running-coach-lfm2.5-1.2b-jp-gguf

running-coach-lfm2.5-1.2b-jp

LiquidAI/LFM2.5-1.2B-JP を LoRA でファインチューニングし、GGUF (Q4_K_M) に量子化したモデルです。 Android アプリ my-running-coach が、走行中・走行後の 日本語のコーチングコメントを端末上で生成するために使います。汎用チャットモデルではありません。

A LoRA fine-tune of LiquidAI/LFM2.5-1.2B-JP, converted to GGUF and quantized to Q4_K_M. It generates short Japanese running-coach comments on-device for the Android app my-running-coach. It is not a general-purpose chat model.

Modifications to the base model / ベースモデルからの変更点

This is a modified version of LiquidAI/LFM2.5-1.2B-JP. The weights were changed by supervised LoRA fine-tuning and then quantized; they are not the original Liquid AI weights. This notice is provided as required by the LFM Open License v1.0.

Nothing else (architecture, context length, vocabulary) was changed.

Files

Files are never overwritten in place. Each retrain is published under a new versioned file name (...-v2.gguf, …), because the consuming app pins the exact byte size and SHA-256 of the file it expects; replacing a file under the same name would make every installed client treat it as corrupt.

Intended use / 想定用途

Three Japanese generation tasks, each driven by a fixed system prompt held by the app:

Outputs must contain no digits, no bullet lines, and no line breaks; the app rejects and discards anything that violates those rules. Prompts outside these three shapes are out of scope.

Training data

3,110 synthetic examples generated from the app's own prompt builders, covering enumerated running situations (pace, distance, laps, heart rate, weather, run history). Teacher outputs were synthesized with a larger model and filtered by a rule-based consistency checker before training. No user data and no personal data of any kind were used.

Evaluation

Held-out: 465 situations × 3 samples = 1,395 generations, same sampler and same llama.cpp backend as the base-model baseline.

Left of each arrow is the unmodified base model, right is this model. The goal of the fine-tune was output variety on coach_comment, which the base model collapsed into 32 distinct strings over 195 generations; this model produces 70. run_review unique rate regressed (98.0% → 80.5%) — training pulled the longer reviews toward a template. It is recorded here rather than hidden because it is a real cost of this fine-tune.

Limitations

• Japanese only. The base model is bilingual, but this fine-tune was trained and evaluated on Japanese only.
• Domain-locked to running coaching. Out-of-domain prompts fall back to base-model behavior with an unhelpful bias toward coaching phrasing.
• Not a safety-tuned model, and not a source of medical or training advice. Output is decoration on top of numbers the app already computed.
• Quantized to Q4_K_M. Other quantizations of this fine-tune have not been evaluated.

Usage

llama-cli -m running-coach-lfm2.5-1.2b-jp-v1.gguf -p "..."

The app runs it through the LEAP SDK (llama.cpp under the hood) on CPU.

License

Licensed under the LFM Open License v1.0 (lfm1.0), inherited from the base model LiquidAI/LFM2.5-1.2B-JP. A copy of the license is included in this repository as LICENSE.

Copyright notices from the original work are retained. Commercial use is restricted by the license's revenue threshold — read LICENSE before using this model commercially.