This is the GGUF format version of the Qwen/Qwen3-30B-A3B-Instruct-2507 model, specifically optimized for text generation tasks. It supports multiple quantization levels, facilitating deployment and operation on various hardware.

Natural Language ProcessingGguf

This is the GGUF format version of the unsloth/Qwen3-30B-A3B-Instruct-2507 model, quantized by machines provided by TensorBlock. This model is a large language model with 30 billion parameters, specifically optimized for instruction following tasks and supporting multiple languages such as Chinese and English.

Natural Language ProcessingTransformers

This is a 5-bit DWQ quantized version in MLX format converted from the Qwen/Qwen3-30B-A3B-Instruct-2507 model using mlx-lm version 0.26.2. It is optimized for Apple Silicon and supports efficient text generation tasks.

Natural Language ProcessingMlx

Qwen3-30B-A3B-Instruct-2507-AWQ is the AWQ int4 quantized version of Qwen3-30B-A3B-Instruct-2507, with a total of 30.5 billion parameters and 3.3 billion active parameters. This model has significant improvements in instruction following, logical reasoning, text understanding, mathematics, science, coding, and tool usage. It supports the ability to understand 256K long contexts and is compatible with Transformers and vLLM for efficient text generation.

Natural Language ProcessingTransformers

Qwen3-30B-A3B-Instruct-2507 is a 30.5 billion parameter mixture of experts model launched by Alibaba Cloud. It uses a non-thinking mode and performs excellently in instruction following, logical reasoning, text understanding, mathematics and science, coding, and tool usage. It supports a 262K long context and multilingual processing.

Natural Language ProcessingTransformers

This is a version of Qwen3-30B-A3B-Instruct-2507 quantized with W8A8 (8-bit weights and activations) based on LLM-Compressor. Compared with the FP16 version, the memory usage is reduced by about 50%, and faster inference speed is achieved on supported hardware, which is particularly suitable for Ampere and older GPU architectures.

Natural Language ProcessingTransformers

A 30B parameter instruction-tuned model developed based on Qwen, processed with MLX 8-bit quantization, specifically optimized for Apple Silicon chips, suitable for various text generation tasks.

Natural Language ProcessingTransformers

Qwen3-30B-A3B-Instruct-2507 is an instruction fine-tuned model with 30 billion parameters based on Qwen. It is specifically optimized for Apple Silicon devices with MLX 4-bit quantization and is suitable for efficient text generation tasks.

Natural Language ProcessingTransformers

Qwen3-30B-A3B-Instruct-2507-FP8 is an updated version of Qwen3-30B-A3B-FP8 in non-thinking mode, with significant improvements in general capabilities, long-tail knowledge coverage, user preference alignment, and long context understanding. It supports a native context length of 262,144.

Natural Language ProcessingTransformers

Qwen3-30B-A3B-Instruct-2507 is a large language model with 30 billion parameters launched by the Tongyi Qianwen team of Alibaba. It is specifically optimized for instruction following tasks. This model supports various natural language processing tasks such as text generation and dialogue interaction, and is open for developers to use through the LM Studio community model program.

Natural Language ProcessingGguf

This is a quantized version of the Qwen3-30B-A3B-Instruct-2507 model. The llama.cpp tool and imatrix technology are used to generate GGUF format files with multiple quantization precisions, facilitating the efficient operation of this large language model in different hardware environments.

Natural Language ProcessingGguf

Qwen3-30B-A3B-Instruct-2507 is an updated version of Qwen3-30B-A3B in non-thinking mode, with significant improvements in general capabilities, long-tail knowledge coverage, user preference alignment, and long context understanding. This is a large language model with 30.5 billion parameters, using the MoE architecture with 3.3 billion activated parameters.

Natural Language ProcessingTransformers