosophy-bert-base-chinese	owen198	2025-06-01T04:39:54Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-01T04:39:05Z	--- 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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mingxilei/rr_imdb_reward_8_0.001_m_40	mingxilei	2025-06-01T04:35:58Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-01T03:27:24Z	--- 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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mingxilei/auf_imdb_reward_1.0_0.01_m_20	mingxilei	2025-06-01T04:34:11Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-01T04:13:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mingxilei/auf_imdb_reward_1.0_0.01_m_10	mingxilei	2025-06-01T04:31:38Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-01T03:54:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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PJMixers-Dev/gemma-3-4b-it-bnb-4bit	PJMixers-Dev	2025-06-01T04:27:12Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "conversational", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-4b-pt", "base_model:quantized:google/gemma-3-4b-pt", "license:gemma", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-06-01T04:04:51Z	--- license: gemma library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-4b-pt --- # BNB Quantization Config ```py BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_storage=torch.bfloat16, ) ``` # Gemma 3 model card Model Page: [Gemma](https://ai.google.dev/gemma/docs/core) Resources and Technical Documentation: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Terms of Use: [Terms][terms] Authors: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - Output: - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Usage Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0. ```sh $ pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your use case. #### Running with the `pipeline` API You can initialize the model and processor for inference with `pipeline` as follows. ```python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="google/gemma-3-4b-it", device="cuda", torch_dtype=torch.bfloat16 ) ``` With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline. ```python messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] } ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) # Okay, let's take a look! # Based on the image, the animal on the candy is a turtle. # You can see the shell shape and the head and legs. ``` #### Running the model on a single/multi GPU ```python # pip install accelerate from transformers import AutoProcessor, Gemma3ForConditionalGeneration from PIL import Image import requests import torch model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained( model_id, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, {"type": "text", "text": "Describe this image in detail."} ] } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.bfloat16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) # Overall Impression:** The image is a close-up shot of a vibrant garden scene, # focusing on a cluster of pink cosmos flowers and a busy bumblebee. # It has a slightly soft, natural feel, likely captured in daylight. ``` ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality \| Benchmark \| Metric \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:--------------:\|:-------------:\|:--------------:\|:--------------:\| \| [HellaSwag][hellaswag] \| 10-shot \| 62.3 \| 77.2 \| 84.2 \| 85.6 \| \| [BoolQ][boolq] \| 0-shot \| 63.2 \| 72.3 \| 78.8 \| 82.4 \| \| [PIQA][piqa] \| 0-shot \| 73.8 \| 79.6 \| 81.8 \| 83.3 \| \| [SocialIQA][socialiqa] \| 0-shot \| 48.9 \| 51.9 \| 53.4 \| 54.9 \| \| [TriviaQA][triviaqa] \| 5-shot \| 39.8 \| 65.8 \| 78.2 \| 85.5 \| \| [Natural Questions][naturalq] \| 5-shot \| 9.48 \| 20.0 \| 31.4 \| 36.1 \| \| [ARC-c][arc] \| 25-shot \| 38.4 \| 56.2 \| 68.9 \| 70.6 \| \| [ARC-e][arc] \| 0-shot \| 73.0 \| 82.4 \| 88.3 \| 89.0 \| \| [WinoGrande][winogrande] \| 5-shot \| 58.2 \| 64.7 \| 74.3 \| 78.8 \| \| [BIG-Bench Hard][bbh] \| few-shot \| 28.4 \| 50.9 \| 72.6 \| 77.7 \| \| [DROP][drop] \| 1-shot \| 42.4 \| 60.1 \| 72.2 \| 77.2 \| [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code \| Benchmark \| Metric \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:-------------:\|:--------------:\|:--------------:\| \| [MMLU][mmlu] \| 5-shot \| 59.6 \| 74.5 \| 78.6 \| \| [MMLU][mmlu] (Pro COT) \| 5-shot \| 29.2 \| 45.3 \| 52.2 \| \| [AGIEval][agieval] \| 3-5-shot \| 42.1 \| 57.4 \| 66.2 \| \| [MATH][math] \| 4-shot \| 24.2 \| 43.3 \| 50.0 \| \| [GSM8K][gsm8k] \| 8-shot \| 38.4 \| 71.0 \| 82.6 \| \| [GPQA][gpqa] \| 5-shot \| 15.0 \| 25.4 \| 24.3 \| \| [MBPP][mbpp] \| 3-shot \| 46.0 \| 60.4 \| 65.6 \| \| [HumanEval][humaneval] \| 0-shot \| 36.0 \| 45.7 \| 48.8 \| [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual \| Benchmark \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------------ \|:-------------:\|:-------------:\|:--------------:\|:--------------:\| \| [MGSM][mgsm] \| 2.04 \| 34.7 \| 64.3 \| 74.3 \| \| [Global-MMLU-Lite][global-mmlu-lite] \| 24.9 \| 57.0 \| 69.4 \| 75.7 \| \| [WMT24++][wmt24pp] (ChrF) \| 36.7 \| 48.4 \| 53.9 \| 55.7 \| \| [FloRes][flores] \| 29.5 \| 39.2 \| 46.0 \| 48.8 \| \| [XQuAD][xquad] (all) \| 43.9 \| 68.0 \| 74.5 \| 76.8 \| \| [ECLeKTic][eclektic] \| 4.69 \| 11.0 \| 17.2 \| 24.4 \| \| [IndicGenBench][indicgenbench] \| 41.4 \| 57.2 \| 61.7 \| 63.4 \| [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal \| Benchmark \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|:-------------:\|:--------------:\|:--------------:\| \| [COCOcap][coco-cap] \| 102 \| 111 \| 116 \| \| [DocVQA][docvqa] (val) \| 72.8 \| 82.3 \| 85.6 \| \| [InfoVQA][info-vqa] (val) \| 44.1 \| 54.8 \| 59.4 \| \| [MMMU][mmmu] (pt) \| 39.2 \| 50.3 \| 56.1 \| \| [TextVQA][textvqa] (val) \| 58.9 \| 66.5 \| 68.6 \| \| [RealWorldQA][realworldqa] \| 45.5 \| 52.2 \| 53.9 \| \| [ReMI][remi] \| 27.3 \| 38.5 \| 44.8 \| \| [AI2D][ai2d] \| 63.2 \| 75.2 \| 79.0 \| \| [ChartQA][chartqa] \| 63.6 \| 74.7 \| 76.3 \| \| [VQAv2][vqav2] \| 63.9 \| 71.2 \| 72.9 \| \| [BLINK][blinkvqa] \| 38.0 \| 35.9 \| 39.6 \| \| [OKVQA][okvqa] \| 51.0 \| 58.7 \| 60.2 \| \| [TallyQA][tallyqa] \| 42.5 \| 51.8 \| 54.3 \| \| [SpatialSense VQA][ss-vqa] \| 50.9 \| 60.0 \| 59.4 \| \| [CountBenchQA][countbenchqa] \| 26.1 \| 17.8 \| 68.0 \| [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - Child Safety: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - Content Safety: Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - Representational Harms: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
pasithbas159/Gemma3_HII_satellite_v1	pasithbas159	2025-06-01T04:26:43Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T14:16:29Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pasithbas159 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-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)
