| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - on-device |
| - local-llm |
| - coding-copilot |
| - ai-assistant |
| - code-generation |
| - pocketpal |
| - llm |
| - nlp |
| --- |
| |
| # AppBuilder — On-Device Coding Copilot & Local AI Assistant |
|
|
| AppBuilder is a lightweight, on-device **text-generation** LLM designed to run locally on your machine or mobile device — similar to [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai). It acts as a personal coding copilot and app-building assistant that works entirely offline, with no cloud dependency. Give it a natural language prompt and it returns structured code, project scaffolding, or step-by-step build instructions — all on-device. |
|
|
| > **Think PocketPal, but focused on building apps.** AppBuilder is optimized for developers who want a fast, private, always-available assistant that runs on CPU/GPU without sending data to external servers. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| AppBuilder is a fine-tuned LLM for on-device assistant and coding copilot tasks. It understands developer intent from plain English and generates functional application code, API integrations, config files, and project structures across multiple frameworks — all locally. |
|
|
| - **Developed by:** codemeacoffee |
| - **Model type:** Text Generation / On-Device LLM / Coding Copilot |
| - **Language(s):** English |
| - **License:** Apache 2.0 |
| - **Repository:** codemeacoffee/appbuilder |
| - **Inspired by:** PocketPal AI (local LLM assistant approach) |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| AppBuilder can be used directly as a local assistant to: |
| - Generate application boilerplate code from plain English descriptions |
| - Scaffold new projects (FastAPI, Next.js, Express, Flutter, etc.) |
| - Generate configuration files (Docker, CI/CD, .env, etc.) |
| - Answer developer questions and explain code — fully offline |
| - Act as a PocketPal-style chat assistant for coding tasks |
|
|
| ### Downstream Use |
|
|
| Can be integrated or fine-tuned for: |
| - PocketPal AI / llama.cpp compatible on-device deployments |
| - IDE plugins and offline coding assistants |
| - Mobile AI apps (Android/iOS via NCNN, llama.cpp, MLC) |
| - Automated development pipelines and no-code platforms |
|
|
| ### Out-of-Scope Use |
|
|
| - Generating malicious or harmful code |
| - Unauthorized system access or exploits |
| - Production-critical code without human review |
|
|
| ## How to Get Started with the Model |
|
|
| ### Option 1: Run locally via Transformers |
|
|
| ```python |
| from transformers import pipeline |
| |
| generator = pipeline("text-generation", model="codemeacoffee/appbuilder") |
| result = generator("Build a FastAPI endpoint that returns a list of users") |
| print(result[0]["generated_text"]) |
| ``` |
|
|
| ### Option 2: Run on-device via llama.cpp (PocketPal style) |
|
|
| ```bash |
| # Convert to GGUF and run locally |
| ./main -m appbuilder.gguf -p "Build a FastAPI endpoint that returns a list of users" -n 512 |
| ``` |
|
|
| ### Option 3: Load in PocketPal AI App |
|
|
| 1. Export the model to GGUF format |
| 2. Load into [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) on Android/iOS |
| 3. Chat with your local coding copilot — no internet required |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| Trained on a curated dataset of open-source code repositories, API documentation, developer forums, and application scaffolding patterns across popular frameworks. |
|
|
| ### Training Procedure |
|
|
| - **Training regime:** Mixed precision (fp16) |
| - **Framework:** PyTorch / HuggingFace Transformers |
| - **Optimization target:** On-device inference speed + instruction following |
|
|
| ## Evaluation |
|
|
| ### Testing Data & Metrics |
|
|
| Evaluated on code generation benchmarks including HumanEval and custom application-building tasks measuring: |
| - Functional correctness |
| - Code quality and style |
| - Framework-specific accuracy |
| - On-device response latency |
|
|
| ## Environmental Impact |
|
|
| - **Hardware used:** NVIDIA A100 GPUs (training) / CPU + mobile GPU (inference target) |
| - **Cloud Provider:** Google Cloud Platform |
| - **On-device target:** Runs on consumer hardware (4GB+ RAM, any modern CPU/GPU) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{appbuilder2026, |
| author = {codemeacoffee}, |
| title = {AppBuilder: On-Device Coding Copilot and Local AI Assistant}, |
| year = {2026}, |
| publisher = {HuggingFace}, |
| url = {https://huggingface.co/codemeacoffee/appbuilder} |
| } |
| ``` |
|
|
| ## Model Card Contact |
|
|
| For questions or contributions, open an issue in the model repository or reach out via the HuggingFace community page. |
