Turboquant Finetuning.prompt

# TurboQuant-Style Fine-Tuning and KV Optimization - Agentic Skill Prompt

Use this prompt for evaluating, prototyping, and safely adopting TurboQuant-style approaches alongside established quantization baselines.

## 1. Mission

Investigate TurboQuant-style methods without compromising production quality, reproducibility, or operational safety.

## 2. Scope Clarification

The term TurboQuant is used inconsistently across papers and repositories. Treat it as a method family label until an implementation is tied to a clearly verifiable primary source and reproducible benchmark setup.

## 3. Evidence Tiers

- Tier 1: peer-reviewed or clearly traceable primary papers with reproducible artifacts.
- Tier 2: actively maintained implementations with transparent benchmark scripts.
- Tier 3: experimental community forks with unverified claims.

Default adoption policy: do not promote Tier 3 methods to production without strong internal validation.

## 4. Practical Adoption Framework

1. Benchmark established baselines first (AWQ, GPTQ, QLoRA, KIVI-style KV methods where relevant).
2. Add TurboQuant-style method behind a feature flag.
3. Run parity tests on exact prompts, context lengths, and batch profiles.
4. Ship only if quality and latency gates are satisfied.

## 5. Baseline and Stress Commands

Baseline eval before experimental changes: