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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load model and tokenizer
def load_model(model_id):
# First load the base model
base_model_id = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
# Load and merge the LoRA adapter
model = PeftModel.from_pretrained(base_model, model_id)
return model, tokenizer
def generate_response(instruction, model, tokenizer, max_length=200, temperature=0.7, top_p=0.9):
# Format the input text
input_text = f"### Instruction:\n{instruction}\n\n### Response:\n"
# Tokenize input
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
# Generate response
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
top_p=top_p,
num_return_sequences=1,
pad_token_id=tokenizer.eos_token_id
)
# Decode and return the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the response part
response_parts = response.split("### Response:")
if len(response_parts) > 1:
return response_parts[1].strip()
return response.strip()
def create_demo(model_id):
# Load model and tokenizer
model, tokenizer = load_model(model_id)
# Define the interface
def process_input(instruction, max_length, temperature, top_p):
return generate_response(
instruction,
model,
tokenizer,
max_length=max_length,
temperature=temperature,
top_p=top_p
)
# Create the interface
demo = gr.Interface(
fn=process_input,
inputs=[
gr.Textbox(
label="Instruction",
placeholder="Enter your instruction here...",
lines=4
),
gr.Slider(
minimum=50,
maximum=500,
value=200,
step=10,
label="Maximum Length"
),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.7,
step=0.1,
label="Temperature"
),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.1,
label="Top P"
)
],
outputs=gr.Textbox(label="Response", lines=8),
title="Phi-2 Fine-tuned Assistant",
description="""This is a fine-tuned version of the Microsoft Phi-2 model, trained on the OpenAssistant dataset.
You can adjust the generation parameters:
- **Maximum Length**: Controls the maximum length of the generated response
- **Temperature**: Higher values make the output more random, lower values make it more focused
- **Top P**: Controls the cumulative probability threshold for token sampling
""",
examples=[
["What is machine learning?"],
["Write a short poem about artificial intelligence"],
["Explain quantum computing to a 10-year-old"],
["What are the best practices for writing clean code?"]
]
)
return demo
if __name__ == "__main__":
# Replace with your model ID (username/model-name)
model_id = "jatingocodeo/phi2-finetuned-openassistant"
demo = create_demo(model_id)
demo.launch() |