Lalamo
JAX infrastructure for model optimisation
Install / Use
/learn @trymirai/LalamoREADME
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lalamo
A set of tools for adapting Large Language Models to on-device inference using the uzu inference engine.
Quick Start
To get the list of supported models, run:
uv run lalamo list-models
To convert a model, run:
uv run lalamo convert MODEL_REPO
Note: on some CPU platform you may be getting an error saying The precision 'F16_F16_F32' is not supported by dot_general on CPU. This is due to a bug in XLA, which causes matmuls inside jax.jit not work correctly on CPUs. The workaround is to set the environment variable JAX_DISABLE_JIT=1 when running the conversion.
After that, you can find the converted model in the models folder. For more options see uv run lalamo convert --help.
Model Support
To add support for a new model, write the corresponding ModelSpec, as shown in the example below:
ModelSpec(
vendor="Google",
family="Gemma-3",
name="Gemma-3-1B-Instruct",
size="1B",
quantization=None,
repo="google/gemma-3-1b-it",
config_type=HFGemma3TextConfig,
weights_type=WeightsType.SAFETENSORS,
)
Optional Features
PyAudio
PyAudio enables audio playback for TTS models and is used as an optional Lalamo feature because it requires PortAudio to be installed.
How to:
- macOS:
brew install portaudio(formula) - Debian/Ubuntu:
apt-get install portaudio19-dev python-all-dev - Other Linux: PortAudio build instructions
Then run :
uv run --with pyaudio lalamo path/to/model --replay
