Edit model card

💨 Vikhr-Gemma-2B-instruct

Мощная инструктивная модель на основе Gemma 2 2B, обученная на русскоязычном датасете GrandMaster-PRO-MAX.

Perplexity (ниже - лучше)

Veles

Model Perplexity
Q4_K 4.7254 +/- 0.03867
Q4_0 4.8067 +/- 0.03922
Q8_0 4.6042 +/- 0.03751
Q4_1 4.7798 +/- 0.03933
F32 4.6013 +/- 0.03749
Q6_K 4.6244 +/- 0.03760
BF16 4.6015 +/- 0.03749
Q2_K 5.6819 +/- 0.04737
Q5_0 4.6876 +/- 0.03855
Q5_K 4.6428 +/- 0.03789
Q3_K_S 5.1485 +/- 0.04257
Q2_K_S 6.3124 +/- 0.05359
F16 4.6013 +/- 0.03749
Q4_K_M 4.7254 +/- 0.03867
Q5_K_M 4.6428 +/- 0.03789
Q5_1 4.6518 +/- 0.03794
Q4_K_S 4.7631 +/- 0.03916
Q5_K_S 4.6509 +/- 0.03803
Q3_K 4.8339 +/- 0.03965
Q3_K_M 4.8339 +/- 0.03965
Q3_K_L 4.7981 +/- 0.03934

Wikitext-2

Model Perplexity
Q4_K 10.4374 +/- 0.07339
Q4_0 10.6480 +/- 0.07452
Q8_0 10.1209 +/- 0.07105
Q4_1 10.5574 +/- 0.07476
F32 10.1191 +/- 0.07099
Q6_K 10.1503 +/- 0.07117
BF16 10.1189 +/- 0.07098
Q2_K 12.8851 +/- 0.09332
Q5_0 10.2551 +/- 0.07251
Q5_K 10.1975 +/- 0.07184
Q3_K_S 11.6028 +/- 0.08333
Q2_K_S 14.7951 +/- 0.10960
F16 10.1191 +/- 0.07099
Q4_K_M 10.4374 +/- 0.07339
Q5_K_M 10.1975 +/- 0.07184
Q5_1 10.2348 +/- 0.07208
Q4_K_S 10.4924 +/- 0.07386
Q5_K_S 10.2098 +/- 0.07198
Q3_K 10.7416 +/- 0.07606
Q3_K_M 10.7416 +/- 0.07606
Q3_K_L 10.6242 +/- 0.07506
Downloads last month
1,628
GGUF
Model size
2.61B params
Architecture
gemma2

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Inference Examples
Inference API (serverless) does not yet support llamacpp models for this pipeline type.

Model tree for Vikhrmodels/Vikhr-Gemma-2B-instruct-GGUF

Base model

google/gemma-2-2b
Quantized
(7)
this model

Dataset used to train Vikhrmodels/Vikhr-Gemma-2B-instruct-GGUF