kurakurai/Luth-LFM2-700M
Luth-LFM2-700M
Luth-LFM2-700M is a French fine-tuned version of LFM2-700M in collaboration with Liquid AI, trained on the 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, along with the Blog we wrote, to further detail our recipe.
Model Details
The model was trained using full fine-tuning on the Luth-SFT dataset with Axolotl. The resulting model was then merged back with LFM2-700M. 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
English Benchmark Scores
Code Example
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("kurakurai/Luth-LFM2-700M")
model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-LFM2-700M")
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
@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}
}