---
tags:
- sentence-transformers
- embeddings
- litert
- tflite
- edge
- on-device
license: mit
base_model: BAAI/bge-small-en-v1.5
pipeline_tag: feature-extraction
---

# bge-small-en-v1.5 - LiteRT

This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) conversion of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) for efficient on-device inference.

## Model Details

| Property | Value |
|----------|-------|
| **Original Model** | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
| **Format** | LiteRT (.tflite) |
| **File Size** | 127.2 MB |
| **Task** | Sentence Embeddings / Retrieval |
| **Max Sequence Length** | 512 |
| **Output Dimension** | 384 |
| **Pooling Mode** | CLS Token Pooling |

## Performance

Benchmarked on AMD CPU (WSL2):

| Metric | Value |
|--------|-------|
| **Inference Latency** | 100.2 ms |
| **Throughput** | 10.0/sec |
| **Cosine Similarity vs Original** | 1.0000 ✅ |

## Quick Start

```python
import numpy as np
from ai_edge_litert.interpreter import Interpreter
from transformers import AutoTokenizer

# Load model and tokenizer
interpreter = Interpreter(model_path="BAAI_bge-small-en-v1.5.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5")

def get_embedding(text: str) -> np.ndarray:
    """Get sentence embedding for input text."""
    encoded = tokenizer(
        text,
        padding="max_length",
        max_length=512,
        truncation=True,
        return_tensors="np"
    )

    interpreter.set_tensor(input_details[0]["index"], encoded["input_ids"].astype(np.int64))
    interpreter.set_tensor(input_details[1]["index"], encoded["attention_mask"].astype(np.int64))
    interpreter.invoke()

    return interpreter.get_tensor(output_details[0]["index"])[0]

# Example
embedding = get_embedding("Hello, world!")
print(f"Embedding shape: {embedding.shape}")  # (384,)
```

## Files

- `BAAI_bge-small-en-v1.5.tflite` - The LiteRT model file

## Conversion Details

- **Conversion Tool**: [ai-edge-torch](https://github.com/google-ai-edge/ai-edge-torch)
- **Conversion Date**: 2026-01-12
- **Source Framework**: PyTorch → LiteRT
- **Validation**: Cosine similarity 1.0000 vs original

## Intended Use

- **Mobile Applications**: On-device semantic search, RAG systems
- **Edge Devices**: IoT, embedded systems, Raspberry Pi
- **Offline Processing**: Privacy-preserving inference
- **Low-latency Applications**: Real-time processing

## Limitations

- Fixed sequence length (512 tokens)
- CPU inference (GPU delegate requires setup)
- Tokenizer loaded separately from original model
- Float32 precision

## License

This model inherits the license from the original:
- **License**: MIT ([source](https://huggingface.co/BAAI/bge-small-en-v1.5))

## Citation

```bibtex
@misc{bge_embedding,
    title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
    author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
    year={2023},
    eprint={2309.07597},
    archivePrefix={arXiv},
}
```

## Acknowledgments

- Original model by [BAAI](https://huggingface.co/BAAI)
- Conversion using [ai-edge-torch](https://github.com/google-ai-edge/ai-edge-torch)

---

*Converted by [Bombek1](https://huggingface.co/Bombek1)*