QLoRA: efficiently LLM Fine-Tuning

Parameter-efficient training (PEFT) techniques offer a way to fine-tune large language models (LLMs) on custom datasets with minimal computational resources. Those techniques only update a small number of parameters during the fine-tuning process, and freeze the rest. 

LoRA is a PET technique that fine-tunes two smaller matrices that approximate the model. Its Quantized version, QLoRA, uses a smaller precision on those two proxy matrices.

Here’s a broad overview of how to use QLoRA to fine-tune an LLM. 

1. Install Required Libraries

• Key Libraries:
  • bitsandbytes for efficient model loading and quantization.
  • transformers for pre-trained models and utilities.
  • peft for parameter-efficient fine-tuning.

2. Prepare the Model for QLoRA

• Configure the model to use 4-bit quantization using BitsAndBytesConfig.
• Load a pre-trained model in 4-bit format using the transformers library and the 4-bit config.

5. Tokenization and Pre-processing

• Configure the tokenizer with necessary settings for optimal memory usage.
• Pre-process the dataset to match the model’s expected input format (completion, summarization, sentiment analysis, etc). 
• Tokenize the pre-processed dataset, batch and shuffle it.

6. Fine-Tuning with QLoRA

• Prepare the model for QLoRA using the prepare_model_for_kbit_training() function of the peft library .
• Define a LoraConfig for fine-tuning, specifying ranks, modules, and other parameters using the peft library, and update the model using this config.
• Use transformers.Trainer with custom training arguments to fine-tune the adapter.

Code Snippet for Fine-Tuning Setup:

from transformers import (TrainingArguments, Trainer)

from peft import (LoraConfig, get_peft_model
                  prepare_model_for_kbit_training)

# Prep QLoRA model
model = prepare_model_for_kbit_training(
    pretrained_model)

# Set LoRA config
lora_config = LoraConfig(r=32, lora_alpha=32,
    target_modules=['q_proj', 'k_proj',
        'v_proj', 'dense'])

# Get PEFT model
peft_model = get_peft_model(
    model, lora_config)

# Training args
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=4,
    logging_dir='./logs')

# Init Trainer
trainer = Trainer(
    model=peft_model,
args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset)

# Fine-tune
trainer.train()