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import gradio as gr
from llama_cpp import Llama
import requests
llm = Llama.from_pretrained(
repo_id="lmstudio-community/Llama-3.2-3B-Instruct-GGUF",
filename="*Q4_K_M.gguf",
verbose=True,
n_ctx=32768,
n_threads=2,
chat_format="chatml"
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
response = llm.create_chat_completion(
messages=messages,
stream=True,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
message_repl = ""
for chunk in response:
if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
message_repl = message_repl + \
chunk['choices'][0]["delta"]["content"]
yield message_repl
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |