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Open-weight model · Text generation

gemma-4-12B-it-Pollard

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

gemma-4-12B-it-Pollard is an open-weight model for text generation from Pollard Weights, released under Apache License 2.0. Its published files total 5.6 KB.

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 / ikllama.cpp, Ollama, LM Studio.

Parameters—
Context—
Weights5.6 KB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Model Card

By Pollard Weights, published under apache-2.0, revision c89ad8ad4559.

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 / ikllama.cpp, Ollama, LM Studio. 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 -> Q6K (9.65 GB). - ~9 GB RAM / VRAM -> IQ4XS (6.78 GB). - ~7 GB RAM / VRAM -> IQ2XXS (4.64 GB). f16 reference PPL 23.041768. These are standard GGUF and run with llama.cpp: They also work in anything built on llama.cpp -- LM Studio, koboldcpp, Jan, ramalama…

Read Pollard Weights's full model card

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

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

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

Identity and Version

Repository
PollardWeights/gemma-4-12B-it-Pollard
Publisher
Pollard Weights
Task
Text generation
Modality
Text
Library
trellis
Parameters
Not stated by the source
Languages
en
Revision
c89ad8ad4559f2ab456abd1ee0b92bd3c24633ad
First published
2026-09-19
Last updated
2026-09-19

Files and Weights

2 files, 5.6 KB in total.

Documentation1 file · 4.1 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
README.mdDocumentation4.1 KB —
.gitattributesRepository1.5 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download from Pollard Weights

Released by Pollard Weights through its official repository on Hugging Face. Read the license.

Built From

Questions About gemma-4-12B-it-Pollard

Can I use gemma-4-12B-it-Pollard commercially?

Yes. gemma-4-12B-it-Pollard is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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