---
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.