LFM2.5-1.2B-JP

LFM2.5-1.2B-JP is a chat model specifically optimized for Japanese. While LFM2 already supported Japanese as one of eight languages, LFM2.5-JP pushes state-of-the-art on Japanese knowledge and instruction-following at its scale. This model is ideal for developers building Japanese-language applications where cultural and linguistic nuance matter.

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

🏃 Inference

LFM2.5 is supported by many inference frameworks. See the Inference documentation for the full list.

Here's a quick start example with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-1.2B-JP"
model = AutoModelForCausalLM.from_pretrained(
 model_id,
 device_map="auto",
 dtype="bfloat16",
# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "What is C. elegans?"

input_ids = tokenizer.apply_chat_template(
 [{"role": "user", "content": prompt}],
 add_generation_prompt=True,
 return_tensors="pt",
 tokenize=True,
).to(model.device)

output = model.generate(
 input_ids,
 do_sample=True,
 temperature=0.3,
 min_p=0.15,
 repetition_penalty=1.05,
 max_new_tokens=512,
 streamer=streamer,
)

• Recommended generation parameters:
  • temperature: 0.3
  • min_p: 0.15
  • repetition_penalty: 1.05

🔧 Fine-Tuning

We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

📊 Performance

Evaluation Notes

• All results are zero-shot evaluations using greedy decoding.
• M-IFEval (ja) scores correspond to the loose evaluation setting.
• JMMLU was evaluated using a prompt format in a similar style to the ArtificialAnalysis methodology (with corresponding parsing logic). The Japanese prompt template used is shown below:

PROMPT_TEMPLATE = """与えられた選択問題に答えてください。回答の最後の行に「答え：{valid_options}」のように出力してください（例：「答え：X」）。

{question}

{options}"""

📬 Contact

• Got questions or want to connect? Join our Discord community
• If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}

Base model