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import gradio as gr
from unsloth import FastLanguageModel
from transformers import TextStreamer
import torch
# Function to load the model
def load_model(model_name, max_seq_length, dtype, load_in_4bit, token=None):
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
token=token
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
return model, tokenizer
# Load the model
model_name = "unsloth/Phi-3-mini-4k-instruct"
token = None # Replace with your token if required
model, tokenizer = load_model(model_name, max_seq_length=2048, dtype=None, load_in_4bit=True, token=token)
def generate_response(instruction, input_text, max_new_tokens):
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
inputs = tokenizer(
[
alpaca_prompt.format(
instruction, # instruction
input_text, # input
"" # output - leave this blank for generation!
)
], return_tensors="pt").to("cpu")
text_streamer = TextStreamer(tokenizer)
output = model.generate(**inputs, streamer=text_streamer, max_new_tokens=max_new_tokens)
response = tokenizer.decode(output[0], skip_special_tokens=True)
return response
# Gradio Interface
iface = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(lines=2, label="Instruction", placeholder="Continue the Fibonacci sequence."),
gr.Textbox(lines=2, label="Input", placeholder="1, 1, 2, 3, 5, 8"),
gr.Slider(1, 2048, value=128, step=1, label="Max New Tokens")
],
outputs=gr.Textbox(label="Response", lines=10),
title="Language Model Chat UI"
)
iface.launch()
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