llm-training

20 projets partagent ce topic GitHub

Les traits relient les membres réellement apparentés entre eux. La taille des points suit les étoiles.

🧬 Membres

Find secrets with Gitleaks 🔑

Low-code framework for building custom LLMs, neural networks, and other AI models

The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer,…

Efficient Triton Kernels for LLM Training

Democratizing Reinforcement Learning for LLMs

H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation:…

OpenLake is a high performance storage engine for efficient LLM inference and GPU Training

verl-agent is an extension of veRL, designed for training LLM/VLM agents via RL. verl-agent is also the…

MoBA: Mixture of Block Attention for Long-Context LLMs

nvidia_gpu_exporter

Nvidia GPU exporter for prometheus using nvidia-smi binary

LLM-PowerHouse-A-Curated-Guide-for-Large-Language-Models-with-Custom-Training-and-Inferencing

LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for…

ICLR2025-Papers-with-Code

历年ICLR论文和开源项目合集，包含ICLR2021、ICLR2022、ICLR2023、ICLR2024、ICLR2025.

LLM-FineTuning-Large-Language-Models

LLM (Large Language Model) FineTuning

awesome-on-policy-distillation

A curated collection of papers, technical reports, frameworks, and tools for on-policy distillation (OPD) of…

Repo for fine-tuning Casual LLMs

Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding,…

LightRFT: Light, Efficient, Omni-modal & Reward-model Driven Reinforcement Fine-Tuning Framework

PMetal: high-performance Apple Silicon framework for local LLM inference, LoRA/QLoRA fine-tuning, serving,…

Full-stack open-source AI engine for building language models — tokenizer training, transformer…

🔗 Familles voisines

llm-inference 76 llm-serving 23 llama2 56 mistral 66 distributed-training 15 gpu 93 ml-infrastructure 4 ml-platform 6

Mesuré à partir des topics GitHub communs aux deux projets, pondérés par leur rareté.