Instructions to use PollardWeights/gemma-4-12B-it-Pollard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PollardWeights/gemma-4-12B-it-Pollard with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use PollardWeights/gemma-4-12B-it-Pollard with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS
# Run inference directly in the terminal:
llama cli -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS
# Run inference directly in the terminal:
llama cli -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS
# Run inference directly in the terminal:
./llama-cli -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Use Docker

docker model run hf.co/PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

LM Studio
Jan

vLLM

How to use PollardWeights/gemma-4-12B-it-Pollard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PollardWeights/gemma-4-12B-it-Pollard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PollardWeights/gemma-4-12B-it-Pollard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Ollama
How to use PollardWeights/gemma-4-12B-it-Pollard with Ollama:
```
ollama run hf.co/PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS
```
Unsloth Desktop

How to use PollardWeights/gemma-4-12B-it-Pollard with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PollardWeights/gemma-4-12B-it-Pollard with Docker Model Runner:
```
docker model run hf.co/PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS
```

Lemonade

How to use PollardWeights/gemma-4-12B-it-Pollard with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Run and chat with the model

lemonade run user.gemma-4-12B-it-Pollard-IQ2_XXS

List all available models

lemonade list

Hermes Agent

How to use PollardWeights/gemma-4-12B-it-Pollard with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PollardWeights/gemma-4-12B-it-Pollard with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "PollardWeights/gemma-4-12B-it-Pollard:IQ2_XXS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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.

Downloads last month: -

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

2-bit

4-bit

6-bit

Model tree for PollardWeights/gemma-4-12B-it-Pollard

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

(318)

this model

PollardWeights
/

gemma-4-12B-it-Pollard

gemma-4-12B-it -- Pollard

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

Model details

Which file should I choose?

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

Download a specific file

How to run

ARM / AVX

Errata

Credits & license

Model tree for PollardWeights/gemma-4-12B-it-Pollard