LFM2.5-JP-202606

🇯🇵 LFM2.5-1.2B-JP-202606

LFM2.5-1.2B-JP-202606 is our latest general purpose Japanese chat model, delivering significant improvements in knowledge, instruction following, math, code, and tool-use over both the models of comparable size and LFM2.5-1.2B-JP. It sets a new benchmark for state-of-the-art performance in Japanese language understanding. Ideal for developers building Japanese-language applications where cultural and linguistic nuance matter.

LFM2.5-1.2B-JP-202606 は、当社の最新の汎用日本語チャットモデルです。知識、指示追従、数学、コード、ツール使用の各領域において、同規模の他モデルおよび LFM2.5-1.2B-JP の双方を大幅に上回る改善を実現しています。日本語全般における最高水準のベンチマーク性能を発揮します。 文化的・言語的なニュアンスが重要となる日本語アプリケーションを構築する開発者に最適です。

Find more information about LFM2.5 in our blog post.

📊 Performance

src="https://cdn-uploads.huggingface.co/production/uploads/64618cf05dba83471db2be9b/7gqajAlXAh52nAz85JoQt.png"
alt="Liquid AI"
width="90%"

/>

We compared LFM2.5-1.2B-JP-202606 with relevant sub-2B models on a diverse suite of benchmarks.

1 JFBench is evaluated using single-instruction prompts. 2 quickTestingOSSHandler is used for models that do not support function calling (sarashina2.2‑1b‑instruct‑v0.1, TinySwallow‑1.5B‑Instruct, llm‑jp‑3.1‑1.8b‑instruct4, and RakutenAI‑2.0‑mini‑instruct).

🗒️ Model Details

LFM2.5-1.2B-JP-202606 is a general-purpose text-only model with the following features:

• Number of parameters: 1.17B
• Number of layers: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
• Training budget: 31.5T tokens
• Context length: 32,768 tokens
• Vocabulary size: 65,536
• Knowledge cutoff: Mid-2024
• Languages: English, Japanese
• Generation parameters:
  • temperature: 0.1
  • top_k: 50
  • repetition_penalty: 1.05

We recommend using it for agentic workflows, tool use, structured outputs, bilingual English–Japanese assistants, and on-device personal-assistant applications. It is not recommended for knowledge-intensive tasks. It performs best when given clear, explicit instructions that define the task, expected behavior, and output format.

エージェント型ワークフロー、ツール使用、構造化出力、日英バイリンガルアシスタント、オンデバイスのパーソナルアシスタントでの利用を推奨します。一方で、詳細な知識を要するのタスクには推奨されません。タスク内容、期待される動作、出力形式を明確かつ具体的に指示することで、最も高い性能を発揮します。

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

You are a helpful assistant trained by Liquid AI.
日本の首都は？

You can use tokenizer.apply_chat_template() to format your messages automatically.

Tool Use

LFM2.5 supports function calling as follows:

1. Function definition: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the tokenizer.apply_chat_template() function with tools.
2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between and special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
3. Function execution: The function call is executed, and the result is returned as a "tool" role.
4. Final answer: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

See the Tool Use documentation for the full guide. Example:

List of tools: [{"name": "get_candidate_status", "description": "採用プロセスにおける候補者の現在のステータスを取得します", "parameters": {"type": "object", "properties": {"cand