Rapid Experimentation for LLM
Fine-Tuning and Post-Training

Rapid Experimentation for LLMFine-Tuning and Post-Training

RapidFire AI is an open-source framework that makes LLM customization
faster, more systematic, and more impactful.

RapidFire AI is an open-source framework that makes LLM customization faster, more systematic, and more impactful.

RapidFire AI is an open-source framework that makes LLM customization
faster, more systematic, and more impactful.

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In the time sequential training evaluates a single model, RapidFire AI tests multiple configurations in parallel, surfaces higher eval scores early, and immediately launches additional informed comparisons in the second step—accelerating discovery within the same wall-time.

Get Started Quickly


pip install rapidfireai

rapidfireai init

rapidfireai start

The Pain of LLM Customization

Customizing open LLMs is slow and expensive. GPU or cost constraints often discourage experimentation, limiting AI's potential impact. Current workflows face key limitations:

Configuration Complexity:

Need to set multiple knobs: LoRA rank, quantization schemes, prompt structure, hyperparameters, reward functions, etc.

Sequential Burden:

GPU constraints and LLM size typically limit teams to trying only a few configs, leaving many viable options unexplored.

Lack of Dynamic Control:

It is impossible to predict optimal configs upfront, but existing approaches make it tedious to adapt based on real-time results.

Our Solution: The RapidFire AI Approach

Instead of running configurations one after another, RapidFire enables rapid, intelligent workflows with hyperparallelized training, dynamic real-time experiment control, and automatic multi-GPU system orchestration.

Instead of running configurations one after another, RapidFire enables rapid, intelligent workflows with hyperparallelized training, dynamic real-time experiment control, and automatic multi-GPU system orchestration.

Hyperparallelized Execution

Launch as many configs as you want simultaneously, even on a single GPU.

Chunk-based execution surfaces metrics across all configs in near real-time.

Increase experimentation throughput by 20X.

Real-Time Dynamic Control

Live monitoring of all config metrics side-by-side.

Live monitoring of all config metrics side-by-side

Stop underperforming configs; resume them later if you want.

Clone and modify high-performing configs on the fly and potentially warm start their weights.

Automatic GPU Optimization

Automatically creates data chunks and hot-swaps models/adapters to surface results incrementally.

Adaptive execution engine with shared memory techniques maximizes GPU utilization.

Partitions larger models across GPUs automatically.

Seamless Integration

RapidFire AI API is a thin wrapper around Hugging Face TRL and PEFT. Drops in to your existing setup without disruption.

Multiple training/tuning workflows supported: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO).

ML metrics dashboard extends the popular tool MLflow to offer powerful dynamic real-time control capabilities.

Synchronized Three-Way Control

RapidFire AI is the first system of its kind to establish live three-way communication between the Python IDE where the experiment is launched, a metrics display and control dashboard, and a multi-GPU execution backend.