kurakurai/Luth-1.7B-Instruct
Luth-1.7B-Instruct
Luth-1.7B-Instruct is a French fine-tuned version of Qwen3-1.7B, trained on the Luth-SFT dataset. The model has drastically improved its French capabilities in instruction following, math, and general knowledge. Additionally, its English capabilities have remained stable and have even increased in some areas.
Our Evaluation, training and data scripts are available on GitHub, along with the Blog we wrote.
Model Details
Luth was trained using full fine-tuning on the Luth-SFT dataset with Axolotl. The resulting model was then merged with the base Qwen3-1.7B model. This process successfully retained the model's English capabilities while improving its performance on most selected benchmarks in both French and English.
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-1.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-1.7B-Instruct")
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}
}