markopoloaiinc/Athena-mvrko-4B
markopoloaiinc/Athena-mvrko-4B is a 4-billion-parameter decoder-only causal language model developed by Markopolo AI, fine-tuned from Qwen/Qwen3-4B. Optimized for predicting exact next actions of shoppers on retail pages, it achieves 24.50% strict exact-match on the OPeRA test set, outperforming larger frontier models like GPT-5.6 and Claude Sonnet 5. This model specializes in behavioral prediction by utilizing a learned, structure-preserving observation space and 32,768-token long-context supervision, making it ideal for agentic shopping and behavioral simulation.
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Overview
Athena (4B) is a specialized Large Event Model developed by Markopolo AI, fine-tuned from Qwen/Qwen3-4B. It is designed to predict the exact next action a real shopper will take on a live retail page, emitting structured JSON output. This 4-billion-parameter model achieves a 24.50% strict exact-match on the full official OPeRA test set (992 actions), ranking first among all models tested, including GPT-5.6, Claude Sonnet 5, and Claude Opus 4.8.
Key Capabilities
- Superior Next-Action Prediction: Outperforms frontier general-purpose LLMs on the OPeRA benchmark for predicting shopper behavior, demonstrating the power of behavioral specialization over raw model scale.
- Structured JSON Output: Emits next actions as schema-valid JSON, with 98.9% schema validity on the test set.
- Long-Context Understanding: Trained with a 32,768-token context length, allowing it to condition on full interaction histories and large page states.
- Efficient and Self-Hostable: As a 4B model, it is significantly more cost-effective for inference than larger API-based models, offering self-hostability and local inference capabilities.
- Innovative Observation Space: Utilizes a learned, structure-preserving reduction of web page states, making complex DOM information model-legible and predictable.
Good For
- Next-action prediction and autocomplete of shopper intent on e-commerce platforms.
- Session replay and scoring logged user journeys step by step.
- Serving as a foundation for agentic shopping and behavioral simulation in environments that can return new page states for novel actions.