litert-tunner 0.3.0

LiteRT tuner Python library.

litert-tunner

Fine-tune fully-quantized INT8 LiteRT (TFLite) models after export — without retraining from scratch.

What is this?

litert-tunner parses an exported INT8 .tflite model, reconstructs it as a Keras 3 model with simulated quantization, lets you fine-tune parameters (biases, scales, weights), and writes them back into the flatbuffer. Graph topology stays intact.

Note: Even though this library simulates the quantization process during fine-tuning, in some cases the resulting INT8 model may still perform worse than a full-precision float32 model.

Installation

From source (recommended for development)

# Clone the repository
git clone https://github.com/kmkolasinski/litert-tunner.git
cd litert-tunner

# Create a virtual environment and install uv
make venv
make init

# Install the project in editable mode with dev dependencies
make install

As a dependency (pip)

pip install litert-tunner

(Core dependencies: keras>=3.0, numpy, tflite>=2.18.0, flatbuffers. tensorflow and ai-edge-litert are only needed for dev/tests).

Quickstart

For a complete, runnable end-to-end example, see the Example Notebook.

import litert_tunner

# 1. Load an INT8 LiteRT model → trainable Keras replica
model = litert_tunner.load_model("model_int8.tflite")

# 2. Inference — should match LiteRT Interpreter output
predictions = model.predict(inputs)

# 3. Prepare for fine-tuning (freeze everything except biases & scales)
litert_tunner.prepare_for_finetuning(model, trainable_pattern=".*bias")

# 4. Fine-tune with any Keras optimizer / loss
model.compile(optimizer="adam", loss="mse")
model.fit(x_train, y_train, epochs=5)

# 5. Export — writes updated parameters back into the flatbuffer
litert_tunner.save_model(model, "model_int8_finetuned.tflite")

Distillation Fine-Tuning (Recommended)

import litert_tunner

# 1. Load an INT8 LiteRT model → trainable Keras replica
student_model = litert_tunner.load_model("model_int8.tflite")
teacher_model = litert_tunner.load_model("model_float32.tflite")

# 2. Freeze everything except biases
litert_tunner.prepare_for_finetuning(student_model, trainable_pattern=".*bias")

# 3. Fine-tune using Trainer (handles distillation & weight drift)
trainer = litert_tunner.Trainer(
    student_model=student_model,
    teacher_model=teacher_model,  # Original float32 model
)
trainer.compile(optimizer="adam", loss="mse")
trainer.fit(train_ds, validation_data=val_ds, epochs=5)

# 4. Save updated parameters to flatbuffer
litert_tunner.save_model(student_model, "model_int8_finetuned.tflite")

Supported Operations

• Linear: FULLY_CONNECTED, CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV
• Arithmetic: ADD, SUB, MUL, DIV, SQUARED_DIFFERENCE, NEG
• Activation: RELU, GELU, LOGISTIC, SOFTMAX
• Pooling: AVERAGE_POOL_2D, MAX_POOL_2D, MEAN
• Reshape/Resize: RESHAPE, TRANSPOSE, PACK, STRIDED_SLICE, RESIZE_NEAREST_NEIGHBOR, SHAPE, EXPAND_DIMS, PAD, TILE
• Other: CONCATENATION, RSQRT, QUANTIZE, DEQUANTIZE

Fused activations (RELU, RELU6, RELU_N1_TO_1) are supported.

License

MIT — Copyright © 2026 Krzysztof Kolasinski