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update webdemo llama3

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This commit is contained in:
HongCheng 2024-04-29 11:38:42 +09:00
parent ac18474b1d
commit f6dec55c06
2 changed files with 4 additions and 4 deletions
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2
app.py
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@ -11,6 +11,6 @@ elif model == "EmoLLM_Model":
os.system("python download_model.py jujimeizuo/EmoLLM_Model") os.system("python download_model.py jujimeizuo/EmoLLM_Model")
os.system('streamlit run web_internlm2.py --server.address=0.0.0.0 --server.port 7860') os.system('streamlit run web_internlm2.py --server.address=0.0.0.0 --server.port 7860')
elif model == "Llama3_Model": elif model == "Llama3_Model":
os.system('streamlit run web_demo-Llama3.py --server.address=0.0.0.0 --server.port 7968') os.system('streamlit run web_demo_Llama3.py --server.address=0.0.0.0 --server.port 7968')
else: else:
print("Please select one model") print("Please select one model")

6
web_demo-Llama3.py → web_demo_Llama3.py
View File

@ -13,7 +13,7 @@ from transformers.utils import logging
from transformers import AutoTokenizer, AutoModelForCausalLM # isort: skip from transformers import AutoTokenizer, AutoModelForCausalLM # isort: skip
warnings.filterwarnings("ignore") # warnings.filterwarnings("ignore")
logger = logging.get_logger(__name__) logger = logging.get_logger(__name__)
@ -151,7 +151,7 @@ def on_btn_click():
@st.cache_resource @st.cache_resource
def load_model(): def load_model():
model_name = "./EmoLLM-Llama3-8B-Instruct2.0" model_name = "./EmoLLM-Llama3-8B-Instruct3.0"
print(model_name) print(model_name)
print('pip install modelscope websockets') print('pip install modelscope websockets')
@ -159,7 +159,7 @@ def load_model():
from modelscope import snapshot_download from modelscope import snapshot_download
#模型下载 #模型下载
model_name = snapshot_download('chg0901/EmoLLM-Llama3-8B-Instruct2.0',cache_dir=model_name) model_name = snapshot_download('chg0901/EmoLLM-Llama3-8B-Instruct3.0',cache_dir=model_name)
print(model_name) print(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16).eval() model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16).eval()