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# Adapted from https://github.com/THUDM/ChatGLM-6B/blob/main/web_demo.py
import argparse
from pathlib import Path
import chatglm_cpp
import gradio as gr
import urllib
DEFAULT_MODEL_PATH = "chatglm3-6b.bin"
testfile = urllib.URLopener()
testfile.retrieve(
"https://huggingface.co/Braddy/chatglm3-6b-chitchat/resolve/main/q5_1.bin?download=true",
DEFAULT_MODEL_PATH
)
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", default=DEFAULT_MODEL_PATH, type=Path, help="model path")
parser.add_argument("--mode", default="chat", type=str, choices=["chat", "generate"], help="inference mode")
parser.add_argument("-l", "--max_length", default=2048, type=int, help="max total length including prompt and output")
parser.add_argument("-c", "--max_context_length", default=512, type=int, help="max context length")
parser.add_argument("--top_k", default=0, type=int, help="top-k sampling")
parser.add_argument("--top_p", default=0.7, type=float, help="top-p sampling")
parser.add_argument("--temp", default=0.95, type=float, help="temperature")
parser.add_argument("--repeat_penalty", default=1.0, type=float, help="penalize repeat sequence of tokens")
parser.add_argument("-t", "--threads", default=0, type=int, help="number of threads for inference")
parser.add_argument("--plain", action="store_true", help="display in plain text without markdown support")
args = parser.parse_args()
pipeline = chatglm_cpp.Pipeline(args.model)
def postprocess(text):
if args.plain:
return f"<pre>{text}</pre>"
return text
def predict(input, chatbot, max_length, top_p, temperature, messages):
chatbot.append((postprocess(input), ""))
messages.append(chatglm_cpp.ChatMessage(role="user", content=input))
generation_kwargs = dict(
max_length=max_length,
max_context_length=args.max_context_length,
do_sample=temperature > 0,
top_k=args.top_k,
top_p=top_p,
temperature=temperature,
repetition_penalty=args.repeat_penalty,
num_threads=args.threads,
stream=True,
)
response = ""
if args.mode == "chat":
chunks = []
for chunk in pipeline.chat(messages, **generation_kwargs):
response += chunk.content
chunks.append(chunk)
chatbot[-1] = (chatbot[-1][0], postprocess(response))
yield chatbot, messages
messages.append(pipeline.merge_streaming_messages(chunks))
else:
for chunk in pipeline.generate(input, **generation_kwargs):
response += chunk
chatbot[-1] = (chatbot[-1][0], postprocess(response))
yield chatbot, messages
yield chatbot, messages
def reset_user_input():
return gr.update(value="")
def reset_state():
return [], []
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">ChatGLM.cpp</h1>""")
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=8)
submitBtn = gr.Button("Submit", variant="primary")
with gr.Column(scale=1):
max_length = gr.Slider(0, 2048, value=args.max_length, step=1.0, label="Maximum Length", interactive=True)
top_p = gr.Slider(0, 1, value=args.top_p, step=0.01, label="Top P", interactive=True)
temperature = gr.Slider(0, 1, value=args.temp, step=0.01, label="Temperature", interactive=True)
emptyBtn = gr.Button("Clear History")
messages = gr.State([])
submitBtn.click(
predict,
[user_input, chatbot, max_length, top_p, temperature, messages],
[chatbot, messages],
show_progress=True,
)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(reset_state, outputs=[chatbot, messages], show_progress=True)
demo.queue().launch(share=False, inbrowser=True) |