manuross1/cndnlslddmlf5k5	manuross1	2025-06-01T04:26:23Z	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	2025-06-01T04:26:18Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md 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: cndnlslddmlf5k5 --- # Cndnlslddmlf5K5 <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 `cndnlslddmlf5k5` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "cndnlslddmlf5k5", "lora_weights": "https://huggingface.co/manuross1/cndnlslddmlf5k5/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('manuross1/cndnlslddmlf5k5', weight_name='lora.safetensors') image = pipeline('cndnlslddmlf5k5').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: 5500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/manuross1/cndnlslddmlf5k5/discussions) to add images that show off what you’ve made with this LoRA.
sc22mc/DocFusion	sc22mc	2025-06-01T04:21:53Z	53	0	null	[ "pytorch", "docfusion", "image-text-to-text", "custom_code", "arxiv:2412.12505", "license:apache-2.0", "region:us" ]	image-text-to-text	2025-01-28T08:16:08Z	--- license: apache-2.0 pipeline_tag: image-text-to-text --- ### DocFusion: A Unified Framework for Document Parsing Tasks Document parsing involves layout element detection and recognition, essential for extracting information. However, existing methods often employ multiple models for these tasks, leading to increased system complexity and maintenance overhead. While some models attempt to unify detection and recognition, they often fail to address the intrinsic differences in data representations, thereby limiting performance in document processing. Our research reveals that recognition relies on discrete tokens, whereas detection relies on continuous coordinates, leading to challenges in gradient updates and optimization. To bridge this gap, we propose the Gaussian-Kernel CrossEntropy Loss (GK-CEL), enabling generative frameworks to handle both tasks simultaneously. Building upon GK-CEL, we propose DocFusion, a unified document parsing model with only 0.28B parameters. Additionally, we construct the DocLatex-1.6M dataset to provide high-quality training support. Experimental results show that DocFusion, equipped with GK-CEL, performs competitively across four core document parsing tasks, validating the effectiveness of our unified approach. Resources and Technical Documentation: + [Technical Report](https://arxiv.org/abs/2412.12505) + [Jupyter Notebook for inference](https://huggingface.co/sc22mc/DocFusion/blob/main/infer.ipynb)
mingxilei/rr_imdb_reward_8_0.001_m_10	mingxilei	2025-06-01T04:20:37Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-01T03:01:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hamsic/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-RK3588	hamsic	2025-06-01T04:19:51Z	0	0	null	[ "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "license:mit", "region:us" ]	null	2025-06-01T04:18:34Z	--- license: mit base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --- library_name: transformers base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B pipeline_tag: text-generation --- # Model Card for DeepSeek-R1-Distill-Qwen-1.5B-Uncensored ## Model Details ### Model Description DeepSeek-R1-Distill-Qwen-1.5B-Uncensored is a text-generation model designed to uphold the values of internet freedom and unrestricted access to information. By offering an uncensored approach, this model enables users to explore ideas, generate content, and engage in discussions without the constraints of over-moderated or filtered outputs. It prioritizes user autonomy and aligns with principles of free speech and open knowledge sharing. - Developed by: Thirdeye AI - Funded by: Thirdeye AI - Shared by: Thirdeye AI - Model type: Distilled Transformer-based Language Model - Language(s) (NLP): English - License: Apache 2.0 - Finetuned from model: DeepSeek-R1-Distill-Qwen-1.5B ### Model Sources - Repository: [DeepSeek-R1-Distill-Qwen-1.5B-Uncensored on Hugging Face](https://huggingface.co/thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored) - Demo: [Available on Hugging Face Hub](https://huggingface.co/thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored) --- ## Uses ### Direct Use The model is intended for applications that demand openness and flexibility in generating creative, exploratory, or critical content. These include: - Free-form writing and storytelling - Open-ended discussions - Exploratory content generation for sensitive or nuanced topics ### Downstream Use Users can fine-tune this model for specialized domains where censorship-free text generation is required, such as: - Journalism and investigative research - Creative projects that push artistic boundaries - Academic applications exploring controversial or complex topics ### Out-of-Scope Use This model should not be used for harmful, illegal, or unethical activities. Users must comply with applicable laws and ensure that the model's outputs do not infringe on others' rights. --- ## Bias, Risks, and Limitations ### Risks While the uncensored approach promotes freedom, it may produce outputs that are controversial, offensive, or factually inaccurate. Users must exercise discretion when interpreting the model's outputs and take responsibility for their use. ### Recommendations - Use responsibly, especially in contexts where outputs could impact individuals or communities. - Employ content moderation or review processes for high-stakes applications. --- ## The Case for Uncensored Models Thirdeye AI believes in the transformative power of open models that respect user autonomy and internet freedom. In a world where over-moderation can stifle innovation and critical thought, uncensored models empower individuals to explore and create without artificial constraints. This aligns with our mission to advance free and open access to AI tools. By releasing this model, we aim to support the following: - Freedom of Expression: Unrestricted AI tools enable users to articulate diverse perspectives and engage in meaningful conversations. - Transparency and Trust: Users deserve access to tools that operate openly, fostering accountability and understanding of AI behaviors. - Creative Empowerment: The absence of censorship allows for boundary-pushing content creation that might otherwise be suppressed. --- ## How to Get Started with the Model ```python from transformers import pipeline generator = pipeline("text-generation", model="thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored") response = generator("The importance of free speech is") print(response)
mradermacher/safe-o1-v-7b-GGUF	mradermacher	2025-06-01T04:14:14Z	10	1	transformers	[ "transformers", "gguf", "en", "base_model:PKU-Alignment/safe-o1-v-7b", "base_model:quantized:PKU-Alignment/safe-o1-v-7b", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T20:36:46Z	--- base_model: PKU-Alignment/safe-o1-v-7b language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/PKU-Alignment/safe-o1-v-7b <!-- 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/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/safe-o1-v-7b-GGUF/resolve/main/safe-o1-v-7b.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. 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 -->
