ZDDC
ea385b5366
ZDDC — Zero Day Document Control. A file-naming convention plus five single-file HTML tools (archive, transmittal, classifier, mdedit, landing) and an optional Go HTTP server (zddc-server) with ACL and a virtual archive index. Self-contained, offline-capable, dependency-free. See README.md for an overview, AGENTS.md and ARCHITECTURE.md for the build/release/architecture detail, bootstrap/README.md for the two-level deployment install pattern, and zddc/README.md for the HTTP server.
167 lines
4.9 KiB
Markdown
167 lines
4.9 KiB
Markdown
# ZDDC Training Data Pipeline
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Adaptive LoRA fine-tuning for **Qwen3-Coder-Next** (`vllm/RedHatAI/Qwen3-Coder-Next-NVFP4`).
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Training data is collected reactively: when Qwen struggles on a task and a stronger model (Sonnet/Opus) is consulted, that interaction is captured as a training example. Over time, domain-specific LoRA adapters are trained to patch Qwen's weak spots.
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---
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## Directory Layout
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```
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training-data/
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├── raw/
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│ └── interactions.jsonl # All captured weak-spot interactions
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├── processed/
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│ ├── all.jsonl # Deduplicated, combined dataset
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│ ├── multi-domain.jsonl # All domains merged
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│ └── <domain>.jsonl # Per-domain splits (auto-generated)
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├── validation/
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│ ├── train.jsonl # 80% split
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│ ├── val.jsonl # 10% split
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│ └── test.jsonl # 10% split (never used during training)
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├── adapters/
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│ └── <domain>-lora-v1/ # Trained LoRA adapter
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│ └── <domain>-lora-v1-merged/ # Merged standalone model
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├── snapshots/
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│ └── v<date>/ # Versioned dataset snapshots
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├── collect-interaction.js # Capture a weak-spot interaction
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├── process.sh # Cluster raw data into domain splits
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├── validate.sh # Check data quality before training
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├── train.sh # Train a LoRA adapter
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└── deploy.sh # Merge adapter into standalone model
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```
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---
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## Workflow
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### Step 1 — Collect a weak-spot interaction
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When Qwen gets stuck or you ask it to consult Sonnet/Opus:
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```bash
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node collect-interaction.js \
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--query "How do I parse ZDDC filenames?" \
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--qwen "[Qwen's suboptimal answer]" \
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--expert "[Sonnet's correct answer]"
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```
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Optionally specify domain explicitly (otherwise auto-detected):
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```bash
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node collect-interaction.js \
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--query "..." \
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--qwen "..." \
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--expert "..." \
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--domain zddc-naming
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```
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Raw interaction is appended to `raw/interactions.jsonl`.
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### Step 2 — Process (after ~50 new interactions)
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```bash
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bash process.sh
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```
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Deduplicates, clusters by domain, creates train/val/test splits.
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### Step 3 — Validate
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```bash
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bash validate.sh
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```
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Checks JSONL validity, domain balance, and split sizes.
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### Step 4 — Train
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```bash
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bash train.sh # train multi-domain adapter
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bash train.sh zddc-naming # train domain-specific adapter
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```
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Outputs LoRA adapter to `adapters/<domain>-lora-v1/`.
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### Step 5 — Deploy (optional)
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```bash
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bash deploy.sh # merge multi-domain adapter
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bash deploy.sh zddc-naming # merge specific adapter
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```
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Merges the LoRA weights into the base model and saves a standalone model.
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---
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## Training Data Format
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Each line in a `.jsonl` file is one training example:
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```json
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{
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"messages": [
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{"role": "user", "content": "Query that exposed weakness"},
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{"role": "assistant", "content": "Qwen's original response"},
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{"role": "user", "content": "consult Sonnet"},
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{"role": "assistant", "content": "Expert's correct response"}
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],
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"metadata": {
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"domain": "zddc-naming",
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"adapter": "lora-v1-zddc_naming",
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"timestamp": "2025-10-31T14:30:00.000Z",
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"interaction_id": "int_1735648200000_abc123",
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"source": "manual-expert-consultation"
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}
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}
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```
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---
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## Auto-Detected Domains
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| Domain | Trigger keywords |
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|--------|----------------|
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| `zddc-naming` | zddc, trackingnumber, revision, status code |
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| `html-architecture` | html, spa, single-file, es module, vanilla js |
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| `build-system` | build.sh, dist/, template.html |
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| `coding-debugging` | debug, error, fix, console |
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| `reasoning-architecture` | reason, analyze, architecture, design |
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| `general-coding` | (default) |
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---
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## LoRA Configuration
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| Parameter | Value | Notes |
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|-----------|-------|-------|
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| Base model | `Qwen/Qwen2.5-7B-Instruct` | Replace with Qwen3 when available on HF |
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| Rank | 64 | Increase to 128 if underfitting |
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| Alpha | 64 | 1:1 with rank |
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| Target modules | q_proj, v_proj, k_proj, o_proj | All attention projections |
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| Dropout | 0.05 | Light regularisation |
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| Learning rate | 1e-4 | Cosine decay with 10% warmup |
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| Epochs | 3 | Monitor val loss to catch overfitting |
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| Batch size | 8 effective | 4 per-device × 2 gradient accumulation |
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| Precision | bfloat16 | Requires Ampere GPU or newer |
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---
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## Hardware Requirements
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| Setup | Min VRAM | Method | Notes |
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|-------|----------|--------|-------|
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| Qwen-7B LoRA | 24 GB | LoRA bf16 | Recommended |
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| Qwen-7B QLoRA | 16 GB | QLoRA 4-bit | Add `--load_in_4bit` flag |
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| Qwen-14B LoRA | 48 GB | LoRA bf16 | Better quality |
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Your system has 96 GB VRAM — full LoRA on Qwen-14B is feasible.
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---
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## When to Retrain
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- Every **50–100 new interactions** collected
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- When a new domain accumulates **200+ examples**
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- After a major project phase where Qwen struggled repeatedly
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