Huggingface Accelerate
OpenRaiser/NanoResearchSimplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
At a glance
git clone --depth 1 https://github.com/OpenRaiser/NanoResearch
cp -r NanoResearch/skills/vendor-ai-research/accelerate ~/.claude/skills/accelerate
Setup, runtime and requirements describe OpenRaiser/NanoResearch, the repo this skill ships in.
Also in OpenRaiser/NanoResearch
View the repoGenerates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components...
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Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directi...
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Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers fr...
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, ge...
Generate a Python code skeleton from an experiment blueprint
Search academic literature and generate research hypotheses
Produce an experiment blueprint from a research hypothesis
Draft a LaTeX research paper from all previous stage outputs
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU mem...