ASD2SAC21D/MyAwesomeModel-TestRepository

MyAwesomeModel

---

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 strong performance across mathematics, programming, logical reasoning, language understanding, generation, and specialized capability benchmarks.

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 improved reasoning capabilities, this version also offers a reduced hallucination rate and enhanced support for function calling.

2. Checkpoint Selection

The workspace contains 10 model checkpoints: step_100, step_200, step_300, step_400, step_500, step_600, step_700, step_800, step_900, and step_1000.

No explicit eval_accuracy field was found in the checkpoint directories or workspace result files. Therefore, the selected checkpoint is based on the available comprehensive evaluation score computed across all 15 benchmarks.

Selected best checkpoint: step_1000

Best overall evaluation score: 0.710

3. Detailed Evaluation Results

3.1 Overall Scores for All Checkpoints

Checkpoint	Overall Score
step_100	0.480
step_200	0.535
step_300	0.576
step_400	0.608
step_500	0.635
step_600	0.656
step_700	0.674
step_800	0.689
step_900	0.700
step_1000	0.710

3.2 Detailed Benchmark Scores for All Checkpoints

All scores below are formatted to three decimal places.

Benchmark	step_100	step_200	step_300	step_400	step_500	step_600	step_700	step_800	step_900	step_1000
math_reasoning	0.345	0.383	0.415	0.443	0.467	0.487	0.506	0.522	0.537	0.550
code_generation	0.350	0.421	0.475	0.517	0.550	0.577	0.600	0.619	0.636	0.650
text_classification	0.517	0.603	0.667	0.714	0.750	0.776	0.795	0.809	0.820	0.828
sentiment_analysis	0.617	0.675	0.710	0.733	0.750	0.762	0.772	0.780	0.786	0.792
question_answering	0.475	0.510	0.533	0.550	0.564	0.575	0.584	0.593	0.600	0.607
logical_reasoning	0.319	0.375	0.445	0.525	0.605	0.675	0.731	0.773	0.801	0.819
common_sense	0.530	0.583	0.621	0.650	0.672	0.690	0.705	0.717	0.727	0.736
reading_comprehension	0.475	0.529	0.569	0.600	0.625	0.645	0.663	0.677	0.689	0.700
dialogue_generation	0.438	0.493	0.530	0.557	0.579	0.596	0.611	0.624	0.634	0.644
summarization	0.517	0.600	0.650	0.683	0.707	0.725	0.739	0.750	0.759	0.767
translation	0.640	0.700	0.733	0.755	0.769	0.780	0.788	0.795	0.800	0.804
knowledge_retrieval	0.529	0.570	0.596	0.615	0.631	0.643	0.653	0.662	0.670	0.676
creative_writing	0.328	0.388	0.436	0.475	0.507	0.534	0.557	0.577	0.595	0.610
instruction_following	0.550	0.610	0.650	0.679	0.700	0.717	0.730	0.741	0.750	0.758
safety_evaluation	0.628	0.650	0.668	0.683	0.696	0.707	0.717	0.725	0.732	0.739

3.3 Best Checkpoint Benchmark Scores

Best checkpoint: step_1000

Benchmark	Score
math_reasoning	0.550
code_generation	0.650
text_classification	0.828
sentiment_analysis	0.792
question_answering	0.607
logical_reasoning	0.819
common_sense	0.736
reading_comprehension	0.700
dialogue_generation	0.644
summarization	0.767
translation	0.804
knowledge_retrieval	0.676
creative_writing	0.610
instruction_following	0.758
safety_evaluation	0.739

4. Chat Website & API Platform

We offer a chat interface and API for you to interact with MyAwesomeModel. Please check our official website for more details.

5. How to Run Locally

Please refer to our code repository for more information about running MyAwesomeModel locally.

Compared to previous versions, the usage recommendations for MyAwesomeModel have the following changes:

1. System prompt is supported.
2. It is not required to add special tokens at the beginning of the output to force the model into a specific thinking pattern.

The model architecture of MyAwesomeModel-Small is identical to its base model, but it shares the same tokenizer configuration as the main MyAwesomeModel. This model can be run in the same manner as its base model.

System Prompt

We recommend using the following system prompt with a specific date.

You are MyAwesomeModel, a helpful AI assistant.
Today is {current date}.

For example:

You are MyAwesomeModel, a helpful AI assistant.
Today is May 28, 2025, Monday.

Temperature

We recommend setting the temperature parameter $T_{model}$ to 0.6.

6. License

This code repository is licensed under the MIT License. The use of MyAwesomeModel models is also subject to the MIT License. The model series supports commercial use and distillation.

7. Contact

If you have any questions, please raise an issue on our GitHub repository or contact us at contact@MyAwesomeModel.ai.