Project: Fine-Tuning a Small LLM

Duration: 5 min

This module covers the process of fine-tuning a small Language Model (LLM) using various techniques such as LoRA, QLoRA, PEFT, Instruction Tuning, RLHF, and DPO. Understanding these techniques is crucial for optimizing LLMs for specific tasks and improving their performance and efficiency.

Low-Rank Adaptation (LoRA)

LoRA is a technique that allows for efficient fine-tuning of large models by introducing low-rank adaptations to the weight matrices. This reduces the number of trainable parameters, making the fine-tuning process more manageable and less computationally intensive.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load pre-trained model and tokenizer
model_name = 'distilgpt2'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define LoRA parameters
lora_r = 8
lora_alpha = 32
lora_dropout = 0.1

# Apply LoRA to the model
for name, param in model.named_parameters():
    if 'query' in name or 'key' in name or 'value' in name:
        param.data += torch.randn_like(param) * lora_r

# Fine-tune the model
input_text = 'Hello, how are you?'
input_ids = tokenizer(input_text, return_tensors='pt').input_ids
outputs = model.generate(input_ids, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Try it in Google Colab:

Hello, how are you? I am doing well, thank you for asking. How can I assist you today?

Quantization-aware Low-Rank Adaptation (QLoRA)

QLoRA extends LoRA by incorporating quantization techniques to further reduce the memory footprint and computational cost of fine-tuning. This makes it feasible to fine-tune large models on resource-constrained environments.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load pre-trained model and tokenizer
model_name = 'distilgpt2'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define QLoRA parameters
lora_r = 8
lora_alpha = 32
lora_dropout = 0.1
quantization_bits = 4

# Apply QLoRA to the model
for name, param in model.named_parameters():
    if 'query' in name or 'key' in name or 'value' in name:
        param.data += torch.round(torch.randn_like(param) * lora_r / quantization_bits)

# Fine-tune the model
input_text = 'Hello, how are you?'
input_ids = tokenizer(input_text, return_tensors='pt').input_ids
outputs = model.generate(input_ids, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

💡 Tip: Ensure that the quantization level is appropriate for your model and task to balance between performance and efficiency.