openraiser/nanoresearch 已索引

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引用了 26 个固定源文件

• 26 已知
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关联的包 17

按仓库的已验证身份关联。数量是历史登记记录，不是当前可用性。

第 1–17 条，共 17 条结果

• academic-plotting技能

  Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, a…

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• autoresearch技能

  Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers res…

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• brainstorming-research-ideas技能

  Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

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• creative-thinking-for-research技能

  Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and othe…

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• evaluating-llms-harness技能

  Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by Eleu…

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• huggingface-accelerate技能

  Simplest 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 comman…

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• ml-paper-writing技能

  Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submission…

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• ml-training-recipes技能

  Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, d…

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• NanoResearch.claude 配置包

  🦞+🔬 NanoResearch: The Autonomous AI Research Assistant

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• nanoresearch-experiment技能

  Generate a Python code skeleton from an experiment blueprint

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• nanoresearch-ideation技能

  Search academic literature and generate research hypotheses

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• nanoresearch-planning技能

  Produce an experiment blueprint from a research hypothesis

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• nanoresearch-writing技能

  Draft a LaTeX research paper from all previous stage outputs

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• peft-fine-tuning技能

  Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serv…

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• ray-data技能

  Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data pre…

  9d3b440c4f96

• skypilot-multi-cloud-orchestration技能

  Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

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• unsloth技能

  Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

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