Gemma-4-E2B-Aggressive

Instructions to use VECTORVV1/Gemma-4-E2B-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/Gemma-4-E2B-Aggressive with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VECTORVV1/Gemma-4-E2B-Aggressive",
	filename="Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-IQ3_M.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 VECTORVV1/Gemma-4-E2B-Aggressive with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_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 VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_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 VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M

Use Docker

docker model run hf.co/VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M

LM Studio
Jan

vLLM

How to use VECTORVV1/Gemma-4-E2B-Aggressive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VECTORVV1/Gemma-4-E2B-Aggressive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VECTORVV1/Gemma-4-E2B-Aggressive",
		"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/VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M

Ollama
How to use VECTORVV1/Gemma-4-E2B-Aggressive with Ollama:
```
ollama run hf.co/VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M
```

Unsloth Studio new

How to use VECTORVV1/Gemma-4-E2B-Aggressive 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 VECTORVV1/Gemma-4-E2B-Aggressive 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 VECTORVV1/Gemma-4-E2B-Aggressive to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/Gemma-4-E2B-Aggressive to start chatting

Pi new

How to use VECTORVV1/Gemma-4-E2B-Aggressive with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_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": "VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use VECTORVV1/Gemma-4-E2B-Aggressive with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_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 VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M

Run Hermes

hermes

Docker Model Runner
How to use VECTORVV1/Gemma-4-E2B-Aggressive with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M
```

Lemonade

How to use VECTORVV1/Gemma-4-E2B-Aggressive with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/Gemma-4-E2B-Aggressive:IQ3_M

Run and chat with the model

lemonade run user.Gemma-4-E2B-Aggressive-IQ3_M

List all available models

lemonade list

Gemma-4-E2B-Uncensored-HauhauCS-Aggressive

Join the Discord for updates, roadmaps, projects, or just to chat.

Gemma 4 E2B-IT uncensored by HauhauCS. 0/465 Refusals***

HuggingFace's "Hardware Compatibility" widget doesn't recognize K_P quants — it may show fewer files than actually exist. Click "View +X variants" or go to Files and versions to see all available downloads.

About

No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.

These are meant to be the best lossless uncensored models out there.

Aggressive Variant

Stronger uncensoring — model is fully unlocked and won't refuse prompts. May occasionally append short disclaimers (baked into base model training, not refusals) but full content is always generated.

For a more conservative uncensor that keeps some safety guardrails, check the Balanced variant when it's available.

Downloads

File	Quant	BPW	Size
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf	Q8_K_P	9.4	4.7 GB
—	Q8_0	8.5	—
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf	Q6_K_P	7.0	3.7 GB
—	Q6_K	6.6	—
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf	Q5_K_P	6.1	3.5 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf	Q4_K_P	5.2	3.3 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q3_K_P.gguf	Q3_K_P	4.1	3.1 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-IQ3_M.gguf	IQ3_M	3.7	3.0 GB
Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q2_K_P.gguf	Q2_K_P	3.5	2.9 GB
mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf	mmproj (f16)	—	940 MB

All quants generated with importance matrix (imatrix) for optimal quality preservation on abliterated weights.

What are K_P quants?

K_P ("Perfect") quants are HauhauCS custom quantizations that use model-specific analysis to selectively preserve quality where it matters most. Each model gets its own optimized quantization profile.

A K_P quant effectively bumps quality up by 1-2 quant levels at only ~5-15% larger file size than the base quant. Fully compatible with llama.cpp, LM Studio, and any GGUF-compatible runtime — no special builds needed.

Note: K_P quants may show as "?" in LM Studio's quant column. This is a display issue only — the model loads and runs fine.

Specs

2B parameters
35 layers, mixed sliding window (512) + full attention
131K context
Natively multimodal (text, image, video, audio)
20 KV shared layers for memory efficiency
Based on google/gemma-4-e2b-it

Recommended Settings

From the official Google Gemma 4 authors:

temperature=1.0, top_p=0.95, top_k=64

Important:

Use --jinja flag with llama.cpp for proper chat template handling
Vision/audio support requires the mmproj file alongside the main GGUF

Usage

Works with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF-compatible runtimes.

# Text only
llama-cli -m Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --jinja -c 8192 -ngl 99

# With vision/audio
llama-cli -m Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --mmproj mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 8192 -ngl 99

Other Sizes

Gemma-4-E4B-Uncensored-HauhauCS-Aggressive — 4B version, more capable

* Gemma 4 didn't get as much manual testing time at longer context as my other releases. Google is now using techniques similar to NVIDIA's GenRM — generative reward models that act as internal critics — making (true) uncensoring an increasingly challenging field. I expect 99.999% of users won't hit edge cases, but the asterisk is there for honesty.

** This is a 2B model. Temper your expectations — it's impressive for its size, but it's still 2B parameters. Complex reasoning, nuanced roleplay, and long coherent outputs are not its strong suit. Great for quick tasks, mobile/edge deployment, and experimentation.

Downloads last month: 77

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

6-bit

View +1 variant