Comments (3)
请更新仓库代码后重新尝试。
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可以启动了,但是推理会报错,报错信息在另一个issue描述
from llama-factory.
通过webui.py 导入13B原模型,用8bit方式会报错,执行代码如下:
python src/web_demo.py --model_name_or_path ../models/Ziya-LLaMA-13B --quantization_bit 8
出错信息如下:Traceback (most recent call last): File "/home/hysz/AI/LLaMA-Efficient-Tuning/src/web_demo.py", line 18, in <module> model, tokenizer = load_pretrained(model_args, finetuning_args) File "/home/hysz/AI/LLaMA-Efficient-Tuning/src/utils/common.py", line 182, in load_pretrained model = model.half() # cast all params to float16 for inference File "/home/hysz/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/modeling_utils.py", line 1896, in half raise ValueError( ValueError: `.half()` is not supported for `4-bit` or `8-bit` models. Please use the model as it is, since the model has already been casted to the correct `dtype`.
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Related Issues (20)
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- 昇腾多卡训练问题
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- llama3-8b-base 微调后重复输出 HOT 1
- 使用Baichuan2-7B-Chat批量推理结果出现乱码 HOT 1
- 在Mac M系芯片的电脑上是否只支持FP32精度的微调啊? HOT 1
- cuda 内存溢出 HOT 1
- 使用自定义数据预训练报错,应当如何排查问题 HOT 3
- 使用hhrlhf数据集时报错 HOT 1
- predict时如何生成多条预测结果 HOT 2
- adapter_name_or_path 继续训练sft的adapter HOT 1
- 推理过程的自回归过程中,如果想要修改生成token对应的logits值,以方便自定义采样过程,如何实现
- VLLM部署api自动使用Ray集群,部署失败 HOT 2
- 训练日志记录不完整 HOT 3
- 显存大小与readme不符 HOT 3
- deepspeed不起作用 HOT 1
- 利用 vLLM 部署 OpenAI API HOT 1
- attn_implementation 不起作用 HOT 1
- 如何使用本地模型进行训练 HOT 1
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