Update README.md

README.md

@@ -13,62 +13,16 @@ pipeline_tag: text-generation
 
 ![Kurakura AI Logo](media/logo_kurakura.png)
 
----
-
-# Luth-1.7B-Instruct
+# Luth-1.7B-Instruct-GGUF
 
 **Luth-1.7B-Instruct** is a French fine-tuned version of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), 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.
 
-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.
-
-## Model Details
-
-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 Qwen3-1.7B 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`.
-
-### Evaluation Visualizations
-
-**French Evaluation:**
-
-![French Evaluation](media/french_evaluation.png)
-
-**English Evaluation:**
-
-![English Evaluation](media/english_evaluation.png)
-
-### French Benchmark Scores
-
-| Benchmark         | Qwen3-1.7B       | SmolLM2-1.7B-Instruct | Qwen2.5-1.5B-Instruct | Luth-1.7B-Instruct   |
-|-------------------|------------------|-----------------------|-----------------------|----------------------|
-| ifeval-fr         | 54.53            | 31.24                 | 32.90                 | <u>57.67</u>         |
-| gpqa-diamond-fr   | 26.90            | 21.83                 | 28.93                 | <u>38.58</u>         |
-| mmlu-fr           | 28.46            | 33.73                 | 46.25                 | <u>49.66</u>         |
-| math-500-fr       | 60.80            | 11.20                 | 32.20                 | <u>64.00</u>         |
-| arc-chall-fr      | 33.28            | 28.57                 | 32.68                 | <u>35.16</u>         |
-| hellaswag-fr      | 24.86            | <u>49.58</u>          | 34.34                 | 31.93                |
-
-### English Benchmark Scores
+Find more details in the original model card: https://huggingface.co/kurakurai/Luth-1.7B-Instruct
 
-| Benchmark         | Qwen3-1.7B       | SmolLM2-1.7B-Instruct | Qwen2.5-1.5B-Instruct | Luth-1.7B-Instruct   |
-|-------------------|------------------|-----------------------|-----------------------|----------------------|
-| ifeval-en         | <u>68.39</u>     | 48.24                 | 39.93                 | 65.80                |
-| gpqa-diamond-en   | <u>31.82</u>     | 24.75                 | 30.30                 | 31.82                |
-| mmlu-en           | 52.74            | 50.27                 | 59.81                 | <u>60.19</u>         |
-| math-500-en       | 69.20            | 22.40                 | 56.00                 | <u>70.00</u>         |
-| arc-chall-en      | 36.09            | 42.32                 | 41.04                 | <u>42.24</u>         |
-| hellaswag-en      | 46.96            | <u>66.94</u>          | 64.48                 | 58.55                |
+## 🏃 How to run Luth
 
-## Citation
+Example usage with [llama.cpp](https://github.com/ggml-org/llama.cpp):
 
-```bibtex
-@misc{luth2025kurakurai,
-  title   = {Luth-1.7B-Instruct},
-  author  = {Kurakura AI Team},
-  year    = {2025},
-  howpublished = {\url{https://huggingface.co/kurakurai/Luth-0.6B}},
-  note    = {Qwen3-1.7B fine-tuned on French datasets}
-}
 ```
+llama-cli -hf kurakurai/Luth-1.7B-Instruct-GGUF
+```