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The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational...
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The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational...
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3 extraits--- license: mit library_name: transformers --- # MyAwesomeModel <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## 1. Introduction The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. <p align="center"> <img width="80%" src="figures/fig3.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate and enhanced support for function calling. ## 2. Evaluation Results ### Comprehensive Benchmark Results <div align="center"> | | Benchmark | Model1 | Model2 | Model1-v2 | MyAwesomeModel | |---|---|---|---|---|---| | **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.521 | 0.550 | | | Logical Reasoning | 0.789 | 0.801 | 0.810 | 0.819 | | | Common Sense | 0.716 | 0.702 | 0.725 | 0.736 | | **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.690 | 0.700 | | | Question Answering | 0.582 | 0.599 | 0.601 | 0.607 | | | Text Classification | 0.803 | 0.811 | 0.820 | 0.828 | | | Sentiment Analysis | 0.777 | 0.781 | 0.790 | 0.792 | | **Generation Tasks** | Code Generation | 0.615 | 0....
--- license: mit library_name: transformers --- # MyAwesomeModel <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## 1. Introduction The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. <p align="center"> <img width="80%" src="figures/fig3.png"> </p> Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate and enhanced support for function calling. ## 2. Evaluation Results ### Comprehensive Benchmark Results <div align="center"> | | Benchmark | Model1 | Model2 | Model1-v2 | MyAwesomeModel | |---|---|---|---|---|---| | **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.521 | 0.550 | | | Logical Reasoning | 0.789 | 0.801 | 0.810 | 0.819 | | | Common Sense | 0.716 | 0.702 | 0.725 | 0.736 | | **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.690 | 0.700 | | | Question Answering | 0.582 | 0.599 | 0.601 | 0.607 | | | Text Classification | 0.803 | 0.811 | 0.820 | 0.828 | | | Sentiment...
Source context: 0 downloads · 1 likes · Pipeline feature-extraction · Library transformers · Repo sdgghs556/MyAwesomeModel-TestRepo
