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[bugfix] fix gptq transformers>=5.0 #9042

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[bugfix] fix gptq transformers>=5.0 #9042

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7ffbb83
fix gptmodel transformers>=5.0
Jintao-Huang Apr 8, 2026
15715cb
update
Jintao-Huang Apr 8, 2026
79b77cf
update
Jintao-Huang Apr 8, 2026
f6bbfc4
update
Jintao-Huang Apr 8, 2026
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3 changes: 2 additions & 1 deletion docs/source/Instruction/Export-and-push.md
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Expand Up @@ -11,7 +11,8 @@ SWIFT支持AWQ、GPTQ、FP8、BNB模型的量化导出。其中使用AWQ、GPTQ

| 量化技术 | 多模态 | 推理加速 | 继续训练 |
| -------- | ------ | -------- | -------- |
| GPTQ | ✅ | ✅ | ✅ |
| GPTQ/GPTQ-V2 | ✅ | ✅ | ✅ |
| FP8 | ✅ | ✅ | ✅ |
| AWQ | ✅ | ✅ | ✅ |
| BNB | ❌ | ✅ | ✅ |

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3 changes: 2 additions & 1 deletion docs/source_en/Instruction/Export-and-push.md
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Expand Up @@ -10,7 +10,8 @@ SWIFT supports quantization exports for AWQ, GPTQ, FP8, and BNB models. AWQ and

| Quantization Technique | Multimodal | Inference Acceleration | Continued Training |
| ---------------------- | ---------- | ---------------------- | ------------------ |
| GPTQ | ✅ | ✅ | ✅ |
| GPTQ/GPTQ-V2 | ✅ | ✅ | ✅ |
| FP8 | ✅ | ✅ | ✅ |
| AWQ | ✅ | ✅ | ✅ |
| BNB | ❌ | ✅ | ✅ |

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2 changes: 2 additions & 0 deletions swift/pipelines/export/quant.py
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Expand Up @@ -280,6 +280,8 @@ def gptq_model_quantize(self, v2: bool = False):
logger.info('Start quantizing the model...')
logger.warning('The process of packing the model takes a long time and there is no progress bar. '
'Please be patient and wait...')
if not hasattr(self.model, 'hf_device_map'):
self.model.hf_device_map = {'': torch.device('cuda:0')}
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@gemini-code-assist gemini-code-assist bot Apr 8, 2026

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high

Hardcoding the device to cuda:0 can lead to failures or incorrect behavior on multi-GPU systems or CPU-only environments. It is safer to use the model's current device when initializing the hf_device_map.

Suggested change
if not hasattr(self.model, 'hf_device_map'):
self.model.hf_device_map = {'': torch.device('cuda:0')}
if not hasattr(self.model, 'hf_device_map'):
self.model.hf_device_map = {'': self.model.device}

with self._patch_gptq_block(self.model, block_name_to_quantize):
gptq_quantizer.quantize_model(self.model, self.tokenizer)
self.model.config.quantization_config.pop('dataset', None)
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