I have loaded an LLM in huggingface with load_in_8bit=True.

I noticed the objects in the state_dict are structured something like

- model.layers.18.self_attn.k_proj.weight
- model.layers.18.self_attn.k_proj.SCB
- model.layers.18.self_attn.k_proj.weight_format

The SCB and weight_format are present only in the quantized model. I think SCB refers to scale and bias that can help us in recreating the original tensor? Weight format is just a string that says “row”

I am not sure about the exact method to dequantize, but I tried the following:

`(weight_SCB.unsqueeze(1) * weight)/127`

This is giving a tensor that is close to the original model (loading without load_in_8bit=True)

However it is not the same.

I think I am doing something wrong in the dequantization process. Would be great if someone could point me to some code or documentation on how I can recreate the exact original tensor (alternatives to huggingface work as well) from the weights.

As a follow up question, I know that for some models there are outlier values that are not quantized even though other values in the tensor are quantized. However I could not find this information in the state_dict. How can we find and handle these values during the dequantization process?