Parameter-efficient instruction tuning: an empirical study. This repository systematically compares various parameter-efficient fine-tuning (PEFT) methods on instruction tuning tasks using the SuperNI dataset. It targets researchers and engineers seeking efficient LLM adaptation, offering empirical insights to guide method selection and reduce computational costs.

How It Works

The project systematically evaluates various parameter-efficient fine-tuning (PEFT) techniques, adapting implementations from established libraries like adapter-transformers and peft. It employs the SuperNI dataset for instruction tuning benchmarks, focusing on empirical comparisons to identify optimal fine-tuning strategies for large language models, aiming to reduce computational cost and memory footprint during adaptation.

Quick Start & Requirements

• Install: Clone peft-private (release-v0.4.0-adapter branch), cd peft-private, pip install -e ., pip install rouge-score.
• Prerequisites: Python 3.8, CUDA 11.3, PyTorch 1.10.2+cu113 (note: peft library requires torch>=1.13.0). GPT2 model required at cache/saved_pretrained/gpt2.
• Platform: Highly optimized for hfai HPC (A100x8 GPUs). Default dataset path: ../../data.
• Docs: Technical report available via arXiv link in citation.

Highlighted Details

• Systematic empirical comparison of PEFT methods for instruction tuning, providing valuable insights into efficiency trade-offs.
• Codebase features robust checkpointing and training state validation, optimized for hfai HPC's pre-emptable environments.
• Utilizes the SuperNI dataset for standardized, reproducible instruction tuning evaluation.
• Supports flexible experiment configuration and submission via hp_run.sh scripts.

Maintenance & Community

No specific community channels, roadmap, or contributor details are provided in the README. The project is associated with an arXiv publication.

Licensing & Compatibility

The repository's license is not specified in the README, posing a potential blocker for commercial use or integration.

Limitations & Caveats

The codebase is heavily optimized for the hfai HPC platform, potentially requiring significant adaptation for other environments due to specific configurations and dependencies. The use of a private peft repository and specific, older PyTorch version dependencies (1.10.2+cu113, despite peft requiring >=1.13.0) may complicate setup and integration. The absence of a specified license is a critical adoption blocker.