declare-lab/instruct-eval
This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
552 stars. No commits in the last 6 months.
Stars
552
Forks
44
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 10, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/declare-lab/instruct-eval"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DaoD/INTERS
This is the repository for our paper "INTERS: Unlocking the Power of Large Language Models in...
Haiyang-W/TokenFormer
[ICLR2025 Spotlightš„] Official Implementation of TokenFormer: Rethinking Transformer Scaling...
hkust-nlp/deita
Deita: Data-Efficient Instruction Tuning for Alignment [ICLR2024]
kehanlu/DeSTA2
Code and model for ICASSP 2025 Paper "Developing Instruction-Following Speech Language Model...
zhilizju/Awesome-instruction-tuning
A curated list of awesome instruction tuning datasets, models, papers and repositories.