| --- |
| license: other |
| license_name: lfm-open-license-v1.0 |
| license_link: LICENSE |
| tags: |
| - torq |
| - synaptics |
| - sl2619 |
| - npu |
| - edge |
| - lfm2 |
| - text-generation |
| base_model: |
| - LiquidAI/LFM2.5-230M |
| pipeline_tag: text-generation |
| --- |
| |
| # 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](https://www.synaptics.com/products/embedded-processors/sl2610-product-line#devKit): Get an SL2600 Machina kit delivered to you |
| - [Torq Examples](https://synaptics-torq.github.io/torq-compiler/v/latest/): Use Torq-examples LiquidAI/LFM2.5-230M scripts to download and deploy on your Machina kit |
|
|
| <img src="astra_sl2600_machina.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 |
|
|
| | 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. |
| |
| ## Tested Platforms |
| |
| - [Synaptics Astra™ Machina SL2619 2GB](https://www.synaptics.com/products/embedded-processors/astra-machina-foundation-series) |
| |
| ## Metrics |
| |
| | Platform | Model / Stage | Environment | NPU Clock | Inference Time | Infer / s | |
| | --------- | --------- | --------- | --------- | --------- | --------- | |
| | SL2619 2GB | LFM2.5-230M | Torq v2.0.0 | 1 GHz | TBD | 7.1 | |
| |
| |
| ## 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](https://github.com/synaptics-torq/torq-examples/tree/main/LiquidAI/LiquidAI-LFM2.5-230M). |
| |
| Check out the [README](https://github.com/synaptics-torq/torq-examples/blob/main/LiquidAI/LiquidAI-LFM2.5-230M/README.md) 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. |
| |
| ## Learn More |
| |
| - [Synaptics AI Developer Zone](https://developer.synaptics.com?utm_source=hf): Get started with documentation, tutorials and resources for your Edge AI journey. |
| - [Torq Compiler Documentation](https://synaptics-torq.github.io/torq-compiler/v/latest/): Learn more about the Torq compiler based on MLIR and IREE. |
| - [Synaptics Astra SDK](https://synaptics-astra.github.io/doc/v/latest/): Learn more about the Yocto Project-based Linux software available for Astra SL processors. |
| - [Astra Support Portal](https://synacsm.atlassian.net/servicedesk/customer/portal/543?utm_source=hf): Connect with our engineering team and community. |
| |
