gszfwsb/Data-Whisperer
Code for ACL 2025 Main paper "Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning".
This project helps machine learning engineers and researchers efficiently fine-tune large language models (LLMs) for specific tasks. It takes an initial dataset and an LLM as input, then identifies the most impactful data points to create a smaller, optimized dataset for training. This results in more efficient and potentially higher-performing task-specific LLMs.
No commits in the last 6 months.
Use this if you need to fine-tune an LLM for a specific application but want to avoid using your entire dataset to save time and computational resources.
Not ideal if you are not working with LLMs, or if you prefer to use your full dataset for fine-tuning without any data selection.
Stars
48
Forks
4
Language
Python
License
—
Category
Last pushed
Aug 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/gszfwsb/Data-Whisperer"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
peremartra/optipfair
Structured pruning and bias visualization for Large Language Models. Tools for LLM optimization...
VainF/Torch-Pruning
[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.
horseee/LLM-Pruner
[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support...
CASIA-LMC-Lab/FLAP
[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
princeton-nlp/LLM-Shearing
[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning