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| import chatglm_cpp | |
| import gradio as gr | |
| from pathlib import Path | |
| model_file_path = "chatglm2-ggml-q4_0.bin" | |
| chatglm_llm = chatglm_cpp.Pipeline(Path(model_file_path)) | |
| examples = [ | |
| "苹果设备购买apple care有什么好处？", | |
| "如何学好历史？", | |
| "如何弘扬中华传统文化？", | |
| "How to promote Chinese traditional culture ?", | |
| "如何进行经济建设？", | |
| "写一段孔子与马克思的对话录。", | |
| ] | |
| def process_stream(instruction, temperature, top_p, top_k, max_new_tokens, seed): | |
| if "[SEP]" not in instruction: | |
| streamer = chatglm_llm.generate(prompt=instruction, | |
| temperature=temperature, | |
| top_p=top_p,top_k=top_k,max_length=max_new_tokens, | |
| stream = True | |
| ) | |
| else: | |
| history = instruction.split("[SEP]") | |
| streamer = chatglm_llm.chat( | |
| history=history, | |
| temperature=temperature, | |
| top_p=top_p,top_k=top_k,max_length=max_new_tokens, | |
| do_sample=False, | |
| stream = True | |
| ) | |
| response = "" | |
| for new_text in streamer: | |
| response += new_text | |
| yield response | |
| with gr.Blocks( | |
| theme=gr.themes.Soft(), | |
| css=".disclaimer {font-variant-caps: all-small-caps;}", | |
| ) as demo: | |
| gr.Markdown( | |
| """<h1><center> ChatGLM2 on CPU in CPP 🐒</center></h1> | |
| This demo uses the [chatglm.cpp](https://github.com/li-plus/chatglm.cpp) library on 2 CPU cores. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| instruction = gr.Textbox( | |
| placeholder="Enter your question or instruction here", | |
| label="Question/Instruction", | |
| elem_id="q-input", | |
| ) | |
| with gr.Accordion("Advanced Options:", open=False): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.8, | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| interactive=True, | |
| info="Higher values produce more diverse outputs", | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| top_p = gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.95, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.01, | |
| interactive=True, | |
| info=( | |
| "Sample from the smallest possible set of tokens whose cumulative probability " | |
| "exceeds top_p. Set to 1 to disable and sample from all tokens." | |
| ), | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| top_k = gr.Slider( | |
| label="Top-k", | |
| value=40, | |
| minimum=5, | |
| maximum=80, | |
| step=1, | |
| interactive=True, | |
| info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.", | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| max_new_tokens = gr.Slider( | |
| label="Maximum new tokens", | |
| value=256, | |
| minimum=0, | |
| maximum=1024, | |
| step=5, | |
| interactive=True, | |
| info="The maximum number of new tokens to generate", | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| seed = gr.Number( | |
| label="Seed", | |
| value=42, | |
| interactive=True, | |
| info="The seed to use for the generation", | |
| precision=0 | |
| ) | |
| with gr.Row(): | |
| submit = gr.Button("Submit") | |
| with gr.Row(): | |
| with gr.Box(): | |
| gr.Markdown("**ChatGLM2-6b**") | |
| output_7b = gr.Markdown() | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[instruction], | |
| cache_examples=False, | |
| fn=process_stream, | |
| outputs=output_7b, | |
| ) | |
| submit.click( | |
| process_stream, | |
| inputs=[instruction, temperature, top_p, top_k, max_new_tokens,seed], | |
| outputs=output_7b, | |
| ) | |
| instruction.submit( | |
| process_stream, | |
| inputs=[instruction, temperature, top_p, top_k, max_new_tokens,seed], | |
| outputs=output_7b, | |
| ) | |
| demo.queue(max_size=4, concurrency_count=1).launch(debug=True) | |