---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: peft
license: mit
tags:
- lora
- peft
- scientific
- research
- academic
- domain-adaptation
- sentence-embeddings
language:
- en
---

# Scientific LoRA Adapter for DomainEmbedder-v2.6

Domain-specific LoRA adapter for scientific/research text embeddings.

## Model Details

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

## Training Data

Trained on 40,000 scientific text pairs from:
- arXiv (document-level)
- arXiv (section-level)
- PubMed Artificial
- Scientific Papers

**Note**: 87.3% real data + 12.7% augmented data (scientific domain had fewer available pairs)

## Training Configuration

| Parameter | Value |
|-----------|-------|
| Epochs | 3 |
| Batch Size | 32 |
| Learning Rate | 2e-4 |
| Loss | Contrastive (InfoNCE) |
| Best Val Loss | 0.0016 |

## Usage

This adapter is part of the DomainEmbedder-v2.6 system. It is selected automatically by the RL policy when scientific 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 scientific LoRA
scientific_model = PeftModel.from_pretrained(base_encoder, 'path/to/scientific_lora')
```

## Author

**Zain Asad**

## License

MIT License

## Framework Versions

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