rahul7star/gemma-gguf

model-apex-i-quality.gguf => is gemma4 apex

model-gemma4-heretic-apexi-quality.gguf => Gemma4 Fast Heretic Model

DEMO

https://huggingface.co/spaces/rahul7star/apex-gguf

Gemma 12 B model

google/gemma-4-12B-it => model-i-gemma-e4-12b-quality.gguf

DEMO https://huggingface.co/spaces/rahul7star/Gguf-gradio

Gemma 12B Heretic

model-gemma-4-12B-it-qat-q4_0-unquantized-heretic-i-quality.gguf

Llama-3.1-Nemotron-Nano-4B-v1.1-heretic

model-iLlama-3.1-Nemotron-Nano-4B-v1.1-heretic-quality.gguf

Knowledge base

understading 46 layers of GEMMA

 Gemma 4 12B model:

### What are the layers?

This line:

```text
gemma4.block_count = 48

means the model has 48 transformer blocks (layers).

Think of them as a pipeline:

Input text
   ↓
Layer 1
   ↓
Layer 2
   ↓
...
   ↓
Layer 48
   ↓
Output probabilities

Every token always passes through all 48 layers during inference (unless you explicitly use layer skipping techniques, which GGUF normally doesn't).

---

What does each layer do?

Very roughly:

Layer Range	Typical Role
1-10	Basic language patterns, spelling, grammar
11-20	Word relationships, syntax
21-35	Facts, concepts, reasoning
36-48	Final prediction and response shaping

This isn't a hard rule, but it's a useful mental model.

---

What does temperature do?

Temperature only affects the final token selection after all 48 layers have finished computing.

The model calculates something like:

Token      Score
----------------
"cat"      0.60
"dog"      0.25
"bird"     0.10
"banana"   0.05

Then temperature modifies those probabilities.

Temperature = 0.2

Very deterministic:

cat 95%
dog 4%
bird 1%

Almost always picks "cat".

---

Temperature = 1.0

Normal:

cat 60%
dog 25%
bird 10%
banana 5%

Balanced.

---

Temperature = 2.0

More random:

cat 35%
dog 28%
bird 22%
banana 15%

Much more variety.

---

So what are these values?

Your GGUF metadata says:

general.sampling.top_k = 64
general.sampling.top_p = 0.95
general.sampling.temp = 1.0

These are just recommended default sampling settings stored inside the GGUF.

They mean:

• temp=1.0 → normal randomness
• top_k=64 → only consider best 64 candidate tokens
• top_p=0.95 → consider tokens whose cumulative probability reaches 95%

Many frontends ignore these and use their own settings.

---

What about Q6_K?

You mentioned:

Q6_K

That's not a layer either.

It means the weights are quantized to roughly 6 bits per weight.

Typical quality ladder:

Q2_K   = very small, lower quality
Q3_K
Q4_K
Q5_K
Q6_K   ← very high quality
Q8_0
F16    = full precision

For a 12B model:

F16  ≈ 24 GB
Q8   ≈ 13 GB
Q6_K ≈ 10 GB
Q5_K ≈ 8 GB
Q4_K ≈ 7 GB

Q6_K is usually very close to F16 quality.

---

Interesting Gemma-specific settings

Your model has:

context_length = 262144

That's 262K context, which is huge.

And:

attention.head_count = 16
key_length = 512
value_length = 512

Meaning each of the 48 layers contains a multi-head attention system with 16 attention heads processing information in parallel.

A simplified picture:

48 Layers
┌──────────────┐
│ Layer 1      │ → 16 attention heads
│ Layer 2      │ → 16 attention heads
│ Layer 3      │ → 16 attention heads
│ ...          │
│ Layer 48     │ → 16 attention heads
└──────────────┘

Total attention computations are happening across all layers every token generation step.

So:

• 48 layers = model depth
• 16 heads per layer = parallel attention mechanisms
• Temperature = randomness of token selection
• Top-k / Top-p = filtering candidate tokens
• Q6_K = quantization level
• Temperature does NOT change which layers are used; all 48 layers run regardless of temperature.