初始化项目，由ModelHub XC社区提供模型
Model: LiquidAI/LFM2.5-1.2B-JP Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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language:
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- en
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- ja
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pipeline_tag: text-generation
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tags:
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- liquid
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- lfm2.5
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- edge
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base_model: LiquidAI/LFM2.5-1.2B-Base
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new_version: LiquidAI/LFM2.5-1.2B-JP-202606
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---
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<div align="center">
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<img
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src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
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alt="Liquid AI"
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style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
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/>
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<div style="display: flex; justify-content: center; gap: 0.5em;">
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<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
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</div>
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</div>
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<br>
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# LFM2.5-1.2B-JP
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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.
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Find more information about LFM2.5 in our [blog post](https://www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai).
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## 🏃 Inference
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LFM2.5 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
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| Name | Description | Docs | Notebook |
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|------|-------------|------|----------|
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| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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Here's a quick start example with `transformers`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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model_id = "LiquidAI/LFM2.5-1.2B-JP"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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dtype="bfloat16",
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# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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prompt = "What is C. elegans?"
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input_ids = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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add_generation_prompt=True,
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return_tensors="pt",
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tokenize=True,
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).to(model.device)
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output = model.generate(
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input_ids,
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do_sample=True,
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temperature=0.3,
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min_p=0.15,
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repetition_penalty=1.05,
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max_new_tokens=512,
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streamer=streamer,
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)
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```
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- **Recommended generation parameters**:
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- `temperature: 0.3`
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- `min_p: 0.15`
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- `repetition_penalty: 1.05`
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## 🔧 Fine-Tuning
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We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.
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| Name | Description | Docs | Notebook |
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|------|-------------|------|----------|
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| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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## 📊 Performance
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| Model | [JMMLU](https://arxiv.org/pdf/2402.14531) | [M-IFEval (ja)](https://arxiv.org/pdf/2502.04688) | [GSM8K (ja)](https://huggingface.co/datasets/SakanaAI/gsm8k-ja-test_250-1319) |
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|-------|------|----------|----------|
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| **LFM2.5-1.2B-JP** | 50.7 | 58.1 | 56.0 |
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| **[LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)** | 47.7 | 41.8 | 46.8 |
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| Qwen3-1.7B (Instruct mode) | 47.7 | 40.3 | 46.0 |
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| Llama 3.2 1B Instruct | 34.0 | 24.1 | 25.2 |
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| TinySwallow-1.5B-Instruct | 48.0 | 36.5 | 47.2 |
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| Gemma-2-Llama-Swallow-2b-it-v0.1 | 48.1 | 33.4 | 34.4 |
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| Gemma-3-1b-it | 34.5 | 26.3 | 33.6 |
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| Granite-4.0-h-1b | 42.2 | 39.3 | 42.8 |
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| Sarashina2.2-1b-instruct-v0.1 | 40.2 | 21.9 | 44.4 |
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**Evaluation Notes**
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- All results are **zero-shot** evaluations using **greedy decoding**.
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- **M-IFEval (ja)** scores correspond to the **loose evaluation setting**.
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- **JMMLU** was evaluated using a prompt format in a similar style to the [ArtificialAnalysis methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking#multiple-choice-questions) (with corresponding parsing logic). The Japanese prompt template used is shown below:
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```
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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」）。
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{question}
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{options}"""
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```
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## 📬 Contact
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- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
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- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
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## Citation
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```bibtex
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@article{liquidai2025lfm2,
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title={LFM2 Technical Report},
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author={Liquid AI},
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journal={arXiv preprint arXiv:2511.23404},
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year={2025}
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}
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```
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