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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 EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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 serving. HuggingFace's official library integrated with transformers ecosystem.
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
Based on SOC occupation classification
Showing SKILL.md
| name | nanoresearch-writing |
| description | Draft a LaTeX research paper from all previous stage outputs |
| version | 0.1.0 |
Take all previous outputs (ideation, planning, experiment results) and produce a complete LaTeX paper draft with figures, tables, and bibliography.
generate_latex: Generate and assemble LaTeX source files for each paper sectioncompile_pdf: Compile the LaTeX source into a PDF documentgenerate_figure: Produce publication-quality figures from experiment resultsideation_output: Path to papers/ideation_output.json from the ideation skillexperiment_blueprint: Path to papers/experiment_blueprint.json from the planning skillexperiment_results: Path to experiments/ directory containing code and results from the experiment skillgenerate_figuregenerate_latexcompile_pdf and verify the outputProduces papers/draft/ directory containing:
main.tex: Complete LaTeX source of the paperreferences.bib: Bibliography file with all citationsfigures/: Generated figures in PDF or PNG formattables/: LaTeX table source filesmain.pdf: Compiled PDF of the paper draft