Fine-Tuning Qwen 4B for TypeScript Code Generation: Strategies for Small Datasets and Overfitting

Optimizing Qwen 4B for Niche Code Generation: Balancing Efficiency and Accuracy

As small language models (SLMs) gain traction in resource-constrained environments, researchers are increasingly turning to models like Qwen-4B for specialized tasks such as TypeScript code generation. However, fine-tuning these models on small, noisy datasets presents unique challenges. A recent inquiry on the r/LocalLLaMA subreddit from a graduate student working on a thesis project highlights common pitfalls: underperformance despite using an A100 GPU, potential overfitting, and data contamination from non-code elements.

The user, who initially attempted fine-tuning Qwen-8B, downsized to the 4B variant to maintain true SLM efficiency—prioritizing inference speed and hardware accessibility over raw performance. Yet, with only 700–800 high-quality {prompt, completion} pairs—some synthetically generated and others distilled from larger models—the model struggled to generalize. The dataset, while curated, contained noise such as image paths, placeholder text, and non-code annotations, raising concerns about the model learning irrelevant patterns.

LoRA Configuration: Less Is More

The original configuration employed LoRA with a rank (r) of 64 and alpha of 128, which may be excessive for such a small dataset. According to best practices in parameter-efficient fine-tuning, higher LoRA ranks increase model capacity but also the risk of memorization. For datasets under 1,000 examples, experts recommend reducing r to 16–32 and lowering lora_alpha to 32–64. This reduces the number of trainable parameters, effectively acting as a regularization mechanism. Additionally, increasing lora_dropout from 0.05 to 0.1–0.15 introduces stochasticity during training, further discouraging overfitting.

Data Quality Over Quantity

With limited data, preprocessing becomes critical. The presence of non-code elements—such as Markdown comments, file paths, or placeholder strings like "TODO: replace with actual image"—can mislead the model into generating irrelevant content. A recommended strategy is to implement automated filtering: use regex patterns to detect and remove lines containing file extensions (.jpg, .png), URL patterns, or non-TypeScript syntax. Tools like CodeBERT or simple syntax validators can help flag malformed code blocks. Furthermore, augmenting the dataset with synthetic but syntactically valid variations (e.g., renaming variables, restructuring conditionals) can increase diversity without introducing noise.

Training Strategy: Smaller Batches, Longer Training

The current setup uses a batch size of 16 with gradient accumulation of 2, effectively simulating a 32-sample batch. For small datasets, this may be too aggressive. Reducing per_device_train_batch_size to 4–8 and increasing num_train_epochs to 5–8 allows the model to iterate more thoroughly over the limited data, improving convergence without increasing memory pressure. The use of cosine learning rate decay with a 5% warmup is appropriate, but consider reducing the base learning rate from 2e-4 to 1e-4 to prevent large parameter updates that destabilize training on sparse data.

Regularization and Evaluation

Weight decay at 0.01 is reasonable, but adding early stopping based on validation loss—triggered after 2–3 consecutive epochs without improvement—can prevent the model from learning noise. The user’s use of packing and max_seq_length=4096 is suitable for code, but ensure that prompts and completions are trimmed to realistic lengths (e.g., 1024–2048 tokens) to avoid diluting signal with padding. Evaluation should include not just loss metrics but also functional tests: measure code compilation success rate, syntax correctness via AST parsing, and task completion accuracy on a held-out benchmark.

Conclusion: Precision Over Power

While the A100 GPU offers substantial compute, success in SLM fine-tuning lies not in hardware, but in precision. For niche code generation with small datasets, the key is disciplined data curation, conservative LoRA parameters, and extended training with robust validation. The Qwen-4B model, when properly tuned, can outperform larger variants in constrained environments—provided the training process respects the limitations of the data. As one Reddit commenter noted, "You’re not training a generalist—you’re sculpting a specialist. Every parameter must earn its place."

For further reading, consult the Hugging Face PEFT documentation and the Unsloth GitHub repository for optimized training pipelines tailored to code generation tasks.