update webdemo llama3
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2
app.py
2
app.py
@ -11,6 +11,6 @@ elif model == "EmoLLM_Model":
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os.system("python download_model.py jujimeizuo/EmoLLM_Model")
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os.system('streamlit run web_internlm2.py --server.address=0.0.0.0 --server.port 7860')
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elif model == "Llama3_Model":
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os.system('streamlit run web_demo-Llama3.py --server.address=0.0.0.0 --server.port 7968')
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os.system('streamlit run web_demo_Llama3.py --server.address=0.0.0.0 --server.port 7968')
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else:
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print("Please select one model")
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@ -13,7 +13,7 @@ from transformers.utils import logging
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from transformers import AutoTokenizer, AutoModelForCausalLM # isort: skip
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warnings.filterwarnings("ignore")
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# warnings.filterwarnings("ignore")
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logger = logging.get_logger(__name__)
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@ -151,7 +151,7 @@ def on_btn_click():
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@st.cache_resource
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def load_model():
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model_name = "./EmoLLM-Llama3-8B-Instruct2.0"
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model_name = "./EmoLLM-Llama3-8B-Instruct3.0"
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print(model_name)
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print('pip install modelscope websockets')
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@ -159,7 +159,7 @@ def load_model():
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from modelscope import snapshot_download
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#模型下载
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model_name = snapshot_download('chg0901/EmoLLM-Llama3-8B-Instruct2.0',cache_dir=model_name)
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model_name = snapshot_download('chg0901/EmoLLM-Llama3-8B-Instruct3.0',cache_dir=model_name)
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print(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16).eval()
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