README.md

---
language:
- en
tags:
- code
- rust
- payment-processing
- curriculum-learning
- continued-pretraining
- hyperswitch
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: Hyperswitch Curriculum Learning Dataset (Unbroken)
---

# Hyperswitch Curriculum Learning Dataset (Unbroken)

A comprehensive dataset for continued pre-training (CPT) of large language models on the [Hyperswitch](https://github.com/juspay/hyperswitch) payment processing codebase, organized into curriculum learning phases with **complete, unbroken entries**.

## 🎯 Dataset Overview

This dataset contains the complete Hyperswitch repository knowledge extracted from:
- **Source code files** (.rs, .toml, .yaml, .json, .md)
- **Git commit history** with full diffs
- **GitHub Pull Requests** with reviews and discussions
- **Test-implementation pairs**

**Key Feature**: Unlike the chunked version, each entry is stored **complete** without breaking at token boundaries, allowing dynamic chunking during training for any sequence length (8K, 16K, 32K, 64K+).

## 📊 Dataset Structure

### Curriculum Learning Phases

The dataset is organized into 3 progressive phases:

#### **Phase 1: Code Foundation** (`phase1_foundation.jsonl`)
- **Content**: Repository files + test-implementation pairs
- **Purpose**: Learn codebase structure, syntax, and testing patterns
- **Training**: 2 epochs
- **Entries**: Complete files and test pairs (unbroken)

#### **Phase 2: Evolution Patterns** (`phase2_evolution.jsonl`)
- **Content**: Git commits (chronological) + small PRs
- **Purpose**: Understand code evolution, change patterns, and incremental development
- **Training**: 2-3 epochs
- **Entries**: Complete commits with full diffs, small PRs (unbroken)

#### **Phase 3: PR Mastery** (`phase3_pr_mastery.jsonl`)
- **Content**: Medium and large PRs with reviews and discussions
- **Purpose**: Master complex changes, code review practices, and collaboration patterns
- **Training**: 3-4 epochs
- **Entries**: Complete PRs with all reviews and comments (unbroken)

## 📁 Data Format

Each entry is a single JSON object per line (JSONL format):

### File Entry
```json
{
  "type": "file",
  "path": "crates/hyperswitch_connectors/src/connectors/paypal/transformers.rs",
  "size_bytes": 140434,
  "training_content": "// File: crates/hyperswitch_connectors/src/connectors/paypal/transformers.rs\n\n<complete_file_content>"
}
```

### Commit Entry
```json
{
  "type": "commit",
  "commit_hash": "73203ebd05beab57f243e8460f259707bb856921",
  "author": "vasanthp-jus",
  "date": "2025-11-27T12:18:26+05:30",
  "message": "fix-postman-collection",
  "training_content": "Commit: \"fix-postman-collection\"\nAuthor: vasanthp-jus\nDate: 2025-11-27T12:18:26+05:30\n\nDiff:\n<complete_git_diff>"
}
```

### PR Entry
```json
{
  "type": "pr_diff",
  "pr_number": 1234,
  "title": "Add PayPal connector support",
  "state": "merged",
  "author": "developer-name",
  "created_at": "2025-11-15T10:30:00Z",
  "training_content": "PR #1234: Add PayPal connector support\n\n<description>\n\nReviews:\n<complete_reviews>\n\nComments:\n<complete_comments>"
}
```

### Test Pair Entry
```json
{
  "type": "test_pair",
  "test_file": "crates/router/tests/connector_tests.rs",
  "impl_file": "crates/router/src/connector.rs",
  "training_content": "Test-Implementation Pair:\n\nTest: <test_content>\n\nImplementation: <impl_content>"
}
```

## 🔢 Dataset Statistics

| Phase | Entries | Content Types | Avg Entry Size |
|-------|---------|---------------|----------------|
| Phase 1 | ~15K | Files, Test Pairs | Varies (complete files) |
| Phase 2 | ~5K | Commits, Small PRs | Varies (complete commits/PRs) |
| Phase 3 | ~1K | Medium/Large PRs | Large (complete PR threads) |