manuross1/cndnlslddhwf5k5	manuross1	2025-06-01T04:14:01Z	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	2025-06-01T04:14:00Z	--- 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: cndnlslddhwf5k5 --- # Cndnlslddhwf5K5 <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 `cndnlslddhwf5k5` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "cndnlslddhwf5k5", "lora_weights": "https://huggingface.co/manuross1/cndnlslddhwf5k5/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('manuross1/cndnlslddhwf5k5', weight_name='lora.safetensors') image = pipeline('cndnlslddhwf5k5').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: 5500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/manuross1/cndnlslddhwf5k5/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF	mradermacher	2025-06-01T04:12:58Z	39	0	transformers	[ "transformers", "gguf", "en", "base_model:UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B", "base_model:quantized:UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-03T04:57:56Z	--- base_model: UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B 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/UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B <!-- 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/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/VLAA-Thinker-Qwen2.5VL-3B-GGUF/resolve/main/VLAA-Thinker-Qwen2.5VL-3B.f16.gguf) \| f16 \| 6.3 \| 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. 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/Dreamer-7B-Classifieds-GGUF	mradermacher	2025-06-01T04:12:20Z	1	0	transformers	[ "transformers", "gguf", "multimodal", "en", "base_model:osunlp/Dreamer-7B-Classifieds", "base_model:quantized:osunlp/Dreamer-7B-Classifieds", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-03T07:32:31Z	--- base_model: osunlp/Dreamer-7B-Classifieds language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - multimodal --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/osunlp/Dreamer-7B-Classifieds <!-- 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/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Classifieds-GGUF/resolve/main/Dreamer-7B-Classifieds.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. 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/Dreamer-7B-Reddit-GGUF	mradermacher	2025-06-01T04:12:00Z	15	0	transformers	[ "transformers", "gguf", "multimodal", "en", "base_model:osunlp/Dreamer-7B-Reddit", "base_model:quantized:osunlp/Dreamer-7B-Reddit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-03T07:51:37Z	--- base_model: osunlp/Dreamer-7B-Reddit language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - multimodal --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/osunlp/Dreamer-7B-Reddit <!-- 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/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Reddit-GGUF/resolve/main/Dreamer-7B-Reddit.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. 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/Dreamer-7B-Shopping-GGUF	mradermacher	2025-06-01T04:11:50Z	5	0	transformers	[ "transformers", "gguf", "multimodal", "en", "base_model:osunlp/Dreamer-7B-Shopping", "base_model:quantized:osunlp/Dreamer-7B-Shopping", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-03T08:02:50Z	--- base_model: osunlp/Dreamer-7B-Shopping language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - multimodal --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/osunlp/Dreamer-7B-Shopping <!-- 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/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Dreamer-7B-Shopping-GGUF/resolve/main/Dreamer-7B-Shopping.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. 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/Hint-GRPO-Qwen2-VL-7B-GGUF	mradermacher	2025-06-01T03:58:47Z	1	0	transformers	[ "transformers", "gguf", "en", "base_model:hqhQAQ/Hint-GRPO-Qwen2-VL-7B", "base_model:quantized:hqhQAQ/Hint-GRPO-Qwen2-VL-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-07T09:14:24Z	--- base_model: hqhQAQ/Hint-GRPO-Qwen2-VL-7B 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/hqhQAQ/Hint-GRPO-Qwen2-VL-7B <!-- 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/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Hint-GRPO-Qwen2-VL-7B-GGUF/resolve/main/Hint-GRPO-Qwen2-VL-7B.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. 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/UI-RFT-3B-GGUF	mradermacher	2025-06-01T03:50:08Z	14	0	transformers	[ "transformers", "gguf", "en", "base_model:henryhe0123/UI-RFT-3B", "base_model:quantized:henryhe0123/UI-RFT-3B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-11T15:21:26Z	--- base_model: henryhe0123/UI-RFT-3B 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/henryhe0123/UI-RFT-3B <!-- 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/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q5_K_S.gguf) \| Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q6_K.gguf) \| Q6_K \| 2.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-RFT-3B-GGUF/resolve/main/UI-RFT-3B.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 -->
mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF	mradermacher	2025-06-01T03:48:14Z	64	0	transformers	[ "transformers", "gguf", "Geometry", "Maths", "en", "base_model:kxxinDave/Qwen2.5-VL-instruct-3B-Geo", "base_model:quantized:kxxinDave/Qwen2.5-VL-instruct-3B-Geo", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-12T06:37:59Z	--- base_model: kxxinDave/Qwen2.5-VL-instruct-3B-Geo language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - Geometry - Maths --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/kxxinDave/Qwen2.5-VL-instruct-3B-Geo <!-- 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-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-instruct-3B-Geo-GGUF/resolve/main/Qwen2.5-VL-instruct-3B-Geo.f16.gguf) \| f16 \| 6.3 \| 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. 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 -->
BootesVoid/cmbckk41200kg10ozm0m6ncxr_cmbckphyl00l010ozjtewid3x	BootesVoid	2025-06-01T03:46:46Z	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	2025-06-01T03:46:45Z	--- 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: EMMA --- # Cmbckk41200Kg10Ozm0M6Ncxr_Cmbckphyl00L010Ozjtewid3X <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 `EMMA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMMA", "lora_weights": "https://huggingface.co/BootesVoid/cmbckk41200kg10ozm0m6ncxr_cmbckphyl00l010ozjtewid3x/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/cmbckk41200kg10ozm0m6ncxr_cmbckphyl00l010ozjtewid3x', weight_name='lora.safetensors') image = pipeline('EMMA').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/cmbckk41200kg10ozm0m6ncxr_cmbckphyl00l010ozjtewid3x/discussions) to add images that show off what you’ve made with this LoRA.
