RapidFire AI
0.12.3
  • Install and Get Started
    • Install and Get Started: RAG and Context Engineering
    • Install and Get Started: Fine-Tuning and Post-Training
  • What Makes RapidFire AI Different?
  • Online Aggregation for Evals
  • Troubleshooting
  • API: Experiment
  • API: Multi-Config Specification
  • APIs for SFT and RFT
  • APIs for RAG and Context Engineering
  • Example Use Case Tutorials
  • ML Metrics Dashboard
  • Dashboard: Interactive Control (IC) Ops
  • Known Issues and Updates Coming Soon
  • Glossary of Key Terms and Concepts
RapidFire AI
  • Install and Get Started
  • View page source

Install and Get Started

  • Install and Get Started: RAG and Context Engineering
    • Step 1: Install dependencies and package
    • Step 2: Initialize RapidFire AI
    • Step 3: Open the tutorial notebooks
      • Quickstart Video (3.5min)
      • Full Usage Walkthrough Video (13.5min)
    • Step 4: Run the notebook cells
    • Step 5: Monitor online aggregation of eval metrics on in-notebook table
    • Step 6: Interactive Control (IC) Ops: Stop, Clone-Modify; check their results
    • Step 7: Inspect results, end experiment, and check logs.
    • Step 8: Venture Beyond!
  • Install and Get Started: Fine-Tuning and Post-Training
    • Google Colab
    • Full Installation
    • Step 1: Install dependencies and package
    • Step 2: Start RapidFire AI server
    • Step 3: Download the tutorial notebooks
      • Quickstart Video (2.5min)
      • Full Usage Walkthrough Video (12min)
    • Step 4: Run the notebook cells
    • Step 5: Monitor training behaviors on ML metrics dashboard
    • Step 6: Interactive Control (IC) Ops: Stop, Clone-Modify; check their results
    • Step 7: End experiment; stop server when done
    • Step 8: Venture Beyond!
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