初始化项目，由ModelHub XC社区提供模型

Model: kurakurai/Luth-LFM2-350M Source: Original Platform
2026-07-29 23:22:13 +08:00
commit a1a9e66d3a
13 changed files with 4399 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,95 @@
+---
+library_name: transformers
+datasets:
+- kurakurai/luth-sft
+language:
+- fr
+- en
+base_model:
+- LiquidAI/LFM2-350M
+pipeline_tag: text-generation
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+tags:
+- liquid
+- lfm2
+- luth
+---
+
+![Luth x LFM2](media/logo_collab.png)
+
+
+# Luth-LFM2-350M
+
+**Luth-LFM2-350M** is a French fine-tuned version of [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) 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.
+
+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.
+
+
+![Luth-LFM2 graph](media/lfm2-luth.png)
+
+
+## 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 LFM2-350M. 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
+
+| Model                 | IFEval<br>French | GPQA-Diamond<br>French | MMLU<br>French | Math500<br>French | Arc-Challenge<br>French | Hellaswag<br>French |
+| --------------------- | ------------------ | ------------------------ | ---------------- | ------------------- | ------------------------- | --------------------- |
+| **Luth-LFM2-350M**    | <u>38.26</u>       | 26.40                    | <u>39.15</u>     | <u>23.00</u>        | <u>34.13</u>              | <u>43.39</u>          |
+| LFM2-350M             | 31.55              | <u>28.93</u>             | 38.63            | 18.00               | 33.36                     | 39.13                 |
+| SmolLM2-360M-Instruct | 21.50              | 28.43                    | 26.14            | 3.20                | 26.60                     | 32.94                 |
+
+
+### English Benchmark Scores
+
+| Model                 | IFEval <br> English | GPQA-Diamond <br> English | MMLU <br> English | Math500 <br> English | Arc-Challenge <br> English | Hellaswag <br> English |
+| --------------------- | ------------------- | ------------------------- | ----------------- | -------------------- | -------------------------- | ---------------------- |
+| **Luth-LFM2-350M**    | <u>57.05</u>        | <u>28.28</u>              | 44.36             | <u>23.20</u>         | 34.81                      | 45.92                  |
+| LFM2-350M             | 56.81               | 27.27                     | <u>44.79</u>      | 20.87                | 34.27                      | 45.07                  |
+| SmolLM2-360M-Instruct | 33.95               | 20.71                     | 26.18             | 3.00                 | <u>35.41</u>               | <u>52.17</u>            |
+
+
+## Code Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("kurakurai/Luth-LFM2-350M")
+model = AutoModelForCausalLM.from_pretrained("kurakurai/Luth-LFM2-350M")
+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{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}
+}
+```