---
base_model: Kwaipilot/KAT-Dev
tags:
- rust
- Hyperswitch
- LoRA
- CPT
- Fine-Tuned
- Causal-LM
pipeline_tag: text-generation
language:
- en
datasets:
- AdityaNarayan/HyperSwitch-Repo-CPT-Dataset
---
# Kwaipilot-KAT-Dev-CPT-LoRA-Adapter-HyperSwitch

A LoRA fine-tuned model based on **Kwaipilot/KAT-Dev** specialized for the [Hyperswitch](https://github.com/juspay/hyperswitch) Rust codebase. This model excels at understanding payment processing patterns, Hyperswitch architecture, and Rust development practices.

## 🎯 Model Description

This LoRA adapter was trained on **16,731 samples** extracted from the Hyperswitch codebase to enhance code understanding, explanation, and generation within the payment processing domain.

- **Base Model**: Kwaipilot/KAT-Dev
- **Training Type**: Causal Language Modeling (CLM) with LoRA
- **Domain**: Payment Processing, Rust Development
- **Specialization**: Hyperswitch codebase patterns and architecture

## 🏆 RustEvo Benchmark

<p float="center">
  <img src="https://huggingface.co/AdityaNarayan/Kwaipilot-KAT-Dev-CPT-LoRA-Adapter-HyperSwitch/resolve/main/bench_asset/rustEvo_bench_chart.png" width="600" />
  <img src="https://huggingface.co/AdityaNarayan/Kwaipilot-KAT-Dev-CPT-LoRA-Adapter-HyperSwitch/resolve/main/bench_asset/rustEvo_bench_table.png" width="600" />
</p>



RustEvo Github Repo : https://github.com/SYSUSELab/RustEvo

## 📊 Training Details

### Dataset Composition
- **Total Samples**: 16,731
  - **File-level samples**: 2,120 complete files
  - **Granular samples**: 14,611 extracted components
    - Functions: 4,121
    - Structs: 5,710  
    - Traits: 223
    - Implementations: 4,296
    - Modules: 261

### LoRA Configuration
```yaml
r: 64                   # LoRA rank
alpha: 128              # LoRA alpha (2*r)
dropout: 0.05           # LoRA dropout
target_modules:         # Applied to all linear layers
  - q_proj, k_proj, v_proj, o_proj
  - gate_proj, up_proj, down_proj
```

### Training Hyperparameters
- **Epochs**: 5
- **Batch Size**: 2 per device (16 effective with gradient accumulation)
- **Learning Rate**: 5e-5 (cosine schedule)
- **Max Context**: 8,192 tokens
- **Hardware**: 2x NVIDIA H200 (80GB each)
- **Training Time**: ~4 hours (2,355 steps)

### Training Results
```
Final Loss: 0.48 (from 1.63)
Perplexity: 1.59 (from 5.12)  
Accuracy: 89% (from 61%)
```

## 🚀 Usage

### Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Kwaipilot/KAT-Dev",
    dtype=torch.bfloat16,
    device_map="auto"
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Kwaipilot/KAT-Dev")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "AdityaNarayan/Kwaipilot-KAT-Dev-CPT-LoRA-Adapter-HyperSwitch")
# Generate code
prompt = """// Hyperswitch payment processing
pub fn validate_payment_method("""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.2,  # Lower temperature for code generation
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Recommended Settings
- **Temperature**: 0.2-0.3 for code generation
- **Temperature**: 0.5-0.7 for explanations and documentation
- **Max tokens**: 1024 for most tasks

## 🛠️ Technical Specifications

- **Context Window**: 8,192 tokens
- **Precision**: bfloat16
- **Memory Usage**: ~78GB VRAM (32B base model)
- **Inference Speed**: Optimized with Flash Attention 2



## 🙏 Acknowledgments

- **Kwaipilot Team** for the excellent KAT-Dev base model
- **Hyperswitch Team** for the open-source payment processing platform
- **Hugging Face** for the transformers and PEFT libraries

## 📞 Citation

```bibtex
@misc{hyperswitch-kat-dev-lora-2024,
  title={Kwaipilot-KAT-Dev-CPT-LoRA-Adapter-HyperSwitch},
  author={Aditya Narayan},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/AdityaNarayan/Kwaipilot-KAT-Dev-CPT-LoRA-Adapter-HyperSwitch}
}
```