r-three / t-few
Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"
☆441Updated last year
Alternatives and similar repositories for t-few:
Users that are interested in t-few are comparing it to the libraries listed below
- Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)☆461Updated 2 years ago
- Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03…☆524Updated last year
- Contriever: Unsupervised Dense Information Retrieval with Contrastive Learning☆705Updated last year
- ☆343Updated 3 years ago
- An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi☆258Updated last year
- Scalable training for dense retrieval models.☆273Updated last year
- MEND: Fast Model Editing at Scale☆241Updated last year
- Fusion-in-Decoder☆558Updated last year
- Run Effective Large Batch Contrastive Learning Beyond GPU/TPU Memory Constraint☆371Updated 10 months ago
- Few-shot Learning of GPT-3☆344Updated last year
- Reading list of Instruction-tuning. A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022).☆759Updated last year
- Code for the ALiBi method for transformer language models (ICLR 2022)☆512Updated last year
- [EMNLP 2023] The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning☆224Updated last year
- DSIR large-scale data selection framework for language model training☆242Updated 9 months ago
- Expanding natural instructions☆970Updated last year
- Accompanying repo for the RLPrompt paper☆313Updated 7 months ago
- OpenICL is an open-source framework to facilitate research, development, and prototyping of in-context learning.☆544Updated last year
- Original Implementation of Prompt Tuning from Lester, et al, 2021☆663Updated last month
- This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.☆540Updated 10 months ago
- Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch☆857Updated last year
- Source Code of Paper "GPTScore: Evaluate as You Desire"☆238Updated last year
- Mass-editing thousands of facts into a transformer memory (ICLR 2023)☆460Updated 11 months ago
- The original implementation of Min et al. "Nonparametric Masked Language Modeling" (paper https//arxiv.org/abs/2212.01349)☆157Updated 2 years ago
- Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning"☆164Updated 3 years ago
- [NeurIPS'22 Spotlight] A Contrastive Framework for Neural Text Generation☆468Updated 10 months ago
- Simple next-token-prediction for RLHF☆222Updated last year
- An Extensible Continual Learning Framework Focused on Language Models (LMs)☆264Updated last year
- Official repository of NEFTune: Noisy Embeddings Improves Instruction Finetuning☆388Updated 8 months ago
- PyTorch + HuggingFace code for RetoMaton: "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML 2022), including an…☆272Updated 2 years ago
- A framework for few-shot evaluation of autoregressive language models.☆102Updated last year