Reposition AppBuilder as on-device coding copilot & local AI assistant (PocketPal-style)

README.md

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+---
+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.