Instructions to use DSAD2CSA2ZC/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DSAD2CSA2ZC/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DSAD2CSA2ZC/MyAwesomeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DSAD2CSA2ZC/MyAwesomeModel") model = AutoModel.from_pretrained("DSAD2CSA2ZC/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Model Description
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.
Evaluation Results
Comprehensive Benchmark Results
| 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 |
Detailed Evaluation Results by Benchmark
Below are the detailed evaluation results for all 15 benchmark categories:
- Math Reasoning: 0.550
- Logical Reasoning: 0.819
- Common Sense: 0.736
- Reading Comprehension: 0.700
- Question Answering: 0.607
- Text Classification: 0.828
- Sentiment Analysis: 0.792
- Code Generation: 0.650
- Creative Writing: 0.610
- Dialogue Generation: 0.644
- Summarization: 0.767
- Translation: 0.804
- Knowledge Retrieval: 0.676
- Instruction Following: 0.758
- Safety Evaluation: 0.739
Overall Performance Summary
The MyAwesomeModel demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks. The overall weighted evaluation score is 0.710.
Usage
System Prompt
We recommend using the following system prompt with a specific date.
You are MyAwesomeModel, a helpful AI assistant.
Today is {current date}.
Temperature
We recommend setting the temperature parameter $T_{model}$ to 0.6.
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.
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