File size: 3,049 Bytes
1ed4c00 a0876f1 1ed4c00 a0876f1 1ed4c00 |
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 69 70 71 72 73 |
from huggingface_hub import InferenceClient
import gradio as gr
# Set up the client for Mistral model inference
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
# Function to format the conversation history
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST] {bot_response} "
prompt += f"[INST] {message} [/INST]</s>"
return prompt
# Text generation function with parameters
def generate(
prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
# Ensure temperature and top_p are correctly set
temperature = max(float(temperature), 1e-2) # Prevent temperature going below 0.01
top_p = float(top_p)
# Keyword arguments for generation configuration
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42, # Ensures results are reproducible
)
# Format the prompt with the user's message and history
formatted_prompt = format_prompt(prompt, history)
# Call the text generation endpoint
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = "" # Initialize an empty string for the output
# Stream the response token by token
for response in stream:
output += response.token.text # Append the generated tokens to output
yield output # Yield partial output for real-time display
return output
# Additional inputs (sliders) for controlling generation parameters
additional_inputs=[
gr.Slider(
label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05,
interactive=True, info="Higher values produce more diverse outputs"
),
gr.Slider(
label="Max new tokens", value=256, minimum=0, maximum=1048, step=64,
interactive=True, info="The maximum numbers of new tokens"
),
gr.Slider(
label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1.0, step=0.05,
interactive=True, info="Higher values sample more low-probability tokens"
),
gr.Slider(
label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05,
interactive=True, info="Penalize repeated tokens"
)
]
# Gradio Chat Interface for the chatbot
gr.ChatInterface(
fn=generate, # The generate function is called when the user submits input
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
additional_inputs=additional_inputs, # Sliders for adjusting generation parameters
title="Mistral 7B v0.3 ChatGPT Clone", # Title for the interface
description="A ChatGPT clone using Mistral 7B model. Adjust parameters to fine-tune the generation."
).launch(show_api=False) # Launch the interface without showing the API key |