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

Libraries

How to use Abiray/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="Abiray/gemma-4-E4B-it-OBLITERATED-GGUF",
	filename="gemma-4-E4B-it-obliterated-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

LM Studio
Jan

vLLM

How to use Abiray/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 "Abiray/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": "Abiray/gemma-4-E4B-it-OBLITERATED-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

How to use Abiray/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 Abiray/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": "Abiray/gemma-4-E4B-it-OBLITERATED-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/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

These are quantized GGUF format files for OBLITERATUS/gemma-4-E4B-it-OBLITERATED.

Available Quantizations

The following quantization methods are provided to suit different memory and performance requirements:

Filename	Quant Type	Description
`gemma-4-E4B-it-obliterated-Q3_K_M.gguf`	Q3_K_M	Very small, high quality loss. Good for extreme low-VRAM scenarios.
`gemma-4-E4B-it-obliterated-Q4_0.gguf`	Q4_0	Legacy format. Fast, but generally superseded by K-quants.
`gemma-4-E4B-it-obliterated-Q4_K_M.gguf`	Q4_K_M	Recommended. Excellent balance of size, speed, and minimal quality loss.
`gemma-4-E4B-it-obliterated-Q5_0.gguf`	Q5_0	Legacy format. Slightly higher quality and larger than Q4_0.
`gemma-4-E4B-it-obliterated-Q5_K_M.gguf`	Q5_K_M	High quality. Recommended if you have enough RAM/VRAM to spare over Q4_K_M.
`gemma-4-E4B-it-obliterated-Q6_K.gguf`	Q6_K	Very high quality. Near-perfect recreation of the original unquantized model.
`gemma-4-E4B-it-obliterated-Q8_0.gguf`	Q8_0	Extremely high quality. Almost indistinguishable from fp16, but requires significant memory.

How to Use with llama.cpp

Once you have downloaded llama.cpp and compiled it, you can run this model via the command line.

Basic CLI usage:

./llama-cli -m gemma-4-E4B-it-obliterated-Q4_K_M.gguf -p "Your prompt goes here" -n 512

Downloads last month: 4,116

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/gemma-4-E4B-it-OBLITERATED-GGUF

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

OBLITERATUS/gemma-4-E4B-it-OBLITERATED

Quantized

(26)

this model

Collection including Abiray/gemma-4-E4B-it-OBLITERATED-GGUF

Gemma 4

Collection

6 items • Updated Apr 16 • 1