Instructions to use senaro/atlas-gemma4-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use senaro/atlas-gemma4-e4b 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 senaro/atlas-gemma4-e4b:BF16
# Run inference directly in the terminal:
llama cli -hf senaro/atlas-gemma4-e4b:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf senaro/atlas-gemma4-e4b:BF16
# Run inference directly in the terminal:
llama cli -hf senaro/atlas-gemma4-e4b:BF16

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 senaro/atlas-gemma4-e4b:BF16
# Run inference directly in the terminal:
./llama-cli -hf senaro/atlas-gemma4-e4b:BF16

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 senaro/atlas-gemma4-e4b:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf senaro/atlas-gemma4-e4b:BF16

Use Docker

docker model run hf.co/senaro/atlas-gemma4-e4b:BF16

LM Studio
Jan
Ollama
How to use senaro/atlas-gemma4-e4b with Ollama:
```
ollama run hf.co/senaro/atlas-gemma4-e4b:BF16
```

Unsloth Studio

How to use senaro/atlas-gemma4-e4b 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 senaro/atlas-gemma4-e4b 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 senaro/atlas-gemma4-e4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for senaro/atlas-gemma4-e4b to start chatting

How to use senaro/atlas-gemma4-e4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf senaro/atlas-gemma4-e4b:BF16

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": "senaro/atlas-gemma4-e4b:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use senaro/atlas-gemma4-e4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf senaro/atlas-gemma4-e4b:BF16

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 "senaro/atlas-gemma4-e4b:BF16" \
  --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"

Docker Model Runner
How to use senaro/atlas-gemma4-e4b with Docker Model Runner:
```
docker model run hf.co/senaro/atlas-gemma4-e4b:BF16
```

Lemonade

How to use senaro/atlas-gemma4-e4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull senaro/atlas-gemma4-e4b:BF16

Run and chat with the model

lemonade run user.atlas-gemma4-e4b-BF16

List all available models

lemonade list

Hermes Agent

How to use senaro/atlas-gemma4-e4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf senaro/atlas-gemma4-e4b:BF16

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 senaro/atlas-gemma4-e4b:BF16

Run Hermes

hermes

Atomic Chat

atlas-gemma4-e4b / README.md

senaro

Update README.md

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metadata

tags:
  - gguf
  - llama.cpp
  - unsloth
  - vision-language-model
license: apache-2.0
datasets:
  - kintsugicollective/atlas-dataset-v8-final
language:
  - en
base_model:
  - google/gemma-4-E4B-it

atlas-gemma4-e4b : GGUF

This variant of Atlas is purely SFT'ed through Unsloth Studio. I wanted to trial Unsloth Studio and also a smaller parameter model for Atlas local deployment.
This model has had NO refusal removal, only SFT using my bespoke data set.

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf senaro/atlas-gemma4-e4b --jinja
For multimodal models: llama-mtmd-cli -hf senaro/atlas-gemma4-e4b --jinja

Available Model files:

gemma-4-e4b-it.Q4_K_M.gguf
gemma-4-e4b-it.BF16-mmproj.gguf

⚠️ Ollama Note for Vision Models

Important: Ollama currently does not support separate mmproj files for vision models.

To create an Ollama model from this vision model:

Place the Modelfile in the same directory as the finetuned bf16 merged model
Run: ollama create model_name -f ./Modelfile (Replace model_name with your desired name)

This will create a unified bf16 model that Ollama can use. This was trained 2x faster with Unsloth