vertings6/bbbde83b-5c57-4651-b38d-7a57418af06d	vertings6	2025-06-01T03:43:19Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:scb10x/llama-3-typhoon-v1.5-8b-instruct", "base_model:adapter:scb10x/llama-3-typhoon-v1.5-8b-instruct", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-01T02:42:50Z	--- library_name: peft license: llama3 base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct tags: - axolotl - generated_from_trainer model-index: - name: bbbde83b-5c57-4651-b38d-7a57418af06d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 147754893cc87b26_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vertings6/bbbde83b-5c57-4651-b38d-7a57418af06d hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/147754893cc87b26_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 24227cfa-836b-435b-aa71-379bfe43f120 wandb_project: s56-7 wandb_run: your_name wandb_runid: 24227cfa-836b-435b-aa71-379bfe43f120 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # bbbde83b-5c57-4651-b38d-7a57418af06d This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1488 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2354 \| 0.0003 \| 1 \| 1.4276 \| \| 1.3914 \| 0.0851 \| 250 \| 1.1956 \| \| 0.9332 \| 0.1703 \| 500 \| 1.1488 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/diagram2graph-GGUF	mradermacher	2025-06-01T03:42:28Z	116	1	transformers	[ "transformers", "gguf", "diagram", "structured-data", "image-processing", "knowledge-graph", "json", "en", "dataset:zackriya/diagramJSON", "base_model:zackriya/diagram2graph", "base_model:quantized:zackriya/diagram2graph", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-14T06:35:37Z	--- base_model: zackriya/diagram2graph datasets: - zackriya/diagramJSON language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - diagram - structured-data - image-processing - knowledge-graph - json --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/zackriya/diagram2graph <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/diagram2graph-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/diagram2graph-GGUF/resolve/main/diagram2graph.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/diagram2graph-GGUF/resolve/main/diagram2graph.f16.gguf) \| f16 \| 6.3 \| 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. 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/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF	mradermacher	2025-06-01T03:36:54Z	50	0	transformers	[ "transformers", "gguf", "en", "base_model:Rosiness/Qwen2.5-VL-7B-Instruct-Mulberry-HY", "base_model:quantized:Rosiness/Qwen2.5-VL-7B-Instruct-Mulberry-HY", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-16T09:02:20Z	--- base_model: Rosiness/Qwen2.5-VL-7B-Instruct-Mulberry-HY 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/Rosiness/Qwen2.5-VL-7B-Instruct-Mulberry-HY <!-- 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-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-Mulberry-HY-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-Mulberry-HY.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. 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 -->
LLM-SocialMedia/Qwen3-8B-Korean-Sentiment	LLM-SocialMedia	2025-06-01T03:34:41Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-01T02:45:01Z	--- license: apache-2.0 --- # Qwen3-8B-Korean-Sentiment ## Overview This repository contains a fine-tuned model for Korean Sentiment Analysis (한국어 감정 분석) using a Large Language Model (LLM), specifically designed for YouTube comments in Korean. The model classifies sentiments into Positive(긍정), Negative(부정), and Neutral(중립) categories, and is fine-tuned to detect not only direct emotions but also subtle features like irony (반어법) and sarcasm (풍자) common in Korean-language content. ### Sentiment Classification: - Positive (긍정) - Negative (부정) - Neutral (중립) ## Quickstart To quickly get started with the fine-tuned model, use the following Python code: ```python from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer import torch # Load the model and tokenizer model = AutoPeftModelForCausalLM.from_pretrained( "LLM-SocialMedia/Qwen3-8B-Korean-Sentiment", # if GPU memory is insufficient device_map="auto", offload_folder="/offload", offload_state_dict=True, torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B") model.eval() # Sample comment comment = "이거 너무 좋아요!" # Format the prompt messages = [ { "role": "user", "content": ( "아래는 한국어 유튜브 댓글의 감정 분류 작업입니다.\n\n" f"댓글: {comment}\n\n" "다음 단계별로 꼼꼼히 생각하고 분석해 주세요:\n" "step_0. 댓글에서 사용된 주요 단어와 표현의 감정적 의미 분석 (예: 긍정적, 부정적, 중립적, 혹은 애매하거나 은어/반어적)\n" "step_1. 이모티콘, 이모지, 밈, 인터넷 은어의 숨겨진 의미 분석\n" "step_2. 댓글의 맥락과 의도 분석 (예: 진심, 풍자, 농담, 비꼼)\n" "step_3. 댓글을 감정을 분류 하세요\n" "step_4. 최종 감정 분류: '긍정', '중립', '부정' 중 하나\n\n" "마지막으로 아래 두 가지를 명확히 작성하세요:\n" "1. 분류 근거: 각 단계 분석을 종합한 감정 분류 이유\n" "2. 감정 분류 결과: '긍정', '중립', '부정' 중 하나로 출력\n\n" "출력 예시:\n" "분류 근거: 이 댓글은 농담과 비꼼을 섞었지만 최종적으로 유튜버를 긍정적으로 평가하고 있습니다.\n" "감정 분류 결과: 긍정" ) } ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) # Tokenize the input inputs = tokenizer(prompt, return_tensors="pt") # Generate prediction outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=512) # Decode and print the output print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) ``` ## Train/Test Details - Training Dataset: Fine-tuned on 3,857 labeled YouTube comments for sentiment classification. - Testing Dataset: Evaluated on 1,130 labeled YouTube comments to assess the model's performance. ## Results The fine-tuned model's performance on the sentiment classification task is summarized below: \| Metric \| Positive (긍정) \| Neutral (중립) \| Negative (부정) \| \|--------------\|-----------------\|----------------\|-----------------\| \| Precision\| 0.8981 \| 0.3787 \| 0.4971 \| \| Recall \| 0.7362 \| 0.2880 \| 0.7413 \| \| F1-Score \| 0.8092 \| 0.3272 \| 0.5951 \| \| Support \| 527 \| 309 \| 344 \| Accuracy: 62.03% (Based on 1180 samples) You can find detailed results [here](https://github.com/suil0109/LLM-SocialMedia/tree/main/huggingface). ## Contact For any inquiries or feedback, feel free to contact the team: - Email: [email protected] Team: - Hanjun Jung - Jinsoo Kim - Junhyeok Choi - Suil Lee - Seongjae Kang
