Fine-Tune Llama 3-8B for Code Generation on Google Colab

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Written by Tamzid Ahmed

June 1, 2026

Fine-tuning Meta’s Llama 3-8B for code generation tasks on Google Colab is a cost-effective way to create specialized AI models that understand programming languages better than general-purpose LLMs. This guide walks through practical steps to optimize the model for Python and other languages using parameter-efficient techniques.

Why Fine-Tune Llama 3-8B for Code Generation?

While Llama 3-8B excels at general text generation, its default training doesn’t prioritize code-specific patterns. Fine-tuning adapts the model to recognize syntax, logic, and context unique to programming languages, significantly improving accuracy for tasks like autocompletion or bug fixing.

Base models often generate syntactically incorrect code or miss domain-specific conventions. Fine-tuning with a code-focused dataset bridges this gap, making the model more reliable for developer workflows without requiring massive computational resources.

Setting Up Your Google Colab Environment

Google Colab provides free access to GPU resources, but proper setup is crucial. Start by selecting a runtime with GPU acceleration (Runtime > Change runtime type > Hardware accelerator > GPU).

Installing Required Libraries

Run these commands in a Colab cell to install necessary packages:

!pip install transformers==4.40.0 peft==0.10.0 bitsandbytes==0.43.0 accelerate datasets

This ensures compatibility with the latest Hugging Face tools and quantization libraries. Always verify versions to avoid runtime errors during fine-tuning.

Preparing the Code Dataset

Use high-quality, curated datasets like CodeSearchNet or filtered GitHub repositories. For Python-specific tasks, consider the Python CodeNet dataset, which contains over 100,000 clean code examples.

Data Cleaning and Formatting

Preprocess data to remove non-code content, ensure consistent indentation, and structure prompts as instruction-response pairs. For example:

{"instruction": "Write a Python function to calculate Fibonacci sequence", "input": "", "output": "def fibonacci(n):n    a, b = 0, 1n    for _ in range(n):n        yield an        a, b = b, a + b"}

This format aligns with instruction-tuning best practices for code generation models.

Applying LoRA for Efficient Fine-Tuning

LoRA (Low-Rank Adaptation) drastically reduces trainable parameters by injecting low-rank matrices into attention layers, preserving performance while cutting memory usage by 90%+ compared to full fine-tuning.

Configuring LoRA Parameters

Use the peft library to apply LoRA:

from peft import LoraConfig

lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

Targeting only query and value projection layers balances efficiency and effectiveness for code generation tasks.

Quantization for Memory Optimization

4-bit quantization via bitsandbytes reduces model size from ~16GB to ~4GB, making training feasible on Colab’s T4 GPU (16GB VRAM). This technique maintains performance while minimizing memory overhead.

Enabling 4-bit Quantization

Load the model with quantization settings:

from transformers import BitsAndBytesConfig

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

This setup uses nested quantization for better accuracy retention and bfloat16 for stable training.

Training and Monitoring the Model

Combine LoRA and quantization with Hugging Face’s Trainer API for streamlined training. Here’s a step-by-step process:

Key Training Steps

  1. Initialize the model with quantization and LoRA adapters
  2. Configure training arguments (batch size, epochs, learning rate)
  3. Start training using Trainer.train()
  4. Monitor metrics like loss and accuracy in real-time via TensorBoard or Colab logs
  5. Save the final adapter weights for deployment

Example training arguments:

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    num_train_epochs=3,
    logging_steps=100,
    save_strategy="epoch",
    fp16=True
)

Adjust batch size based on Colab’s GPU memory limits—typically 2-4 samples per batch with gradient accumulation for stability.

Evaluating and Deploying the Model

After training, evaluate the model on a test set of code generation tasks. Use metrics like pass@k (pass rate for k samples) to measure accuracy.

Deploy the fine-tuned model using Hugging Face’s transformers pipeline or integrate it into IDE plugins for real-time code suggestions. Always validate outputs against known test cases before production use.

Conclusion

Fine-tuning Llama 3-8B for code generation on Google Colab is achievable with LoRA and 4-bit quantization, making it accessible even with free-tier resources. By focusing on efficient parameter adaptation and memory optimization, you can create a specialized model that significantly outperforms base LLMs in coding tasks. Start with a small, high-quality dataset and iterate—your first fine-tuned model could be ready in under 2 hours on Colab. For next steps, explore deploying your model via Hugging Face Spaces or integrating it into developer tools for immediate productivity gains.

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