ml-training

name	ml-training
description	ML model training on cloud GPU — platform selection, LoRA/QLoRA fine-tuning, cost estimation, human-AI collaboration via browser MCP
version	0.1.0
type	reference-based
keywords	["fine-tune","微调","LoRA","QLoRA","train model","训练模型","Colab","Kaggle","RunPod","cloud GPU","云GPU","云训练","training data","训练数据","GPU hours","model training","模型训练","personality clone","个性克隆"]

ML Training Capability Pack

Cross-agent portable judgment for ML model training on cloud GPU. Covers platform selection, LoRA/QLoRA fine-tuning for LLM and voice models, data preparation, cost estimation, and human-AI collaboration workflows. CONSUMES: Training data (JSONL/ShareGPT/audio+transcript pairs), base model name, hardware constraints, budget. PRODUCES: Platform recommendation, tool selection, training configuration, cost estimate. INTERFACE: ai-voice-production pack defers voice training platform selection to this pack's platform-selection.md. This pack defers voice-specific tool selection (GPT-SoVITS, VoxCPM2 configs, audio quality thresholds) to ai-voice-production pack. When both packs load for voice training: this pack takes precedence for platform/cost decisions; ai-voice-production takes precedence for tool selection and audio quality.

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Step 0: Prerequisites

• Python 3.10+ — required by Unsloth, LlamaFactory, Axolotl
• pip or uv — install in virtual environment (never global)
• Cloud account — at least one of: Google (Colab/Kaggle), RunPod, Vast.ai
• Chrome + Claude MCP extension — for browser-automated Colab workflows (optional)

Verify: python3 --version

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Step 1: Context Detection

User Signal	Load Reference
fine-tune, 微调, LoRA, QLoRA, train model, 训练模型	references/lora-finetune.md
Colab, Kaggle, RunPod, Vast.ai, cloud GPU, 云GPU, 云训练	references/platform-selection.md
training data, 训练数据, chat logs, 聊天记录, data prep	references/data-preparation.md
cost, 成本, pricing, 多少钱, budget, GPU hours	references/cost-estimation.md
browser automation, MCP, Colab操作, 人机协作	references/mcp-collaboration.md
personality clone, 个性克隆, sound like, 像某人	references/lora-finetune.md + references/data-preparation.md

Multi-signal: Load all matched references. Cross-reference links are provided within files.

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Step 2: Decision Entry Point

Q1 — What type of model?

• LLM (text: Qwen/Llama/Mistral) → load lora-finetune.md
• Voice (TTS/cloning: VoxCPM2/GPT-SoVITS) → defer to ai-voice-production pack for tool selection; load platform-selection.md for GPU choice
• Image/other → out of scope, state clearly

Q2 — What hardware do you have locally?

• Apple Silicon 8-16GB → cloud training mandatory for 7B+ LoRA 16-bit; local only for QLoRA 2-bit or inference
• NVIDIA GPU ≥16GB → local QLoRA possible; cloud for LoRA 16-bit or larger models
• No GPU → cloud mandatory

Q3 — What's your budget?

• $0 → Colab Free / Kaggle (with gotcha awareness — read platform-selection.md)
• $10-50/mo → Colab Pro (student free w/ .edu) / RunPod

• $50/mo → RunPod Secure / Lambda (if uptime critical)

Q4 — What tool?

• Minimal setup + Colab/Kaggle → Unsloth (pre-built notebooks, 70% less VRAM claim)
• Widest model support (100+ LLMs) → LlamaFactory (WebUI + CLI)
• MLOps pipeline reproducibility → Axolotl (YAML config)
• Voice → GPT-SoVITS or VoxCPM2 (defer to ai-voice-production pack)

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Step 3: Apply Rules

Read matched reference(s) and apply rules directly. Rules are concrete decision parameters — not step-by-step tutorials.

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Quick Rule Index

Platform Selection (references/platform-selection.md)

• Budget Decision Tree: $0 / $10-50 / $50+ routing with gotcha awareness → §Budget Decision Tree
• 5 Platform Gotchas: Colab anti-abuse, Kaggle idle timeout, RunPod storage, Vast.ai security, hyperscaler egress → §Hidden Limitations
• Free Tier Comparison: T4 vs P100, time limits, quota burn risks → §Platform Comparison Table

LoRA Fine-Tuning (references/lora-finetune.md)

• Fine-tune vs RAG Threshold: <50 examples = prompting, 100-500 = classification, 500-2K = generation → §When to Fine-Tune
• VRAM Requirements: 4-bit QLoRA 6GB, 8-bit 10GB, 16-bit LoRA 16GB → §VRAM Requirements
• Rank Selection Table: 16 (style), 32 (multi-turn), 64 (complex reasoning) → §Configuration
• Tool Head-to-Head: Unsloth vs LlamaFactory vs Axolotl → §Tool Comparison

Data Preparation (references/data-preparation.md)

• LLM Data Pipeline: chat logs → clean → ShareGPT JSON format → §LLM Data Pipeline
• Voice Data Pipeline: audio → VAD → transcript → annotation list → §Voice Data Pipeline
• AI Bootstrap Technique: frontier model generates synthetic training pairs → §AI Bootstrap

MCP Collaboration (references/mcp-collaboration.md)

• PAUSE Protocol: auth triggers, forbidden tools during PAUSE, resume procedure → §PAUSE Protocol
• Chrome MCP Tool Map: 10 tools for Colab automation → §Chrome MCP Tools
• Security Rules: no reading auth pages, navigate away before resuming → §Security Rules

Cost Estimation (references/cost-estimation.md)

• VRAM-to-Cost Mapping: method × platform cost formula → §VRAM-to-Cost
• "Can I Do It Free?" Tree: quick-check decision flow → §Free Tier Decision Tree

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Anti-Skip Table

Shortcut Attempt	Required Action
"I'll just use Colab Free"	MUST read platform-selection.md gotchas — anti-abuse termination kills SSH/remote desktop, Drive I/O errors after 10K files, idle quota burn
"200 examples should be enough for everything"	MUST check task-type threshold in lora-finetune.md — classification 100-500, generation 500-2K, personality needs AI bootstrap to reach target
"I'll fine-tune first, test later"	MUST check fine-tune vs prompting/RAG threshold in lora-finetune.md — if <50 examples, use few-shot prompting first
"My Mac can handle it"	MUST check VRAM requirements in lora-finetune.md — 7B LoRA 16-bit needs 16GB VRAM, 8GB Mac = cloud mandatory for anything above 2-bit QLoRA
"I'll skip data cleaning"	MUST read data-preparation.md quality rule — 117 clean segments outperformed 248 dirty segments at same step count (Colin dogfood)