---
license: other
license_name: lfm-open-license-v1.0
license_link: LICENSE
base_model: LiquidAI/LFM2.5-1.2B-JP
language:
  - ja
  - en
tags:
  - litert
  - litert-lm
  - litertlm
  - on-device
  - edge
  - hybrid
  - liquid
  - japanese
pipeline_tag: text-generation
library_name: litert-lm
---

# LFM2.5-1.2B-JP — LiteRT-LM

[LiquidAI/LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) converted to the **LiteRT-LM** (`.litertlm`) format for on-device inference with Google's [LiteRT-LM](https://github.com/google-ai-edge/litert-lm) runtime (requires **litert-lm ≥ 0.14** / a recent AI Edge Gallery). Sibling of [litert-community/LFM2.5-1.2B-Instruct](https://huggingface.co/litert-community/LFM2.5-1.2B-Instruct).

LFM2.5-1.2B-JP is the **Japanese-optimized chat variant** of Liquid AI's hybrid conv-attention flagship — a 1.2B model tuned for natural Japanese conversation that runs fully on-device at ~100 tok/s on a laptop CPU.

**日本語に最適化された 1.2B オンデバイスモデルです。** スマホの CPU だけで動作し、ネット接続なしで自然な日本語チャットができます。AI Edge Gallery にインポートするだけで試せます。

| File | Recipe | Size | GSM8K (n=100) |
|---|---|---|---|
| `LFM2.5-1.2B-JP_int8.litertlm` | int8 dynamic (linears + embedding; convs float) | 1.24 GB | **65%** (bf16 reference: 63%) |
| `LFM2.5-1.2B-JP_int4.litertlm` | int4 blockwise-32 + OCTAV linears, int8 embedding, convs float | 736 MB | 55% |

| | |
|---|---|
| **Context (KV cache)** | 4096 max |
| **Backend** | **CPU** (the hybrid conv graph is not supported by current GPU delegates) |
| **Template** | bundled — full chat template (tool-calling supported) |
| **Base model** | LiquidAI/LFM2.5-1.2B-JP (LFM Open License v1.0) |

## Quality

GSM8K (English, greedy, 0-shot CoT, max-tokens 1024, n=100, same harness for all rows): PyTorch bf16 63% · **LiteRT int8 65%** (full parity, +2pt within noise) · int4 55%. English math undersells a Japanese-optimized tune — it is reported for quantization-fidelity transparency, not as this model's headline; both files pass an 8-question sanity gate (7/8, zero degenerate) and Japanese conversation quality was verified by inspection. Note the int8 file quantizes **linears only** (convs stay float): quantizing this tune's convs costs ~9pt, unlike the Instruct sibling where conv-int8 is free.

## Usage

```bash
litert-lm run ./LFM2.5-1.2B-JP_int8.litertlm --prompt "海について一文で書いてください。"
```

## Run on Android

Install a recent [Google AI Edge Gallery](https://github.com/google-ai-edge/gallery), import this repo (or `adb push` a file and use local import: menu → Models → “+” → From local model file), select the **CPU** backend, and chat — in Japanese.

## Speed

Same architecture as the Instruct sibling: on a Mac M4 Max CPU the int8 file decodes at ~100 tok/s; on a Pixel 8a int8 decodes at ~19 tok/s and int4 at ~31 tok/s (int4 is ~1.7× faster on phone-class memory bandwidth as well as 41% smaller). First device load compiles the graph and can take about a minute; later loads are instant.

## Conversion notes

Converted with released [`litert-torch`](https://github.com/google-ai-edge/litert) 0.9.1 with the same exporter fix as the Instruct sibling: the stock LFM2 short-conv block saves its conv state from padded prefill columns, corrupting the first generated token of nearly every reply; the fix derives the chunk's valid length from the attention mask in-graph and gathers the state from the last valid columns (verified token-identical to an exact per-token reference loop). Multi-length prefill signatures (1–1024). Quantization: export-time int8 including convs, or post-hoc int4 on linears only — post-hoc conv quantization breaks generation.

## License and changes

Distributed under the **LFM Open License v1.0** (see `LICENSE`, inherited from the base model). Note the license's commercial-use limitation for organizations above US$10M annual revenue. **Changes from the original work:** weights converted from safetensors bf16 to LiteRT flatbuffers and quantized as described above; tokenizer and chat template repackaged unmodified; exporter conv-state fix as described in Conversion notes. This repository is a community conversion and is not affiliated with Liquid AI.