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kontextdev/
embeddinggemma-300m-litertlm

Name: embeddinggemma-300m-litertlm
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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 runtime. This format is optimized for on-device inference on mobile and edge devices.

Model Details

Property	Value
Base Model	google/embeddinggemma-300m
Source TFLite	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 to the LiteRT-LM .litertlm bundle format using Google's official tooling:

Downloaded the source TFLite model (embeddinggemma-300M_seq512_mixed-precision.tflite)
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"

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:

#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

Tokenization: Currently uses a simplified character-based tokenizer. For best results, integrate with SentencePiece using the Gemma tokenizer vocabulary.
Model Inference: The current wrapper uses placeholder inference. Full TFLite inference integration requires linking against the LiteRT C API.
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 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}
}

kontextdev/embeddinggemma-300m-litertlm