Results obtained through the Serbian LLM evaluation, released by Aleksa Gordić: serbian-llm-eval

Evaluation was conducted on a 4-bit version of the model due to hardware resource constraints.

MODEL	ARC-E	ARC-C	Hellaswag	BoolQ	Winogrande	OpenbookQA	PiQA
*Yugo55-GPT-v4-4bit	51.41	36.00	57.51	80.92	65.75	34.70	70.54
Yugo55A-GPT	51.52	37.78	57.52	84.40	65.43	35.60	69.43

Quant. preference

Quant.	Description
not_quantized	Recommended. Fast conversion. Slow inference, big files.
fast_quantized	Recommended. Fast conversion. OK inference, OK file size.
quantized	Recommended. Slow conversion. Fast inference, small files.
f32	Not recommended. Retains 100% accuracy, but super slow and memory hungry.
f16	Fastest conversion + retains 100% accuracy. Slow and memory hungry.
q8_0	Fast conversion. High resource use, but generally acceptable.
q4_k_m	Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
q5_k_m	Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
q2_k	Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
q3_k_l	Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
q3_k_m	Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
q3_k_s	Uses Q3_K for all tensors
q4_0	Original quant method, 4-bit.
q4_1	Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
q4_k_s	Uses Q4_K for all tensors
q4_k	alias for q4_k_m
q5_k	alias for q5_k_m
q5_0	Higher accuracy, higher resource usage and slower inference.
q5_1	Even higher accuracy, resource usage and slower inference.
q5_k_s	Uses Q5_K for all tensors
q6_k	Uses Q8_K for all tensors
iq2_xxs	2.06 bpw quantization
iq2_xs	2.31 bpw quantization
iq3_xxs	3.06 bpw quantization
q3_k_xs	3-bit extra small quantization

datatab/Yugo55A-GGUF

Yugo55A-GPT.GGUF

Full Weights Model

🏆 Results

Quant. preference

datatab/
Yugo55A-GGUF