---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: peft
license: mit
tags:
- lora
- peft
- code
- programming
- software
- domain-adaptation
- sentence-embeddings
language:
- en
---

# Code LoRA Adapter for DomainEmbedder-v2.6

Domain-specific LoRA adapter for code/programming text embeddings.

## Model Details

| Property | Value |
|----------|-------|
| **Base Model** | sentence-transformers/all-MiniLM-L6-v2 |
| **Parent System** | DomainEmbedder-v2.6 |
| **Domain** | Code / Programming |
| **LoRA Rank** | 16 |
| **LoRA Alpha** | 32 |
| **Target Modules** | query, value |
| **Trainable Params** | 147,456 (0.645%) |

## Training Data

Trained on 40,000 code-related text pairs from:
- Code Alpaca
- MBPP (Mostly Basic Python Problems)
- Code Contests
- Python Instructions

## Training Configuration

| Parameter | Value |
|-----------|-------|
| Epochs | 3 |
| Batch Size | 32 |
| Learning Rate | 2e-4 |
| Loss | Contrastive (InfoNCE) |
| Best Val Loss | 0.0039 |

## Usage

This adapter is part of the DomainEmbedder-v2.6 system. It is selected automatically by the RL policy when code-related content is detected.

```python
from peft import PeftModel
from transformers import AutoModel

# Load base encoder
base_encoder = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

# Apply code LoRA
code_model = PeftModel.from_pretrained(base_encoder, 'path/to/code_lora')
```

## Author

**Zain Asad**

## License

MIT License

## Framework Versions

- PEFT 0.18.1
- Transformers 4.x
- PyTorch 2.x