Update README.md

README.md

@@ -29,6 +29,8 @@ Luth was trained using full fine-tuning on the Luth-SFT dataset with [Axolotl](h
 
 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)
@@ -37,6 +39,29 @@ We used LightEval for evaluation, with custom tasks for the French benchmarks. T
 
 ![English Evaluation](media/english_evaluation.png)
 
+### French Benchmark Scores
+
+| Benchmark         | Qwen3-0.6B       | Qwen2.5-0.5B-Instruct | Luth-0.6B-0.3   |
+|-------------------|------------------|-----------------------|-----------------|
+| ifeval-fr         | 44.45            | 22.18                 | <u>48.24</u>    |
+| gpqa-diamond-fr   | 28.93            | 23.86                 | <u>33.50</u>    |
+| mmlu-fr           | 27.16            | 35.04                 | <u>40.23</u>    |
+| math-500-fr       | 29.20            | 10.00                 | <u>43.00</u>    |
+| arc-chall-fr      | 31.31            | 28.23                 | <u>33.88</u>    |
+| hellaswag-fr      | 25.11            | <u>51.45</u>          | 45.70           |
+
+### English Benchmark Scores
+
+| Benchmark         | Qwen3-0.6B       | Qwen2.5-0.5B-Instruct | Luth-0.6B-0.3   |
+|-------------------|------------------|-----------------------|-----------------|
+| ifeval-en         | <u>57.86</u>     | 29.21                 | 53.97           |
+| gpqa-diamond-en   | <u>29.80</u>     | 26.77                 | 28.28           |
+| mmlu-en           | 36.85            | 43.80                 | <u>48.10</u>    |
+| math-500-en       | 45.00            | 31.80                 | <u>47.80</u>    |
+| arc-chall-en      | 33.62            | 32.17                 | <u>35.92</u>    |
+| hellaswag-en      | 42.91            | <u>49.56</u>          | 46.96           |
+
+
 ## Citation
 
 ```bibtex