data-labeling
claudeData labeling: annotation guidelines, platforms, inter-annotator agreement, active learning, weak supervision, and label error detection.
1 skill · 1 command · 1 agent
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claude
llm-fine-tuning
LLM fine-tuning: LoRA and QLoRA, instruction datasets, DPO and PPO alignment, adapter merging, and evaluation.
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Plugin llm-fine-tuning from marketplace libre-mlops in HermeticOrmus/LibreMLOps-Claude-Code. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (1)
llm-tuning-patternsLLM fine-tuning patterns with code: QLoRA with SFTTrainer, LoRA configuration choices, instruction dataset preparation, DPO preference training, adapter merging and export, and lm-evaluation-harness runs. Use when fine-tuning or aligning an LLM.
Slash commands (1)
/llm-fine-tuning:fine-tuneFormat an instruction dataset, generate a QLoRA or SFT training script, evaluate with lm-evaluation-harness, or merge a LoRA adapter into its base model.
Subagents (1)
llm-tunerUse this agent when adapting a large language model to a task or domain: preparing instruction datasets, configuring LoRA or QLoRA with PEFT, running supervised fine-tuning or preference alignment (DPO, PPO) with trl, working out the memory math, and evaluating the model before and after tuning.
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