139 lines
7.1 KiB
Markdown
139 lines
7.1 KiB
Markdown
---
|
||
library_name: transformers
|
||
license: other
|
||
license_name: lfm1.0
|
||
license_link: LICENSE
|
||
language:
|
||
- en
|
||
- ja
|
||
pipeline_tag: text-generation
|
||
tags:
|
||
- liquid
|
||
- lfm2.5
|
||
- edge
|
||
base_model: LiquidAI/LFM2.5-1.2B-Base
|
||
new_version: LiquidAI/LFM2.5-1.2B-JP-202606
|
||
---
|
||
|
||
<div align="center">
|
||
<img
|
||
src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
|
||
alt="Liquid AI"
|
||
style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
|
||
/>
|
||
<div style="display: flex; justify-content: center; gap: 0.5em;">
|
||
<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>
|
||
</div>
|
||
</div>
|
||
|
||
<br>
|
||
|
||
# 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](https://www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai).
|
||
|
||
|
||
## 🏃 Inference
|
||
|
||
LFM2.5 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
|
||
|
||
| Name | Description | Docs | Notebook |
|
||
|------|-------------|------|----------|
|
||
| [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> |
|
||
| [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> |
|
||
| [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> |
|
||
|
||
Here's a quick start example with `transformers`:
|
||
|
||
```python
|
||
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 |
|
||
|------|-------------|------|----------|
|
||
| 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> |
|
||
| 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> |
|
||
| 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> |
|
||
|
||
## 📊 Performance
|
||
|
||
| 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) |
|
||
|-------|------|----------|----------|
|
||
| **LFM2.5-1.2B-JP** | 50.7 | 58.1 | 56.0 |
|
||
| **[LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/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](https://artificialanalysis.ai/methodology/intelligence-benchmarking#multiple-choice-questions) (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](https://discord.com/invite/liquid-ai)
|
||
- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
|
||
|
||
## Citation
|
||
|
||
```bibtex
|
||
@article{liquidai2025lfm2,
|
||
title={LFM2 Technical Report},
|
||
author={Liquid AI},
|
||
journal={arXiv preprint arXiv:2511.23404},
|
||
year={2025}
|
||
}
|
||
``` |