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?
QuantFactory/Phi-3-mini-4k-geminified-GGUF
This is quantized version of failspy/Phi-3-mini-4k-geminified created using llama.cpp
Original Model Card
Phi-3-mini-128k-instruct- abliterated-v3 -geminified
Credit to u/Anduin1357 on reddit for the name who wrote this comment
My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon
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 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' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.