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README.md

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+---
+title: "Norobara-ZLoss-8x7B Quantized in GGUF"
+tags:
+  - GGUF
+language: en
+---
+![Image description](https://i.postimg.cc/MGwhtFfF/tsune-fixed.png)
+
+# Tsunemoto GGUF's of Norobara-ZLoss-8x7B
+
+This is a GGUF quantization of Norobara-ZLoss-8x7B.
+
+## Original Repo Link:
+[Original Repository](https://huggingface.co/Doctor-Shotgun/Norobara-ZLoss-8x7B)
+
+## Original Model Card:
+---
+
+# Norobara-ZLoss-8x7B
+
+This is an experimental instruct-tuned [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)-based model trained using [Charles Goddard](https://huggingface.co/chargoddard)'s ZLoss and Megablocks-based fork of transformers.
+
+It primarily uses the Capybara and No Robots datasets (thus the name). The goal was to create an uncensored general instruction following model, as well as test various loss implementations while we figure out how the heck to train Mixtral properly.
+
+[Exl2 Quants](https://huggingface.co/royallab/Norobara-ZLoss-8x7B-exl2)
+
+## Usage:
+The intended prompt format is a modified multi-turn Alpaca instruction format:
+```
+### Instruction:
+{system prompt}
+
+### Input:
+{user message}
+
+### Response:
+{model response}
+
+### Input:
+{user message}
+
+### Response:
+{model response}
+
+(etc.)
+```
+
+## Bias, Risks, and Limitations
+The model will show biases present in the base model. No ethical alignment was applied to prevent the generation of toxic or harmful outputs (in fact the opposite, with examples from toxic-DPO included), so generate at your own risk.
+## Training Details
+This model was trained as a QLora adapter for 3 epochs using a single H100 GPU for around 13 hours.

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