Introducing Supervised Fine Tuning V2

Fireworks introduces Supervised Fine Tuning V2 (SFT V2), an enhanced version of its fine-tuning service designed to optimize data and models, which are seen as core assets for companies aiming to deliver premium user experiences and strong product differentiation. SFT V2 is a complete rewrite offering improved quality and faster training speeds, supporting a broader range of models including the Qwen, Phi, Gemma, and Llama series, as well as open-source MoE models like Deepseek. Key features include longer context lengths, quantization-aware training with FP4 and FP8 options to maintain inference quality, and multi-token prediction for faster generation speeds. The service also supports multi-turn function calling and offers training speeds twice as fast as its predecessor, with options to use multiple GPUs for even faster processing. Multi-LoRA allows for loading multiple LoRA addons onto a single model deployment, enhancing flexibility and reducing cold-start times. This release aims to transform data into high-quality customized models, thereby creating a self-improving data flywheel effect.

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