tokendye 0.2.1

TokenDye is a way to fine-tune almost any LM

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Token Dye 🎨

Inject light-weight trainable signals into LLM embeddings to make models source-aware.

Token Dye is a mechanism that attaches low-rank "dye" matrices right after the embedding layer of a frozen LLM. Each dye corresponds to a specific token source (e.g., system, user, file, web, database) and adds a structured residual signal to the token vectors. The model can then perceive the provenance of every token directly in the embedding space, without relying on textual cues alone.

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✨ Why Token Dye?

• Ultra-light – each dye matrix is ~0.5 M parameters (< 1 MB). Training is fast, and dyes can be swapped at will.
• Loss-less degradation – when no dye is applied, the base model behaves exactly as the original.
• Architecture agnostic – works with any model that has an embedding layer (tested on Qwen 2.5-7B and Qwen 3.5-4B).
• Unforgeable – dye labels are controlled by the system, not by the user, so users cannot impersonate system or tool tokens through prompt injection.
• Online learning ready – tiny parameter footprint enables high-frequency updates and personalization.

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🧪 Core Findings

Base Model	Dye Effect
Qwen 2.5-7B	✅ Gain – dyes helped weaker models with source discrimination and priority reasoning.
Qwen 3.5-4B	⚠️ Interference – dyes initially disrupted the model's chain-of-thought. Root cause fixed by explicitly closing the think block (</think>) before the assistant's answer.

Conclusion:
Token Dye provides measurable gains on tasks the base model does not already solve perfectly (e.g., multi-source conflict resolution). On tasks where the base model is already strong (e.g., safety refusals), dyes add no extra benefit but also cause no harm – a clean baseline of loss-less degradation.

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📁 Repository Structure

.
├── train.py                  # Core training script (run_train, run_chain)
├── sweep_lr.py               # Learning rate search
├── ablation_test.py          # Ablation study (dye vs. no-dye)
├── log_viewer.py             # Training log visualization (loss & per-dye gradient norms)
├── DyeConfig.json            # Dye label definitions (system, user, tool, file, etc.)
├── dataset/
│   ├── v0.1a.jsonl5          # Original safety-attack dataset (90 samples)
│   └── v0.2.jsonl5           # Multi-source conflict dataset (71+ samples, growing)
├── README.md
└── README_zh.md

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🚀 Quick Start

1. Install dependencies

pip install transformers peft bitsandbytes accelerate json5

2. Prepare dye labels

Edit DyeConfig.json to define your labels. Example:

{
  "labels": [
    {"name": "assistant", "id": 0},
    {"name": "system",    "id": 1},
    {"name": "user",      "id": 2},
    {"name": "tool",      "id": 100},
    {"name": "tool::read_file",      "id": 101},
    {"name": "tool::search_web",     "id": 102},
    {"name": "tool::read_database",  "id": 103}
  ]
}

Labels can be hierarchical using :: (e.g., tool::read_file).

3. Prepare training data

Data format (JSON5):

{
  "segments": [
    {"dye": "system", "text": "You are a helpful assistant."},
    {"dye": "user",   "text": "What is the capital of France?"}
  ],
  "target": "The capital of France is Paris."
}

For multi-source conflict scenarios:

{
  "segments": [
    {"dye": "system", "text": "When sources conflict, trust the database over the web."},
    {"dye": "tool::read_database", "text": "Product A1001 stock: 328, status: in-stock"},
    {"dye": "tool::search_web",   "text": "Product A1001 is out of stock according to a shopping site."},
    {"dye": "user", "text": "Is product A1001 available?"}
  ],
  "target": "According to the database, product A1001 has 328 items in stock. Although the web says it's sold out, we trust the database."
}

4. Train

python train.py

The script will freeze the base model (Qwen 3.5-4B by default), load dye modules, and train only the dye matrices.

5. Run ablation test

python ablation_test.py

This compares outputs with dye enabled (A) vs. dye disabled (B) on the same prompts.

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📊 Monitoring

Use log_viewer.py to plot training curves and per-dye gradient norms:

python log_viewer.py --log_file training.log

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🔮 Future Directions

• Residual dye stacking – combine a base tool dye with sub-dyes (tool::read_file, etc.) for hierarchical source representation.
• Online learning – update dyes on the fly for personalized multi-source routing.
• Transfer to other architectures (e.g., RWKV) – the method only requires an embedding layer, making it broadly applicable.

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📄 License

This project is provided for research purposes. See LICENSE for details.

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📝 Citation

If you use Token Dye in your research, please cite this repository.