Optimizing Llama 3 Fine-Tuning for Python Code Generation using Unsloth

Unsloth is currently the only way to fine-tune Llama 3 on a single consumer GPU without losing your mind over OOM (Out of Memory) errors. If you're trying to make Llama 3 better at a specific Python framework or your own internal API, standard LoRA is too slow. Unsloth’s optimized kernels cut memory usage by about 60% and speed up training by 2x, which is the difference between a 10-hour run and a 4-hour run.

The biggest hurdle with code generation is the formatting. If your dataset isn't strictly aligned with the Llama 3 chat template, the model will start hallucinating <|eot_id|> tokens or failing to close function blocks. I've found that using a strict "Instruction -> Input -> Output" mapping is non-negotiable.

Here is the boilerplate I use to initialize the model for code tasks. I always stick to the 4-bit quantized version because the perplexity hit is negligible for Python generation, but the VRAM savings are massive.

from unsloth import FastLanguageModel
import torch

max_seq_length = 4096 # Code needs larger windows for context
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit",
    max_seq_length = max_seq_length,
    load_in_4bit = True,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 16, # Rank 16 is usually the sweet spot for code
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj"],
    lora_alpha = 16,
    lora_dropout = 0, 
    bias = "none",    
)

One specific "gotcha" I hit: the default learning rate for general text often washes out the precise syntax required for Python. I dropped my learning rate to 2e-4 and used a cosine scheduler. If you go too high, the model starts ignoring the indentation rules of Python, which kills the code's executability.

For the dataset, don't just dump raw .py files. The model learns better when it sees the "thought process." I wrap my training pairs in a specific prompt format:

### Instruction:
Write a Python function to [task] using [library].

### Response:

python
[code here]

To maximize productivity, I use the following training hyperparameters in the SFTTrainer:

Learning Rate: 2e-4
Epochs: 3 (going beyond 3 usually leads to overfitting on specific variable names)
Weight Decay: 0.01
Optimizer: adamw_8bit (essential for VRAM)
Max Steps: Set based on dataset size, but keep an eye on the loss curve

The real productivity gain comes when you export the model to GGUF format via Unsloth. You can push it directly to Hugging Face and then pull it into Cursor or a local Ollama instance. This loop—train in Unsloth, deploy in Ollama, use as a custom model in Cursor—is the fastest iteration cycle I've found for creating a "domain expert" AI that actually knows your codebase without needing a 100k token context window.

One final tip: disable gradient_checkpointing if you have enough VRAM (24GB+), as it slightly speeds up the training process. If you're on a 16GB card, leave it on, or you'll hit a wall the moment you encounter a long code snippet in your training set.