Liquid AI's LFM2-350M hybrid conv/attention LM, exported as a single fused graph.

This is a loom.cpp export: a single self-describing GGUF that carries its own graph topologies, tokenizer (if any) and driver script, produced by loom-exporter.

Exported from LiquidAI/LFM2-350M. Weights are unmodified; this repo packages the same parameters into loom.cpp's GGUF format.

LFM Open License v1.0 -- inherited from the base model above.

en, ar, zh, fr, de, ja, ko, es

Run it with loom-py -- loom-py-rt on PyPI:

The call above is the high-level door: one per task, named for the modality pair it maps between, with the windowing, sampling and assembly this model needs already applied. Under it, model.infer(...) passes your arguments straight to the driver this GGUF embeds -- which is where you go for a knob the door does not name.

model.driver_source prints that driver, including a header comment documenting every argument it accepts for this model, and is the authority on it. See loom-py for the API and loom.cpp for what the engine does between the two.