Text Generation
Safetensors
MLX
mlx-vlm
gemma4
lora
ui
ux
frontend
design-review
conversational
4-bit precision
Instructions to use RayyTien/gemma-4-e2b-uiux-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RayyTien/gemma-4-e2b-uiux-lora with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("RayyTien/gemma-4-e2b-uiux-lora") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use RayyTien/gemma-4-e2b-uiux-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 "RayyTien/gemma-4-e2b-uiux-lora"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "RayyTien/gemma-4-e2b-uiux-lora" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use RayyTien/gemma-4-e2b-uiux-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 "RayyTien/gemma-4-e2b-uiux-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 RayyTien/gemma-4-e2b-uiux-lora
Run Hermes
hermes
- OpenClaw new
How to use RayyTien/gemma-4-e2b-uiux-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 "RayyTien/gemma-4-e2b-uiux-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 "RayyTien/gemma-4-e2b-uiux-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"
- MLX LM
How to use RayyTien/gemma-4-e2b-uiux-lora with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "RayyTien/gemma-4-e2b-uiux-lora"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "RayyTien/gemma-4-e2b-uiux-lora" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RayyTien/gemma-4-e2b-uiux-lora", "messages": [ {"role": "user", "content": "Hello"} ] }'
| set -euo pipefail | |
| MODEL="${MODEL:-mlx-community/gemma-4-e2b-it-4bit}" | |
| DATASET="${DATASET:-./data}" | |
| ITERS="${ITERS:-50}" | |
| MAX_SEQ_LENGTH="${MAX_SEQ_LENGTH:-1024}" | |
| ADAPTER_PATH="${ADAPTER_PATH:-./adapters/gemma-e2b-code-smoke}" | |
| RESUME_ADAPTER_PATH="${RESUME_ADAPTER_PATH:-}" | |
| STEPS_PER_SAVE="${STEPS_PER_SAVE:-25}" | |
| cmd=( | |
| python -m mlx_vlm.lora | |
| ) | |
| if [[ -n "$RESUME_ADAPTER_PATH" ]]; then | |
| cmd+=(--adapter-path "$RESUME_ADAPTER_PATH") | |
| fi | |
| "${cmd[@]}" \ | |
| --model-path "$MODEL" \ | |
| --dataset "$DATASET" \ | |
| --split train \ | |
| --iters "$ITERS" \ | |
| --batch-size 1 \ | |
| --max-seq-length "$MAX_SEQ_LENGTH" \ | |
| --grad-checkpoint \ | |
| --train-on-completions \ | |
| --lora-rank 8 \ | |
| --lora-alpha 16 \ | |
| --learning-rate 2e-5 \ | |
| --steps-per-report 5 \ | |
| --steps-per-eval 25 \ | |
| --steps-per-save "$STEPS_PER_SAVE" \ | |
| --output-path "$ADAPTER_PATH" | |