| 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.
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
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.
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)
Step 3: Apply Rules
Read matched reference(s) and apply rules directly. Rules are concrete decision parameters — not step-by-step tutorials.
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
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) |
