LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
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Updated
Dec 22, 2024 - Python
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
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 & MPP-Qwen-Next(Multimodal Pipeline Parallel based on Qwen-LM). Support [video/image/multi-image] {sft/conversations}. Don't let the poverty limit your imagination! Train your own 8B/14B LLaVA-training-like MLLM on RTX3090/4090 24GB.
Best practices & guides on how to write distributed pytorch training code
llama2 finetuning with deepspeed and lora
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
DeepSpeed教程 & 示例注释 & 学习笔记 (大模型高效训练)
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
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
Train llm (bloom, llama, baichuan2-7b, chatglm3-6b) with deepspeed pipeline mode. Faster than zero/zero++/fsdp.
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.
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