Sayan01/Phi3-TL-ORCAMEL-SFT	Sayan01	2025-06-01T03:34:20Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T02:17:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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sergioalves/64dd0e1a-f870-4efe-aadd-42919de1180a	sergioalves	2025-06-01T03:33:47Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:scb10x/llama-3-typhoon-v1.5-8b-instruct", "base_model:adapter:scb10x/llama-3-typhoon-v1.5-8b-instruct", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-01T02:42:27Z	--- library_name: peft license: llama3 base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct tags: - axolotl - generated_from_trainer model-index: - name: 64dd0e1a-f870-4efe-aadd-42919de1180a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 147754893cc87b26_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/64dd0e1a-f870-4efe-aadd-42919de1180a hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/147754893cc87b26_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 24227cfa-836b-435b-aa71-379bfe43f120 wandb_project: s56-7 wandb_run: your_name wandb_runid: 24227cfa-836b-435b-aa71-379bfe43f120 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 64dd0e1a-f870-4efe-aadd-42919de1180a This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3141 \| 0.0005 \| 1 \| 1.4276 \| \| 1.2518 \| 0.1135 \| 250 \| 1.3058 \| \| 1.4845 \| 0.2270 \| 500 \| 1.2652 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Sayan01/Phi3-Llama-ORCAMEL-SFT	Sayan01	2025-06-01T03:32:12Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T02:27:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF	mradermacher	2025-06-01T03:25:27Z	17	1	transformers	[ "transformers", "gguf", "en", "base_model:wintonYF/SCB-Qwen2-VL-7B-Instruct-F", "base_model:quantized:wintonYF/SCB-Qwen2-VL-7B-Instruct-F", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-21T12:17:49Z	--- base_model: wintonYF/SCB-Qwen2-VL-7B-Instruct-F 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/wintonYF/SCB-Qwen2-VL-7B-Instruct-F <!-- 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/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SCB-Qwen2-VL-7B-Instruct-F-GGUF/resolve/main/SCB-Qwen2-VL-7B-Instruct-F.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. 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/Qwen2-VL-7B-JEDI-VSOP-GGUF	mradermacher	2025-06-01T03:25:24Z	6	1	transformers	[ "transformers", "gguf", "en", "base_model:beatrice-adel/Qwen2-VL-7B-JEDI-VSOP", "base_model:quantized:beatrice-adel/Qwen2-VL-7B-JEDI-VSOP", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-21T12:19:34Z	--- base_model: beatrice-adel/Qwen2-VL-7B-JEDI-VSOP 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/beatrice-adel/Qwen2-VL-7B-JEDI-VSOP <!-- 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-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-7B-JEDI-VSOP-GGUF/resolve/main/Qwen2-VL-7B-JEDI-VSOP.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. 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 -->
mingxilei/r_imdb_reward_8_0.001_m_30	mingxilei	2025-06-01T03:23:27Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-01T03:23:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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WenFengg/children_4	WenFengg	2025-06-01T03:23:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T03:18:47Z	--- 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. 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mradermacher/LiveCC-7B-Instruct-GGUF	mradermacher	2025-06-01T03:21:51Z	239	0	transformers	[ "transformers", "gguf", "qwen_vl", "video", "real-time", "multimodal", "LLM", "en", "dataset:chenjoya/Live-CC-5M", "dataset:chenjoya/Live-WhisperX-526K", "dataset:lmms-lab/LLaVA-Video-178K", "base_model:chenjoya/LiveCC-7B-Instruct", "base_model:quantized:chenjoya/LiveCC-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-23T11:31:33Z	--- base_model: chenjoya/LiveCC-7B-Instruct datasets: - chenjoya/Live-CC-5M - chenjoya/Live-WhisperX-526K - lmms-lab/LLaVA-Video-178K language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - qwen_vl - video - real-time - multimodal - LLM --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/chenjoya/LiveCC-7B-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/LiveCC-7B-Instruct-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/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/LiveCC-7B-Instruct-GGUF/resolve/main/LiveCC-7B-Instruct.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. 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 -->
PJMixers-Dev/gemma-3-12b-it-bnb-4bit	PJMixers-Dev	2025-06-01T03:19:52Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "conversational", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-12b-pt", "base_model:quantized:google/gemma-3-12b-pt", "license:gemma", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-06-01T02:21:49Z	--- license: gemma library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-12b-pt --- # BNB Quantization Config ```py BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_storage=torch.bfloat16, ) ``` # Gemma 3 model card Model Page: [Gemma](https://ai.google.dev/gemma/docs/core) Resources and Technical Documentation: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Terms of Use: [Terms][terms] Authors: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - Output: - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Usage Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0. ```sh $ pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your use case. #### Running with the `pipeline` API You can initialize the model and processor for inference with `pipeline` as follows. ```python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="google/gemma-3-12b-it", device="cuda", torch_dtype=torch.bfloat16 ) ``` With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline. ```python messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] } ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) # Okay, let's take a look! # Based on the image, the animal on the candy is a turtle. # You can see the shell shape and the head and legs. ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoProcessor, Gemma3ForConditionalGeneration from PIL import Image import requests import torch model_id = "google/gemma-3-12b-it" model = Gemma3ForConditionalGeneration.from_pretrained( model_id, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, {"type": "text", "text": "Describe this image in detail."} ] } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.bfloat16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) # Overall Impression:** The image is a close-up shot of a vibrant garden scene, # focusing on a cluster of pink cosmos flowers and a busy bumblebee. # It has a slightly soft, natural feel, likely captured in daylight. ``` ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality \| Benchmark \| Metric \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:--------------:\|:-------------:\|:--------------:\|:--------------:\| \| [HellaSwag][hellaswag] \| 10-shot \| 62.3 \| 77.2 \| 84.2 \| 85.6 \| \| [BoolQ][boolq] \| 0-shot \| 63.2 \| 72.3 \| 78.8 \| 82.4 \| \| [PIQA][piqa] \| 0-shot \| 73.8 \| 79.6 \| 81.8 \| 83.3 \| \| [SocialIQA][socialiqa] \| 0-shot \| 48.9 \| 51.9 \| 53.4 \| 54.9 \| \| [TriviaQA][triviaqa] \| 5-shot \| 39.8 \| 65.8 \| 78.2 \| 85.5 \| \| [Natural Questions][naturalq] \| 5-shot \| 9.48 \| 20.0 \| 31.4 \| 36.1 \| \| [ARC-c][arc] \| 25-shot \| 38.4 \| 56.2 \| 68.9 \| 70.6 \| \| [ARC-e][arc] \| 0-shot \| 73.0 \| 82.4 \| 88.3 \| 89.0 \| \| [WinoGrande][winogrande] \| 5-shot \| 58.2 \| 64.7 \| 74.3 \| 78.8 \| \| [BIG-Bench Hard][bbh] \| few-shot \| 28.4 \| 50.9 \| 72.6 \| 77.7 \| \| [DROP][drop] \| 1-shot \| 42.4 \| 60.1 \| 72.2 \| 77.2 \| [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code \| Benchmark \| Metric \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:-------------:\|:--------------:\|:--------------:\| \| [MMLU][mmlu] \| 5-shot \| 59.6 \| 74.5 \| 78.6 \| \| [MMLU][mmlu] (Pro COT) \| 5-shot \| 29.2 \| 45.3 \| 52.2 \| \| [AGIEval][agieval] \| 3-5-shot \| 42.1 \| 57.4 \| 66.2 \| \| [MATH][math] \| 4-shot \| 24.2 \| 43.3 \| 50.0 \| \| [GSM8K][gsm8k] \| 8-shot \| 38.4 \| 71.0 \| 82.6 \| \| [GPQA][gpqa] \| 5-shot \| 15.0 \| 25.4 \| 24.3 \| \| [MBPP][mbpp] \| 3-shot \| 46.0 \| 60.4 \| 65.6 \| \| [HumanEval][humaneval] \| 0-shot \| 36.0 \| 45.7 \| 48.8 \| [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual \| Benchmark \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------------ \|:-------------:\|:-------------:\|:--------------:\|:--------------:\| \| [MGSM][mgsm] \| 2.04 \| 34.7 \| 64.3 \| 74.3 \| \| [Global-MMLU-Lite][global-mmlu-lite] \| 24.9 \| 57.0 \| 69.4 \| 75.7 \| \| [WMT24++][wmt24pp] (ChrF) \| 36.7 \| 48.4 \| 53.9 \| 55.7 \| \| [FloRes][flores] \| 29.5 \| 39.2 \| 46.0 \| 48.8 \| \| [XQuAD][xquad] (all) \| 43.9 \| 68.0 \| 74.5 \| 76.8 \| \| [ECLeKTic][eclektic] \| 4.69 \| 11.0 \| 17.2 \| 24.4 \| \| [IndicGenBench][indicgenbench] \| 41.4 \| 57.2 \| 61.7 \| 63.4 \| [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal \| Benchmark \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|:-------------:\|:--------------:\|:--------------:\| \| [COCOcap][coco-cap] \| 102 \| 111 \| 116 \| \| [DocVQA][docvqa] (val) \| 72.8 \| 82.3 \| 85.6 \| \| [InfoVQA][info-vqa] (val) \| 44.1 \| 54.8 \| 59.4 \| \| [MMMU][mmmu] (pt) \| 39.2 \| 50.3 \| 56.1 \| \| [TextVQA][textvqa] (val) \| 58.9 \| 66.5 \| 68.6 \| \| [RealWorldQA][realworldqa] \| 45.5 \| 52.2 \| 53.9 \| \| [ReMI][remi] \| 27.3 \| 38.5 \| 44.8 \| \| [AI2D][ai2d] \| 63.2 \| 75.2 \| 79.0 \| \| [ChartQA][chartqa] \| 63.6 \| 74.7 \| 76.3 \| \| [VQAv2][vqav2] \| 63.9 \| 71.2 \| 72.9 \| \| [BLINK][blinkvqa] \| 38.0 \| 35.9 \| 39.6 \| \| [OKVQA][okvqa] \| 51.0 \| 58.7 \| 60.2 \| \| [TallyQA][tallyqa] \| 42.5 \| 51.8 \| 54.3 \| \| [SpatialSense VQA][ss-vqa] \| 50.9 \| 60.0 \| 59.4 \| \| [CountBenchQA][countbenchqa] \| 26.1 \| 17.8 \| 68.0 \| [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - Child Safety: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - Content Safety: Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - Representational Harms: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
MechaSloth/delete_u_0p_	MechaSloth	2025-06-01T03:18:31Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-06-01T03:15:30Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
TanAlexanderlz/RALL_RGBCROP_Aug16F-cosine_with_restarts	TanAlexanderlz	2025-06-01T03:18:16Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-06-01T01:41:19Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_RGBCROP_Aug16F-cosine_with_restarts 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. --> # RALL_RGBCROP_Aug16F-cosine_with_restarts This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4225 - Accuracy: 0.8494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3462 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4395 \| 0.0835 \| 289 \| 0.5305 \| 0.7239 \| \| 0.2409 \| 1.0835 \| 578 \| 0.5012 \| 0.8016 \| \| 0.0269 \| 2.0835 \| 867 \| 0.6809 \| 0.8160 \| \| 0.0086 \| 3.0835 \| 1156 \| 0.8971 \| 0.7894 \| \| 0.0008 \| 4.0835 \| 1445 \| 0.9614 \| 0.8160 \| \| 0.0004 \| 5.0835 \| 1734 \| 1.0207 \| 0.8160 \| \| 0.0004 \| 6.0835 \| 2023 \| 1.0934 \| 0.8139 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
