gemma-4-12B-it -- Pollard

Pollard shrank this model: 23.81 GB (f16) -> 4.64 GB -- 80% smaller, 5.1x down.

The smallest rung here; larger, higher-fidelity rungs are listed below.

format this model's size

f16 23.81 GB

Q8_0 ~12.62 GB

Q6_K ~9.76 GB

Q4_K_M ~6.91 GB

PollardMix (this repo's IQ2_XXS) 4.64 GB

format	this model's size
f16	23.81 GB
Q8_0	~12.62 GB
Q6_K	~9.76 GB
Q4_K_M	~6.91 GB
PollardMix (this repo's IQ2_XXS)	4.64 GB

Pollard builds of google/gemma-4-12B-it made with Pollard Weights -- a ladder of measured-allocation quants (bits placed by per-layer sensitivity, not a uniform crush).

Standard GGUF -- every file here runs in stock llama.cpp / ik_llama.cpp, Ollama, LM Studio.

Model details


Parameter count	~11.9B
Architecture	`gemma4_unified`
Input support	text, image, audio
imatrix	yes -- see calibration
Perplexity measured	yes -- table below

Which file should I choose?

Every rung is the same weights, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context:

~12 GB RAM / VRAM -> Q6_K (9.65 GB).
~9 GB RAM / VRAM -> IQ4_XS (6.78 GB).
~7 GB RAM / VRAM -> IQ2_XXS (4.64 GB).

Available files (Calib 3.0 held-out (prose/code/math/chat/multilingual), ctx 2048, 60 chunks)

f16 reference PPL 23.041768.

file	PPL	size	Mean KLD	notes
`gemma-4-12B-it-Pollard-IQ2_XXS.gguf`	56.8803	4.64 GB	2.3671	smallest -- +147% vs f16
`gemma-4-12B-it-Pollard-IQ4_XS.gguf`	29.803	6.78 GB	0.7497	recommended default -- +29% vs f16
`gemma-4-12B-it-Pollard-Q6_K.gguf`	26.6124	9.65 GB	0.334	highest fidelity here -- +15% vs f16

Download a specific file

pip install -U "huggingface_hub[cli]"
hf download PollardWeights/gemma-4-12B-it-Pollard \
  --include "gemma-4-12B-it-Pollard-IQ2_XXS.gguf" --local-dir ./

How to run

These are standard GGUF and run with llama.cpp:

llama-server -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

or from a local file:

llama-cli    -m gemma-4-12B-it-Pollard-IQ2_XXS.gguf -ngl 99 -p "Explain why the sky is blue."
llama-server -m gemma-4-12B-it-Pollard-IQ2_XXS.gguf -ngl 99      # OpenAI-compatible API + web UI at :8080

They also work in anything built on llama.cpp -- LM Studio, koboldcpp, Jan, ramalama, Ollama (ollama run hf.co/PollardWeights/gemma-4-12B-it-Pollard).

ARM / AVX

llama.cpp repacks weights into an interleaved layout at load time for faster inference on ARM and AVX machines -- no special file needed, online repacking covers these quants. The old Q4_0_4_4/4_8/8_8 variants are not required.

Errata

Every file here loads in stock llama.cpp -- verified from the tensor types with pollard-ggufcheck, not assumed from the filenames.
Measured allocation places bits by per-layer sensitivity under a size budget.
Single machine; replication invited.

Credits & license

Base model: google/gemma-4-12B-it
Quantization tooling: llama.cpp (ggml-org)
Method + tooling: Pollard Weights -- measure first, no claim before a number.
License: apache-2.0, inherited from the base model.

Built with Pollard Weights -- frontier models, small hardware, no compromise.

PollardWeights/gemma-4-12B-it-Pollard