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import argparse |
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from pathlib import Path |
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import chatglm_cpp |
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import gradio as gr |
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import urllib |
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DEFAULT_MODEL_PATH = "chatglm3-6b.bin" |
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testfile = urllib.URLopener() |
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testfile.retrieve( |
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"https://huggingface.co/Braddy/chatglm3-6b-chitchat/resolve/main/q5_1.bin?download=true", |
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DEFAULT_MODEL_PATH |
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) |
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parser = argparse.ArgumentParser() |
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parser.add_argument("-m", "--model", default=DEFAULT_MODEL_PATH, type=Path, help="model path") |
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parser.add_argument("--mode", default="chat", type=str, choices=["chat", "generate"], help="inference mode") |
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parser.add_argument("-l", "--max_length", default=2048, type=int, help="max total length including prompt and output") |
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parser.add_argument("-c", "--max_context_length", default=512, type=int, help="max context length") |
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parser.add_argument("--top_k", default=0, type=int, help="top-k sampling") |
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parser.add_argument("--top_p", default=0.7, type=float, help="top-p sampling") |
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parser.add_argument("--temp", default=0.95, type=float, help="temperature") |
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parser.add_argument("--repeat_penalty", default=1.0, type=float, help="penalize repeat sequence of tokens") |
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parser.add_argument("-t", "--threads", default=0, type=int, help="number of threads for inference") |
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parser.add_argument("--plain", action="store_true", help="display in plain text without markdown support") |
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args = parser.parse_args() |
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pipeline = chatglm_cpp.Pipeline(args.model) |
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def postprocess(text): |
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if args.plain: |
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return f"<pre>{text}</pre>" |
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return text |
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def predict(input, chatbot, max_length, top_p, temperature, messages): |
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chatbot.append((postprocess(input), "")) |
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messages.append(chatglm_cpp.ChatMessage(role="user", content=input)) |
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generation_kwargs = dict( |
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max_length=max_length, |
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max_context_length=args.max_context_length, |
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do_sample=temperature > 0, |
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top_k=args.top_k, |
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top_p=top_p, |
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temperature=temperature, |
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repetition_penalty=args.repeat_penalty, |
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num_threads=args.threads, |
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stream=True, |
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) |
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response = "" |
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if args.mode == "chat": |
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chunks = [] |
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for chunk in pipeline.chat(messages, **generation_kwargs): |
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response += chunk.content |
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chunks.append(chunk) |
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chatbot[-1] = (chatbot[-1][0], postprocess(response)) |
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yield chatbot, messages |
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messages.append(pipeline.merge_streaming_messages(chunks)) |
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else: |
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for chunk in pipeline.generate(input, **generation_kwargs): |
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response += chunk |
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chatbot[-1] = (chatbot[-1][0], postprocess(response)) |
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yield chatbot, messages |
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yield chatbot, messages |
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def reset_user_input(): |
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return gr.update(value="") |
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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.HTML("""<h1 align="center">ChatGLM.cpp</h1>""") |
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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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user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=8) |
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submitBtn = gr.Button("Submit", variant="primary") |
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with gr.Column(scale=1): |
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max_length = gr.Slider(0, 2048, value=args.max_length, step=1.0, label="Maximum Length", interactive=True) |
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top_p = gr.Slider(0, 1, value=args.top_p, step=0.01, label="Top P", interactive=True) |
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temperature = gr.Slider(0, 1, value=args.temp, step=0.01, label="Temperature", interactive=True) |
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emptyBtn = gr.Button("Clear History") |
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messages = gr.State([]) |
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submitBtn.click( |
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predict, |
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[user_input, chatbot, max_length, top_p, temperature, messages], |
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[chatbot, messages], |
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show_progress=True, |
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) |
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submitBtn.click(reset_user_input, [], [user_input]) |
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emptyBtn.click(reset_state, outputs=[chatbot, messages], show_progress=True) |
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demo.queue().launch(share=False, inbrowser=True) |
