instruction-following

Code and documentation to train Stanford's Alpaca models, and generate the data.

✨✨Latest Advances on Multimodal Large Language Models

An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.

Must-read Papers on LLM Agents.

An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.

An Open-sourced Knowledgable Large Language Model Framework.

This repository collects papers for "A Survey on Knowledge Distillation of Large Language Models". We break down KD into Knowledge Elicitation and Distillation Algorithms, and explore the Skill & Vertical Distillation of LLMs.

A collection of open-source dataset to train instruction-following LLMs (ChatGPT,LLaMA,Alpaca)

A simulation framework for RLHF and alternatives. Develop your RLHF method without collecting human data.

PhoGPT: Generative Pre-training for Vietnamese (2023)

Reading list of Instruction-tuning. A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022).

A collection of ChatGPT and GPT-3.5 instruction-based prompts for generating and classifying text.

Code to accompany the Universal Deep Research paper (https://arxiv.org/abs/2509.00244)

[ICLR'25] BigCodeBench: Benchmarking Code Generation Towards AGI

[NeurIPS'23] "MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing".

EVE Series: Encoder-Free Vision-Language Models from BAAI

[ICLR 2024] Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

[CVPR 2025] UniGoal: Towards Universal Zero-shot Goal-oriented Navigation

[EMNLP 2023] Lion: Adversarial Distillation of Proprietary Large Language Models

This framework works as a form of user/machine calibration, with a focus on user-context and user-intent, deconstructing your ideas logically from A to B to Z.