File size: 1,575 Bytes
1e2d981 6da1c26 1e2d981 637f425 c2aa89c 637f425 c2aa89c 637f425 c2aa89c 1e2d981 68eded2 1e2d981 6da1c26 1e2d981 6da1c26 d1a0edb 1e2d981 8a8d916 1e2d981 02743b6 6da1c26 02743b6 1e2d981 6da1c26 1e2d981 8a8d916 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import gradio as gr
from llama_cpp import Llama
model = "Qwen/Qwen2-7B-Instruct-GGUF"
llm = Llama.from_pretrained(
repo_id=model,
filename="qwen2-7b-instruct-q4_k_m.gguf",
verbose=False,
use_mmap=False,
use_mlock=True,
n_threads=2,
n_threads_batch=2,
n_ctx=40000,
)
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 = llm.create_chat_completion(
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
return response["choices"][0]["message"]["content"]
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are a helpful assistant.",
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)",
),
],
description=model,
)
if __name__ == "__main__":
demo.launch()
|