Instructions to use Adiuk/eyla-gemma4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Adiuk/eyla-gemma4-lora with MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir eyla-gemma4-lora Adiuk/eyla-gemma4-lora

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Adiuk/eyla-gemma4-lora 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 Adiuk/eyla-gemma4-lora
# Run inference directly in the terminal:
llama cli -hf Adiuk/eyla-gemma4-lora

Install from WinGet (Windows)

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

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 Adiuk/eyla-gemma4-lora
# Run inference directly in the terminal:
./llama-cli -hf Adiuk/eyla-gemma4-lora

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 Adiuk/eyla-gemma4-lora
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Adiuk/eyla-gemma4-lora

Use Docker

docker model run hf.co/Adiuk/eyla-gemma4-lora

LM Studio
Jan
Ollama
How to use Adiuk/eyla-gemma4-lora with Ollama:
```
ollama run hf.co/Adiuk/eyla-gemma4-lora
```

Unsloth Studio

How to use Adiuk/eyla-gemma4-lora 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 Adiuk/eyla-gemma4-lora 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 Adiuk/eyla-gemma4-lora to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use Adiuk/eyla-gemma4-lora with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Adiuk/eyla-gemma4-lora"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Adiuk/eyla-gemma4-lora"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Adiuk/eyla-gemma4-lora with Docker Model Runner:
```
docker model run hf.co/Adiuk/eyla-gemma4-lora
```

Lemonade

How to use Adiuk/eyla-gemma4-lora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Adiuk/eyla-gemma4-lora

Run and chat with the model

lemonade run user.eyla-gemma4-lora-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Adiuk/eyla-gemma4-lora with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Adiuk/eyla-gemma4-lora"

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 Adiuk/eyla-gemma4-lora

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Adiuk/eyla-gemma4-lora with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Adiuk/eyla-gemma4-lora"

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 "Adiuk/eyla-gemma4-lora" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Eyla · Gemma-4-12B tool-calling LoRA (experimental)

MLX LoRA adapter checkpoints (training steps 100–500) fine-tuning Gemma-4-12B for Eyla agent tool-calling, plus a merged Q4_K_M GGUF (eyla-gemma4-q4km.gguf).

Honest status: in our agent evals the stock Gemma-4-12B QAT outperformed this fine-tune (7/8 vs 1/8 tasks). Kept public for reproducibility; if you want a working Eyla tool-caller, use eyla-qwen3-8b-tools-v2 instead. Gemma license terms apply.

Author & research

🌐 Website: arifadito.com
💻 GitHub: github.com/Adiuk24
📄 Eyla identity paper: arXiv:2604.00009
🔧 Rust ML training verification tooling: gradient-flow-arbiter

Downloads last month: 245

MLX

Hardware compatibility

Quantized

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Paper for Adiuk/eyla-gemma4-lora

Eyla: Toward an Identity-Anchored LLM Architecture with Integrated Biological Priors -- Vision, Implementation Attempt, and Lessons from AI-Assisted Development

Paper • 2604.00009 • Published Mar 9