Instructions to use Adiuk/eyla-gemma4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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 Desktop
- Pi
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"
| { | |
| "adapter_path": "/Users/adi/noor/gemma4-bangla-lora", | |
| "batch_size": 1, | |
| "clear_cache_threshold": 0, | |
| "config": null, | |
| "data": "/Users/adi/noor/data/gemma4_eyla_system", | |
| "fine_tune_type": "lora", | |
| "grad_accumulation_steps": 2, | |
| "grad_checkpoint": true, | |
| "iters": 300, | |
| "learning_rate": 5e-05, | |
| "lora_parameters": { | |
| "rank": 8, | |
| "dropout": 0.0, | |
| "scale": 20.0 | |
| }, | |
| "lr_schedule": null, | |
| "mask_prompt": true, | |
| "max_seq_length": 256, | |
| "model": "google/gemma-4-E4B-it", | |
| "num_layers": 16, | |
| "optimizer": "adam", | |
| "optimizer_config": { | |
| "adam": {}, | |
| "adamw": {}, | |
| "muon": {}, | |
| "sgd": {}, | |
| "adafactor": {} | |
| }, | |
| "project_name": null, | |
| "report_to": null, | |
| "resume_adapter_file": "/Users/adi/noor/gemma4-bangla-lora/adapters.safetensors", | |
| "save_every": 100, | |
| "seed": 42, | |
| "steps_per_eval": 100, | |
| "steps_per_report": 10, | |
| "test": false, | |
| "test_batches": 500, | |
| "train": true, | |
| "val_batches": 5 | |
| } |