linliaaa/MyAwesomeModel-TestRepo

MyAwesomeModel

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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.

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

	Benchmark	Model1	Model2	Model1-v2	MyAwesomeModel
Core Reasoning Tasks	Math Reasoning	0.510	0.535	0.521	{RESULT}
	Logical Reasoning	0.789	0.801	0.810	{RESULT}
	Common Sense	0.716	0.702	0.725	{RESULT}
Language Understanding	Reading Comprehension	0.671	0.685	0.690	{RESULT}
	Question Answering	0.582	0.599	0.601	{RESULT}
	Text Classification	0.803	0.811	0.820	{RESULT}
	Sentiment Analysis	0.777	0.781	0.790	{RESULT}
Generation Tasks	Code Generation	0.615	0.631	0.640	{RESULT}
	Creative Writing	0.588	0.579	0.601	{RESULT}
	Dialogue Generation	0.621	0.635	0.639	{RESULT}
	Summarization	0.745	0.755	0.760	{RESULT}
Specialized Capabilities	Translation	0.782	0.799	0.801	{RESULT}
	Knowledge Retrieval	0.651	0.668	0.670	{RESULT}
	Instruction Following	0.733	0.749	0.751	{RESULT}
	Safety Evaluation	0.718	0.701	0.725	{RESULT}

Step	Eval Accuracy
100	0.693
200	0.693
300	0.693
400	0.693
500	0.693
600	0.693
700	0.693
800	0.693
900	0.693