一、chatglm6b web服务
- from transformers import AutoModel, AutoTokenizer
- import gradio as gr
- #model_name_or_path="THUDM/ChatGLM-6B"#"conf/snapshots"
- model_name_or_path="../conf/snapshots"
- tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
- model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True).half().quantize(4).cuda()
- model = model.eval()
-
- MAX_TURNS = 20
- MAX_BOXES = MAX_TURNS * 2
-
-
- def predict(input, history=None):
- if history is None:
- history = []
- response, history = model.chat(tokenizer, input, history)
- updates = []
- for query, response in history:
- updates.append(gr.update(visible=True, value="用户:" + query))
- updates.append(gr.update(visible=True, value="AI小助手:" + response))
- if len(updates) < MAX_BOXES:
- updates