This project provides a quantized version (Int4) of Qwen/Qwen3-VL-32B-Thinking, aiming to lower the hardware threshold for model deployment and make the knowledge of advanced visual language models accessible to everyone.

MultimodalGguf

This is a 🤗 Transformers model published on the Hugging Face Hub, and its model card is automatically generated by the system. Currently, most detailed information in the card (such as developers, model type, language, license, etc.) is pending completion.

Natural Language ProcessingTransformers

Gemma-UA-Cardio consists of two professional large language models specifically designed for the Ukrainian cardiology field. They are based on Google's Gemma and MedGemma models and have undergone two-stage fine-tuning (language adaptation and domain specialization). They can accurately provide cardiovascular medical information and answer clinical questions in Ukrainian, serving as auxiliary tools for medical professionals.

Natural Language ProcessingTransformersOther

This is a LoRA fine-tuned model based on Qwen-Image-Edit-2509, specifically designed to solve the problem of secondary illumination processing for images. It can remove the illumination effect of the original image and regenerate realistic lighting and shadows for the target image according to the illumination and hue of the reference image, achieving the migration and fusion of image styles.

Computer VisionDiffusers

CodeV is a 7-billion-parameter vision-language model fine-tuned based on Qwen2.5-VL-7B-Instruct. Through two-stage training of supervised fine-tuning (SFT) and reinforcement learning (RL) based on tool-aware policy optimization (TAPO), it aims to achieve reliable and interpretable visual reasoning. It represents visual tools as executable Python code and ensures the consistency between tool usage and question evidence through a reward mechanism, solving the problem of irrelevant tool invocation under high accuracy.

MultimodalTransformersMultiple Languages

ActIO-UI-7B-RLVR is a 7-billion-parameter visual language model released by Uniphore, specifically designed for computer interface automation tasks. It is based on Qwen2.5-VL-7B-Instruct and optimized through supervised fine-tuning and reinforcement learning with verifiable rewards. It performs excellently in tasks such as GUI navigation, element positioning, and interaction planning, and has achieved the leading level among open-source 7B models in the WARC-Bench benchmark test.

MultimodalTransformersEnglish

This is an open-source large language model with 20 billion parameters focusing on the field of network security. It is based on the GPT-OSS architecture and fine-tuned by integrating multiple network security instruction datasets. The model provides various quantization versions, which are convenient for deployment in resource-constrained environments and can be used to generate network security-related texts, codes, and analysis reports.

Natural Language ProcessingTransformersEnglish

This is a quantized version of TheDrummer's Magidonia-24B-v4.3 large language model. By using the imatrix quantization technology of llama.cpp, GGUF format files with various precisions from BF16 to IQ2_XS are generated, aiming to run the model more efficiently under different hardware conditions (especially in resource-constrained environments) while maintaining the model performance as much as possible.

Natural Language ProcessingGguf

This project is a quantized version of TheDrummer's Cydonia-24B-v4.3 model. It uses llama.cpp for quantization processing, aiming to provide model files of different quantization types to meet the needs of different hardware and performance. It is a large language model with 24B parameters, mainly used for text generation tasks.

Natural Language ProcessingGguf

Z-Image-Re-Turbo is a text-to-image generation model that has been optimized for de-reduction and re-acceleration based on the Z-Image-De-Turbo model. This model aims to balance the convenience during training and the speed during inference. It restores the fast generation ability close to the original Turbo model while maintaining the same training-friendly features as Z-Image-De-Turbo, enabling it to be perfectly compatible with a large number of trained LoRA models in the Z-Image ecosystem.

MultimodalDiffusers

INTELLECT-3 is a Mixture of Experts (MoE) inference model developed by Prime Intellect with a parameter scale of 106B (12B active parameters). It is supervised and fine-tuned based on GLM-4.5-Air-Base and trained through large-scale reinforcement learning, showing excellent performance in mathematics, coding, and inference tasks.

Natural Language ProcessingGguf

This is the GGUF quantization format version of the NousResearch/Hermes-4.3-36B model, quantized by MaziyarPanahi. GGUF is an efficient model format designed to optimize local deployment and inference performance, supporting multiple quantization levels (2-bit to 8-bit), and suitable for a wide range of text generation tasks.

Natural Language ProcessingGguf

This is a cross-encoder model fine-tuned on the LangCache sentence pair dataset using the sentence-transformers library, based on the Alibaba-NLP/gte-reranker-modernbert-base model. It is specifically designed to calculate the semantic similarity score between text pairs, aiming to provide efficient text matching and reordering capabilities for the LangCache semantic cache system.

Natural Language ProcessingSentence-transformersEnglish

This is a cross-encoder model fine-tuned by Redis, specifically designed for sentence pair re-ranking in semantic caching scenarios. It is based on the gte-reranker-modernbert-base model of Alibaba-NLP and trained on the LangCache sentence pair dataset. It can efficiently calculate the semantic similarity score between two texts to determine whether they express the same or similar meanings.

Natural Language ProcessingSentence-transformersEnglish

OpenThinker-Agent-v1-SFT is an agent model obtained through supervised fine-tuning (SFT) based on Qwen/Qwen3-8B. It is the first-stage model of the complete training process (SFT + RL) of OpenThinker-Agent-v1, specifically optimized for agent tasks (such as terminal operations and code repair).

Natural Language ProcessingTransformersOther

OpenThinker - Agent - v1 is an open - source intelligent agent model that is subsequently trained based on Qwen3 - 8B. It is designed for terminal operations and software engineering tasks. It is first trained on a high - quality supervised fine - tuning dataset and then further optimized through reinforcement learning. It performs excellently in agent benchmark tests such as Terminal - Bench 2.0 and SWE - Bench.

Natural Language ProcessingTransformersOther

This is a CrossEncoder model developed by Redis and fine-tuned for the LangCache semantic caching task. It is based on the mature `cross-encoder/ms-marco-MiniLM-L6-v2` model and trained on a dataset of over 1 million LangCache sentence pairs. It is specifically designed to calculate the semantic relevance score between two texts to optimize the cache hit rate.

Natural Language ProcessingSentence-transformersEnglish

GigaChat3-10B-A1.8B is an efficient dialogue model in the GigaChat series. Based on the Mixture-of-Experts (MoE) architecture, it has a total of 10 billion parameters and 1.8 billion active parameters. It adopts the innovative Multi-head Latent Attention (MLA) and Multi-token Prediction (MTP) technologies, aiming to optimize inference throughput and generation speed. The model is trained on 20T tokens of diverse data and supports 10 languages including Chinese, suitable for dialogue scenarios requiring quick responses.

Natural Language ProcessingGgufMultiple Languages

VoxCPM is an innovative tokenizer-free end-to-end text-to-speech (TTS) system that overcomes the limitations of discrete tokenization by modeling speech in a continuous space. It has two core capabilities: context-aware speech generation and realistic zero-shot voice cloning. It can automatically adjust the prosody and style according to the text content and clone the speaker's timbre, accent, and emotion with just a short reference audio.

Audio ProcessingSafetensorsMultiple Languages

LongCat Image Editing is the image editing version of Longcat-Image. It is an open-source image editing model that supports both Chinese and English. It has reached the advanced level of open-source models in terms of instruction following, image quality, and visual consistency, and has excellent precise editing and consistency preservation capabilities.

MultimodalTransformersMultiple Languages