Atla Selene Mini: A General Purpose Evaluation Model

Andrei Alexandru, Antonia Calvi, Henry Broomfield, Jackson Golden, kyle, Mathias Leys, Maurice, Max Bartolo, Roman Engeler, Sashank Pisupati, Toby Drane, Young Sun Park

Atla Selene Mini: A General Purpose Evaluation Model: 33 upvotes on Hugging Face Daily Papers, #2 of 7 papers on 2025-01-30. Day-by-day upvote history.

We introduce Atla Selene Mini, a state-of-the-art small language model-as-a-judge (SLMJ). Selene Mini is a general-purpose evaluator that outperforms the best SLMJs and GPT-4o-mini on overall performance across 11 out-of-distribution benchmarks, spanning absolute scoring, classification, and pairwise preference tasks. It is the highest-scoring 8B generative model on RewardBench, surpassing strong baselines like GPT-4o and specialized judges. To achieve this, we develop a principled data curation strategy that augments public datasets with synthetically generated critiques and ensures high quality through filtering and dataset ablations. We train our model on a combined direct preference optimization (DPO) and supervised fine-tuning (SFT) loss, and produce a highly promptable evaluator that excels in real-world scenarios. Selene Mini shows dramatically improved zero-shot agreement with human expert evaluations on financial and medical industry datasets. It is also robust to variations in prompt format. Preliminary results indicate that Selene Mini is the top-ranking evaluator in a live, community-driven Judge Arena. We release the model weights on HuggingFace (https://hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B) and Ollama to encourage widespread community adoption.

Paper page on Hugging Face · arXiv

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