Upload folder using huggingface_hub
Browse files- api.py +2 -2
- cli_demo.py +2 -2
- web_demo.py +3 -3
- web_demo_old.py +26 -82
api.py
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@@ -50,7 +50,7 @@ async def create_item(request: Request):
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if __name__ == '__main__':
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tokenizer = AutoTokenizer.from_pretrained("THUDM/
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model = AutoModel.from_pretrained("THUDM/
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model.eval()
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uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
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if __name__ == '__main__':
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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model.eval()
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uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
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cli_demo.py
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@@ -4,8 +4,8 @@ import signal
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from transformers import AutoTokenizer, AutoModel
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import readline
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tokenizer = AutoTokenizer.from_pretrained("THUDM/
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model = AutoModel.from_pretrained("THUDM/
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model = model.eval()
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os_name = platform.system()
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from transformers import AutoTokenizer, AutoModel
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import readline
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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model = model.eval()
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os_name = platform.system()
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web_demo.py
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@@ -2,8 +2,8 @@ from transformers import AutoModel, AutoTokenizer
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import gradio as gr
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import mdtex2html
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tokenizer = AutoTokenizer.from_pretrained("THUDM/
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model = AutoModel.from_pretrained("THUDM/
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model = model.eval()
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"""Override Chatbot.postprocess"""
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@@ -98,4 +98,4 @@ with gr.Blocks() as demo:
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emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
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demo.queue().launch(share=
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import gradio as gr
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import mdtex2html
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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model = model.eval()
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"""Override Chatbot.postprocess"""
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emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
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demo.queue().launch(share=False, inbrowser=True)
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web_demo_old.py
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@@ -1,101 +1,45 @@
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from transformers import AutoModel, AutoTokenizer
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import gradio as gr
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import mdtex2html
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tokenizer = AutoTokenizer.from_pretrained("THUDM/
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model = AutoModel.from_pretrained("THUDM/
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model = model.eval()
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def
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if
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for i, (message, response) in enumerate(y):
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y[i] = (
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None if message is None else mdtex2html.convert((message)),
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None if response is None else mdtex2html.convert(response),
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)
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return y
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gr.Chatbot.postprocess = postprocess
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def parse_text(text):
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"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
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lines = text.split("\n")
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lines = [line for line in lines if line != ""]
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count = 0
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split('`')
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] = f'<br></code></pre>'
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", "\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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line = line.replace("*", "*")
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line = line.replace("_", "_")
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line = line.replace("-", "-")
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line = line.replace(".", ".")
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line = line.replace("!", "!")
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>"+line
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text = "".join(lines)
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return text
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def predict(input, chatbot, max_length, top_p, temperature, history):
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chatbot.append((parse_text(input), ""))
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for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
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temperature=temperature):
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def reset_state():
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return [], []
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with gr.Blocks() as demo:
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gr.
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chatbot = gr.Chatbot()
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with gr.Row():
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with gr.Column(scale=4):
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container=False)
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with gr.Column(min_width=32, scale=1):
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submitBtn = gr.Button("Submit", variant="primary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
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top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
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temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
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submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
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show_progress=True)
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submitBtn.click(reset_user_input, [], [user_input])
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emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
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demo.queue().launch(share=True, inbrowser=True)
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from transformers import AutoModel, AutoTokenizer
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import gradio as gr
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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model = model.eval()
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MAX_TURNS = 20
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MAX_BOXES = MAX_TURNS * 2
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def predict(input, max_length, top_p, temperature, history=None):
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if history is None:
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history = []
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for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
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temperature=temperature):
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updates = []
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for query, response in history:
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updates.append(gr.update(visible=True, value="用户:" + query))
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updates.append(gr.update(visible=True, value="ChatGLM-6B:" + response))
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if len(updates) < MAX_BOXES:
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updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
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yield [history] + updates
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with gr.Blocks() as demo:
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state = gr.State([])
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text_boxes = []
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for i in range(MAX_BOXES):
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if i % 2 == 0:
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text_boxes.append(gr.Markdown(visible=False, label="提问:"))
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else:
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text_boxes.append(gr.Markdown(visible=False, label="回复:"))
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with gr.Row():
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with gr.Column(scale=4):
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txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter", lines=11).style(
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container=False)
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with gr.Column(scale=1):
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max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
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top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
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temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
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button = gr.Button("Generate")
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button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
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demo.queue().launch(share=False, inbrowser=True)
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