LowResAdapt: Principled LoRA Fine-Tuning CLI for Low-Resource Languages

A command-line toolkit that recommends and executes staged LoRA fine-tuning of a multilingual base model for a target low-resource language, with compute-budget guidance baked in.

Difficulty: 1-week | Stack: Python, HuggingFace Transformers, PEFT (LoRA), datasets, Typer CLI, Weights & Biases (optional logging)

Who this is for

Individual developers and researchers who want to adapt a multilingual model (e.g., mT5-small, Qwen2.5-0.5B) to an underrepresented language but don’t know how to set training epochs, learning rates, or data-mix ratios without expensive trial and error.

Build steps

1. Build a Typer CLI with a plan subcommand: takes target language BCP-47 code, corpus size (tokens), and available GPU hours; outputs a recommended training schedule (epochs per phase, learning rate, warmup, LoRA rank) based on a simple heuristic table derived from published low-resource LLM papers.
2. Implement a two-phase training loop: Phase 1 does language-model continuation on the target-language corpus; Phase 2 mixes in multilingual data at a tunable ratio to prevent catastrophic forgetting.
3. Add a perplexity-curve subcommand that evaluates the checkpoint after each epoch on a held-out slice and plots perplexity vs. compute to surface the diminishing-returns knee point.
4. Package everything so a user can run lowresadapt plan --lang sw --tokens 10M --gpu-hours 8 and then lowresadapt train --config plan_output.yaml with a single base model download.
5. Write a minimal eval harness that runs the adapted model on a small culturally-sourced prompt set and compares output quality to the unmodified base model side-by-side.

Risks

• The heuristic budget table will be wrong for unusual model architectures or tokenizers that handle the target language poorly — users need to know these are starting points, not guarantees.
• LoRA fine-tuning on very small corpora (<1M tokens) often causes overfitting within a few hundred steps; without careful early-stopping the model will memorize training data rather than generalize.
• HuggingFace PEFT API surface changes frequently — pinning exact dependency versions is critical or the CLI breaks on fresh installs.