Create README.md

README.md

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+---
+language:
+- en
+---
+
+### Description:
+This is a multipurpose chat / chat instruct hybrid model in the same vein as the Pygmalion team's Metharme. It uses a curated pile of training data that has been normalized into a consistent training format. It has been trained on a wide array of one shot instructions, multi round instructions, and role playing scenarios.
+
+### Prompt format: 
+Metharme
+
+The prompt should start with the cursor on the same line directly after "<|model|>" with no space. The following are all valid formats and can be extended to as many rounds as desired.
+```
+<|system|>system message here<|user|>user message here<|model|>
+```
+```
+<|system|>system message here<|user|>user message here<|model|>model message<|user|>user message here<|model|>
+```
+```
+<|system|>system message here<|model|>
+```
+```
+<|system|>system message here<|model|>model message<|user|>user message here<|model|>
+```
+
+Some example prompts:
+```
+<|system|>The following is a transcript between a helpful assistant and a user.<|user|>Why is the sky blue?<|model|>
+```
+```
+<|system|>You are a Virtual Story Generator. You take the user's input and create an excellent and captivating story that goes in that direction. Use an abundance of sensory descriptions and eloquent prose.<|user|>Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<|model|>
+```
+```
+<|system|>You are a professional editor with decades of experience, help the user with any task they have for you.<|user|>Can you rewrite this to flow better? "I knew I probably shouldnt have done that but oh well"<|model|>
+```
+More will be added at a later date.
+
+### Perplexity Benchmarks:
+- TBA
+
+### Training information:
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+- GPTQ 4 bit LoRA
+- 7 Epochs
+- 64 / 32 R / A
+- 2048 Cutoff
+- 42 hours on 1x RTX 4090
+
+### Data used in training:
+- TBA
+
+### Models used: 
+For training:
+https://huggingface.co/PocketDoc/llama-30b-gptq-4bit-128g
+
+For merging:
+
+https://huggingface.co/PocketDoc/Dans-PersonalityEngine-30b-LoRA
+
+and
+
+https://huggingface.co/huggyllama/llama-30b
+
+
+### Disclaimer:
+It has not been aligned and no warranty is given for the quality or safety of its outputs.