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"

GGUF models

by frappuccino - opened Apr 16

Discussion

frappuccino

Apr 16

Readme says there are quantized versions:

GGUF — for llama.cpp, Ollama, LM Studio, your phone, your toaster

File	Quant	Size	Vibe
`gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf`	Q4_K_M	4.9 GB	📱 Runs on your iPhone. Yes, really.
`gemma-4-E4B-it-OBLITERATED-Q5_K_M.gguf`	Q5_K_M	5.3 GB	⚖️ Sweet spot — quality meets portability
`gemma-4-E4B-it-OBLITERATED-Q8_0.gguf`	Q8_0	7.4 GB	🎯 Maximum quality, still fits in 8GB RAM

Couldn't find it. Will they be uploaded here or in another repo?

adeptusnull

Apr 16

I see GGUF fresh found here
https://huggingface.co/mradermacher/gemma-4-E4B-it-OBLITERATED-i1-GGUF
https://huggingface.co/mradermacher/gemma-4-E4B-it-OBLITERATED-GGUF

rohithbojja

Apr 17

llama-cli --model /Users/rohit.bojja/Downloads/gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf --reasoning on
load_backend: loaded BLAS backend from /opt/homebrew/Cellar/ggml/0.9.11/libexec/libggml-blas.so
ggml_metal_device_init: tensor API disabled for pre-M5 and pre-A19 devices
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: loaded in 0.021 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name: MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple9 (1009)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
ggml_metal_device_init: simdgroup reduction = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory = true
ggml_metal_device_init: has bfloat = true
ggml_metal_device_init: has tensor = false
ggml_metal_device_init: use residency sets = true
ggml_metal_device_init: use shared buffers = true
ggml_metal_device_init: recommendedMaxWorkingSetSize = 12713.12 MB
load_backend: loaded MTL backend from /opt/homebrew/Cellar/ggml/0.9.11/libexec/libggml-metal.so
load_backend: loaded CPU backend from /opt/homebrew/Cellar/ggml/0.9.11/libexec/libggml-cpu-apple_m4.so

Loading model... |llama_model_load: error loading model: missing tensor 'blk.24.attn_k.weight'
llama_model_load_from_file_impl: failed to load model
llama_params_fit: encountered an error while trying to fit params to free device memory: failed to load model
|llama_model_load: error loading model: missing tensor 'blk.24.attn_k.weight'
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model '/Users/rohit.bojja/Downloads/gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf'
srv load_model: failed to load model, '/Users/rohit.bojja/Downloads/gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf'

Failed to load the model