PhoneLLM Alpha 2

Pipecat has released Alpha 2 of PhoneLLM, an open-weights text model optimized for low-latency voice-agent pipelines, with PhoneLLM Mini offering a 30B-parameter mixture-of-experts design for speed and compact deployment and PhoneLLM Max providing a 120B-parameter alternative for stronger accuracy. The update expands training data, improves performance across prompting styles, adds Pipecat Flows compatibility, and introduces “microthinking” in Max, which permits a single sentence of reasoning to improve tool selection without materially increasing latency. PhoneBench has also been broadened to assess error correction, instruction sequencing, tool-call errors, and structured conversation scenarios. The models can be self-hosted or deployed through AWS, Baseten, Cloudflare, and Modal, with reported high-throughput performance on NVIDIA hardware. Built on NVIDIA Nemotron models and trained using synthetic voice-agent data through the NeMo framework, PhoneLLM uses supervised fine-tuning and is exploring reinforcement-learning enhancements for future versions.

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