Hugging Face GPU Grant Request

Project Title: Code Agents Beta: Collaborative AI for Rapid Prototyping

Summary

This Hugging Face Space launches the beta version of Code Agents, an interactive multi-agent framework where AI roles—Designer, Implementer, Validator, Refiner, and Deployer—team up to prototype full-stack applications from user prompts. As a purely personal project—not affiliated with any company—I chose to develop and maintain this beta voluntarily to keep the app evolving. It serves as the dedicated environment where new features are developed and iterated upon, emphasizing real-time feedback loops, error recovery, and modular experimentation using open-source models like Qwen3-0.6B and DeepSeek-Coder exclusively for inference. No training or fine-tuning is involved, prioritizing stability and user-driven iterations.

Motivation: Why GPU Acceleration Is Essential

Beta testing demands snappy responsiveness to gather actionable feedback from early adopters—yet CPU-only inference on models like the 1.3B DeepSeek-Coder drags generations to 2–5 minutes per agent step, stifling interactivity and slowing my development cycle. A GPU (T4, A10G, or similar) would slash this to under 10 seconds, enabling seamless beta sessions, iterative refinements, and faster prototyping for new features. This request adheres strictly to Hugging Face GPU Grant policies: inference-only on pre-trained models, with a focus on community beta validation.

Why Persistent Storage Is Required

Beta workflows generate transient prototypes, debug logs, user feedback artifacts, and versioned ZIPs. 20 GB of persistent storage ensures beta testers can resume sessions, compare iterations, and export results without resets—critical for multi-user stress testing, data retention during the beta phase, and accelerating my ongoing development efforts.

Models in Use

• Qwen/Qwen3-0.6B (current; lightweight 0.6B model for efficient agent coordination)
• deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct (1.3B; planned for precise code synthesis in beta)
• microsoft/Phi-3-mini-4k-instruct (3.8B; planned for robust validation and error-handling logic)
• Qwen/Qwen2.5-Coder-7B-Instruct (7B; planned for advanced refactoring tasks)
• codellama/CodeLlama-13b-Instruct-hf (13B; planned for handling larger beta-scale prototypes)

All models leverage Hugging Face Transformers with quantization for optimized inference.

Community Impact and Value

Code Agents Beta serves as a public testing ground for agentic AI in software prototyping, empowering HF users to experiment with prompt-to-app pipelines (e.g., CLI tools or web dashboards). My continued development has been entirely voluntary, driven by a passion to make AI and Hugging Face a better place through open-source contributions and accessible tools. It promotes collaboration by surfacing beta insights—such as agent handoff efficiencies or model swap benchmarks—directly to the community, accelerating adoption of small, specialized LLMs over monolithic alternatives.

Future Enhancements Post-Grant

Securing GPU access will unlock beta-scale expansions:

• Add support for 20B+ open-source models (e.g., a fine-tuned Llama-3.1-70B variant or emerging GPT-NeoX-20B evolutions) to tackle enterprise-grade prototypes with multi-language and framework support.
• Roll out a beta-exclusive model selector UI, enabling testers to A/B compare models (e.g., 0.6B for speed vs. 13B for accuracy) and contribute anonymized metrics to refine the system.

Specific Request

• GPU Tier: T4 or A10G (suitable for parallel beta inference on these models)
• Persistent Storage: 20 GB