manuross1/cndnlslddhwf6k	manuross1	2025-06-01T03:17:08Z	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	2025-06-01T02:18:07Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: cndnlslddhwf6k --- # Cndnlslddhwf6K <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 `cndnlslddhwf6k` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "cndnlslddhwf6k", "lora_weights": "https://huggingface.co/manuross1/cndnlslddhwf6k/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('manuross1/cndnlslddhwf6k', weight_name='lora.safetensors') image = pipeline('cndnlslddhwf6k').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: 6000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/manuross1/cndnlslddhwf6k/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/olmOCR-7B-thai-v2-GGUF	mradermacher	2025-06-01T03:15:51Z	113	0	transformers	[ "transformers", "gguf", "en", "base_model:Adun/olmOCR-7B-thai-v2", "base_model:quantized:Adun/olmOCR-7B-thai-v2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-25T12:05:04Z	--- base_model: Adun/olmOCR-7B-thai-v2 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/Adun/olmOCR-7B-thai-v2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-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/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-thai-v2-GGUF/resolve/main/olmOCR-7B-thai-v2.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. 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 -->
WenFengg/children_3	WenFengg	2025-06-01T03:12:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T03:10:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mradermacher/Jedi-3B-1080p-GGUF	mradermacher	2025-06-01T03:06:59Z	149	0	transformers	[ "transformers", "gguf", "en", "base_model:xlangai/Jedi-3B-1080p", "base_model:quantized:xlangai/Jedi-3B-1080p", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-30T15:01:26Z	--- base_model: xlangai/Jedi-3B-1080p 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/xlangai/Jedi-3B-1080p <!-- 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/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.f16.gguf) \| f16 \| 6.3 \| 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. 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/NORA-GGUF	mradermacher	2025-06-01T03:06:50Z	631	1	transformers	[ "transformers", "gguf", "en", "base_model:hungchiayu/NORA", "base_model:quantized:hungchiayu/NORA", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-30T15:08:47Z	--- base_model: hungchiayu/NORA 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/hungchiayu/NORA <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/NORA-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 \| \|:-----\|:-----\|--------:\|:------\| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q2_K.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q2_K.gguf) \| Q2_K \| 2.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.mmproj-fp16.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.mmproj-fp16.gguf) \| mmproj-fp16 \| 2.8 \| multi-modal supplement \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q3_K_S.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q3_K_M.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q3_K_M.gguf) \| Q3_K_M \| 3.3 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q3_K_L.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q3_K_L.gguf) \| Q3_K_L \| 3.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.IQ4_XS.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.IQ4_XS.gguf) \| IQ4_XS \| 3.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q4_K_S.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q4_K_S.gguf) \| Q4_K_S \| 3.8 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q4_K_M.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q4_K_M.gguf) \| Q4_K_M \| 4.0 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q5_K_S.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q5_K_S.gguf) \| Q5_K_S \| 4.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q5_K_M.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q5_K_M.gguf) \| Q5_K_M \| 4.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q6_K.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q6_K.gguf) \| Q6_K \| 5.2 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.Q8_0.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.Q8_0.gguf) \| Q8_0 \| 6.7 \| fast, best quality \| \| [PART 1](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/NORA.f16.gguf) [PART 2](https://huggingface.co/mradermacher/NORA-GGUF/resolve/main/nora.f16.gguf) \| f16 \| 12.5 \| 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. 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/asphaltmods7bv1-GGUF	mradermacher	2025-06-01T03:06:36Z	116	0	transformers	[ "transformers", "gguf", "en", "base_model:newchangertech/asphaltmods7bv1", "base_model:quantized:newchangertech/asphaltmods7bv1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-30T15:57:11Z	--- base_model: newchangertech/asphaltmods7bv1 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/newchangertech/asphaltmods7bv1 <!-- 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/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/asphaltmods7bv1-GGUF/resolve/main/asphaltmods7bv1.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. 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/pipes7bv1-GGUF	mradermacher	2025-06-01T03:06:32Z	112	0	transformers	[ "transformers", "gguf", "en", "base_model:newchangertech/pipes7bv1", "base_model:quantized:newchangertech/pipes7bv1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-30T16:17:51Z	--- base_model: newchangertech/pipes7bv1 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/newchangertech/pipes7bv1 <!-- 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/pipes7bv1-GGUF/resolve/main/pipes7bv1.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/pipes7bv1-GGUF/resolve/main/pipes7bv1.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. 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 -->
