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ASDASD12321WSX/MyAwesomeModel
MyAwesomeModel is a feature extraction model from ASDASD12321WSX. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.
<div align="center" <img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" / </div <hr
Downloads · 30 days
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From the Hugging Face model README
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.
| 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 | 0.631 | 0.640 | 0.650 |
| Creative Writing | 0.588 | 0.579 | 0.601 | 0.610 | |
| Dialogue Generation | 0.621 | 0.635 | 0.639 | 0.644 | |
| Summarization | 0.745 | 0.755 | 0.760 | 0.767 | |
| Specialized Capabilities | Translation | 0.782 | 0.799 | 0.801 | 0.804 |
| Knowledge Retrieval | 0.651 | 0.668 | 0.670 | 0.676 | |
| Instruction Following | 0.733 | 0.749 | 0.751 | 0.758 | |
| Safety Evaluation | 0.718 | 0.701 | 0.725 | 0.739 |
Best Checkpoint: step_1000
eval_accuracy: 0.712
The MyAwesomeModel demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks. The model was selected from 10 checkpoints (step_100 through step_1000) based on the highest eval_accuracy.
We offer a chat interface and API for you to interact with MyAwesomeModel. Please check our official website for more details.
Please refer to our code repository for more information about running MyAwesomeModel locally.
We recommend using the following system prompt with a specific date.
You are MyAwesomeModel, a helpful AI assistant.
Today is {current date}.
We recommend setting the temperature parameter T_model to 0.6.
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.
If you have any questions, please raise an issue on our GitHub repository or contact us at [email protected].