AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. 8-Flash-Next (125B) on 6GB, and DeepSeek-V3 (671B) on 12GB. 8-Flash-Next (125B) under 6GB.
Global
Fine-tune an 8B model on a 4 GB laptop GPU. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. 32 GB.
Local UI to run and train LLMs and diffusion models. 8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.
#1057 · +25 · 1d ·GitHub
🐧 PenguinHarness: Local-first multi-agent app development platform - create, fine-tune, deploy AI apps automatically!
> 第八章聚焦 GRPO、OPD、Search-R1 与 ReTool。更多 Agentic RL 算法、训练代码与实验实践，可以前往作者持续维护的另一个仓库 agentic-rl-lab；该仓库更新频率更高，会持续跟进新的算法与环境。
✅ Optimized performance - Models designed to maximize performance, reduce costs, and speed up training.
QLoRA uses bitsandbytes for quantization and is integrated with Hugging Face's PEFT and transformers libraries. QLoRA was developed by members of the University of Washington's UW NLP group.
Halo is an open-source framework built by White Circle for training large language and multimodal models
#8320 · +0 · 1d ·GitHub
A conversational drone-swarm choreography agent built on the ReAct paradigm, running on top of a deployed SwarmGPT service. 5B distilled Qwen model (LoRA adapter) that replaces the cloud teacher for choreography planning.