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

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

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

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

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

This is the GGUF quantized version of the large language model Mistral-Large-3-675B-Instruct-2512 developed by Mistral AI. The original model has 675 billion parameters and is designed for instruction-following tasks. This project uses the llama.cpp tool and combines it with the imatrix calibration dataset to generate more than 20 different precision quantized model files from Q8_0 to IQ1_S, aiming to balance model performance, inference speed, and storage/memory usage, enabling it to run on a wider range of hardware.

Natural Language ProcessingGgufMultiple Languages

Rnj-1-instruct is an 8-billion-parameter open-weight dense model trained from scratch by Essential AI. It is optimized for code, STEM fields, agent capabilities, and tool invocation. It performs excellently in multiple programming languages and also demonstrates strong capabilities in mathematics and science, comparable to the most advanced open-weight models.

Natural Language ProcessingTransformers

This model is an MLX format conversion version of the Ministral-3-3B-Instruct-2512 instruction fine-tuning model released by Mistral AI. It is a large language model with a parameter scale of 3B, specifically optimized for following instructions and dialogue tasks, and supports multiple languages. The MLX format enables it to run efficiently on Apple Silicon devices.

Natural Language ProcessingMlxMultiple Languages

D-ART - LoRA - Z-Image-Turbo is an image generation model based on the Tongyi-MAI/Z-Image-Turbo base model. It is a LoRA (Low-Rank Adaptation) adapter designed to inject a specific 'D-ART' artistic style into the generated images. This style is characterized by generating illustrations of confident and dynamic young female characters, especially emphasizing the curves and dynamic poses of the characters. Users can activate this style by using specific trigger words to generate diverse images with unified aesthetic features.

Computer VisionDiffusers

This is a hybrid - language AI model specifically designed for call record analysis, capable of handling call transcription content in Hindi, English, and mixed Hindi - English. The model is fine - tuned based on Qwen2.5 - 1.5B - Instruct and has powerful multilingual understanding and information extraction capabilities.

Natural Language ProcessingTransformersMultiple Languages

This model is obtained through distillation training using the high-reasoning-difficulty Gemini 3 Pro preview dataset based on the Qwen3-4B-Thinking-2507 base model. It focuses on improving complex reasoning abilities in the fields of coding and science. Through training with specific datasets, it aims to efficiently transfer the reasoning abilities of large models (such as Gemini 3 Pro) to a smaller-scale model.

Natural Language ProcessingTransformers

This model is a quantized (Int8 / Q8) version of mixedbread-ai/mxbai-edge-colbert-v0-17m, exported specifically for use with transformers.js. It includes a custom projection layer required for the 48-dimensional ColBERT architecture, aiming to provide efficient vector retrieval capabilities.

Natural Language ProcessingTransformers

Zenyx_114M-Tiny-Edu-Instruct is an experimental small instruction fine-tuned language model with approximately 114 million parameters. It is built on the TinyEdu-50M base model, pre-trained on the FineWeb-Edu dataset, and fine-tuned on a mixed dataset of OpenHermes-2.5 and CodeFeedback-Filtered. This model aims to explore the limits of instruction fine-tuning under a minimal architecture and verify that the loss converges to approximately 1.04.

Natural Language ProcessingPytorchEnglish

Olmo-3-7B-Instruct-AIO-GGUF is a GGUF quantized version based on the Olmo-3-7B-Instruct model developed by the Allen Institute for AI. This is an autoregressive language model with 7 billion parameters, trained on datasets such as Tulu 2 and UltraFeedback through supervised fine-tuning and direct preference optimization, and performs excellently in question-answering and instruction following.

Natural Language ProcessingTransformersEnglish

This is a quantized version of ai-sage's GigaChat3-10B-A1.8B model, using the imatrix quantization technology of llama.cpp, which can run more efficiently under different hardware conditions. The model supports Russian and English and is mainly used for text generation tasks.

Natural Language ProcessingGgufMultiple Languages

This is the GGUF quantized version of allenai's Olmo-3-32B-Think model. It undergoes various quantization processes using the llama.cpp tool, aiming to enhance the model's performance and efficiency in specific environments. It offers multiple quantization options from Q2 to Q8 to meet different hardware configurations and performance requirements.

Natural Language ProcessingGgufEnglish

Qwen-SEA-LION-v4-4B-VL is a vision-language model with 4 billion parameters built on the Qwen3-VL-4B-Instruct architecture. It has been specifically instruction fine-tuned for the Southeast Asian region, possessing multilingual and multicultural capabilities, supporting English and seven Southeast Asian languages, and retaining strong vision-language understanding capabilities.

MultimodalTransformersMultiple Languages

This is the GGUF quantized version of the ai-sage/GigaChat3-10B-A1.8B-bf16 model, offering a variety of quantization options, from high-precision Q8_0 to extremely compressed smol-IQ1_KT, to meet the deployment requirements under different hardware conditions. This model supports a 32K context length, adopts the MLA architecture, and is optimized for dialogue scenarios.

Natural Language ProcessingGgufOther