Fine-Tuning LLaMA-2 7B for Python Code Generation

1. Problem & Constraint

Large Language Models have transformed software development, but their scaling creates a deployment barrier: high-performing models require substantial GPU memory and are commonly accessed through cloud APIs. For students, researchers, and small teams, cloud-only access introduces cost, latency, privacy concerns, and external dependence.

Smaller open models like LLaMA-2 7B can run on consumer GPUs, but their code-generation quality is weaker than that of larger or code-specialized models. This motivates methods that adapt smaller models to specialized tasks without full parameter fine-tuning.

Parameter-Efficient Fine-Tuning (PEFT) provides a practical solution. LoRA injects low-rank trainable matrices into transformer layers, reducing trainable parameter count. QLoRA extends this by quantizing the base model to 4-bit precision and training LoRA adapters on top, enabling 7B-scale adaptation on modest hardware.

The project objective was deliberately practical: improve executable Python generation enough to measure a gain over the untuned chat model, then package the result for responsive local inference on a consumer GPU.

2. Data Collection & Curation

Source Datasets

Filtering Pipeline

The raw datasets contained general scripting tasks, web-development snippets, machine-learning examples, duplicates, empty descriptions, comments, docstrings, and non-functional fragments. We applied a multi-stage filtering pipeline:

Impact of Repetition Threshold

Dataset Distillation Summary

3. Methodology

Key Pivot: Base Model to Chat Model

An initial base-model strategy produced inconsistent output structure and weak task alignment, which made Pass@k evaluation noisy. Moving to LLaMA-2 Chat forced a clearer instruction template and stable response boundary. The pivot was less about choosing a fashionable model and more about making outputs easier to execute, compare, and score.

Prompt Formatting

All examples were converted into the LLaMA-2 native instruction format using the chat template with [INST] markers. The tokenizer maximum length was set to 512 tokens with right-side padding, and the padding token was mapped to the end-of-sequence token so the model could learn clear termination behavior during generation.

Completion-Only Instruction Masking

Standard supervised fine-tuning can compute loss over every token in the prompt-response sequence. Here, tokens belonging to the instruction and template prefix were assigned the label -100, which PyTorch cross-entropy ignores. This aligned the optimization objective with the response tokens after the [/INST] marker instead of spending loss on reproducing the prompt.

Hyperparameter Optimization

We performed hyperparameter optimization with Optuna using a reduced 5% subset of the training data. Each trial used an 80/20 train-validation split. We ran 45 trials with Tree-structured Parzen Estimator search and pruning for underperforming trials.

QLoRA Fine-Tuning

The pretrained LLaMA-2 7B Chat weights were loaded in 4-bit NormalFloat format with double quantization. LoRA adapters were inserted into attention and feed-forward decoder modules while keeping the original base-model weights frozen. Only adapter parameters were trainable, representing less than 1% of total model parameters.

Training was performed on Kaggle dual-T4 GPUs using cross-entropy loss on response tokens only. Training was stopped during the second epoch because validation loss continued to decrease only marginally (below ~0.001), reducing overfitting risk.

4. Results

Quantization Impact

Quantization substantially reduced memory requirements. The 81.4% memory reduction enabled full training and deployment workflow without large data-center GPUs. We observed only a small quantitative performance difference between non-quantized and quantized inference.

HumanEval Pass@k Comparison

BLEU & CodeBLEU

BLEU remained limited because many programming problems permit multiple correct implementations with different surface forms. CodeBLEU is more informative because it considers code structure, syntax, and data-flow. Functional testing (HumanEval) remains the more important criterion for code generation.

Local Ollama Inference

Evaluation Evidence

5. Limitations

Limited dataset scope. The final dataset focuses on Python and algorithmic tasks. It does not cover the full diversity of real software engineering work (web frameworks, data pipelines, concurrent programming, etc.).

Metrics cannot guarantee correctness. BLEU and CodeBLEU measure surface similarity, not functional correctness. A syntactically similar solution can still fail hidden test cases.

Narrow functional evaluation. HumanEval contains only 164 problems and may not represent broader production scenarios. More diverse benchmarks (MBPP, SWE-bench) would provide a fuller picture.

Fine-tuning cannot replace code-specific pretraining. The fine-tuned model remains significantly below CodeLlama on HumanEval, indicating that task-specific QLoRA improves LLaMA-2 but cannot fully compensate for limited code-specific pretraining.

Single-language deployment. Deployment was tested for Python code generation only and was not extended to multilingual code generation.

No component ablation. The final gain reflects the complete pipeline—data curation, prompt format, masking, hyperparameters, and QLoRA—not a controlled estimate of any one component's contribution.

The defensible conclusion is that parameter-efficient adaptation improved this base model within the measured task and hardware envelope—not that it produced a production-grade coding assistant.

6. Practical Recommendations

For teams considering similar fine-tuning projects on constrained hardware:

7. Reproducibility

Environment: Python 3.10+, PyTorch, Hugging Face Transformers, PEFT, bitsandbytes, Optuna, Kaggle dual-T4 GPUs.

Base model: meta-llama/Llama-2-7b-chat-hf (4-bit NF4 quantized).

Evaluation: HumanEval (164 Python problems), BLEU, CodeBLEU.

Deployment: Ollama local inference on consumer NVIDIA GPU.

8. Future Work

• Repeat the experiment with a newer code-specialized base model to separate the value of adaptation from the limitations of LLaMA-2 pretraining.
• Expand the dataset with multilingual programming examples, edge cases, debugging tasks, and repository-level contexts.
• Evaluate preference optimization or feedback-based tuning after establishing stronger supervised baselines.
• Use curriculum-based fine-tuning to present simpler tasks before harder edge cases.
• Integrate with development environments for real-time code assistance and richer human-in-the-loop evaluation.
• Evaluate on additional benchmarks such as MBPP and SWE-bench for broader coverage.