How is the scale same. The scale and alpha/beta shouls change based on statergy (Min_Max,Percentile,MSE)

This is the subtle part. There are two different concepts of "strategy" that are easy to conflate.

Your code allows different strategies:

MinMax
Percentile
MSE
CrossEntropy

These strategies determine how you choose the quantization range (alpha, beta), and therefore the scale.

However, GGML Q4_0 does not use those strategies.

In your getAlphaBeta() you have:

if dtype == "I4" && quantExportFormat == types.GGUF {
    absMax := 0.0
    extreme := 0.0
    for _, w := range modelWeight {
        x := float64(w)
        ax := math.Abs(x)
        if ax > absMax {
            absMax = ax
            extreme = x
        }
    }
    return extreme, extreme
}

Notice what this does.

For GGUF Q4_0, you completely bypass:

* MinMax
* Percentile
* MSE

Every one of them.

You always return

alpha = extreme
beta  = extreme

So for your implementation, the scale is independent of the strategy.

⸻

Why does llama.cpp produce the same scale?

Because Q4_0 is defined as a symmetric, one-scale-per-32-values quantizer.

The block contains only

FP16 scale
16 packed bytes

There is no metadata to store:

* percentile
* clipping threshold
* optimized alpha
* optimized beta

So the scale is fundamentally constrained.

⸻

The optimization happens elsewhere

Suppose the block is

[-0.003
 0.008
 0.010
 ...
 0.041]

The scale becomes approximately

0.041 / -8

That stays fixed.

Now there are many possible integer vectors

[-2,-2,-1,...]
[-2,-1,-2,...]
[-1,-2,-2,...]
...

whose reconstructed values are all near the originals.

The optimizer searches among these integer assignments to reduce the total block error.

It doesn't necessarily change the scale.

⸻

If you changed the strategy…

Suppose you forced

Percentile 99%

Then you might choose

alpha = -0.032
beta  = 0.033

instead of

±0.041

Now the scale becomes

0.033 / -8

which is completely different.

But then you are no longer producing GGML Q4_0.

You are producing your own quantizer that happens to use the same storage layout.

⸻

In other words

There are two separate optimization problems:

1. Range selection

Choose

alpha
beta

Examples:

* MinMax
* Percentile
* MSE

This changes the scale.

⸻

2. Integer assignment

Given a fixed scale, choose

q0...q31

to minimize

Σ(original_i - q_i * scale)²

This changes the quantized values, but the scale can remain unchanged.

⸻

So when I said "the scale is the same," I meant:

For llama.cpp's Q4_0 implementation, the optimizer is not optimizing the range (alpha/beta); it is optimizing the integer assignments for a scale derived from the block's extreme value.

If your objective is to reproduce llama.cpp byte-for-byte, then your getAlphaBeta() for GGUF Q4_0 is already following the correct idea (choose the extreme value). The remaining difference lies in how the 32 quantized integers are chosen after the scale has been fixed.

`For llama.cpp's Q4_0 implementation, the optimizer is not optimizing the range (alpha/beta); it is optimizing the integer assignments for a scale derived from the block's extreme value.

model

rants