| name | ml-training |
| description | ML model training on cloud GPU capability pack. Covers platform selection, LoRA and QLoRA fine-tuning, cost estimation, and human-AI collaboration via browser MCP. Use for any ML model fine-tuning, cloud GPU training, or model adaptation task. |
| 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)
- jq + awk — required by
scripts/dataset-check.sh / cost-estimate.sh / vram-fit.sh
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 (~70% VRAM reduction via
use_gradient_checkpointing="unsloth", 2-5× speedup)
- Widest models + GUI → LlamaFactory v0.9.4 (LlamaBoard no-code UI + CLI)
- RL / post-training pipeline → Axolotl v0.17.0 (single YAML, GRPO/DPO/KTO/ORPO)
- 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. Run the deterministic checks (Step 4) instead of eyeballing VRAM/cost/schema.
Step 4: Validation Scripts (don't punt determinism to prose)
| Script | Checks |
|---|
scripts/vram-fit.sh <params_B> <method> <gpu_gb> | Does the config fit the card? (qlora4/lora8/lora16, unsloth-qlora4) |
scripts/cost-estimate.sh <num_examples> [epochs] [batch] [sec/step] [rate] | GPU-hour cost from the formula + 2026 rate anchors |
scripts/dataset-check.sh <file.jsonl> | JSONL validity + schema detect (ShareGPT/ChatML/Alpaca/preference) + size verdict + dedup |
Quick Rule Index (router — depth lives in references/)
Each entry is a POINTER, not the answer. The concrete numbers (ranks, VRAM, prices,
learning rates, quotas) are in the linked reference so this index stays a true router.
Platform Selection → references/platform-selection.md
- Budget decision tree ($0 / $10-50 / $50+) → §Budget Decision Tree
- Free-tier hard limits (Colab/Kaggle quotas, session caps) → §Free Tier Hard Limits
- The 5 platform gotchas (anti-abuse, idle timeout, ephemeral storage, untrusted hosts, egress) → §Hidden Limitations
- VRAM → card mapping → §VRAM → Platform mapping
LoRA Fine-Tuning → references/lora-finetune.md
- When to fine-tune (dataset-size → task threshold) → §When to Fine-Tune
- VRAM requirements + Unsloth gradient-checkpointing flag → §VRAM Requirements
- LoRA vs QLoRA quality/VRAM tradeoff + 2026 default config → §LoRA vs QLoRA
- Rank (r) + alpha rule → §Rank Selection
- Learning rate (SFT vs DPO/GRPO) → §Learning Rate
- Overfitting tripwires + training budget → §Overfitting Tripwires
- Preference tuning (DPO/GRPO config) → §Preference Tuning
- Version-pinned tool comparison → §Tool Comparison
Data Preparation → references/data-preparation.md
- Quality > quantity rule → §Dataset Quality Rule
- LLM data pipeline + schemas (ShareGPT/ChatML/Alpaca) → §LLM Data Pipeline
- Preference-pair data (DPO/GRPO triples) → §Preference-Pair Data
- Voice data pipeline (defer audio thresholds) → §Voice Data Pipeline
- AI bootstrap to reach the example floor → §AI Bootstrap
MCP Collaboration → references/mcp-collaboration.md
- PAUSE protocol (auth triggers, forbidden tools, resume) → §PAUSE Protocol
- Chrome MCP tool map → §Chrome MCP Tool Map
- Security rules → §Security Rules
Cost Estimation → references/cost-estimation.md
- Cost formula → §Cost Formula
- 2026 price anchors → §Cloud GPU Price Anchors
- "Can I do it free?" tree → §"Can I Do It Free?" 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 — practical floor 100, sweet spot 500-10000; personality needs AI bootstrap to reach ~500. Run scripts/dataset-check.sh for the size verdict |
| "I'll fine-tune first, test later" | MUST check fine-tune threshold in lora-finetune.md — if <100 examples, use few-shot prompting/RAG first (below practical floor) |
| "My Mac can handle it" | MUST check VRAM in lora-finetune.md — 7-8B LoRA 16-bit needs ~16GB, 8GB Mac = cloud mandatory above QLoRA 4-bit. Run scripts/vram-fit.sh to confirm |
| "I'll reuse the SFT learning rate for DPO" | MUST check lora-finetune.md §Learning Rate — DPO/GRPO LR is 5e-6, ~40× lower than SFT 2e-4; reusing 2e-4 diverges |
| "I'll skip data cleaning" | MUST read data-preparation.md quality rule — 500 clean examples outperform 5,000 noisy; 117 clean > 248 dirty segments at same step count (Colin dogfood) |
