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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import time
import gc
import os
import psutil
# Configuration
BASE_MODEL = "microsoft/phi-2"
ADAPTER_MODEL = "pradeep6kumar2024/phi2-qlora-assistant"
DEBUG = False # Set to True to enable debug prints
# Memory monitoring
def get_memory_usage():
process = psutil.Process(os.getpid())
return process.memory_info().rss / (1024 * 1024) # MB
class ModelWrapper:
def __init__(self):
self.model = None
self.tokenizer = None
self.loaded = False
def load_model(self):
if not self.loaded:
try:
# Force CPU usage
os.environ["CUDA_VISIBLE_DEVICES"] = ""
device = torch.device("cpu")
# Clear memory
gc.collect()
if DEBUG:
print(f"Memory before loading: {get_memory_usage():.2f} MB")
print("Loading tokenizer...")
self.tokenizer = AutoTokenizer.from_pretrained(
BASE_MODEL,
trust_remote_code=True,
padding_side="left"
)
self.tokenizer.pad_token = self.tokenizer.eos_token
if DEBUG:
print(f"Memory after tokenizer: {get_memory_usage():.2f} MB")
print("Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float32,
device_map="cpu",
trust_remote_code=True,
use_flash_attention_2=False,
low_cpu_mem_usage=True,
offload_folder="offload"
)
if DEBUG:
print(f"Memory after base model: {get_memory_usage():.2f} MB")
print("Loading LoRA adapter...")
self.model = PeftModel.from_pretrained(
base_model,
ADAPTER_MODEL,
torch_dtype=torch.float32,
device_map="cpu"
)
# Free up memory
del base_model
gc.collect()
if DEBUG:
print(f"Memory after adapter: {get_memory_usage():.2f} MB")
self.model.eval()
print("Model loading complete!")
self.loaded = True
except Exception as e:
print(f"Error during model loading: {str(e)}")
raise
def generate_response(self, prompt, max_length=256, temperature=0.7, top_p=0.9):
if not self.loaded:
self.load_model()
try:
# Use shorter prompts to save memory
if "function" in prompt.lower() and "python" in prompt.lower():
enhanced_prompt = f"""Write Python function: {prompt}"""
elif any(word in prompt.lower() for word in ["explain", "what is", "how does", "describe"]):
enhanced_prompt = f"""Explain briefly: {prompt}"""
else:
enhanced_prompt = prompt
if DEBUG:
print(f"Enhanced prompt: {enhanced_prompt}")
# Tokenize input with shorter max length
inputs = self.tokenizer(
enhanced_prompt,
return_tensors="pt",
truncation=True,
max_length=256, # Reduced for memory
padding=True
).to("cpu")
# Generate with minimal parameters
start_time = time.time()
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_length=min(max_length, 256), # Strict limit
min_length=10, # Reduced minimum
temperature=min(0.5, temperature),
top_p=min(0.85, top_p),
do_sample=True,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
num_return_sequences=1,
early_stopping=True,
num_beams=1, # Greedy decoding to save memory
length_penalty=0.6
)
# Decode response
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
if DEBUG:
print(f"Raw response: {response}")
# Clean up the response
if response.startswith(enhanced_prompt):
response = response[len(enhanced_prompt):].strip()
if DEBUG:
print(f"After prompt removal: {response}")
# Basic cleanup only
cleaned_response = response.replace("Human:", "").replace("Assistant:", "")
if DEBUG and cleaned_response != response:
print(f"After conversation removal: {cleaned_response}")
response = cleaned_response
# Ensure code examples are properly formatted
if "```python" not in response and "def " in response:
response = "```python\n" + response + "\n```"
# Simple validation
if len(response.strip()) < 10:
if DEBUG:
print("Response validation failed - using fallback")
if "function" in prompt.lower():
fallback_response = """```python
def add_numbers(a, b):
return a + b
```"""
else:
fallback_response = "I apologize, but I couldn't generate a response. Please try with a simpler prompt."
response = fallback_response
# Clear memory after generation
gc.collect()
generation_time = time.time() - start_time
return response, generation_time
except Exception as e:
print(f"Error during generation: {str(e)}")
raise
# Initialize model wrapper
model_wrapper = ModelWrapper()
def generate_text(prompt, max_length=256, temperature=0.5, top_p=0.85):
"""Gradio interface function"""
try:
if not prompt.strip():
return "Please enter a prompt."
response, gen_time = model_wrapper.generate_response(
prompt,
max_length=max_length,
temperature=temperature,
top_p=top_p
)
return f"Generated in {gen_time:.2f} seconds:\n\n{response}"
except Exception as e:
print(f"Error in generate_text: {str(e)}")
return f"Error generating response: {str(e)}\nPlease try again with a shorter prompt."
# Create a very lightweight Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(
label="Enter your prompt",
placeholder="Type your prompt here...",
lines=3
),
gr.Slider(
minimum=64,
maximum=256,
value=192,
step=32,
label="Maximum Length",
info="Keep this low for CPU"
),
gr.Slider(
minimum=0.1,
maximum=0.7,
value=0.4,
step=0.1,
label="Temperature",
info="Lower is better for CPU"
),
gr.Slider(
minimum=0.5,
maximum=0.9,
value=0.8,
step=0.1,
label="Top P",
info="Controls diversity"
),
],
outputs=gr.Textbox(label="Generated Response", lines=6),
title="Phi-2 QLoRA Assistant (CPU-Optimized)",
description="""This is a lightweight CPU version of the fine-tuned Phi-2 model.
Tips:
- Keep prompts short and specific
- Use lower maximum length (128-192) for faster responses
- Use lower temperature (0.3-0.5) for more reliable responses
""",
examples=[
[
"Write a Python function to calculate factorial",
192,
0.4,
0.8
],
[
"Explain machine learning simply",
192,
0.4,
0.8
],
[
"Write a short email to schedule a meeting",
192,
0.4,
0.8
]
],
cache_examples=False,
concurrency_limit=1 # Use the correct parameter for limiting concurrency
)
if __name__ == "__main__":
# Using the modern approach without queue method
demo.launch(max_threads=1) # Limit the number of worker threads |