LFM2-350M (monolithic export)
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.
Original model
Exported from LiquidAI/LFM2-350M. Weights are unmodified; this repo packages the same parameters into
loom.cpp's GGUF format.
License
LFM Open License v1.0 -- inherited from the base model above.
Language(s)
en, ar, zh, fr, de, ja, ko, es
Usage
Run it with loom-py -- loom-py-rt on PyPI:
pip install -U "loom-py-rt[hub]"
import loom
model = loom.Model.from_pretrained("loom-ai-org/lfm2-350m-monolithic-loom")
print(model.text2text.chat("Who discovered Brazil?", max_new_tokens=256))
# The same model, one turn at a time, and with the decode rule named rather than inherited:
print(model.chat([("user", "Name one river in Brazil.")], temperature=0.0))
The layer underneath
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.
Files
lfm2-350m-monolithic.gguf-- the model, exported with loom-exporter.