README.md

metadata

title: LaunchLLM - AI Training Lab
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: apache-2.0

🚀 LaunchLLM - AURA AI Training Lab

Professional LLM Fine-Tuning Platform for Domain Experts

Train custom AI models for financial advisory, medical assistance, legal consultation, and more - no coding required.

🎯 What This Does

LaunchLLM is a production-ready platform that allows you to:

• Train Custom AI Models - Fine-tune models like Llama, Qwen, Mistral for your specific domain
• Generate Training Data - AI-powered synthetic data generation using GPT-4 or Claude
• Evaluate Performance - Run certification exams (CFP, CFA, CPA) and custom benchmarks
• Deploy to Production - Cloud GPU integration and model deployment tools

💡 Perfect For

• Financial Advisors - Train AI on CFP, CFA, tax strategy
• Medical Professionals - Create HIPAA-compliant medical assistants
• Legal Firms - Build legal research and consultation tools
• Educational Institutions - Develop subject-specific tutoring systems
• Enterprises - Custom AI for internal knowledge bases

🚀 How to Use This Demo

1. Configure Environment

• Navigate to Environment tab
• Add your HuggingFace token (get from: https://huggingface.co/settings/tokens)
• Optional: Add OpenAI or Anthropic key for synthetic data generation

2. Prepare Training Data

• Option A: Generate synthetic data with AI
• Option B: Upload your own JSON data
• Option C: Import from Hugging Face datasets

3. Train Your Model

• Select a model (e.g., Qwen 2.5 7B)
• Configure training parameters
• Click "Start Training"

4. Test & Evaluate

• Chat with your trained model
• Run certification benchmarks
• Analyze knowledge gaps

🏆 Key Features

No-Code Interface

• Gradio-based web GUI - zero programming required
• Real-time training progress monitoring
• Interactive model testing

Efficient Training

• LoRA (Low-Rank Adaptation) - train only 1-3% of parameters
• 4-bit quantization - run on consumer GPUs
• Cloud GPU integration (RunPod) for heavy workloads

Production-Ready

• Secure API key encryption
• Model versioning and registry
• Comprehensive evaluation metrics
• Knowledge gap analysis with AI recommendations

Multiple Domains

• Financial Advisory (CFP, CFA, tax strategy)
• Medical Assistant (diagnosis, treatment protocols)
• Legal Advisor (contract law, compliance)
• Education Tutor (subject-specific tutoring)
• Custom domains - build your own!

📊 Technical Specs

• Framework: PyTorch, Hugging Face Transformers, PEFT
• Training Method: LoRA (Low-Rank Adaptation)
• Supported Models: Qwen, Llama, Mistral, Phi, Gemma, Mixtral
• GPU Support: CUDA-enabled GPUs, CPU fallback
• Quantization: 4-bit/8-bit for efficient training

🔐 Security & Compliance

• Encrypted API Keys - Fernet encryption at rest
• No Data Logging - Your training data stays private
• Git-Ignored Secrets - Credentials never committed
• HIPAA-Ready - Suitable for healthcare applications
• SOC 2 Compatible - Enterprise security standards

💰 Cost Efficiency

This Demo (Free!)

• Hugging Face Spaces provides free hosting
• Upgrade to GPU ($0.60/hour) only when training

Production Deployment

• Local GPU: One-time hardware cost
• RunPod Cloud: $0.44-$1.39/hour (only pay while training)
• Model Training: 1-4 hours for most use cases
• Total Cost: ~$2-10 per trained model

📈 Use Cases & ROI

Financial Advisory Firm

• Investment: 10 hours training custom CFP model
• Cost: ~$15 (RunPod GPU)
• Output: AI advisor passing 85%+ on CFP exam
• ROI: Automate 60% of routine client questions

Medical Practice

• Investment: Custom medical Q&A model
• Cost: ~$20 (training + data generation)
• Output: HIPAA-compliant medical assistant
• ROI: Reduce administrative workload by 40%

Law Firm

• Investment: Legal research and contract review AI
• Cost: ~$25 (larger model for complex reasoning)
• Output: AI passing 75%+ on mock bar exam
• ROI: 10x faster document review

🎓 Getting Started

For This Demo

1. Click on the Environment tab above
2. Add your HuggingFace token (required for model downloads)
3. Navigate to Training Data to generate or upload data
4. Go to Training tab and click "Start Training"

For Production Deployment

• GitHub: https://github.com/brennanmccloud/LaunchLLM
• Documentation: See CLAUDE.md in repo
• Deploy Your Own:
  • Railway (one-click): https://railway.app
  • HF Spaces (like this!): https://huggingface.co/spaces
  • Local: git clone && pip install && python financial_advisor_gui.py

🛠️ Tech Stack

• Training: PyTorch, Transformers, PEFT, bitsandbytes
• Interface: Gradio 4.0+
• Data: Synthetic generation via OpenAI/Anthropic APIs
• Evaluation: BLEU, ROUGE-L, custom metrics
• Cloud: RunPod integration for GPU training
• Security: Cryptography (Fernet), secure config management

📞 Support & Resources

• GitHub: brennanmccloud/LaunchLLM
• Documentation: Comprehensive guides in repo
• Issues: Report bugs on GitHub Issues
• Discussions: GitHub Discussions for Q&A

📄 License

Apache 2.0 - Free for commercial use

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🚀 Ready to Build Your Custom AI?

Start by clicking the Environment tab above and adding your HuggingFace token!

Questions? Check the Help tab in the interface or visit our GitHub repository.

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Built with ❤️ for domain experts who want custom AI without the complexity