---
license: mit
tags:
  - coreml
  - phi-2
  - code-generation
  - html
  - css
  - javascript
  - ios
  - macos
  - apple
  - on-device
pipeline_tag: text-generation
base_model: microsoft/phi-2
---

# 🌐 WebICoder v3 — CoreML

**Generate complete, production-ready HTML/CSS websites directly on your iPhone, iPad or Mac — no internet required.**

WebICoder v3 is a fine-tuned [Phi-2](https://huggingface.co/microsoft/phi-2) (2.7B parameters) model, optimized for on-device HTML code generation using Apple's CoreML framework.

## 📦 Available Models

| Model | Size | Precision | Best For |
|-------|------|-----------|----------|
| `WebICoder-v3-fp16.mlpackage` | ~5.5 GB | FP16 | Mac (M1/M2/M3) — maximum quality |
| `WebICoder-v3-8bit.mlpackage` | ~2.8 GB | INT8 | iPad — good quality, smaller |
| `WebICoder-v3-4bit.mlpackage` | ~1.4 GB | 4-bit | iPhone — smallest, good quality |

## 🚀 Quick Start (Swift / Xcode)

### 1. Download the model
```bash
# Install Git LFS first
git lfs install
git clone https://huggingface.co/nexsendev/webicoder-v3-coreml
```

### 2. Add to your Xcode project
Drag the `.mlpackage` file into your Xcode project. Xcode will automatically compile it.

### 3. Run inference
```swift
import CoreML
import Tokenizers

// Load model
let config = MLModelConfiguration()
config.computeUnits = .cpuAndNeuralEngine
let model = try MLModel(contentsOf: modelURL, configuration: config)

// Tokenize input
let tokenizer = try await AutoTokenizer.from(pretrained: "nexsendev/webicoder-v3-coreml")
let prompt = "Create a modern landing page for a coffee shop"
let inputIds = tokenizer.encode(text: prompt)

// Run inference
let inputArray = try MLMultiArray(shape: [1, inputIds.count as NSNumber], dataType: .int32)
for (i, id) in inputIds.enumerated() {
    inputArray[i] = NSNumber(value: id)
}

let prediction = try model.prediction(from: MLDictionaryFeatureProvider(
    dictionary: ["input_ids": inputArray, "attention_mask": maskArray]
))
```

## 🐍 Usage with Python (macOS only)

```python
import coremltools as ct
import numpy as np
from transformers import AutoTokenizer

# Load
model = ct.models.MLModel("WebICoder-v3-fp16.mlpackage")
tokenizer = AutoTokenizer.from_pretrained("nexsendev/webicoder-v3-coreml")

# Generate
prompt = "Create a modern landing page for a coffee shop with dark theme"
tokens = tokenizer.encode(prompt, return_tensors="np").astype(np.int32)
mask = np.ones_like(tokens, dtype=np.int32)

result = model.predict({"input_ids": tokens, "attention_mask": mask})
logits = result["logits"]
```

## 💬 Prompt Format

The model works best with descriptive prompts:

```
Create a modern landing page for a coffee shop with:
- Dark theme with warm colors
- Hero section with a background image
- Menu section with cards
- Contact form
- Responsive design
```

The model will output a complete, standalone HTML file with embedded CSS.

## ⚙️ Hardware Requirements

| Model | Min RAM | Recommended Device |
|-------|---------|-------------------|
| FP16 | 6 GB | Mac M1/M2/M3/M4 |
| 8-bit | 3 GB | iPad Pro, iPad Air |
| 4-bit | 2 GB | iPhone 15, iPhone 16 |

All models require **iOS 17+** or **macOS 14+**.

## 📝 Details

- **Base model**: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) (2.7B parameters)
- **Fine-tuning**: Trained on curated HTML/CSS website examples
- **Input**: Natural language description of a website
- **Output**: Complete HTML with embedded CSS and JavaScript
- **Context length**: 4096 tokens
- **License**: MIT