LiquidAI/LFM2.5-1.2B-JP · Hugging Face

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LiquidAI

LFM2.5-1.2B-JP

like 148

Liquid AI 3.77k

Text Generation Transformers Safetensors English Japanese lfm2 liquid lfm2.5 edge conversational

arxiv: 2402.14531

arxiv: 2502.04688

arxiv: 2511.23404

License: lfm1.0

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Instructions to use LiquidAI/LFM2.5-1.2B-JP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
Transformers

How to use LiquidAI/LFM2.5-1.2B-JP with Transformers:

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-1.2B-JP") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)

Load model directly

from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-JP") model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-JP") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
vLLM

How to use LiquidAI/LFM2.5-1.2B-JP with vLLM:

Install from pip and serve model

Install vLLM from pip:

pip install vllm

Start the vLLM server:

vllm serve "LiquidAI/LFM2.5-1.2B-JP"

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/LFM2.5-1.2B-JP", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-JP

SGLang

How to use LiquidAI/LFM2.5-1.2B-JP with SGLang:

Install from pip and serve model

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server \ --model-path "LiquidAI/LFM2.5-1.2B-JP" \ --host 0.0.0.0 \ --port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/LFM2.5-1.2B-JP", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'

Use Docker images

docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "LiquidAI/LFM2.5-1.2B-JP" \ --host 0.0.0.0 \ --port 30000

Call the server using curl (OpenAI-compatible API):

Docker Model Runner

How to use LiquidAI/LFM2.5-1.2B-JP with Docker Model Runner:

docker model run hf.co/LiquidAI/LFM2.5-1.2B-JP

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.
LFM2.5-1.2B-JP
🏃 Inference
🔧 Fine-Tuning
📊 Performance
📬 Contact
Citation

Liquid AI

Try LFM • Docs • LEAP • Discord

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.

Name | Description | Docs | Notebook

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.

Name | Description | Docs | Notebook

📊 Performance

Model | JMMLU | M-IFEval (ja) | GSM8K (ja)

LFM2.5-1.2B-JP | 50.7 | 58.1 | 56.0 LFM2.5-1.2B-Instruct | 47.7 | 41.8 | 46.8 Qwen3-1.7B (Instruct mode) | 47.7 | 40.3 | 46.0 Llama 3.2 1B Instruct | 34.0 | 24.1 | 25.2 TinySwallow-1.5B-Instruct | 48.0 | 36.5 | 47.2 Gemma-2-Llama-Swallow-2b-it-v0.1 | 48.1 | 33.4 | 34.4 Gemma-3-1b-it | 34.5 | 26.3 | 33.6 Granite-4.0-h-1b | 42.2 | 39.3 | 42.8 Sarashina2.2-1b-instruct-v0.1 | 40.2 | 21.9 | 44.4

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.