Spaces:
Running
Running
File size: 1,821 Bytes
5c9319c b1c7718 9171f49 79cade0 9171f49 79cade0 b1c7718 79cade0 b1c7718 fd38834 b1c7718 79cade0 b1c7718 79cade0 b1c7718 79cade0 b1c7718 b4d7ce7 b1c7718 04ec251 b1c7718 79cade0 b1c7718 79cade0 b1c7718 5c9319c b1c7718 5c9319c |
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 68 |
# #refer llama recipes for more info https://github.com/huggingface/huggingface-llama-recipes/blob/main/inference-api.ipynb
# #huggingface-llama-recipes : https://github.com/huggingface/huggingface-llama-recipes/tree/main
import gradio as gr
from openai import OpenAI
import os
ACCESS_TOKEN = os.getenv("HF_TOKEN")
client = OpenAI(
base_url="https://integrate.api.nvidia.com/v1",
api_key=ACCESS_TOKEN,
)
def respond(
message,
history: list[tuple[str, str]],
max_tokens,
temperature,
top_p,
):
messages = []
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 = ""
for message in client.chat.completions.create(
model="nvidia/llama-3.1-nemotron-70b-instruct",
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
messages=messages,
):
token = message.choices[0].delta.content
if token is not None:
response += token
yield response
chatbot = gr.Chatbot(height=600)
service = gr.ChatInterface(
respond,
additional_inputs=[
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Максимальная длина ответа"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Температура"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="top_p",
),
],
fill_height=True,
chatbot=chatbot,
theme=gr.themes.Soft(),
)
if __name__ == "__main__":
service.launch()
|