LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
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Updated
May 18, 2024 - Python
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO)
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
An Open-sourced Knowledgable Large Language Model Framework.
Best practice for training LLaMA models in Megatron-LM
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and finetune GPT-NEO (2.7 B) on a single GPU with Huggingface Transformers using DeepSpeed
Collaborative Training of Large Language Models in an Efficient Way
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.
llama2 finetuning with deepspeed and lora
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
A full pipeline to finetune ChatGLM LLM with LoRA and RLHF on consumer hardware. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the ChatGLM architecture. Basically ChatGPT but with ChatGLM
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.
A full pipeline to finetune Alpaca LLM with LoRA and RLHF on consumer hardware. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the Alpaca architecture. Basically ChatGPT but with Alpaca
DeepSpeed教程 & 示例注释 & 学习笔记 (大模型高效训练)
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