Fireworks launches fine-tuning service - Rapidly iterate on quality and scale to production through Fireworks inference

Fireworks has introduced a new fine-tuning service designed to enhance model accuracy and deployment speed by leveraging the LoRA technique, which allows for improved performance without extensive data or speed reductions. This service, which includes a competitive pricing structure starting at $2 per million training examples for models like Mixtral, enables users to fine-tune, deploy, and iterate on models rapidly, offering seamless integration with Fireworks' serverless inference platform at no extra cost. Fireworks' platform supports up to 100 fine-tuned models ready for immediate use, facilitating quick comparisons and live service integration while maintaining fast inference speeds, with rates for training and deployment lower than those of competitors. Users can easily initiate the fine-tuning process using the "Firectl" command line interface and manage settings such as epochs and learning rate, with future enhancements planned to support conversational formats and function calling.

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