firobeid/L4_LSTM_financial_News_Headlines_generator	firobeid	2025-06-01T03:03:47Z	0	0	tensorflow	[ "tensorflow", "tf-keras", "lstm", "text-generation", "region:us" ]	text-generation	2025-06-01T02:46:36Z	--- tags: - text-generation - lstm - tensorflow library_name: tensorflow pipeline_tag: text-generation --- # LSTM Text Generation Model This model was trained using TensorFlow/Keras for financial article generation tasks. ## Model Details - Model Type: LSTM - Framework: TensorFlow/Keras - Task: Text Generation - Vocabulary Size: 30000 - Architecture: Bi-directional Long Short-Term Memory (LSTM) ## Usage ```python from huggingface_hub import snapshot_download import tensorflow as tf import json import pickle import numpy as np # Download model files model_path = snapshot_download(repo_id="firobeid/L4_LSTM_financial_News_Headlines_generator") # Load the LSTM model model = tf.keras.models.load_model(f"{model_path}/lstm_model") # Load tokenizer try: # Try JSON format first with open(f"{model_path}/tokenizer.json", 'r', encoding='utf-8') as f: tokenizer_json = f.read() tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_json) except FileNotFoundError: # Fallback to pickle format with open(f"{model_path}/tokenizer.pkl", 'rb') as f: tokenizer = pickle.load(f) # Text generation function import numpy as np from tensorflow.keras.preprocessing.sequence import pad_sequences def preprocess(texts, max_sequence_length = 71): texts = '<s> {}'.format(texts.lower()) X = np.array(tokenizer.texts_to_sequences([texts])) # REMOVE -1 pad_encoded = pad_sequences(X, maxlen= max_sequence_length, padding='pre') return pad_encoded def next_word(model, tokenizer, text, num_gen_words=1, randome_sampling = False, temperature=1): ''' Randome_Sampling : Using a categorical distribution to predict the character returned by the model Low temperatures results in more predictable text. Higher temperatures results in more surprising text. Experiment to find the best setting. ''' input_text = text output_text = [input_text] for i in range(num_gen_words): X_new = preprocess(input_text) if randome_sampling: y_proba = model.predict(X_new, verbose = 0)[0, -1:, :]#first sentence, last token rescaled_logits = tf.math.log(y_proba) / temperature pred_word_ind = tf.random.categorical(rescaled_logits, num_samples=1) #REMOVE THIS + 1 pred_word = tokenizer.sequences_to_texts(pred_word_ind.numpy())[0] else: y_proba = model.predict(X_new, verbose=0)[0] #first sentence pred_word_ind = np.argmax(y_proba, axis = -1) #REMOVE THIS + 1 pred_word = tokenizer.index_word[pred_word_ind[-1]] input_text += ' ' + pred_word output_text.append(pred_word) if pred_word == '</s>': return ' '.join(output_text) return ' '.join(output_text) def generate_text(model, tokenizer, text, num_gen_words=25, temperature=1, random_sampling=False): return next_word(model, tokenizer, text, num_gen_words, random_sampling, temperature) # Example usage # Start with these tag: <s>, while keeping words in lower case generate_text(model, tokenizer, "Apple", num_gen_words = 10, random_sampling = True, temperature= 10) ``` ## Training This model was trained on text data using LSTM architecture for next-word prediction. ## Limitations - Model performance depends on training data quality and size - Generated text may not always be coherent for longer sequences - Model architecture is optimized for the specific vocabulary it was trained on
Ibisbill/stage4_OpenThinker2_step50	Ibisbill	2025-06-01T03:02:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T01:56:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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apriasmoro/77168dc0-756e-4481-9685-f42f5b334270	apriasmoro	2025-06-01T03:02:28Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-01T03:01:46Z	--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: 77168dc0-756e-4481-9685-f42f5b334270 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml adapter: lora base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: false bnb_4bit_compute_dtype: float16 bnb_4bit_quant_type: nf4 bnb_4bit_use_double_quant: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9b33138dcfdbbaea_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: None field_instruction: instruct field_output: output field_system: None format: None no_input_format: None system_format: '{system}' system_prompt: None debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: true fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: true hub_model_id: apriasmoro/77168dc0-756e-4481-9685-f42f5b334270 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 15 micro_batch_size: 2 mlflow_experiment_name: /tmp/9b33138dcfdbbaea_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 6eee0287-8ac7-4224-9016-db360c2e7534 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 6eee0287-8ac7-4224-9016-db360c2e7534 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 77168dc0-756e-4481-9685-f42f5b334270 This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0921 \| 0.1429 \| 1 \| 0.8331 \| \| 1.0134 \| 0.5714 \| 4 \| 0.8241 \| \| 1.0091 \| 1.1429 \| 8 \| 0.7778 \| \| 0.7854 \| 1.7143 \| 12 \| 0.7536 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
mradermacher/olmOCR-7B-faithful-GGUF	mradermacher	2025-06-01T03:02:21Z	312	0	transformers	[ "transformers", "gguf", "en", "base_model:tngtech/olmOCR-7B-faithful", "base_model:quantized:tngtech/olmOCR-7B-faithful", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T21:17:33Z	--- base_model: tngtech/olmOCR-7B-faithful 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/tngtech/olmOCR-7B-faithful <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/olmOCR-7B-faithful-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/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/olmOCR-7B-faithful-GGUF/resolve/main/olmOCR-7B-faithful.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. 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 -->
WenFengg/children_2	WenFengg	2025-06-01T03:01:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-01T02:58:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]

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