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README.md

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+---
+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**: 2048 tokens
+- **License**: MIT

WebICoder-v3-4bit.mlpackage/Data/com.apple.CoreML/model.mlmodel

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WebICoder-v3-4bit.mlpackage/Manifest.json

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+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
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+            "author": "com.apple.CoreML",
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+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "01098715-dfec-4c6d-b565-c52a0cc32503"
+}

WebICoder-v3-8bit.mlpackage/Data/com.apple.CoreML/model.mlmodel

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WebICoder-v3-8bit.mlpackage/Manifest.json

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+            "path": "com.apple.CoreML/weights"
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+}

WebICoder-v3-fp16.mlpackage/Data/com.apple.CoreML/model.mlmodel

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WebICoder-v3-fp16.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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WebICoder-v3-fp16.mlpackage/Manifest.json

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+        }
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+}

config.json

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+{
+  "architectures": [
+    "PhiForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 50256,
+  "dtype": "float16",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "hidden_act": "gelu_new",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 10240,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "phi",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 50256,
+  "partial_rotary_factor": 0.4,
+  "qk_layernorm": false,
+  "resid_pdrop": 0.1,
+  "rope_parameters": {
+    "partial_rotary_factor": 0.4,
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "vocab_size": 51200
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "5.0.0"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "is_local": false,
+  "model_max_length": 2048,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<|endoftext|>"
+}