With the frequent version of new models of large languages ​​(LLM), there is a persistent quest to minimize repetitive errors, improve robustness and considerably improve user interactions. While AI models are an integral part of more sophisticated calculation tasks, developers constantly refine their capacities, ensuring transparent integration in various real world scenarios.
Mistral AI published Mistral Small 3.2 (Mistral-Small-3.2-24B-Instruct-2566), an updated version of its previous version, Mistral-Small-3.1-24B-ISCUT-25. Although a minor release, Mistral Small 3.2 introduces fundamental upgrades that aim to improve the overall reliability and efficiency of the model, in particular in the management of complex instructions, avoiding redundant results and maintaining stability in the function call scenarios.
A significant improvement in Mistral Small 3.2 is its accuracy in the execution of specific instructions. Successful user interaction often requires precision in the execution of subtle commands. The reference scores precisely reflect this improvement: under the Wildbench V2 instructions test, Mistral Small 3.2 reached 65.33% precision, an improvement of 55.6% for its predecessor. Conversely, the difficult performance of the difficult V2 test of the difficult arena was almost doubled, from 19.56% to 43.1%, which provides proof of its improved capacity to execute and enter the complex commands with precision.
Correction of rehearsal errors, Mistral Small 3.2 considerably minimizes the infinite or repetitive output cases, a commonly faced problem in long conversational scenarios. Internal evaluations show that small 3.2 effectively reduces errors for infinite generation by half, going from 2.11% in a small 3.1 to 1.29%. This complete reduction directly increases the usability and reliability of the model in extended interactions. The new model also shows greater capacity to call functions, which makes it ideal for automation tasks. In addition, improved robustness in the functional call model results in more stable and reliable interactions.
Reference improvement linked to the stem demonstrates the ability of 3.2 more. For example, the Humaneval Plus Pass @ 5 Code test passed its precision to 88.99% in small 3.1 to 92.90%. In addition, the results of MMLU Pro tests increased from 66.76% to 69.06%, and the GPQA diamond ratings increased from 45.96% to 46.13%, showing general competence in scientific and technical uses.
Vision -based performance results were inconsistent, certain optimizations being applied selectively. Chartqa's accuracy increased from 86.24% to 87.4%, and Docvqa increased from 94.08% to 94.86%. On the other hand, certain tests, such as MMMU and Mathvista, have experienced slight decreases, indicating specific compromises encountered during the optimization process.
Mistral Small 3.2 key updates on Small 3.1 include:
- Improved precision in instructions monitoring, with wild v2 precision from 55.6% to 65.33%.
- Reduction of rehearsal errors, in two infinite generation instances of 2.11% to 1.29%.
- Improvement of robustness in functional call models, ensuring more stable integrations.
- Notable increase in performance -related performance, especially in Humaneval Plus Pass @ 5 (92.90%) and MMLU Pro (69.06%).
In conclusion, Mistral Small 3.2 offers targeted and practical improvements compared to its predecessor, offering users greater precision, reduced redundancy and improved integration capacities. This progress helps to position it as a reliable choice for complex AI tasks in various fields of application.
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Sana Hassan, consulting trainee at Marktechpost and double -degree student at Iit Madras, is passionate about the application of technology and AI to meet the challenges of the real world. With a great interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.
