Conciseness

Dense tables and code with no explanations of concepts Claude already knows, but the docs/ link catalog is listed three times — the Quick Reference table, per-section 'Sources' lines, and the References section (~70 redundant lines). Efficient with minor trimming opportunities, fitting 4 rather than the 'some unnecessary explanation' anchor at 3.

Actionability

Mostly executable: the SFT workflow is copy-paste ready, reward functions and GRPOConfig are concrete, and troubleshooting gives exact env vars. Minor gaps keep it from 5 — Workflow 2 passes an undefined 'dataset' to GRPOTrainer, Workflow 3 uses an undefined 'raw_dataset', and the TTS workflow shows no trainer code.

Workflow Clarity

Each workflow has a sequenced checklist with preparation steps, monitoring guidance ('Monitor reward curves and KL divergence'), and the Common Issues table provides error-to-fix feedback loops. Minor validation gaps — no explicit verify-before-deploy step (e.g., test the exported GGUF in Ollama) — place it at 4 rather than the explicit-checkpoint anchor at 5.

Progressive Disclosure

The body is structured as an overview pointing to one-level-deep docs/*.md references, but none of the ~30 referenced files exist in the bundle (no docs/, references/, scripts/, or assets/ directories) — every navigation link is dead, so the promised detail is inaccessible. Scoring against the actual bundle structure, this is 'references are buried/absent' territory at 2 rather than 3's 'references present but not clearly signaled'.

Specificity

Names the domain and multiple concrete actions — 'single-GPU LoRA/QLoRA SFT, GRPO/RL reasoning training, vision/TTS fine-tuning, and GGUF export to Ollama/vLLM/llama.cpp' — comprehensively covering the tool's capability surface, matching the 'lists multiple specific concrete actions; comprehensive coverage' anchor rather than the minor-gaps anchor at 4.

Completeness

Explicitly answers both 'what' ('Fast LLM fine-tuning with Unsloth - 2-5x faster training, 50-80% less VRAM') and 'when' via a concrete 'Use for single-GPU LoRA/QLoRA SFT, GRPO/RL reasoning training, vision/TTS fine-tuning, and GGUF export' clause, matching the 'clearly and explicitly answers both what AND when with concrete trigger phrases' anchor.

Trigger Term Quality

Includes strong natural keywords users would say — 'fine-tuning', 'LoRA', 'QLoRA', 'GRPO', 'GGUF', 'Ollama', 'vLLM', plus model names — but misses common variations like 'RLHF', 'instruction tuning', 'reward-based training', or 'train a model on my GPU'. Good coverage with a few natural terms missing fits 4; it exceeds the 'some relevant keywords but missing variations' anchor at 3.

Distinctiveness Conflict Risk

'Unsloth' plus tool-specific terms (GRPO, GGUF export, 4-bit quant naming) anchor a clear niche, but the description does not distinguish itself from adjacent fine-tuning stacks, so a generic request like 'fine-tune a LoRA' could overlap with PEFT/Axolotl-style skills — 'mostly distinct; minor overlap risk with closely related skills'.