DOMINO

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 12)

DOMINO: Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

DOMINO 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DOMINO: Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

DOMINO learns a **minimal sufficient representation** of a target domain from limited reference examples via soft prompt tuning and contrastive learning.

## Method Overview

![DOMINO Architecture](https://raw.githubusercontent.com/tongye98/DOMINO/main/assets/arch.png)

DOMINO trains two types of learnable continuous soft tokens:

- **Domain-level soft tokens**: Shared across all reference samples, capturing generalizable domain patterns.
- **Sample-level soft tokens**: Unique per sample, encoding sample-specific information.

The training objective consists of two parts:

1. **Domain-level learning:** Trains the domain-level soft tokens to reconstruct all reference samples, capturing common features/patterns across the domain.

2. **Contrastive learning:** Each sample has its own private soft tokens. The model learns to use private tokens only for its own sample. This forces domain-level tokens to focus on shared knowledge, not sample-specific details.

The final loss balances both objectives. During synthesis, only domain-level tokens are used, producing diverse in-domain samples rather than memorized copies of the reference data.

## Directory Structure

```
DOMINO/
├── domino/
│   ├── contrastive/           # Core method: public+private soft token contrastive learning
│   │   ├── model.py           # PublicPrivateContrastiveModel
│   │   ├── train.py           # Training script (HuggingFace Trainer)
│   │   ├── dataset.py         # Dataset loader
│   │   └── generate.py        # Synthetic data generation (transformers/vLLM)
│   │
│   ├── soft_prompt/           # Baseline: shared public soft prompt tuning
│   │   ├── model.py, train.py, dataset.py, generate.py
│   │
│   ├── pipeline/              # Synthetic data quality control pipeline
│   │   ├── code_generation/   # Quality assessment, response generation, filtering
│   │   └── code_execution/    # Quality assessment, response generation, correctness check
│   │
│   ├── evaluation/            # LiveCodeBench evaluation harness
│   │   ├── code_generation/   # Pass@k computation, sandboxed testing
│   │   └── code_execution/    # Execution correctness, metrics
│   │
│   └── utils/                 # Embedding extraction, statistics, LLM query
│
├── scripts/                   # Example training and pipeline scripts
├── configs/                   # DeepSpeed ZeRO configurations
└── examples/                  # Visualization and analysis scripts
```

## Requirements

- Python 3.10+
- PyTorch 2.0+
- transformers
- vLLM
- DeepSpeed
- tree-sitter + tree-sitter-python
- scikit-learn, matplotlib, numpy, scipy
- pyext (for code generation evaluation)

Install dependencies:
```bash
pip install torch transformers vllm deepspeed tree-sitter tree-sitter-python scikit-learn matplotlib numpy scipy pyext datasets
```

## Quick Start

### Step 1: Prepare Reference Data

Prepare your reference dataset in JSONL format:
```jsonl
{"question_content": "Write a function to ...", ...}
```

### Step 2: Train DOMINO Soft Tokens

```bash
cd scripts
bash train_contrastive.sh
```

This trains public and private soft tokens via contrastive learning on the reference dataset.

### Step 3: Generate Synthetic Data

```bash
bash pipeline_codegen.sh
```

This generates synthetic code problems, filters by quality, and produces instruction-response pairs.

### Step 4: (Optional) SFT with Synthetic Data

Use the generated instruction-response pairs to fine-tune a base LLM using your preferred framework (e.g., LLaMA-Factory).

### Step 5: Evaluate

```bash
python -m domino.evaluation.code_generation.evaluate \
    --data_path ./data/test.jsonl \
    --model_path ./path/to/your/model
```

<!-- ## Citation

```
@software{domino2025,
  title={DOMINO: Domain-level \& Sample-level Contrastive Soft Prompt Tuning for Code Generation},
  author={Your Name},
  year={2025},
  url={https://github.com/yourusername/DOMINO}
}
``` -->

## License

Apache 2.0

DOMINO

DOMINO 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

DOMINO

DOMINO 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README