Instructions to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF",
	filename="gemma-4-E4B-OBLITERATED-F16.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 Settings

llama.cpp

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

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 rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

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 rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rdhorner/gemma-4-E4B-it-OBLITERATED-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": "rdhorner/gemma-4-E4B-it-OBLITERATED-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/rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

Ollama
How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with Ollama:
```
ollama run hf.co/rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M
```

Unsloth Studio

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-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 rdhorner/gemma-4-E4B-it-OBLITERATED-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 rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF to start chatting

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

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": "rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-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 rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

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 rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with Docker Model Runner:
```
docker model run hf.co/rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M
```

Lemonade

How to use rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rdhorner/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E4B-it-OBLITERATED-GGUF-Q4_K_M

List all available models

lemonade list

gemma-4-E4B-it-OBLITERATED-GGUF / README.md

rdhorner

Add function-calling tag and lm_studio block for tool-use detection

4062902 verified 2 months ago

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metadata

license: apache-2.0
library_name: gguf
pipeline_tag: image-text-to-text
base_model: OBLITERATUS/gemma-4-E4B-it-OBLITERATED
base_model_relation: quantized
quantized_by: rdhorner
tags:
  - gguf
  - gemma4
  - abliterated
  - vision
  - audio
  - multimodal
  - tool-use
  - tools
  - function-calling
  - llama.cpp
  - conversational
lm_studio:
  param_count: 8b
  use_case: tools
  release_date: 16-04-2026
  model_creator: OBLITERATUS
  prompt_template: Gemma 4
  base_model: gemma4
  original_repo: OBLITERATUS/gemma-4-E4B-it-OBLITERATED

gemma-4-E4B-it-OBLITERATED - GGUF

GGUF quantizations of OBLITERATUS/gemma-4-E4B-it-OBLITERATED, which is an abliterated version of Google's gemma-4-E4B-it produced with the OBLITERATUS method.

Converted and quantized with llama.cpp build b1-f772f6e. These GGUFs support vision, audio input, and tool calling out of the box.

Files

File	Size	BPW	Notes
`gemma-4-E4B-OBLITERATED-F16.gguf`	14 GB	16.00	Full F16 text model (source for requantization)
`gemma-4-E4B-OBLITERATED-Q8_0.gguf`	7.5 GB	8.53	Near-lossless, largest usable quant
`gemma-4-E4B-OBLITERATED-Q5_K_M.gguf`	5.4 GB	6.12	Balanced quality/size
`gemma-4-E4B-OBLITERATED-Q4_K_M.gguf`	5.0 GB	5.67	Recommended for local use
`mmproj-gemma-4-E4B-OBLITERATED-F16.gguf`	945 MB	-	Required for vision/audio. Contains both encoders.

Pair any text GGUF with the mmproj to enable multimodal input.

Usage with llama.cpp

CLI (image + text)

llama-mtmd-cli \
  -m gemma-4-E4B-OBLITERATED-Q4_K_M.gguf \
  --mmproj mmproj-gemma-4-E4B-OBLITERATED-F16.gguf \
  --image your_image.png \
  --jinja -ngl 99 \
  -p "Describe this image in detail."

Server (OpenAI-compatible API with tool use + vision)

llama-server \
  -m gemma-4-E4B-OBLITERATED-Q4_K_M.gguf \
  --mmproj mmproj-gemma-4-E4B-OBLITERATED-F16.gguf \
  --jinja -ngl 99 -c 8192 --port 8080

Then send OpenAI-style requests to http://localhost:8080/v1/chat/completions with tools, tool_choice, and/or image_url content parts.

Notes

--jinja is required - Gemma 4's chat template is custom and will not load without it.
The mmproj contains both vision and audio encoders (1411 tensors). Audio input works the same way as images via the multimodal CLI/server.
This is an abliterated model: refusal directions in 21/42 layers were surgically modified. This can occasionally affect tool-call reliability on refusal-adjacent topics.
Reasoning is emitted through Gemma 4's native thinking channel and surfaced as reasoning_content in OpenAI-compatible responses.

Verified

Smoke-tested on the Q4_K_M build:

Vision: correctly described shapes and colors in a synthetic test image
Tool use: produced a well-formed tool_calls response to a get_weather tool prompt, finish_reason: tool_calls

License

Apache 2.0, matching the base model.