This is a quantized version of the Qwen3-4B-Instruct-2507-heretic model. Multiple quantization types of model files are generated using the llama.cpp tool and the imatrix dataset, which can run in various environments and provide users with more options.

Natural Language ProcessingGguf

This is a mixed quantization version based on the Qwen3 4B Instruct model, using MXFP4 and mixed weight techniques to achieve a smaller file size and higher inference speed while maintaining nearly lossless precision.

Natural Language ProcessingSafetensorsOther

OmniDimen-V1.1-4B-Emotion is an emotion recognition and emotion-aware text generation model fine-tuned based on Qwen3-4B-Instruct-2507. It is specifically designed to accurately identify the emotional tendency of text and generate matching emotional content, providing users with a more emotionally resonant interaction experience.

Natural Language ProcessingGgufMultiple Languages

Qwen3-4B-I-1509 is a dedicated model fine-tuned based on the Qwen3-4B-Instruct-2507 base model, focusing on tool usage and function call generation tasks. This model is trained using a reinforcement learning method with multi-signal rewards and demonstrates good performance in multiple evaluation benchmarks.

Natural Language ProcessingTransformersEnglish

OmniDimen-4B-Emotion is an emotion recognition and emotion - aware text generation model fine - tuned based on Qwen3-4B-Instruct-2507, specifically designed for emotional interaction and dialogue generation with emotion perception ability.

Natural Language ProcessingGgufMultiple Languages

Qwen3 4B Instruct is a 4-billion parameter instruction fine-tuning model based on the Qwen3 architecture, supporting multilingual text generation tasks. The model offers various quantization versions, ranging from 2-bit to 32-bit, providing users with flexible choices for balancing performance and efficiency.

Natural Language ProcessingGgufMultiple Languages

This project provides a quantized version of the Qwen/Qwen3-4B-Instruct-2507 model in GGUF format, supporting multiple quantization levels (2-bit to 8-bit). It is designed for efficient text generation tasks and can run in a local environment.

Natural Language ProcessingGguf

This is a GGUF quantized version converted from the Qwen3-4B-Instruct model, using Q8 quantization technology, suitable for the llama.cpp inference framework. The model is optimized through model fusion technology and performs excellently in academic benchmark tests such as MMLU and GPQA.

Natural Language ProcessingTransformersEnglish

Qwen3-4B-Instruct-2507 is a large language model with 4 billion parameters launched by Alibaba Cloud. Based on the Unsloth Dynamic 2.0 quantization technology, it has significant improvements in general capabilities, long-tail knowledge coverage, user preference alignment, and long context understanding.

Natural Language ProcessingTransformers

Qwen3-4B-Instruct-2507 is an updated version of Qwen3-4B in non-thinking mode, with significant improvements in general capabilities, long-tail knowledge coverage, user preference alignment, and long context understanding. It uses the Unsloth Dynamic 2.0 quantization technology and performs excellently in accuracy.

Natural Language ProcessingTransformers

Qwen3-4B-Instruct-2507-AWQ is an updated version of Qwen3-4B in non-thinking mode, with significant improvements in general capabilities, long-tail knowledge coverage, user preference alignment, and long context understanding. It can be used in various scenarios such as text generation, providing users with high-quality language interaction experiences.

Natural Language ProcessingTransformers

Based on the Qwen architecture, the Qwen3-4B-Instruct-2507 model is launched by the community and 4-bit quantized using MLX, specifically optimized for Apple chips. This model provides an efficient and high-performance solution for text generation tasks and is part of the LM Studio Community Model Highlights Program.

Natural Language ProcessingTransformers