metadata
base_model_relation: quantized
quantized_by: Quant-Cartel
base_model: rAIfle/Acolyte-22B
e88 88e d8
d888 888b 8888 8888 ,"Y88b 888 8e d88
C8888 8888D 8888 8888 "8" 888 888 88b d88888
Y888 888P Y888 888P ,ee 888 888 888 888
"88 88" "88 88" "88 888 888 888 888
b
8b,
e88'Y88 d8 888
d888 'Y ,"Y88b 888,8, d88 ,e e, 888
C8888 "8" 888 888 " d88888 d88 88b 888
Y888 ,d ,ee 888 888 888 888 , 888
"88,d88 "88 888 888 888 "YeeP" 888
PROUDLY PRESENTS
Acolyte-22B-exl2-longcal
Quantized using 115 rows of 8192 tokens from the default ExLlamav2-calibration dataset.
Branches:
main
--measurement.json
- 8b8h -- 8bpw, 8bit lm_head
- 4.63b6h -- 4.63bpw, 6bit lm_head
- 3.09b6h -- 3.09bpw, 6bit lm_head
- 2.32b6h -- 2.32bpw, 6bit lm_head
Original model link: rAIfle/Acolyte-22B
Original model README below.
Acolyte-22B
LoRA of a bunch of random datasets on top of Mistral-Small-Instruct-2409, then SLERPed onto base at 0.5. Decent enough for its size. Check the LoRA for dataset info.
Use Mistral V2 & V3
template.