Instructions to use RayyTien/gemma-4-e2b-uiux-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

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"}
     ]
   }'

Gemma 4 E2B UI/UX LoRA Adapter

Bundled MLX repo containing the mlx-community/gemma-4-e2b-it-4bit base model files plus a UI/UX LoRA adapter.

This is not a fused model. The base weights and LoRA adapter are stored in the same repository so users can download one repo, then pass the same directory as both --model and --adapter-path.

The adapter was tuned on UI/UX frontend guidance converted from:

nextlevelbuilder/ui-ux-pro-max-skill - MIT
pbakaus/impeccable - Apache-2.0

Intended Use

Frontend UI/UX critique, React/Next.js/Tailwind implementation guidance, design anti-pattern review, accessibility and responsive layout recommendations.

Usage

pip install mlx-vlm
hf download RayyTien/gemma-4-e2b-uiux-lora --local-dir ./gemma-e2b-uiux

python -m mlx_vlm.generate \
  --model ./gemma-e2b-uiux \
  --adapter-path ./gemma-e2b-uiux \
  --system "You are a senior frontend engineer and UI/UX reviewer. Give concrete implementation-ready guidance." \
  --prompt "Review this SaaS dashboard UI: nested cards, purple gradients, tiny gray labels, and no loading state." \
  --max-tokens 320 \
  --temperature 0.0

Training Summary

See training_args.json and dataset_summary.json.

Important caveat: this adapter was trained locally with a patched mlx-vlm Gemma 4 loader because the unpatched loader did not instantiate K/V projections for weights present in the checkpoint. If vanilla mlx-vlm fails to load this model, use the same local patch or wait for upstream Gemma 4 loader support to catch up.

Known Limitations

This is a bundled base-model-plus-adapter repo, not a fused full fine-tuned checkpoint.
The adapter improves UI/UX vocabulary and critique style, but should still be evaluated against real frontend tasks before production use.
The converted dataset is guidance-heavy; add more paired bad/good UI implementation examples for stricter behavior.

Downloads last month: 17

Safetensors

Model size

1B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for RayyTien/gemma-4-e2b-uiux-lora

Base model

mlx-community/gemma-4-e2b-it-4bit

Adapter

(4)

this model