base_model: mistralai/Mistral-Nemo-Base-2407
license: apache-2.0
datasets:
- BeaverAI/Nemo-Inst-Tune-ds
language:
- en
library_name: transformers
mistral-doryV2-12b - EXL2 8bpw max
This is a 8bpw EXL2 quant of BeaverAI/mistral-doryV2-12b
This quant was made using exllamav2-0.1.8 with default dataset. I used a slightly modified quantization script to force use of highest bpw methods for all layers in the model (which is usually "1:8b_128g s4") to ensure max quality.
I also added a small fix in config file to set max default context at 128k as original Mistral-Nemo should have.
I tested this quant shortly in some random RPs (including ones over 8k context) and it seems to work fine.
Prompt Templates
Uses Alpaca-like format like mentioned below.
Original readme below
Dory 12b (v2)
(redone) redone instruct finetune of mistral nemo 12b's base. not (E)RP-focused, leave that to drummer.
thanks to twisted again for the compute :3
Prompting
alpaca-like:
### System:
[Optional system prompt]
### Instruction:
[Query]
### Response:
[Response]</s>
### Instruction:
[...]
Training details
Rank 64 QDoRA, trained on the following data mix:
- All of kalomaze/Opus_Instruct_3k
- All conversations with a reward model rating above 5 in Magpie-Align/Magpie-Gemma2-Pro-Preview-Filtered
- 50k of Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- All stories above 4.7 rating and published before 2020 in Fizzarolli/FallingThroughTheSkies-592k-Filtered-Filtered