How to use :
!pip install peft accelerate bitsandbytes
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Function to generate and solve problems using the fine-tuned model
def generate_and_solve_problems(model, tokenizer, num_problems=5):
"""
Generate and solve math and reasoning problems using the fine-tuned model.
Parameters:
model: Fine-tuned language model
tokenizer: Corresponding tokenizer
num_problems: Number of problems to generate and solve
"""
# Prompt template
test_prompt = """Below is a math problem. Solve the problem step by step and provide a detailed explanation.
### Problem:
{}
### Solution:"""
# Sample test problems
test_problems = [
"A car travels at 40 mph for 2 hours, then at 60 mph for another 3 hours. How far does it travel in total?",
"If the sum of three consecutive integers is 72, what are the integers?",
"A train leaves Station A at 10:00 AM traveling at 50 mph. Another train leaves Station A at 12:00 PM traveling at 70 mph on the same track. At what time will the second train catch up to the first?",
"A rectangle has a length of 12 units and a width of 8 units. If the length is increased by 50% and the width is reduced by 25%, what is the new area of the rectangle?",
"If a person invests $1000 in a savings account that earns 5% annual interest compounded yearly, how much money will be in the account after 10 years?"
]
# Use only the specified number of problems
test_problems = test_problems[:num_problems]
for problem in test_problems:
# Create the prompt
prompt = test_prompt.format(problem)
# Tokenize and generate response
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True).to("cuda")
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=512,
temperature=0.7,
top_p=0.9,
do_sample=True,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Print the problem and the solution
print(response)
print("\n" + "="*50 + "\n")
# Example usage with model and tokenizer
base_model_name = "unsloth/phi-3-mini-4k-instruct-bnb-4bit"
lora_model_name = "Vijayendra/Phi3-LoRA-GSM8k"
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
# Load the fine-tuned LoRA model
model = PeftModel.from_pretrained(base_model, lora_model_name)
model.eval()
# Call the function to solve problems
generate_and_solve_problems(model, tokenizer)
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