Dans-PersonalityEngine-30b

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.

The training parameters were suboptimal for the most recent run and I decided to stop after 2 epochs as 3 likely would have overtrained it. I plan on iterating the model and improving it further when I have access to more funds to do so.

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"/>

• GPTQ 4 bit LoRA
• 2 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.