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README.md
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---
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license: mit
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license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
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language:
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- multilingual
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pipeline_tag: text-generation
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tags:
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- nlp
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- code
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inference:
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parameters:
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temperature: 0.7
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widget:
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- messages:
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- role: user
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content: What's the difference between a banana and a strawberry?
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---
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# QuantFactory/Phi-3-mini-4k-geminified-GGUF
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This is quantized version of [failspy/Phi-3-mini-4k-geminified](https://huggingface.co/failspy/Phi-3-mini-4k-geminified) created using llama.cpp
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# Original Model Card
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# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
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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/)
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[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)
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## What's this?
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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.
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## Summary
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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.
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This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
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