Synaptics/liquidAI-LFM2p5-230M-LLM

LFM2.5-230M — Torq build (Synaptics SL2619 NPU)

Pre-compiled Torq VMFB build of LiquidAI's LFM2.5 230M text language model, ready to run on the Synaptics SL2619 edge NPU. LFM2 is a hybrid architecture that combines short convolutions with grouped-query attention. The transformer runs on the NPU in bf16; the token embeddings run on the host CPU.

Contents

File	Size	Role
model.vmfb	461 MB	monolithic build — decoder + LM head in one graph (logits output)
body.vmfb	327 MB	split build — decoder body only, emits hidden states (pairs with lm_head.vmfb)
lm_head.vmfb	134 MB	split build — standalone LM head (hidden → 65 536 logits)
token_embeddings.npy	134 MB	CPU embedding lookup table (bf16)
config.json	—	model configuration
tokenizer.json, tokenizer_config.json	—	tokenizer + tokenizer config
onnx/model.onnx (+ model.onnx_data)	~952 MB	reference ONNX export for non-Torq runtimes (e.g. onnxruntime)

Monolithic vs. split

Two equivalent ways to run the model (same weights — body 327 MB + lm_head 134 MB ≈ the 461 MB monolithic build):

• model.vmfb (monolithic): one graph that outputs logits directly. Simplest to run.
• body.vmfb + lm_head.vmfb (split): the decoder body emits hidden states and the LM head is applied only when sampling. Prefill tokens then skip the large [1024 → 65 536] LM-head projection, which lowers time-to-first-token — pick this when TTFT matters.

The onnx/ export is provided for reference / portability to other runtimes.

Model details

• Architecture: LFM2 (Lfm2ForCausalLM) — hybrid short-convolution + grouped-query attention.
• Hidden size: 1024 · Layers: 14 · Attention heads: 16 (8 KV heads, GQA) · Intermediate size: 2560.
• Vocabulary: 65 536 · Context length: up to 128 k.
• Precision: bf16 on the NPU.
• Target: Synaptics SL2619, compiled with the Torq compiler.

Quick start

Runs on the Synaptics Torq runtime via synaptics-torq/torq-examples. Place the model files in a directory and invoke the Torq LLM runner with either model.vmfb (monolithic) or body.vmfb + lm_head.vmfb (split, lower TTFT), alongside token_embeddings.npy, config.json, and tokenizer.json.

License

Derived from LiquidAI's LFM2.5 230M and distributed under LiquidAI's LFM Open License v1.0 — see LiquidAI on Hugging Face for the full terms.