--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-8B --- # Qwen3-8B-Instruct-2512-SFT **NOTE：This model is the Instruct-aligned variant, and it will not generate ```` blocks in its outputs. Additionally, there is no need to specify enable_thinking=False anymore.** Among them, the 8B and 14B SFT and DFT variants are obtained via full-parameter fine-tuning, while the 32B models are trained using LoRA due to hardware resource constraints.
The dataset used is the Chinese Distillation Dataset based on Qwen3-235B-2507.available at:[**Chinese-Qwen3-235B-2507-Distill-data-110k**](https://www.modelscope.cn/datasets/swift/Chinese-Qwen3-235B-2507-Distill-data-110k)
For details and code regarding model training and quantization, please see[Training and Quantization Guide](https://www.modelscope.cn/learn/3000)
Here is the list of models released in this version:

Model	4-bit AWQ		8-bit FP8	GPTQ		NVIDIA FP4		Weight-Activation
Model	AWQ	AWQ-asym	8-bit FP8	INT4	INT8	NVFP4	NVFP4-A16	W4A16	W8A8
Qwen3-8B-Instruct-2512-DFT	AWQ	awq-asym	FP8	GPTQ(int4)	GPTQ(int8)	NVFP4	NVFP4A16	W4A16	W8A8
Qwen3-8B-Instruct-2512-SFT	AWQ	awq-asym	FP8	GPTQ(int4)	GPTQ(int8)	NVFP4	NVFP4A16	W4A16	W8A8
Qwen3-14B-Instruct-2512-DFT	AWQ	awq-asym	FP8	GPTQ(int4)	GPTQ(int8)	NVFP4	NVFP4A16	W4A16	W8A8
Qwen3-14B-Instruct-2512-SFT	AWQ	awq-asym	FP8	GPTQ(int4)	GPTQ(int8)	NVFP4	NVFP4A16	W4A16	W8A8
Qwen3-32B-Instruct-2512-DFT	AWQ	awq-asym	FP8	GPTQ(int4)	GPTQ(int8)	NVFP4	NVFP4A16	W4A16	W8A8

### 【Dependencies】 ``` vllm>=0.10.2 transformers>=4.56.1 ```