Quantization made by Richard Erkhov.
Tito-7B-slerp is a merge of the following models using
mergekit:
1slices:
2 - sources:
3 - model: gordicaleksa/YugoGPT
4 layer_range: [0, 32]
5 - model: mlabonne/AlphaMonarch-7B
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: mlabonne/AlphaMonarch-7B
9parameters:
10 t:
11 - filter: self_attn
12 value: [0, 0.5, 0.3, 0.7, 1]
13 - filter: mlp
14 value: [1, 0.5, 0.7, 0.3, 0]
15 - value: 0.6
16dtype: bfloat16
Evaluations on Serbian LLM eval suite (or rather, performance and knowledge of Serbian):
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.
If we try to replicate OpenLLM Leaderboard results on available Serbian datasets (running an appropriate amount of shots instead of 0), we get:
Note that YugoGPT, Llama3 and SambaLingo are all base models, unlike Tito and Perucac.
Detailed results can be found
here