Stars
A powerful tool for creating fine-tuning datasets for LLM
Tools for understanding how transformer predictions are built layer-by-layer
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
The unofficial python package that returns response of Google Bard through cookie value.
LLM training code for Databricks foundation models
Instruction Tuning with GPT-4
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
KoAlpaca: ํ๊ตญ์ด ๋ช ๋ น์ด๋ฅผ ์ดํดํ๋ ์คํ์์ค ์ธ์ด๋ชจ๋ธ (KoAlpaca: An open-source language model to understand Korean instructions)
Reading list of Instruction-tuning. A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022).
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
[ICML 2023] Exploring the Benefits of Training Expert Language Models over Instruction Tuning
[AAAI 2024] Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
Crosslingual Generalization through Multitask Finetuning
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your dataโฆ
Space-efficient graph data converter
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Polyglot: Large Language Models of Well-balanced Competence in Multi-languages
A framework for few-shot evaluation of autoregressive language models.
[EMNLP 2023 Findings] Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt
Must-read papers on prompt-based tuning for pre-trained language models.
Convert Machine Learning Code Between Frameworks
๐ค The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
TUTA and ForTaP for Structure-Aware and Numerical-Reasoning-Aware Table Pre-Training
ICLR 2022 Paper, SOTA Table Pre-training Model, TAPEX: Table Pre-training via Learning a Neural SQL Executor
Package to compute Mauve, a similarity score between neural text and human text. Install with `pip install mauve-text`.
Code associated with the Don't Stop Pretraining ACL 2020 paper
The corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!