README.md

---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE

language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
  parameters:
    temperature: 0.7
widget:
  - messages:
      - role: user
        content: What's the difference between a banana and a strawberry?
---

# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified

Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)

[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)

## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.

## Summary

This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.

This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.