Update README.md

README.md

@@ -44,6 +44,7 @@ Evaluations on Serbian LLM eval suite (or rather, performance and knowledge of S
 | [Mustra-7B](https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.1) | 52.95 | 33.70 | 45.89     | **77.55** | 64.17      | **30.60**      | 67.25 | 15.40   | 34.84    | 46.93 |
 | [Tito-7B](https://huggingface.co/Stopwolf/Tito-7B)   | 55.43 | **34.73** | 48.19     | 77.37 | **65.27**      | 30.00      | 67.30 | **16.7**    | 35.38    | **47.82** |
 | [YugoGPT](https://huggingface.co/gordicaleksa/YugoGPT)   | **57.79** | **34.73** | **49.89**     | 69.45 | 64.56      | 28.20      | **72.03** | 15.82   | **36.14**    | 47.62 |
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 Here, all benchmarks were done 0-shot, on the exception of NQ Open and TriviaQA which were done in 5-shot manner, in order to be comparable to Mistral paper.
 
 
@@ -52,6 +53,7 @@ Evalutaions on Open LLM Leaderboard (or rather, performance and knowledge of Eng
 |---------|-------|-----------|------------|------|-------|-------------|-------|
 | Tito-7B | 68.08 | 86.37     | 81.69      |64.01 | 63.61 | 57.01       | 70.13 |
 | YugoGPT | 58.10 | 81.44     | 76.56      |60.68 | 30.70 | 36.60       | 57.34 |
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 Here, Winogrande, GSM8k, MMLU were done in 5-shot manner, Hellaswag in 10-shot manner, and finally ARC in 25-shot manner.
 
 If we try to replicate these approaches on available Serbian datasets (running an appropriate amount of shots instead of 0), we get: