app.py

import gradio as gr
from transformers import pipeline
import torch
import subprocess
import spaces


@spaces.GPU
def _build_flash_attn():
    subprocess.check_call("pip install flash-attn", shell=True)
_build_flash_attn() # This is how we'll build flash-attn.
# Initialize the model pipeline
generator = pipeline('text-generation', model='mistralai/Mistral-7B-v0.1', torch_dtype=torch.bfloat16, use_flash_attention_2=True)
@spaces.GPU
def generate_text(prompt, temperature, top_p, top_k, repetition_penalty, max_length):
    # Generate text using the model
    generator.model.cuda()
    outputs = generator(
        prompt, 
        max_new_tokens=max_length,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        return_full_text=False
    )
    # Extract the generated text and return it
    generated_text = outputs[0]['generated_text']
    return generated_text
# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(label="Prompt", lines=2, placeholder="Type a prompt..."),
        gr.inputs.Slider(minimum=0.1, maximum=2.0, step=0.01, default=0.8, label="Temperature"),
        gr.inputs.Slider(minimum=0.0, maximum=1.0, step=0.01, default=0.95, label="Top p"),
        gr.inputs.Slider(minimum=0, maximum=100, step=1, default=40, label="Top k"),
        gr.inputs.Slider(minimum=1.0, maximum=2.0, step=0.01, default=1.10, label="Repetition Penalty"),
        gr.inputs.Slider(minimum=5, maximum=4096, step=5, default=1024, label="Max Length")
    ],
    outputs=gr.outputs.Textbox(label="Generated Text"),
    title="Text Completion Model",
    description="Try out the Mistral-7B model for free! Note this is the pretrained model and is not fine-tuned for instruction."
)

iface.launch()