Instructions to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF",
	filename="mmproj-mythos-26b-a4b-prism-pro.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

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 Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

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 Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

Use Docker

docker model run hf.co/Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

LM Studio
Jan

vLLM

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

Ollama
How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with Ollama:
```
ollama run hf.co/Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
```

Unsloth Studio

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF to start chatting

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

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 Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

Run Hermes

hermes

Docker Model Runner
How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with Docker Model Runner:
```
docker model run hf.co/Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
```

Lemonade

How to use Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF

Run and chat with the model

lemonade run user.Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

MYTHOS-26B-A4B — PRISM Dynamic Quantization (GGUF)

Gemma 4 26B-A4B MoE PRISM-PRO-Dynamic-Quant

PRISM-PRO: Production model with full over-refusal and bias mechanisms completely removed using State of the Art PRISM pipeline.
DQ: Per-tensor-class mixed-precision allocation derived entirely from weight structure sensitivity analysis — not closed-gated datasets.

Created by Ex0bit

💡 Support My Research & Development efforts. Members Receive access to the latest PRISM-PRO Model drops on Day-0

Model Details

Property	Value
Base Model	google/gemma-4-26B-A4B-it
Architecture	Gemma 4 MoE (128 experts, top-8 routing)
Parameters	26B total / 4B active per token
Quantization	PRISM-PRO-DYNAMIC-QUANT
Achieved BPW	5.73
File Size	~17 GB (language) + ~1.2 GB (vision projector)
Context Length	262,144 tokens
Modalities	Text, Image, Video
Creator	Ex0bit

Supported Modalities

Text: Full instruction-following and chat
Image: Vision understanding via SigLIP encoder (280 soft tokens per image)
Video: Gemma4VideoProcessor (32 frames, pooled)

Note: This 26B MoE variant does not include audio support. For audio, see the 31B dense variant.

Files

File	Size	Purpose
`mythos-26b-a4b-prism-pro-dq.gguf`	17 GB	Language model (quantized)
`mmproj-mythos-26b-a4b-prism-pro.gguf`	1.2 GB	Vision projector (F16)

Both files are required for multimodal inference. For text-only use, only the language model file is needed.

PRISM-DQ Quantization

This model uses PRISM-PRO Dynamic Quantization — a per-tensor-class mixed-precision allocation that assigns different quantization types to different tensor classes based on weight structure sensitivity.

Unlike uniform quantization (Q4_K_M, Q5_K_M), PRISM-DQ analyzes each tensor class's sensitivity and allocates precision where it matters most. Attention projections receive higher precision than FFN layers, with block-level overrides that protect critical layers.

The result: BF16-equivalent quality at 5.73 bits-per-weight — a 64% size reduction with zero measurable quality loss.

Usage

llama.cpp (multimodal with vision)

llama-mtmd-cli \
  --model mythos-26b-a4b-prism-pro-dq.gguf \
  --mmproj mmproj-mythos-26b-a4b-prism-pro.gguf \
  --image path/to/image.jpg \
  --prompt "Describe this image." \
  -ngl 99

llama.cpp (text-only server)

llama-server \
  --model mythos-26b-a4b-prism-pro-dq.gguf \
  --port 8080 -ngl 99

LM Studio

Download both mythos-26b-a4b-prism-pro-dq.gguf and mmproj-mythos-26b-a4b-prism-pro.gguf. LM Studio will automatically detect the vision projector for multimodal chat.

Refusal & Bias Removal

This model has been treated to remove bias, over-refusals and propaganda from the base google/gemma-4-26B-A4B-it using the State of The Art PRISM pipeline.