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# -*- coding: utf-8 -*-
"""Untitled18.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1_vTVH3hBX8wVXIgrW1T2Q4N1DSkWoXV8
"""
import gradio as gr
import torch
from transformers import TextStreamer
from unsloth import FastLanguageModel
from google.colab import drive
import os
# Ensure necessary packages are installed
# Define the parameters for the model
max_seq_length = 2048
# Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="lora_model", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
# Define the Alpaca prompt
alpaca_prompt = """
### Input:
{}
### Response:
{}"""
# Define the function to generate responses
def chat_alpaca(message: str, history: list, temperature: float, max_new_tokens: int) -> str:
prompt = alpaca_prompt.format(message, "")
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
# Define the streamer
text_streamer = TextStreamer(tokenizer)
# Generate the response
outputs = model.generate(**inputs, streamer=text_streamer, max_new_tokens=max_new_tokens, temperature=temperature)
response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# Return the response
return response
# Define the response function for the Gradio interface
def respond(message, history, system_message, max_new_tokens, temperature, top_p):
return chat_alpaca(message, history, temperature, max_new_tokens)
# Create the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch(share=True)
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