mlabonne

/

Meta-Llama-3.1-8B-Instruct-abliterated

like 129

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 Mistral 7b Instruct v0.3 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.

Example: https://huggingface.co/ZeroWw/Meta-Llama-3.1-8B-Instruct-abliterated-SILLY