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---
license: agpl-3.0
datasets:
- kalomaze/Opus_Instruct_25k
- kalomaze/Opus_Instruct_3k
- MangoHQ/Claude-Data-Anon-Killed
---
GGUF: [Here](https://huggingface.co/MangyMango/Ohashi-9B-V1-GGUF)
EXL2 6.0BPW (Thx Lucy <3): [Here](https://huggingface.co/MangyMango/Ohashi-9B-V1-EXl2-6.0BPW)
More quants will be up soon.
# Model Details
I took Gemma-2 base and trained a LoRa with 2 million tokens worth of Claude data and merged via Axolotl CLI
Data used was similar to what Magnum Models are trained off hence Claude Shannon for the card image.
# Prompting
In testing it worked well with basic sampler settings (specifically i used Simple~1 included within ST); it was coherent and stable throughout my testing aswell as being quite proactive. I used the Gemma2 format provided in SillyTavern to test and i found no refusals within RP even when doing extreme NSFW activites - When i was using it as an assistant though i found many refusals but all of them were easily dealt with by using MooreRP, a custom prompt / context template to uncensor the model
MooreRP links:
Context Template: https://files.catbox.moe/b1lpao.json
Instruct Mode: https://files.catbox.moe/21joxa.json
(Made by @a.lk on Discord)
# Config
LoRa for this model was trained in Axoltol for 2 epochs at a rank of 32 and a LR of 2e-5 on 2xRTX 6000s (Provided by Kubernetes Bad) and using the [CustomGemma2 Prompt strategy](https://github.com/xzuyn/axolotl/blob/prompt_formats/src/axolotl/prompt_strategies/customgemma2.py)
# Credits
Thanks to [Kubernetes Bad](https://huggingface.co/kubernetes-bad) for providing compute for this train, [Lucy Knada](https://huggingface.co/lucyknada), [Nopm](https://huggingface.co/Nopm), [Kalomaze](https://huggingface.co/kalomaze) and the rest of [Anthracite](https://huggingface.co/anthracite-org) for providing help to do the train. (But not Alpin) |