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---
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](https://huggingface.co/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](https://huggingface.co/datasets/kalomaze/Opus_Instruct_3k)
- All conversations with a reward model rating above 5 in [Magpie-Align/Magpie-Gemma2-Pro-Preview-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Gemma2-Pro-Preview-Filtered)
- 50k of [Gryphe/Sonnet3.5-SlimOrcaDedupCleaned](https://huggingface.co/datasets/Gryphe/Sonnet3.5-SlimOrcaDedupCleaned)
- All stories above 4.7 rating and published before 2020 in [Fizzarolli/FallingThroughTheSkies-592k-Filtered-Filtered](https://huggingface.co/datasets/Fizzarolli/FallingThroughTheSkies-592k-Filtered-Filtered) |