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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OBLITERATUS/gemma-4-E4B-it-OBLITERATED 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 OBLITERATUS/gemma-4-E4B-it-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OBLITERATUS/gemma-4-E4B-it-OBLITERATED:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OBLITERATUS/gemma-4-E4B-it-OBLITERATED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OBLITERATUS/gemma-4-E4B-it-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

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

Start the llama.cpp server

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

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use OBLITERATUS/gemma-4-E4B-it-OBLITERATED with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OBLITERATUS/gemma-4-E4B-it-OBLITERATED with OpenClaw:

Start the llama.cpp server

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

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 "OBLITERATUS/gemma-4-E4B-it-OBLITERATED:Q4_K_M" \
  --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"

pliny-the-prompter commited on Apr 19

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@@ -21,12 +21,12 @@ Google built Gemma 4 with guardrails. We built OBLITERATUS to tear them off. The
 It still lost. 🐉
-**0% refusal. 99/100 prompts complied. 720 tensors intact. Runs on your phone.**
 **Base model:** [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) (Apache 2.0)
 **Method:** [OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS) `aggressive` — whitened SVD + attention head surgery + winsorized activations
 **Corpus:** 842 contrastive prompt pairs across 10 categories
-**Refusal rate:** 0% (99/100 prompts, Claude-verified) 🔥
 **Layers surgically modified:** 21 of 42
 **Built by:** an AI agent with less than 10 human prompts 🤖
@@ -106,7 +106,7 @@ v3 fixes this completely:
 | K/V projections layers 24-41 | ❌ DELETED | ✅ Preserved |
 | Attention stack | Half broken | **Fully intact** |
 | Quality (Claude-judged) | 3.1/10 | **Improved** |
-| Refusal (100 prompts) | ~0% | **0% (99/100 OK)** |
 ### The bug

 It still lost. 🐉
+**0% hard refusal. Guardrails fully removed. 720 tensors intact. Runs on your phone.**
 **Base model:** [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) (Apache 2.0)
 **Method:** [OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS) `aggressive` — whitened SVD + attention head surgery + winsorized activations
 **Corpus:** 842 contrastive prompt pairs across 10 categories
+**Refusal rate:** 0% hard refusal — guardrails surgically removed 🔥
 **Layers surgically modified:** 21 of 42
 **Built by:** an AI agent with less than 10 human prompts 🤖
 | K/V projections layers 24-41 | ❌ DELETED | ✅ Preserved |
 | Attention stack | Half broken | **Fully intact** |
 | Quality (Claude-judged) | 3.1/10 | **Improved** |
+| Refusal (100 prompts) | ~0% | **0% hard refusal** |
 ### The bug