stalkiq/stalkiq-ios-app-generator

StalkIQ iOS App Generator

A fine-tuned LLM that generates complete, production-ready SwiftUI iOS application codebases from natural language descriptions.

What It Does

Give it an app idea, get back a full Xcode project with:

• project.yml (XcodeGen)
• SwiftUI views, view models, models
• Info.plist, LaunchScreen.storyboard, Assets.xcassets
• MVVM architecture, dark gradient UI theme
• iOS 16+ deployment target

Usage

With Ollama (Recommended)

# Download and create the model
echo 'FROM hf.co/stalkiq/stalkiq-ios-app-generator/stalkiq.gguf' > Modelfile
ollama create stalkiq -f Modelfile

# Generate an app
ollama run stalkiq "Create an iOS app called RecipeAI that identifies food from photos using AI"

With Python (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("stalkiq/stalkiq-ios-app-generator")
tokenizer = AutoTokenizer.from_pretrained("stalkiq/stalkiq-ios-app-generator")

prompt = "### Instruction:\nCreate an iOS app called RecipeAI that identifies food from photos\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

API

# Via Ollama API (after creating the model)
curl http://localhost:11434/api/generate -d '{
  "model": "stalkiq",
  "prompt": "Create an iOS app called WeatherAI that shows forecasts from sky photos"
}'

Prompt Format

### Instruction:
{your app description here}

### Response:

Model Details

• Base Model: TinyLlama-1.1B-Chat-v1.0
• Fine-tuning: QLoRA on StalkIQ iOS app training data
• Architecture: LlamaForCausalLM (1.1B parameters)
• Tensor Type: FP16
• Files: safetensors (for transformers) + GGUF (for Ollama/llama.cpp)

Built By

StalkIQ — AI-powered web and mobile applications.