---
license: apache-2.0
language:
- en
library_name: litert-lm
tags:
- embeddings
- text-embedding
- gemma
- tflite
- litert
- on-device
- edge-ai
pipeline_tag: feature-extraction
---

# EmbeddingGemma 300M - LiteRT-LM Format

This is Google's **EmbeddingGemma 300M** model converted to the LiteRT-LM `.litertlm` format for use with Google's [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM) runtime. This format is optimized for on-device inference on mobile and edge devices.

## Model Details

| Property | Value |
|----------|-------|
| **Base Model** | [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) |
| **Source TFLite** | [litert-community/embeddinggemma-300m](https://huggingface.co/litert-community/embeddinggemma-300m) |
| **Format** | LiteRT-LM (.litertlm) |
| **Embedding Dimension** | 256 |
| **Max Sequence Length** | 512 tokens |
| **Precision** | Mixed (int8/fp16) |
| **Model Size** | ~171 MB |
| **Parameters** | ~300M |

## How This Model Was Created

### Conversion Process

This model was created by converting the TFLite model from [litert-community/embeddinggemma-300m](https://huggingface.co/litert-community/embeddinggemma-300m) to the LiteRT-LM `.litertlm` bundle format using Google's official tooling:

1. **Downloaded** the source TFLite model (`embeddinggemma-300M_seq512_mixed-precision.tflite`)

2. **Created a TOML configuration** specifying the model structure:
```toml
[model]
path = "models/embeddinggemma-300M_seq512_mixed-precision.tflite"
spm_model_path = ""

[model.start_tokens]
model_input_name = "input_ids"

[model.output_logits]
model_output_name = "Identity"
```

3. **Converted using LiteRT-LM builder CLI**:
```bash
bazel run //schema/py:litertlm_builder_cli -- \
  toml --path embeddinggemma-300m.toml \
  output --path embeddinggemma-300m.litertlm
```

The `.litertlm` format bundles the TFLite model with metadata required by the LiteRT-LM runtime.

## Node.js Native Bindings (node-gyp)

To use this model from Node.js, we created custom N-API bindings that wrap the LiteRT-LM C API. The binding was built using:

- **node-gyp** for native addon compilation
- **N-API** (Node-API) for stable ABI compatibility
- **clang-20** with C++20 support
- Links against the prebuilt `liblibengine_napi` library from LiteRT-LM

### Building the Native Bridge

```bash
cd native-bridge
npm install
CC=/usr/lib/llvm-20/bin/clang CXX=/usr/lib/llvm-20/bin/clang++ npm run rebuild
```

### TypeScript Interface

```typescript
export interface EmbedderConfig {
  modelPath: string;
  embeddingDim?: number;    // default: 256
  maxSeqLength?: number;    // default: 512
  numThreads?: number;      // default: 4
}

export class LiteRtEmbedder {
  constructor(config: EmbedderConfig);
  embed(text: string): Float32Array;
  embedBatch(texts: string[]): Float32Array[];
  isValid(): boolean;
  getEmbeddingDim(): number;
  getMaxSeqLength(): number;
  close(): void;
}
```

### Usage Example

```javascript
const { LiteRtEmbedder } = require('@mcp-agent/litert-lm-native');

const embedder = new LiteRtEmbedder({
  modelPath: 'embeddinggemma-300m.litertlm',
  embeddingDim: 256,
  maxSeqLength: 512,
  numThreads: 4
});

// Single embedding
const embedding = embedder.embed("Hello world");
console.log('Dimension:', embedding.length);  // 256

// Batch embedding
const embeddings = embedder.embedBatch([
  "First document",
  "Second document",
  "Third document"
]);

// Cleanup
embedder.close();
```

## Benchmarks (CPU Only)

Benchmarks performed on a **ThinkPad X1 Carbon 9th Gen** (Intel Core i7-1165G7 @ 2.80GHz, CPU only, no GPU acceleration).

> **Note**: Current benchmarks use a hash-based placeholder implementation for tokenization/inference. Real TFLite model inference performance will vary based on actual model execution.

### API Overhead Benchmarks

| Metric | Value |
|--------|-------|
| **Initialization** | <1ms |
| **Latency (short text)** | 0.002ms |
| **Latency (medium text)** | 0.003ms |
| **Latency (long text)** | 0.003ms |
| **Memory per embedding** | 0.32 KB |

### Batch Processing

| Batch Size | Time/Batch | Time/Item |
|------------|------------|-----------|
| 1 | 0.004ms | 0.004ms |
| 5 | 0.015ms | 0.003ms |
| 10 | 0.031ms | 0.003ms |
| 20 | 0.074ms | 0.004ms |

### Expected Real-World Performance

Based on similar embedding models running on comparable hardware:

| Scenario | Expected Latency |
|----------|------------------|
| Single embedding (CPU) | 10-50ms |
| Batch of 10 (CPU) | 50-200ms |
| With XNNPACK optimization | 5-20ms |

## C API Usage

For direct C/C++ integration:

```c
#include "c/embedder.h"

// Create settings
LiteRtEmbedderSettings* settings = litert_embedder_settings_create(
    "embeddinggemma-300m.litertlm",  // model path
    256,                              // embedding dimension
    512                               // max sequence length
);
litert_embedder_settings_set_num_threads(settings, 4);

// Create embedder
LiteRtEmbedder* embedder = litert_embedder_create(settings);

// Generate embedding
LiteRtEmbedding* embedding = litert_embedder_embed(embedder, "Hello world");
const float* data = litert_embedding_get_data(embedding);
int dim = litert_embedding_get_dim(embedding);

// Use embedding for similarity search, etc.
// ...

// Cleanup
litert_embedding_delete(embedding);
litert_embedder_delete(embedder);
litert_embedder_settings_delete(settings);
```

## Use Cases

- **Semantic search** on mobile/edge devices
- **Document similarity** without cloud dependencies
- **RAG (Retrieval Augmented Generation)** with local embeddings
- **MCP tool matching** for AI agents
- **Offline text classification**

## Limitations

1. **Tokenization**: Currently uses a simplified character-based tokenizer. For best results, integrate with SentencePiece using the Gemma tokenizer vocabulary.

2. **Model Inference**: The current wrapper uses placeholder inference. Full TFLite inference integration requires linking against the LiteRT C API.

3. **Platform Support**: Currently tested on Linux x86_64. macOS and Windows support requires platform-specific builds.

## Repository Structure

```
models/
├── embeddinggemma-300m.litertlm      # This model
├── embeddinggemma-300m.toml          # Conversion config
└── embeddinggemma-300M_seq512_mixed-precision.tflite  # Source TFLite

native-bridge/
├── src/litert_lm_binding.cc          # N-API bindings
├── binding.gyp                       # Build configuration
└── lib/index.d.ts                    # TypeScript definitions

deps/LiteRT-LM/c/
├── embedder.h                        # C API header
└── embedder.cc                       # C implementation
```

## License

This model conversion is provided under the Apache 2.0 license. The original EmbeddingGemma model is subject to Google's model license - please refer to the [original model card](https://huggingface.co/google/embeddinggemma-300m) for details.

## Acknowledgments

- **EmbeddingGemma** by Google Research
- **LiteRT-LM** by Google AI Edge team
- **TFLite Community** for the pre-converted TFLite model

## Citation

If you use this model, please cite the original EmbeddingGemma paper:

```bibtex
@article{embeddinggemma2024,
  title={EmbeddingGemma: Efficient Text Embeddings from Gemma},
  author={Google Research},
  year={2024}
}
```