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README.md

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+---
+tags:
+- sentence-transformers
+- embeddings
+- litert
+- tflite
+- edge
+- on-device
+license: mit
+base_model: intfloat/multilingual-e5-small
+pipeline_tag: feature-extraction
+---
+
+# multilingual-e5-small - LiteRT
+
+This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) conversion of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) for efficient on-device inference.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Original Model** | [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) |
+| **Format** | LiteRT (.tflite) |
+| **File Size** | 449.0 MB |
+| **Task** | Multilingual Sentence Embeddings (100 languages) |
+| **Max Sequence Length** | 512 |
+| **Output Dimension** | 384 |
+| **Pooling Mode** | Mean Pooling |
+
+## Performance
+
+Benchmarked on AMD CPU (WSL2):
+
+| Metric | Value |
+|--------|-------|
+| **Inference Latency** | 91.9 ms |
+| **Throughput** | 10.9 tokens/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="intfloat_multilingual-e5-small.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-small")
+
+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
+
+- `intfloat_multilingual-e5-small.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/intfloat/multilingual-e5-small))
+
+## Citation
+
+```bibtex
+@article{wang2024multilingual,
+    title={Multilingual E5 Text Embeddings: A Technical Report},
+    author={Wang, Liang and Yang, Nan and Huang, Xiaolong and others},
+    journal={arXiv preprint arXiv:2402.05672},
+    year={2024}
+}
+```
+
+## Acknowledgments
+
+- Original model by [intfloat](https://huggingface.co/intfloat)
+- Conversion using [ai-edge-torch](https://github.com/google-ai-edge/ai-edge-torch)
+
+---
+
+*Converted by [Bombek1](https://huggingface.co/Bombek1)*