An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO)
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Updated
Jun 2, 2024 - Python
An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO)
Personal Project: MPP-Qwen14B(Multimodal Pipeline Parallel-Qwen14B). Don't let the poverty limit your imagination! Train your own 14B LLaVA-like MLLM on RTX3090/4090 24GB.
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
All about large language models
Collaborative Training of Large Language Models in an Efficient Way
llm-inference is a platform for publishing and managing llm inference, providing a wide range of out-of-the-box features for model deployment, such as UI, RESTful API, auto-scaling, computing resource management, monitoring, and more.
A toy large model for recommender system based on LLaMA2/SASRec/Meta's generative recommenders. Besides, note and experiments of official implementation for Meta's generative recommenders.
An Open-sourced Knowledgable Large Language Model Framework.
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
Shaping Language Models with Cognitive Insights
Minimal yet high performant code for pretraining llms. Attempts to implement some SOTA features. Implements training through: Deepspeed, Megatron-LM, and FSDP. WIP
Train llm (bloom, llama, baichuan2-7b, chatglm3-6b) with deepspeed pipeline mode. Faster than zero/zero++/fsdp.
Application of the L2HMC algorithm to simulations in lattice QCD.
The official implementation of paper "Demystifying Instruction Mixing for Fine-tuning Large Language Models"
Natural Language Processing (NLP) and Large Language Models (LLM) with Fine-Tuning LLM and Trainer with DeepSpeed
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