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import streamlit as st
from huggingface_hub import InferenceClient
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
# Create text input for user message
message_input = st.text_input("You:", "")
# Create text input for system prompt
system_prompt_input = st.text_input("System Prompt:", "You are a helpful assistant.")
# Create sliders for temperature, max new tokens, top-p, and repetition penalty
temperature_slider = st.slider("Temperature", 0.0, 1.0, 0.9)
max_new_tokens_slider = st.slider("Max new tokens", 0, 1048, 256)
top_p_slider = st.slider("Top-p (nucleus sampling)", 0.0, 1.0, 0.95)
repetition_penalty_slider = st.slider("Repetition penalty", 1.0, 2.0, 1.0)
# Create button to generate response
if st.button("Generate"):
# Create empty list to store conversation history
history = []
# Call generate function with user message, system prompt, and slider values
output = generate(message_input, history, system_prompt_input, temperature=temperature_slider, max_new_tokens=max_new_tokens_slider, top_p=top_p_slider, repetition_penalty=repetition_penalty_slider)
# Display generated response
st.write("Assistant:", output)
# Add user message and generated response to conversation history
history.append((message_input, output)) |