Claude Code plugin for LLM fine-tuning, distillation, and evaluation — decide whether you need fine-tuning at all, distill your LLM logs into small local models (MLX/LoRA), evaluate with held-out discipline, and learn the why at every step.
Skill Claude CodeCodex
Build a training dataset for fine-tuning, distillation, or continued pretraining. Use when the user wants to turn logs/CSV/JSONL into fine-tuning data, label data with an LLM, distill a teacher model's outputs, generate synthetic training examples from nothing, chunk raw domain text for CPT, deduplicate a dataset…
Skill Claude CodeCodex
The tunelab front door — decides whether a task needs fine-tuning at all, by running EXPERIMENTS on the user's data, not just interviewing. Use whenever the user wants to fine-tune, distill, or train a small/local model, cut their LLM API bill, replace frontier calls with something cheaper or faster, build a…
Skill Claude CodeCodex
Evaluate a fine-tuned, distilled, or continued-pretrained model with held-out test discipline — the honest scoreboard at the end of the tunelab pipeline. Pre-registers the acceptance bar and metric set BEFORE results exist, runs the untouched test split through base and tuned models, scores classification (accuracy…
Skill Claude CodeCodex
The tunelab capstone — drives a self-improving AI system. Use when the user wants a deployed model/cascade/workflow to keep getting better from feedback, run champion/challenger experiments to discover the best architecture, set up a data flywheel with retrain triggers, or automate "is a new model better than what we…
Skill Claude CodeCodex
Drive a local MLX-LM training run on Apple Silicon (LoRA/QLoRA, full fine-tuning, CPT) after tune-decide has validated a Level 2-3 plan. Use to pick a base model and hyperparameters, launch/monitor/resume a detached mlxlm.lora run, diagnose loss curves, run continued pretraining on a validated corpus, or fuse adapters…