kurakurai
/

Luth-LFM2-1.2B

@@ -1,199 +1,96 @@
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 library_name: transformers
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 ---
 library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+datasets:
+- kurakurai/luth-sft
+language:
+- fr
+- en
+base_model:
+- LiquidAI/LFM2-1.2B
+pipeline_tag: text-generation
+tags:
+- liquid
+- lfm2
+- luth
 ---
+![Luth x LFM2](media/logo_collab.png)
+# Luth-LFM2-1.2B
+**Luth-LFM2-1.2B** is a French fine-tuned version of [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2BM), 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.
+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.
+## 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 LFM2-1.2B. 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
+| Benchmark         | LFM2-1.2B        | Luth-LFM2-1.2B     |
+|-------------------|------------------|-----------------------|
+| ifeval-fr         | 53.23            | <u>61.74</u>          |
+| gpqa-fr           | 25.77            | <u>26.61</u>          |
+| mmlu-fr           | 47.59            | <u>48.02</u>          |
+| math-500-fr       | 35.80            | <u>47.00</u>          |
+| kholle            | 44.57            | <u>48.57</u>          |
+| arc-chall-fr      | <u>39.44</u>     |         39.01         |
+| hellaswag-fr      | 33.05            | <u>36.81</u>          |
+### English Benchmark Scores
+| Benchmark         | LFM2-1.2B        | Luth-LFM2-1.2B     |
+|-------------------|------------------|-----------------------|
+| ifeval-en         | <u>69.87</u>     | 69.69                 |
+| gpqa-en           |     26.68        | <u>28.03</u>          |
+| mmlu-en           | <u>55.17</u>     | 54.52                 |
+| math-500-en       | 44.60            | <u>50.20</u>          |
+| arc-chall-en      | 42.66            | <u>43.26</u>          |
+| hellaswag-en      | 57.64            | <u>58.42</u>          |
+## Code Example
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("kurakurai/Luth-LFM2-1.2B")
+model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-LFM2-1.2B")
+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{luthlfm2kurakurai,
+  title   = {Luth-LFM2-1.2B},
+  author  = {Kurakura AI Team},
+  year    = {2025},
+  howpublished = {\url{https://huggingface.co/kurakurai/Luth-LFM2-1.2B}},
+  note    = {LFM2-1.2B fine-tuned on French datasets}
+}
+```