lmdeploy [Bug] llama3和Chinese-LLaMA-Alpaca-3 无论是pipeline方式，还是lmdeploy chat方式都无法生成结果（llama2都是好的）

Checklist

[ ] 1. I have searched related issues but cannot get the expected help.
[ ] 2. The bug has not been fixed in the latest version.

Describe the bug

start_chinese_llama3.py

import lmdeploy
from lmdeploy import pipeline
pipe = lmdeploy.pipeline("/home/zhanghui/models/ChineseAlpacaGroup/llama-3-chinese-8b-instruct", model_name='llama3')

response=pipe(['你是谁'])
print(response)

start_meta_llama3.py

from lmdeploy import pipeline

pipe = pipeline('/home/zhanghui/models/LLM-Research/Meta-Llama-3-8B-Instruct', model_name='llama3')

responese = pipe([
    'Who are you?',
    'Hello!'
])

print(responese)

start_chinese_llama2.py

import lmdeploy
from lmdeploy import pipeline
pipe = lmdeploy.pipeline("/mnt/d/models/chinese-alpaca-2-7b-hf", model_name='llama2')

response=pipe(['你是谁'])
print(response)

start_meta_llama2.py

import lmdeploy
from lmdeploy import pipeline
pipe = lmdeploy.pipeline("/mnt/d/models/meta-llama/Llama-2-7b-chat-hf", model_name='llama2')

response=pipe(['你是谁'])
print(response)

Reproduction

llama3代 python start_chinese_llama3.py

python start_meta_llama3.py

llama2代 python start_chinese_llama2.py

python start_meta_llama2.py

Environment

(lmdeploy0430) zhanghui@zhanghui:~/Chinese-LLaMA-Alpaca-3$  lmdeploy check_env
sys.platform: linux
Python: 3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.6, V11.6.124
GCC: gcc (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0
PyTorch: 2.1.2+cu121
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.1
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.9.7  (built against CUDA 11.8)
    - Built with CuDNN 8.9.2
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

LMDeploy: 0.4.0+1e8d7b8
transformers: 4.38.2
gradio: Not Found
fastapi: 0.110.3
pydantic: 2.7.1
triton: 2.1.0

Error traceback

No response

May 01 '24 02:05 zhanghui-china

Have you tried the official llama3-8b-instruct?

May 01 '24 11:05 lvhan028

Have you tried the official llama3-8b-instruct?

我下载的应该是modelscope官方的。https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct/summary

这个链接就是官方认证的。

May 02 '24 16:05 zhanghui-china

有5万次下载量·呢！ 4b8ae083922417dbc1f4ff4d267b20d

May 02 '24 16:05 zhanghui-china

I cannot reproduce your issue

May 03 '24 08:05 lvhan028

Please pass log_level=INFO to the pipeline and run it again. Let's check what the log says

May 03 '24 08:05 lvhan028

我从huggingface官网下载了权重文件。

··· import os

os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'

command_str = 'huggingface-cli download --token hf_Bxxx --resume-download meta-llama/Meta-Llama-3-8B-Instruct --local-dir ' + os.environ.get('HOME') + '/models/meta-llama/Meta-Llama-3-8B-Instruct1'

os.system(command_str) ··· 下载结果如下：

进入lmdeploy 0.4.0环境 conda deactivate conda activate lmdeploy040 lmdeploy chat /home/zhanghui/models/meta-llama/Meta-Llama-3-8B-Instruct1 --model-name llama3