**Total**: ~21K complete, unbroken entries

## 💡 Unbroken vs Chunked

### Unbroken (This Dataset)
✅ Complete semantic units preserved  
✅ No artificial breaks in code/diffs  
✅ Flexible for any sequence length  
✅ Chunk dynamically during training  
✅ Smaller dataset file size (no overlap)  

### Chunked (Alternative)
- Pre-chunked at fixed token limit (e.g., 8K)
- Ready for immediate training
- Fixed sequence length
- Includes chunk overlap for continuity

## 🚀 Usage

### Loading the Dataset

```python
import json

def load_phase(phase_file):
    """Load a curriculum phase."""
    entries = []
    with open(phase_file, 'r', encoding='utf-8') as f:
        for line in f:
            entries.append(json.loads(line))
    return entries

# Load Phase 1
phase1 = load_phase('phase1_foundation.jsonl')
```

### Dynamic Chunking for Training

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your-model")
max_length = 32768  # 32K tokens

def chunk_entry(entry, tokenizer, max_length):
    """Chunk a complete entry for training."""
    text = entry['training_content']
    
    # Tokenize
    tokens = tokenizer(text, truncation=False, return_tensors='pt')
    
    # Split into chunks if needed
    chunks = []
    token_ids = tokens['input_ids'][0]
    
    for i in range(0, len(token_ids), max_length):
        chunk = token_ids[i:i + max_length]
        chunks.append(chunk)
    
    return chunks

# Process entries
for entry in phase1:
    chunks = chunk_entry(entry, tokenizer, max_length)
    for chunk in chunks:
        # Use chunk for training
        pass
```

### Recommended Training Schedule

```python
# Phase 1: Code Foundation (2 epochs)
train(phase1_foundation, epochs=2, lr=1e-5)

# Phase 2: Evolution Patterns (2-3 epochs)
train(phase2_evolution, epochs=3, lr=8e-6)

# Phase 3: PR Mastery (3-4 epochs)
train(phase3_pr_mastery, epochs=4, lr=5e-6)
```

## 🎓 Curriculum Learning Benefits

- **Progressive complexity**: Start simple, increase difficulty
- **Better convergence**: 25-40% improvement over random training
- **Domain adaptation**: Learn repository-specific patterns
- **Code understanding**: Syntax → Changes → Collaboration
- **Efficient training**: Focused learning objectives per phase

## 📝 Technical Details

### Repository
- **Source**: [Hyperswitch](https://github.com/juspay/hyperswitch)
- **Language**: Primarily Rust
- **Domain**: Payment processing, financial technology
- **Components**: Connectors, API models, routing logic, state machines

### Data Collection
- **Files**: Pattern-based extraction (Rust, TOML, YAML, JSON, Markdown)
- **Commits**: Full git history from repository inception
- **PRs**: Merged and closed PRs with reviews and comments via GitHub API
- **Tests**: Automatic pairing of test files with implementations


## 🔧 Sequence Length Flexibility

This unbroken dataset works with any sequence length:

| Sequence Length | Use Case | Chunking Strategy |
|----------------|----------|-------------------|
| 8K tokens | Base models | Chunk with overlap |
| 16K tokens | Extended context | Fewer chunks needed |
| 32K tokens | Long context models | Most files fit whole |
| 64K+ tokens | Ultra-long context | Complete commits/PRs |


## 🙏 Acknowledgments

- **Hyperswitch Team** at Juspay for the amazing open-source payment processing platform
- Dataset curated and organized by **Aditya Narayan**
- Dataset generated using custom extraction pipeline with curriculum organization

## 📧 Contact & Citation

If you use this dataset, please cite:

```bibtex
@dataset{hyperswitch_curriculum2025,
  title = {AdityaNarayan/HS-Repo-Curriculum-Learning},
  author = {Aditya Narayan},
  year = {2025},
  url = {https://huggingface.co/datasets/AdityaNarayan/HS-Repo-Curriculum-Learning},
  publisher = {HuggingFace},
  note = {Dataset derived from Hyperswitch repository}
}
```