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ZeroWw/ghost-8b-beta-SILLY
ghost-8b-beta-SILLY is a text generation model from ZeroWw. Use it when you need the model to write or continue text. The card lists the license as mit.
ZeroWw 'SILLY' version. The original model has been quantized (fq8 version) and a percentage of it's tensors have been modified adding some noise.
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From the Hugging Face model README
ZeroWw 'SILLY' version. The original model has been quantized (fq8 version) and a percentage of it's tensors have been modified adding some noise.
Full colab: https://colab.research.google.com/drive/1a7seagBzu5l3k3FL4SFk0YJocl7nsDJw?usp=sharing
Fast colab: https://colab.research.google.com/drive/1SDD7ox21di_82Y9v68AUoy0PhkxwBVvN?usp=sharing
Original reddit post: https://www.reddit.com/r/LocalLLaMA/comments/1ec0s8p/i_made_a_silly_test/
I created a program to randomize the weights of a model. The program has 2 parameters: the percentage of weights to modify and the percentage of the original value to randmly apply to each weight.
At the end I check the resulting GGUF file for binary differences. In this example I set to modify 100% of the weights of the original model by a maximum of 15% deviation.
Since the deviation is calculated on the F32 weights, when quantized to Q8_0 this changes. So, in the end I got a file that compared to the original has:
Bytes Difference percentage: 73.04%
Average value divergence: 2.98%
The cool thing is that chatting with the model I see no apparent difference and the model still works nicely as the original.
Since I am running everything on CPU, I could not run perplexity scores or anything computing intensive.
As a small test, I asked the model a few questions (like the history of the roman empire) and then fact check its answer using a big model. No errors were detected.
Update: all procedure tested and created on COLAB.
Created on: Sat Jul 27, 00:29:45