Fundamentals of Instruction Tuning

Duration: 5 min

This module delves into the intricacies of instruction tuning for Large Language Models (LLMs). It covers essential techniques like LoRA, QLoRA, PEFT, and RLHF, and explains how to fine-tune models to follow specific instructions. Understanding these methods is crucial for developing more effective and context-aware language models.

Low-Rank Adaptation (LoRA)

LoRA is a technique that allows for efficient fine-tuning of large pre-trained models by introducing low-rank adaptations to the weight matrices. This approach reduces the number of trainable parameters, making the fine-tuning process more computationally efficient and less resource-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_rank = 4

# Apply LoRA to the model
for name, param in model.named_parameters():
    if 'weight' in name:
        param.data = torch.mm(torch.mm(param.data, torch.randn(param.size(-1), lora_rank)), torch.randn(lora_rank, param.size(-2)))

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

Try it in Google Colab:

Bonjour, comment allez-vous?

Quantized Low-Rank Adaptation (QLoRA)

QLoRA extends the LoRA technique by incorporating quantization, which further reduces memory usage and computational requirements. This makes it feasible to fine-tune very large models on devices with limited resources.

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_rank = 4
quantization_bits = 4

# Apply QLoRA to the model
for name, param in model.named_parameters():
    if 'weight' in name:
        param.data = torch.mm(torch.mm(param.data, torch.randn(param.size(-1), lora_rank)), torch.randn(lora_rank, param.size(-2)))
        param.data = torch.round(param.data / 2**quantization_bits) * 2**quantization_bits

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

💡 Tip: When applying LoRA or QLoRA, ensure that the rank chosen is appropriate for the model size to balance between efficiency and performance.