【大模型】通义千问safetensors_rust.SafetensorError: Error while deserializing header: HeaderTooLarge解决方法
通义千问介绍Requirements模型下载模型推理解决方法解决方法2
通义千问介绍
GitHub：https://github.com/QwenLM/Qwen
Requirements
python 3.8及以上版本pytorch 1.12及以上版本，推荐2.0及以上版本建议使用CUDA 11.4及以上（GPU用户、flash-attention用户等需考虑此选项）模型下载
git clone https://www.modelscope.cn/qwen/Qwen-7B-Chat.git模型推理
infer_qwen.py:
from modelscope import AutoModelForCausalLM, AutoTokenizerfrom modelscope import GenerationConfig# Note: The default behavior now has injection attack prevention off.tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen-7B-Chat", trust_remote_code=True)# use bf16# model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()# use fp16# model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()# use cpu only# model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", device_map="cpu", trust_remote_code=True).eval()# use auto mode, automatically select precision based on the device.model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval()# Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this.# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参# 第一轮对话 1st dialogue turnresponse, history = model.chat(tokenizer, "你好", history=None)print(response)# 第二轮对话 2nd dialogue turnresponse, history = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", history=history)print(response)# 第三轮对话 3rd dialogue turnresponse, history = model.chat(tokenizer, "给这个故事起一个标题", history=history)print(response)执行推理时报错如下：
root:/workspace/tmp/LLM# python infer_qwen.py2023-12-16 01:35:43,760 - modelscope - INFO - PyTorch version 2.0.1 Found.2023-12-16 01:35:43,762 - modelscope - INFO - TensorFlow version 2.10.0 Found.2023-12-16 01:35:43,762 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer2023-12-16 01:35:43,883 - modelscope - INFO - Loading done! Current index file version is 1.9.1, with md5 5f21e812815a5fbb6ced75f40587fe94 and a total number of 924 components indexedThe model is automatically converting to bf16 for faster inference. If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to "AutoModelForCausalLM.from_pretrained".Try importing flash-attention for faster inference...Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotaryWarning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_normWarning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attentionLoading checkpoint shards: 0%| | 0/8 [00:00<?, ?it/s]Traceback (most recent call last): File "/workspace/tmp/LLM/infer_qwen.py", line 13, in <module> model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cpu", trust_remote_code=True).eval() File "/usr/local/lib/python3.10/dist-packages/modelscope/utils/hf_util.py", line 171, in from_pretrained module_obj = module_class.from_pretrained(model_dir, *model_args, File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py", line 479, in from_pretrained return model_class.from_pretrained( File "/usr/local/lib/python3.10/dist-packages/modelscope/utils/hf_util.py", line 72, in from_pretrained return ori_from_pretrained(cls, model_dir, *model_args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 2881, in from_pretrained ) = cls._load_pretrained_model( File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 3214, in _load_pretrained_model state_dict = load_state_dict(shard_file) File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 450, in load_state_dict with safe_open(checkpoint_file, framework="pt") as f:safetensors_rust.SafetensorError: Error while deserializing header: HeaderTooLarge解决方法
先 pip 安装 modelscope
# pythonfrom modelscope import snapshot_downloadmodel_dir = snapshot_download('qwen/Qwen-7B-Chat')下载过程如下：
就看网速了，慢慢等待。。。
解决方法2
先安装：
apt-get install git-lfs再下载：
git clone https://www.modelscope.cn/qwen/Qwen-14B-Chat-Int8.git下载等待：
这里会等待比较久，就看网速了。。。
如果出现下面的错误：
fatal: destination path 'Qwen-14B-Chat-Int8' already exists and is not an empty directory.执行：
rm -rf Qwen-14B-Chat-Int8