Moving from the rationale for Supervised Fine-Tuning (SFT) and dataset curation, we now focus on the practical aspects of implementing the SFT process for Large Language Models (LLMs). This stage involves taking a pre-trained base model and further training it on your specific demonstration dataset to align its behavior before the more complex Reinforcement Learning (RL) phase. Careful attention to implementation details is significant for achieving a well-performing SFT model, which forms the foundation for successful RLHF.

Choosing the Base Model and Framework

The starting point for SFT is typically a large, pre-trained language model. Popular choices include models from the GPT family, Llama, Mistral, or other open-source alternatives. The choice depends on factors like task requirements, computational resources, and licensing constraints.

Most SFT implementations leverage established deep learning frameworks like PyTorch or TensorFlow, often in conjunction with libraries specifically designed for transformer models, such as Hugging Face's transformers. These libraries provide pre-built model architectures, tokenizers, and training utilities that significantly simplify the fine-tuning process. Using transformers, you can load a pre-trained model and its corresponding tokenizer with just a few lines of code:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meta-llama/Llama-2-7b-hf" # Example model
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Data Preparation and Formatting

The curated SFT dataset, consisting of prompt-response pairs, needs to be formatted correctly for the model. This involves:

Structuring Input: Concatenate prompts and responses into a single sequence. A common format is to structure the input like: "<s>[INST] Prompt text [/INST] Response text </s>". Special tokens (like <s>, </s>, [INST], [/INST]) delineate the different parts of the input and signal the start/end of turns or sequences. These tokens depend on the specific base model being used; consult the model's documentation.

Tokenization: Convert the structured text sequences into numerical input IDs that the model can understand. The tokenizer associated with the base model must be used. Pay attention to padding (adding special tokens to make sequences in a batch the same length) and truncation (cutting sequences longer than the model's maximum context length).

# Example using Hugging Face tokenizer
prompt = "Explain the difference between supervised and unsupervised learning."
response = "Supervised learning uses labeled data..."
formatted_text = f"<s>[INST] {prompt} [/INST] {response} </s>"

# Tokenize the formatted text
inputs = tokenizer(formatted_text, return_tensors="pt", padding=True, truncation=True, max_length=1024) 
# inputs['input_ids'], inputs['attention_mask']

Masking Labels: During training, the model should only learn to predict the response tokens, not the prompt tokens. We achieve this by setting the label IDs corresponding to the prompt tokens to a special value (often -100), which is ignored by the standard cross-entropy loss function. Only the response tokens contribute to the loss calculation.

Training Configuration

Configuring the training loop involves setting up the optimizer, loss function, and various hyperparameters that govern the learning process.

Optimizer and Loss Function

Optimizer: The AdamW optimizer is a common and effective choice for training transformer models. It extends the Adam optimizer with decoupled weight decay, which can improve generalization.
Loss Function: For language modeling tasks like SFT, the standard loss function is Cross-Entropy Loss. It measures the difference between the probability distribution predicted by the model for the next token and the actual next token in the sequence. Libraries like PyTorch handle the calculation efficiently, especially when combined with label masking as described above.

Key Hyperparameters

Tuning hyperparameters is essential for stable and effective SFT. Some of the most important ones include:

Learning Rate: This determines the step size during optimization. For fine-tuning large models, smaller learning rates (e.g., $1e^{-5}$ to $5e^{-5}$ ) are typically used to avoid disrupting the pre-trained weights too drastically. A learning rate schedule, often involving a warm-up phase followed by linear or cosine decay, is standard practice. This helps stabilize training initially and allows for finer adjustments later.

A typical learning rate schedule starts at zero, increases linearly during a warm-up period, and then decays linearly towards zero over the remaining training steps.

Batch Size: The number of examples processed in each training step. Larger batch sizes provide more stable gradient estimates but require more GPU memory. If memory is limited, gradient accumulation can simulate a larger batch size by accumulating gradients over several smaller steps before performing an optimizer update.
Number of Epochs: An epoch is one full pass through the entire training dataset. Fine-tuning usually requires only a few epochs (e.g., 1-5), as the model is already pre-trained. Training for too long can lead to overfitting on the SFT dataset.
Weight Decay: A regularization technique (often included in the AdamW optimizer) that penalizes large weights, helping to prevent overfitting. Typical values are small, like 0.01.
Gradient Clipping: Prevents exploding gradients (gradients becoming excessively large) by capping their maximum norm. This improves training stability, especially early in training. A common clipping value is 1.0.
Sequence Length: The maximum number of tokens processed by the model at once. Longer sequences capture more context but require significantly more memory and computation. Choose a length appropriate for your task and hardware constraints, often truncating or sliding windows over longer examples.

Parameter-Efficient Fine-Tuning (PEFT)

Full fine-tuning, where all model parameters are updated, can be computationally expensive for billion-parameter models. Parameter-Efficient Fine-Tuning (PEFT) methods offer alternatives that update only a small subset of parameters or add new, trainable modules.

Low-Rank Adaptation (LoRA): A popular PEFT technique. LoRA freezes the original pre-trained weights and injects smaller, trainable "rank decomposition" matrices into specific layers (typically attention layers). During training, only these smaller matrices are updated, drastically reducing the number of trainable parameters and memory requirements. The original weights remain unchanged, preserving much of the pre-trained knowledge. At inference time, the learned LoRA matrices can often be merged back into the original weights without adding latency. Libraries like Hugging Face's peft provide easy implementations of LoRA.

Diagram illustrating the LoRA mechanism. The input x is processed by both the frozen pre-trained weights W0 and the low-rank decomposition matrices A and B. Only A and B are updated during training.

Hardware and Efficiency Considerations

GPUs: Fine-tuning large LLMs typically requires powerful GPUs with significant memory (e.g., NVIDIA A100s or H100s).
Distributed Training: For very large models or datasets, distributed training frameworks like DeepSpeed or PyTorch's Fully Sharded Data Parallel (FSDP) might be necessary to distribute the model and data across multiple GPUs or machines.
Mixed-Precision Training: Using lower-precision floating-point formats like FP16 (16-bit floating point) or BF16 (bfloat16) can significantly speed up training and reduce memory usage with minimal impact on model quality. Libraries often provide simple flags to enable this.

Saving and Checkpointing

Regularly save model checkpoints during training. This allows you to:

Resume training if interrupted.
Select the best performing model based on evaluation metrics on a validation set.
Keep track of different versions of the model.

When using PEFT methods like LoRA, you often only need to save the small adapter weights, which are much smaller than the full model, making checkpointing very efficient.

By carefully configuring these implementation details, you can effectively fine-tune your base LLM on the demonstration data, creating a strong SFT model ready for the subsequent reward modeling and RL fine-tuning stages of the RLHF pipeline. The next section will cover how to evaluate the performance of this SFT model.