Lora fine-tunning对Qwen3.5-4B模型进行微调，经过GPTQ int4量化之后的模型不能正常输出，等待很久全是！！！

Checklist / 检查清单

• I have searched existing issues, and this is a new question or discussion topic. / 我已经搜索过现有的 issues，确认这是一个新的问题与讨论。

Question Description / 问题描述

使用Lora fine-tunning对Qwen3.5-4B模型进行微调，融合后模型可以正常推理，但是经过GPTQ int4量化之后的模型不能正常输出，等待很久全是！！！的输出。
遇到的问题有：
1、python -m pip install -U accelerate optimum transformers
python -m pip install gptqmodel --no-build-isolation
2、CUDA_VISIBLE_DEVICES=0 swift export --model ./output/washer_tasks_system/v1-20260315-114809/checkpoint-260-merged --quant_method gptq_v2 --quant_bits 4 --dataset ./out/dishwasher_mix_sft/quant_mix.jsonl --quant_n_samples 256 --max_length 2048 --output_dir output/washer_qwen3.5_4b_gptq
运行量化指令之后，报错

然后打了补丁：
/home/super/anaconda3/envs/swift401/lib/python3.11/site-packages/optimum/gptq/quantizer.py中：
self.select_quant_linear(device_map=model.hf_device_map, pack=True)
改成下面这样：
device_map = getattr(model, "hf_device_map", {"": 0})
self.select_quant_linear(device_map=device_map, pack=True)
3、执行推理

Start by reproducing the swift export GPTQ int4 command for the merged Qwen3.5-4B checkpoint and inspect the reported error. Compare the behavior of optimum/gptq/quantizer.py with and without the shown hf_device_map fallback, then run inference on the quantized output. Done means the quantized model produces normal output rather than repeated exclamation marks.