RapidFire AI has officially integrated with Hugging Face’s Transformers Reinforcement Learning (TRL) library, enabling users to accelerate fine-tuning and post-training experiments by up to 20 times. This integration allows TRL users to seamlessly discover, install, and run RapidFire AI to compare multiple configurations concurrently—without major code changes and without increasing GPU demands. The key innovation lies in RapidFire AI’s adaptive, chunk-based scheduling system. Instead of training configurations one after another, RapidFire AI splits datasets into chunks and cycles through multiple configurations at chunk boundaries. This enables earlier, apples-to-apples comparisons and maximizes GPU utilization across multiple models and hyperparameter sets—even on a single GPU. Teams often lack the time or budget to test multiple configurations, despite the significant gains in evaluation metrics that such experimentation can yield. RapidFire AI solves this by enabling real-time, concurrent training and monitoring. Internal benchmarks show a 16 to 24 times improvement in experimentation throughput compared to sequential approaches. The platform establishes live, three-way communication between your IDE, a metrics dashboard, and a multi-GPU execution backend. Users gain immediate insights and control through Interactive Control Ops (IC Ops), allowing them to stop, resume, delete, or clone ongoing experiments directly from the dashboard—no job restarts or manual GPU management required. Promising configurations can be cloned with modified hyperparameters and optionally warm-started from the parent model’s weights, all without disrupting the workflow. RapidFire AI offers drop-in replacements for TRL’s standard configs: RFSFTConfig, RFDPOConfig, and RFGRPOConfig. These allow users to maintain their existing TRL workflows while unlocking far greater concurrency and control. For example, a minimal SFT training setup using multiple LoRA configurations can run concurrently on a single GPU. Instead of waiting for one config to finish before starting the next, both train simultaneously. On a 2-GPU machine, this reduces time to a comparative decision from ~15 minutes (sequential) to ~5 minutes (concurrent), while boosting GPU utilization from 60% to over 95%. Benchmark results across various scenarios demonstrate consistent speedups: - 4 configs on 1 GPU: 16× faster - 8 configs on 1 GPU: 20× faster - 4 configs on 2 GPUs: 15× faster These results were achieved using NVIDIA A100 40GB GPUs with models like TinyLlama-1.1B and Llama-3.2-1B. Getting started is simple: - Install via pip: pip install rapidfireai - Authenticate with Hugging Face - Initialize and start the service: rapidfireai init and rapidfireai start - Access the dashboard at http://localhost:3000 to monitor and control experiments in real time The integration supports all major TRL trainers and is fully open source. Users can explore it via an interactive Colab notebook, consult comprehensive documentation at oss-docs.rapidfire.ai, or join the community on Discord. RapidFire AI was built to address the inefficiency of testing one configuration at a time—wasting both time and valuable GPU resources. With this official integration, TRL users can now iterate faster, experiment smarter, and deploy higher-performing models more efficiently. Try it today and share your feedback: How much faster is your experimentation loop? What features should we build next? The journey is just beginning.