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from huggingface_hub import InferenceClient
import gradio as gr
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, temperature=0.2, max_new_tokens=30000, top_p=0.9, repetition_penalty=1.0,
):
temperature = max(float(temperature), 0.01)
top_p = max(min(float(top_p), 1.0), 0.0)
repetition_penalty = max(float(repetition_penalty), 0.01)
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(prompt, history)
# Generate text
response = client.text_generation(formatted_prompt, **generate_kwargs)
generated_text = response["generated_text"]
return generated_text
iface = gr.Interface(
fn=generate,
inputs=["text", "text", gr.inputs.Slider(0.1, 2.0), gr.inputs.Slider(100, 50000), gr.inputs.Slider(0.1, 1.0)],
outputs="text",
title="Text Generation"
)
iface.launch()
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