Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2
Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.1.3
Other EXL2 quants: | Quant | Model Size | lm_head | | ----- | ---------- | ------- | |<center>[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)</center> | <center>1217 MB</center> | <center>6</center> | |<center>[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)</center> | <center>1342 MB</center> | <center>6</center> | |<center>[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)</center> | <center>1558 MB</center> | <center>6</center> | |<center>[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)</center> | <center>1774 MB</center> | <center>6</center> | |<center>[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)</center> | <center>1882 MB</center> | <center>6</center> | |<center>[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)</center> | <center>1990 MB</center> | <center>6</center> | |<center>[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)</center> | <center>2099 MB</center> | <center>6</center> | |<center>[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)</center> | <center>2423 MB</center> | <center>6</center> | |<center>[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)</center> | <center>2870 MB</center> | <center>8</center> | |<center>[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)</center> | <center>3089 MB</center> | <center>8</center> | |<center>[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)</center> | <center>3620 MB</center> | <center>8</center> |
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.