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82a09fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # Hyperswitch Token-Aware CPT Dataset
This dataset contains **1,076 samples** of Rust code from the [Hyperswitch](https://github.com/juspay/hyperswitch) payment router project, optimized for Continued Pre-Training (CPT) with the **Kwaipilot/KAT-Dev** tokenizer.
## Dataset Statistics
- **Total Samples**: 1,076
- **Total Tokens**: 5,687,255
- **Mean Tokens per Sample**: 5,285
- **Token Range**: 2,001 - 15,609
### Token Distribution
- **< 4k tokens**: 38.1% of samples
- **4k-10k tokens**: 52.0% of samples
- **10k+ tokens**: 9.9% of samples
### Granularity Types
- **file**: 721 samples (single large files)
- **module**: 180 samples (multiple files from same module)
- **combined_files**: 160 samples (small files combined by crate)
- **crate**: 15 samples (entire small crates)
## Top Crates
1. **router** - 371 samples
2. **hyperswitch_connectors** - 336 samples
3. **analytics** - 54 samples
4. **diesel_models** - 39 samples
5. **api_models** - 28 samples
## Sample Structure
Each sample contains:
- `id`: Unique identifier
- `type`: Sample type (always "clm" for causal language modeling)
- `granularity`: Level of code organization (file/module/combined_files/crate)
- `content`: Full code with path metadata in format:
```
<path>
Repository: hyperswitch
Crate: [crate_name]
File: [file_path]
Tokens: [token_count]
</path>
<file>
[actual code content]
</file>
```
- `metadata`: Contains crate, file info, and token count
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("archit11/hyperswitch-token-aware-cpt")
# Access samples
sample = dataset['train'][0]
print(f"Tokens: {sample['metadata']['token_count']:,}")
print(f"Crate: {sample['metadata']['crate']}")
print(f"Granularity: {sample['granularity']}")
# Filter by token count
medium_samples = dataset['train'].filter(
lambda x: 4000 <= x['metadata']['token_count'] < 10000
)
# Filter by crate
router_samples = dataset['train'].filter(
lambda x: x['metadata']['crate'] == 'router'
)
```
## Training Recommendations
- **Context Length**: 16k tokens (max sample is 15,609 tokens)
- **Tokenizer**: Kwaipilot/KAT-Dev
- **Suggested Batch Size**: 1-2 samples per batch (due to large context)
- **Format**: Samples are pre-formatted with `<path>` and `<file>` tags
## Source
- **Repository**: https://github.com/juspay/hyperswitch
- **Language**: Rust
- **License**: Apache 2.0
## Generation Method
Samples were generated using token-aware strategies:
1. **Large files** (2k-16k tokens) included as-is
2. **Small files** combined within same crate until reaching 2k+ tokens
3. **Module clusters** grouped by directory structure
4. **Complete crates** for small crates that fit within context
All token counts measured using the Kwaipilot/KAT-Dev tokenizer.
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