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--- |
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library_name: transformers |
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license: other |
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license_name: lfm1.0 |
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license_link: LICENSE |
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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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- LiquidAI/LFM2-1.2B |
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pipeline_tag: text-generation |
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tags: |
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- liquid |
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- lfm2 |
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- luth |
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--- |
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# Luth-LFM2-1.2B |
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**Luth-LFM2-1.2B** is a French fine-tuned version of [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B) 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 or slightly improved such as in Maths. |
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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, to further detail our recipe. |
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## Model Details |
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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 LFM2-1.2B. This process successfully retained the model's English capabilities while improving its performance in French. |
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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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| **Benchmark** | **LFM2-1.2B** | **Luth-LFM2-1.2B** | |
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|-------------------|------------------|-----------------------| |
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| **IFEval-fr (strict prompt)** | 53.60 | <u>60.44</u> | |
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| **GPQA-fr** | 25.77 | <u>27.02</u> | |
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| **MMLU-fr** | 47.59 | <u>47.98</u> | |
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| **MATH-500-fr** | 35.80 | <u>47.20</u> | |
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| **Arc-Chall-fr** | <u>39.44</u> | 39.01 | |
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| **Hellaswag-fr** | 33.05 | <u>36.76</u> | |
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### English Benchmark Scores |
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| **Benchmark** | **LFM2-1.2B** | **Luth-LFM2-1.2B** | |
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|-------------------|------------------|-----------------------| |
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| **IFEval-en (strict prompt)**| 70.43 | <u>70.61</u> | |
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| **GPQA-en** | 26.68 | <u>28.21</u> | |
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| **MMLU-en** | <u>55.18</u> | 54.59 | |
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| **MATH-500-en** | 44.60 | <u>50.20</u> | |
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| **Arc-Chall-en** | 43.09 | <u>43.26</u> | |
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| **Hellaswag-en** | 57.64 | <u>58.46</u> | |
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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-LFM2-1.2B") |
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model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-LFM2-1.2B") |
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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{luthlfm2kurakurai, |
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title = {Luth-LFM2-1.2B}, |
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author = {Kurakura AI Team}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co/kurakurai/Luth-LFM2-1.2B}}, |
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note = {LFM2-1.2B fine-tuned on French datasets} |
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} |
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``` |
