Kimi-K2-Thinking is a large language model in MLX format converted by mlx-community from the original model of moonshotai. The conversion is carried out using version 0.28.4 of mlx-lm, retaining the chain of thought reasoning ability of the original model.

Natural Language ProcessingMlx

This is an enhanced multimodal vision-language model based on the Qwen3-VL-8B-Thinking model. It is extended to 12B parameters through Brainstorm 20x technology and uses NEO Imatrix-enhanced GGUF quantization. The model has powerful image understanding, text generation, and multimodal reasoning capabilities, with significant improvements in visual perception, text quality, and creative scenarios.

MultimodalGgufMultiple Languages

This is the GGUF quantized version of the Qwen/Qwen3-Coder-30B-A3B-Instruct language model, specifically optimized for code generation and programming tasks. It is converted using FP32 precision and is suitable for local inference deployment.

Natural Language ProcessingGgufMultiple Languages

This is the MXFP4_MOE quantized version of the MiniMax-M2-THRIFT model. It has been compressed based on the original model, including 25% expert pruning (from 256 to 192) and setting top_k=8. Meanwhile, it retains the characteristics of the encoding model and can be used for text generation tasks.

Natural Language ProcessingGguf

This is a 🤗 Transformers model published on the Hugging Face Hub. Specific information needs to be obtained from the model page. This model is based on the Transformer architecture and is suitable for various natural language processing tasks.

Natural Language ProcessingTransformers

The aquif-3.5 series is the masterpiece released on November 3, 2025, including two versions, Plus and Max, which provide advanced inference capabilities and an unprecedented 1 million token context window, achieving state-of-the-art performance in their respective categories.

Natural Language ProcessingTransformersMultiple Languages

A static quantized version of UME-R1-7B, supporting multiple tasks such as sentence similarity, embedding, zero-shot image classification, and video text-to-text. It offers various quantization types to meet different needs, from the lightweight Q2_K to the high-quality Q8_0 version.

MultimodalTransformersEnglish

Qwen3-Coder-REAP-363B-A35B is a sparse mixture-of-experts model obtained by pruning 25% of the experts from Qwen3-Coder-480B-A35B-Instruct using the REAP method. While maintaining performance close to the original model, it significantly reduces the parameter scale and memory requirements, making it particularly suitable for code generation and intelligent coding scenarios with limited resources.

Natural Language ProcessingTransformersEnglish

NV-Reason-CXR-3B GGUF is a quantized version of the NVIDIA NV-Reason-CXR-3B vision-language model, optimized for edge device deployment. This is a model with 3 billion parameters, focusing on chest X-ray analysis. It has been converted to the GGUF format and quantized for efficient operation on mobile devices, desktops, and embedded systems.

MultimodalGgufEnglish

Aquif-3.5-Max-42B-A3B is a large language model with 42 billion parameters, which has undergone MXFP4_MOE quantization processing. It optimizes inference efficiency while maintaining high-quality text generation capabilities. This model is based on an advanced mixture-of-experts architecture and is suitable for various natural language processing tasks.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the aquif-3.5-Plus-30B-A3B model. Through quantization technology, the model's resource occupancy is significantly reduced while maintaining similar performance, which has high engineering practice value.

Natural Language ProcessingGguf

This project is the MXFP4_MOE quantized version of the Kanana 1.5-15.7B-A3B instruction model, which can improve the model's operating efficiency in specific scenarios. This model is optimized based on kakaocorp/kanana-1.5-15.7b-a3b-instruct, using a mixture-of-experts architecture and MXFP4 quantization technology.

Natural Language ProcessingGguf

This is a text generation model optimized using PEFT (Parameter-Efficient Fine-Tuning) and LoRA techniques based on the Google Gemma-2B-IT base model. The model is optimized for specific tasks while maintaining the capabilities of the base model through parameter-efficient methods.

Natural Language ProcessingPeft

Kimi K2 Thinking is the latest generation of open-source thinking model developed by Moonshot AI. It has powerful deep reasoning ability and tool call function. The model adopts a mixture-of-experts architecture, supports native INT4 quantization, has a 256k context window, and performs excellently in multiple benchmark tests.

Natural Language ProcessingTransformers

This is a GGUF format version converted from the Qwen3-VL-30B-A3B-Instruct model, specifically optimized for llama.cpp. This model is a multimodal visual language model with 30 billion parameters, supporting image understanding and text generation tasks.

MultimodalGguf

Melta is a unique small language model developed based on the Discord bot Melta27. It has a cute, charming, and enthusiastic personality, and is specifically designed to generate original, unique, and cute text content.

Natural Language ProcessingTransformersFrench

This is a static quantized version of the yanolja/YanoljaNEXT-Rosetta-27B-2511 model, providing weight files of various quantization types and supporting multilingual translation tasks. The model is specifically optimized for 32 languages, including major languages such as Chinese, English, Japanese, and Korean.

Natural Language ProcessingTransformersMultiple Languages

Qwen-Edit-Skin is a LoRA model fine-tuned based on the Qwen/Qwen-Image-Edit-2509 model, focusing on enhancing the realism and details of human skin in images and generating more natural and delicate skin textures.

Computer VisionDiffusers

Apollo-V0.1-4B-Thinking is a large language model based on 4B parameters, adopting a chain of thought reasoning architecture and specifically optimized for reasoning tasks. This model offers multiple quantization versions and can run efficiently in different hardware environments.

Natural Language ProcessingGguf

This is a quantized version model based on Qwen3-VL-30B, specifically designed for image-text to text conversion tasks. The model optimizes the original model through quantization technology, providing a more efficient inference solution.

MultimodalGguf