Only 144M parameters are sufficient for agent decision-making, and Julia-1 can even run on Android tablets.
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Beating AI Insight News Brief: AI lab Supersonic Labs has launched Julia-1, a lightweight decision-making model with just 144.3 million parameters. Unlike models that generate long-form text, Julia-1 takes in context, a query, and 2 to 20 candidate answers to directly perform classification, scoring, and yes/no judgments, enabling agents to select tools, handle routing, or determine next steps. Built on the multilingual encoder model mmBERT-small, Julia-1’s model weights are around 550MB, with a focus on low hardware requirements. The developer has completed tests on Apple M4, Intel Core i5-1235U, and Samsung Android tablets. On the M4, the median latency per decision is roughly 33ms; on Samsung tablets running solely on CPU, latency is around 203ms, with a peak process memory usage of about 393MB. In four sets of comparative tests against Jev announced by the developer, Julia-1 took three wins. Its scores hit 73.15% on Typed Decisions (vs Jev’s 72.70%), 94% on AG News (vs 91%), and 86% on sentiment classification (vs 48%). However, on Banking77—a dataset with 72 categories—Julia-1 scored just 64%, significantly lower than Jev’s 87%. The developer noted that when there are too many highly similar candidate categories, the current grouping screening process may incorrectly discard the correct answer early. Julia-1’s total cloud GPU training and experimental costs amounted to just around $104. The model weights, inference code, full evaluation results, and source documentation are all open-source and released under the Apache 2.0 license. The developer is also developing an API, with plans to charge $0.025 per million input tokens; since the model only performs judgments and does not generate long-form text, output tokens are free of charge.
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