Packs

1 pack

Fine-Tune Transformer Model

Fine-tune transformer language models using TRL with support for SFT, DPO, GRPO, and reward model training.

8 skills · pack

Results for “training”

7 skills

dual-folder-workflow

Workflow for private repos + Colab training. Trigger when: (1) training on Colab with private repo, (2) separating dev from production, (3) avoiding untested code in production.

Builds a personalized fitness system that assesses your body, goals, and constraints, then designs training, nutrition, and progress tracking around your real life.

cx-skills-matrix

Maps support capabilities, plans cross-training, and sizes multi-skilling against real proficiency and routing cost.

More results

tao-launch-workflow

Collects launch inputs and runs preflight checks before executing TAO workflows such as AutoML, training, evaluation, inference, export, TensorRT engine generation, or DEFT jobs on supported platforms.

Builds a personalized fitness system by assessing your body, goals, and constraints, then designs training, nutrition, and recovery plans that adapt to real-life disruptions.

Interactive training for the GitHub Copilot CLI with guided lessons, quizzes, scenario challenges, and a full reference covering slash commands, shortcuts, modes, agents, skills, MCP, and configuration.

36.2k · bundle

alterlab-geniml

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.