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Synaptics/LiquidAI-LFM2.5-230M

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LFM2.5-230M — Torq build (Synaptics SL2610-Series Torq NPU)

<img src="synaptics-logo-full-color.png" alt="Synaptics" width="600"/></img>

This repository provides compiled model files for LiquidAI's LFM2.5 230M text language model, ready to run on the Synaptics SL2610-series Torq NPU. The model can act as a skill-selection layer, taking natural-language instruction and decompose it into a sequence of tool calls.

Quick start guide:

  • —Buy a Machina kit: Get an SL2600 Machina kit delivered to you
  • —Torq Examples: Use Torq-examples LiquidAI/LFM2.5-230M scripts to download and deploy on your Machina kit

<img src="astrasl2600machina.png" alt="SL2600 Machina kit" width="400"/></img>

Model Overview

LFM2.5-230M is a general-purpose text-only model. It is a hybrid architecture that combines short convolutions with grouped-query attention.

The transformer runs on the Torq NPU in bf16; the token embeddings run on the host CPU.

Model Features

Contents

FileSizeRole
model.vmfb461 MBmonolithic build — decoder + LM head in one graph (logits output)
body.vmfb327 MBsplit build — decoder body only, emits hidden states (pairs with lm_head.vmfb)
lm_head.vmfb134 MBsplit build — standalone LM head (hidden → 65 536 logits)
token_embeddings.npy134 MBCPU embedding lookup table (bf16)
config.json—model configuration
tokenizer.json, tokenizer_config.json—tokenizer + tokenizer config
onnx/model.onnx (+ model.onnx_data)~952 MBreference 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.

Tested Platforms

Metrics

PlatformModel / StageEnvironmentNPU ClockInference TimeInfer / s
SL2619 2GBLFM2.5-230MTorq v2.0.01 GHzTBD7.1

Deployment

The models have been tested with the following environment.

  • —Torq Compiler: v2.0.0
  • —Torq Runtime: v2.0.0 included in Astra SDK release scarthgap_6.12_v2.4.0

Usage Tutorials / Example Apps

A usage example is provided in the Torq Examples / LiquidAI-LFM2.5-230M.

Check out the README for instructions.

Usage Notes:

  • —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 & attribution

This repository is a redistribution of a model created by Liquid AI, Inc., licensed under the LFM Open License v1.0. Copies of the license and the attribution notices are included alongside the model files:

  • —[LICENSE](https://huggingface.co/Synaptics/LiquidAI-LFM2.5-230M/blob/main/LICENSE) — a verbatim copy of the LFM Open License v1.0.
  • —[NOTICE](https://huggingface.co/Synaptics/LiquidAI-LFM2.5-230M/blob/main/NOTICE) — the copyright, patent, trademark, and attribution notices retained from the original Work (per Section 4(c) of the license).

Original model: [LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) · Copyright © Liquid AI, Inc.

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