A detailed Tutorial showing the process of fine-tuning a causal language model using LoRA (Low-Rank Adaptation).

This tutorial demonstrates how to fine-tune a pre-trained Llama language model for chat-based interactions. We leverage the Guanaco dataset, designed for instruction tuning, and employ Low-Rank Adaptation (LoRA) for efficient fine-tuning. LoRA reduces the number of trainable parameters, making the process faster and less resource-intensive.

1. Setup:

   • Install required libraries: transformers, datasets, evaluate, peft, trl, and bitsandbytes.
   • Import necessary modules from these libraries.
   • Define the paths to the base model, dataset, and the name for the new fine-tuned model.

2. Load Model and Dataset:

   • Load the pre-trained Llama model using AutoModelForCausalLM, utilizing available devices for efficient computation.
   • Load the Guanaco dataset, which contains conversational data for instruction tuning.
   • Load the tokenizer associated with the pre-trained model and set appropriate padding configurations.

3. Pre-Fine-Tuning Inference (Optional):

   • Create a simple text generation pipeline using the pre-trained model and tokenizer.
   • Run inference with a sample prompt to see the model's baseline performance before fine-tuning.

4. Configure LoRA and Training:

   • Define LoRA parameters (e.g., lora_alpha, lora_dropout, r) for efficient fine-tuning.
   • Set up training parameters (e.g., num_train_epochs, per_device_train_batch_size, learning_rate).
   • Create an SFTTrainer instance, combining the model, dataset, LoRA configuration, tokenizer, and training parameters.

5. Fine-Tune the Model:

   • Initiate training by calling trainer.train(). The model will learn to generate responses that are more aligned with the conversational style and instructions in the Guanaco dataset.
   • Monitor the training progress, including loss and other metrics.

6. Save the Fine-Tuned Model:

   • After training, save the fine-tuned model and tokenizer to a specified directory (new_model).

7. Post-Fine-Tuning Inference:

   • Create another text generation pipeline using the fine-tuned model.
   • Run inference again with the same sample prompt to compare the model's responses before and after fine-tuning. You should observe improvements in the quality and relevance of the generated text.

• Hardware Requirements: Fine-tuning large language models can be computationally intensive. Ensure you have sufficient GPU memory and processing power.
• Dataset Quality: The quality of the fine-tuning dataset (Guanaco in this case) significantly impacts the performance of the final model.
• Hyperparameter Tuning: Experiment with different LoRA and training parameters to optimize the fine-tuning process and achieve the best results.