90 lines
4.2 KiB
Markdown
90 lines
4.2 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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datasets:
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- kurakurai/luth-sft
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language:
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- fr
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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pipeline_tag: text-generation
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---
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# Luth-0.6B-Instruct
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**Luth-0.6B-Instruct** is a French fine-tuned version of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), trained on the [Luth-SFT](https://huggingface.co/datasets/kurakurai/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.
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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.
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## Model Details
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Luth 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 with the base Qwen3-0.6B model. This process successfully retained the model's English capabilities while improving its performance on nearly all selected benchmarks in both French and English.
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## Benchmark Results
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We used LightEval for evaluation, with custom tasks for the French benchmarks. The models were evaluated with a `temperature=0`.
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### French Benchmark Scores
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| Model | IFEval<br>French | GPQA-Diamond<br>French | MMLU<br>French | Math500<br>French | Arc-Challenge<br>French | Hellaswag<br>French |
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|------------------------|-----------------|-----------------------|----------------|-----------------|------------------------|-------------------|
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| **Luth-0.6B-Instruct** | <u>48.24</u> | <u>34.52</u> | <u>40.12</u> | <u>44.00</u> | <u>33.88</u> | 45.58 |
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| Llama-3.2-1B | 27.79 | 25.38 | 25.49 | 15.80 | 29.34 | 25.09 |
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| Qwen3-0.6B | 44.86 | 26.90 | 27.13 | 29.20 | 31.57 | 25.10 |
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| Qwen2.5-0.5B-Instruct | 22.00 | 25.89 | 35.04 | 12.00 | 28.23 | <u>51.45</u> |
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### English Benchmark Scores
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| Model | IFEval<br>English | GPQA-Diamond<br>English | MMLU<br>English | Math500<br>English | Arc-Challenge<br>English | Hellaswag<br>English |
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|------------------------|-----------------|------------------------|----------------|------------------|-------------------------|--------------------|
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| **Luth-0.6B-Instruct** | 53.73 | 25.76 | <u>48.12</u> | <u>48.80</u> | <u>36.09</u> | 47.03 |
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| Llama-3.2-1B | 44.05 | 25.25 | 31.02 | 26.40 | 34.30 | <u>55.84</u> |
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| Qwen3-0.6B | <u>57.18</u> | <u>29.29</u> | 36.79 | 43.40 | 33.70 | 42.92 |
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| Qwen2.5-0.5B-Instruct | 29.70 | <u>29.29</u> | 43.80 | 32.00 | 32.17 | 49.56 |
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## Code Example
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("kurakurai/Luth-0.6B-Instruct")
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model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-0.6B-Instruct")
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messages = [
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{"role": "user", "content": "Quelle est la capitale de la France?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(
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tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[-1] :], skip_special_tokens=True
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)
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)
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```
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## Citation
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```bibtex
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@misc{luth2025kurakurai,
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title = {Luth: Efficient French Specialization for Small Language Models and Cross-Lingual Transfer},
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author = {Lasbordes, Maxence and Gad, Sinoué},
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year = {2025},
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howpublished = {\url{https://arxiv.org/abs/2510.05846}},
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note = {arXiv:2510.05846}
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}
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```
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