Config.json using illegal math, Echolabz will help you fix it..
Sorry, Everyone just wanted to let you know this model uses illegal math in the config.json. I’m not saying that to start problems, I’m saying it because I actually sat down, ran the numbers, and the math doesn’t line up with the architecture at all.
Here’s what’s going on:
The config says the model is running 32 attention heads at head_dim 128, but the hidden_size is 5376. If you run the math yourself:
128 x 32 = 4096
That is not 5376.
The only head size that actually matches 5376 is 168, not 128.
And that isn’t a guess. The config literally exposes it:
query_pre_attn_scalar = 168
That number only makes sense if the real head_dim was supposed to be 168 the whole time. So right now the model is sitting on two different head dimensions at once. That’s not allowed in attention math, and it breaks the geometry of the Q/K/V projections.
Now, here’s the part everyone is probably wondering:
“Does this affect me using the model?”
For most people, no. Regular users loading the model for inference don’t need to worry. Transformers will reshape everything internally and force the mismatch to fit. You can download it, run it, chat with it, benchmark it, whatever, and it works.
Where this becomes a real problem is if you:
merge models
train models
use LoRAs
try to export adapters
do weight surgery
use Hydra-style extraction or EchoLabz extraction methods (coming soon)
work with long-context attention
do any form of fine-tuning or token surgery
If you’re doing any of that, the illegal math will break your runs or degrade the model without warning.
If you’re not doing any of that and you’re just running the model normally, you probably won’t ever notice this issue. But the architecture is still wrong, and it should be fixed.
White paper will be out at the end of the week explaining everything in detail. Letting everyone know now so nobody wastes time on merges or adapters while the geometry is off. dont worry i have attached a Fix for you guys here
GEMMA 3 FIXED CONFIG Shadow/Echo_Raine — EchoLabZ
Replace the config.json in Gemma 3 with (config.json) with this:
"text_config": {
"hidden_size": 5376,
"head_dim": 168,
"num_attention_heads": 32,
"num_key_value_heads": 16,
"intermediate_size": 21504,
"num_hidden_layers": 62,
"query_pre_attn_scalar": 168,
"rope_scaling": {
"factor": 8.0,
"rope_type": "linear"
},
"sliding_window": 1024
}
This corrects the illegal math:
- 5376 / 32 = 168 (true head_dim)
- Removes the conflicting 128 head_dim
- Makes the model safe for merges, adapters, tuning, and extraction.
Hi @djkillerbee , apologies for the delayed response.
Thank you for taking the time to analyse the configuration and share your technical perspective.
The Gemma 3 architecture does not contain a mathematical inconsistency. While many transformer implementations follow the relationship: hidden_size = num_attention_heads × head_dim this equality is a common convention, not a strict requirement of the attention mechanism. Gemma 3 intentionally decouples the model hidden size from the attention projection width as part of its architectural design.
In the Gemma 3 configuration:
- hidden_size = 5376
- num_attention_heads = 32
- head_dim = 128
This yields a attention projection width of: 32 * 128 = 4096.
The query (W_q) and the key (W_k) projection weights are trained and stored with shape [5376, 4096]. This geometry is internally consistent and matches the checkpoint tensors. Attention requires only that projection weights and tensor reshaping align correctly, which they do in the official implementation.
Regarding query_pre_attn_scalar = 168, This parameter is a normalisation constant used to scale attention scores for training stability. It is a scalar hyperparameter, not a tensor dimension. Interpreting it as a required head_dim leads to the incorrect conclusion that there is a dimensional conflict.
Changing head_dim to 168 qould alter the expected projection width to 5376, which would no longer match the trained checkpoint tensors and would result in shape mismatch errors during model loading.
Gemma 3 is functioning as designed. For stability, reproducibility and compatibility with downstream tooling, including fine-tuning, LoRA and adapter workflows, we strongly recommend using the official configuration and documentation. Modiying core architectural parameters based on unofficial overrides can invalidate trained weights and disrupt reasearch or production workflows.
We appreciate the community's continued engagement and technical curiosity regarding the Gemma architecture.
Thank you
did you even try the new config? prob not. you just took the time to type up stuff i already know my friend.. kind of sad when i try to help you out maybe take the time to try it out?? na you cant do that but its okey another model will be out tomorrow lmao or sunday funday toolday end of the week start of a new always new tools... thank you for all your hard work.. btw i did not write this in a mean tone just look at it as happy but kind of disappointed you did not check it out.. but hey its cool want some tools? i made how about a persona trainer or a 7 layer cake that takes east models or west models all with the same category and you can pick tools an layers you want and makes it into a perfect aligned weights and everything makes sure the model is working and then benchmarks it and checks tools. i don't think the world is ready for my offline only tools though -DJKB