This project provides the GGUF format file of the Qwen3-4B-Thinking-2507 model. It is a large language model with 4B parameters and chain of thought reasoning ability, supporting multiple quantization versions for easy deployment and operation on various hardware.

Natural Language ProcessingGguf

This model is a knowledge distillation model based on the Qwen3-4B architecture, trained with approximately 54.4 million tokens generated by Gemini 2.5 Flash, aiming to integrate the behavior, inference process, and knowledge of Gemini-2.5 Flash into a single dataset.

Natural Language ProcessingTransformersEnglish

This is a mixed quantization version based on the Qwen3 4B model, using the MXFP4_MOE mixed weight technology. It achieves a smaller file size and higher inference speed while maintaining near-lossless accuracy. By carefully combining MXFP4 and high-precision embedding/output weights, the model reaches an accuracy level close to Q8 quantization and has a throughput at the Q4 - Q6 level.

Natural Language ProcessingSafetensorsOther

This model is based on Qwen3-4B-Thinking-2507 and fine-tuned on 1000 examples of GPT-5-Codex. It focuses on text generation tasks and uses Unsloth technology to achieve a 2-fold increase in training speed.

Natural Language ProcessingTransformersEnglish

This is a GGUF format version converted from the Qwen/Qwen3-4B-Thinking-2507 model, using Q8_0 quantization, which can be used compatibly with llama.cpp. This model has chain-of-thought reasoning ability and has 4 billion parameters.

Natural Language ProcessingTransformers

A professional data science agent model fine-tuned based on Qwen3-4B-Thinking-2507, specifically optimized for the Jupyter Notebook environment, capable of executing Python code, analyzing datasets, and providing step-by-step reasoning.

Natural Language ProcessingTransformersMultiple Languages

This is an MLX version memory agent model with 8-bit precision, trained based on Qwen3-4B-Thinking-2507, specifically designed to handle information retrieval, update, and clarification tasks in the memory system.

Natural Language ProcessingTransformers

This is an enhanced language model based on the Qwen3-4B-Thinking-2507 base model, obtained by merging multiple fine-tuned models through the TIES method. It focuses on improving code generation and mathematical reasoning abilities while maintaining excellent general language understanding capabilities.

Natural Language ProcessingTransformersOther

This is an enhanced language model based on the Qwen3-4B-Thinking-2507 model. It is obtained by merging multiple Qwen3 fine-tuned models using the TIES method, specifically optimized for code generation and mathematical reasoning abilities.

Natural Language ProcessingTransformers

Qwen3-4B-Thinking-2507 is an upgraded version of the Qwen3-4B model, significantly improving thinking ability, reasoning quality, and depth, and performing excellently in various tasks, supporting 256K long context understanding.

Natural Language ProcessingTransformers

Qwen3-4B-Thinking-2507-AWQ is a quantized version based on the Qwen3-4B-Thinking-2507 base model. It performs excellently in multiple tasks such as reasoning, coding, and multi-language processing, and has significantly improved thinking ability and long context understanding ability.

Natural Language ProcessingTransformers

This is the 4-bit quantized version of the Qwen3-4B-Thinking-2507 model, optimized for the MLX framework. The original model is a large language model with 4 billion parameters developed by the Qwen team, capable of chain of thought reasoning and supporting various natural language processing tasks.

Natural Language ProcessingMlx