---
library_name: transformers
datasets:
- kurakurai/luth-sft
language:
- fr
- en
base_model:
- LiquidAI/LFM2-350M
pipeline_tag: text-generation
license: other
license_name: lfm1.0
license_link: LICENSE
tags:
- liquid
- lfm2
- luth
---

![Luth x LFM2](media/logo_collab.png)


# Luth-LFM2-350M

**Luth-LFM2-350M** is a French fine-tuned version of [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) in collaboration with Liquid AI, trained on the [Luth-SFT](https://huggingface.co/datasets/kurakurai/luth-sft) dataset. The model has improved its French capabilities in instruction following, math, and general knowledge. Additionally, its English capabilities have remained stable.

Our Evaluation, training and data scripts are available on [GitHub](https://github.com/kurakurai/Luth), along with the [Blog](https://huggingface.co/blog/MaxLSB/luth) we wrote, to further detail our recipe.


![Luth-LFM2 graph](media/lfm2-luth.png)


## Model Details

The model was trained using full fine-tuning on the Luth-SFT dataset with [Axolotl](https://github.com/axolotl-ai-cloud/axolotl). The resulting model was then merged back with LFM2-350M. This process successfully retained the model's English capabilities while improving its performance in French.

## Benchmark Results

We used LightEval for evaluation, with custom tasks for the French benchmarks. The models were evaluated with a `temperature=0`.

### French Benchmark Scores

| Model                 | IFEval<br>French | GPQA-Diamond<br>French | MMLU<br>French | Math500<br>French | Arc-Challenge<br>French | Hellaswag<br>French |
| --------------------- | ------------------ | ------------------------ | ---------------- | ------------------- | ------------------------- | --------------------- |
| **Luth-LFM2-350M**    | <u>38.26</u>       | 26.40                    | <u>39.15</u>     | <u>23.00</u>        | <u>34.13</u>              | <u>43.39</u>          |
| LFM2-350M             | 31.55              | <u>28.93</u>             | 38.63            | 18.00               | 33.36                     | 39.13                 |
| SmolLM2-360M-Instruct | 21.50              | 28.43                    | 26.14            | 3.20                | 26.60                     | 32.94                 |


### English Benchmark Scores

| Model                 | IFEval <br> English | GPQA-Diamond <br> English | MMLU <br> English | Math500 <br> English | Arc-Challenge <br> English | Hellaswag <br> English |
| --------------------- | ------------------- | ------------------------- | ----------------- | -------------------- | -------------------------- | ---------------------- |
| **Luth-LFM2-350M**    | <u>57.05</u>        | <u>28.28</u>              | 44.36             | <u>23.20</u>         | 34.81                      | 45.92                  |
| LFM2-350M             | 56.81               | 27.27                     | <u>44.79</u>      | 20.87                | 34.27                      | 45.07                  |
| SmolLM2-360M-Instruct | 33.95               | 20.71                     | 26.18             | 3.00                 | <u>35.41</u>               | <u>52.17</u>            |


## Code Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kurakurai/Luth-LFM2-350M")
model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-LFM2-350M")
messages = [
    {"role": "user", "content": "Quelle est la capitale de la France?"},
]
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=100)
print(
    tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[-1] :], skip_special_tokens=True
    )
)
```

## Citation

```bibtex
@misc{luth2025kurakurai,
  title        = {Luth: Efficient French Specialization for Small Language Models and Cross-Lingual Transfer},
  author       = {Lasbordes, Maxence and Gad, Sinoué},
  year         = {2025},
  howpublished = {\url{https://arxiv.org/abs/2510.05846}},
  note         = {arXiv:2510.05846}
}
```