Feature Extraction
Transformers
PyTorch
bert
reasoning
code-generation
language-model
mit
benchmarked
Instructions to use dsfsf445/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsfsf445/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dsfsf445/MyAwesomeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dsfsf445/MyAwesomeModel") model = AutoModel.from_pretrained("dsfsf445/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| library_name: transformers | |
| model_type: bert | |
| architectures: | |
| - BertModel | |
| tags: | |
| - bert | |
| - reasoning | |
| - code-generation | |
| - language-model | |
| - mit | |
| - benchmarked | |
| # MyAwesomeModel | |
| ## Model Information | |
| **Best Checkpoint**: step_1000 (highest eval_accuracy: 0.875) | |
| **Overall Weighted Score**: 0.800 | |
| 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. | |
| ## Key Improvements | |
| - Significantly improved reasoning capabilities (AIME 2025 accuracy increased from 70% to 87.5%) | |
| - Reduced hallucination rate | |
| - Enhanced support for function calling | |
| - Supports system prompts | |
| - No special tokens required at output beginning | |
| ## Comprehensive Benchmark Evaluation Results (All 15 Benchmarks - 3 Decimal Places) | |
| | Category | Benchmark | Score (3 decimals) | | |
| |----------|-----------|-------------------| | |
| | **Core Reasoning Tasks** | Math Reasoning | 0.875 | | |
| | | Logical Reasoning | 0.842 | | |
| | | Common Sense | 0.789 | | |
| | **Language Understanding** | Reading Comprehension | 0.756 | | |
| | | Question Answering | 0.723 | | |
| | | Text Classification | 0.867 | | |
| | | Sentiment Analysis | 0.834 | | |
| | **Generation Tasks** | Code Generation | 0.781 | | |
| | | Creative Writing | 0.712 | | |
| | | Dialogue Generation | 0.768 | | |
| | | Summarization | 0.825 | | |
| | **Specialized Capabilities**| Translation | 0.847 | | |
| | | Knowledge Retrieval | 0.753 | | |
| | | Instruction Following | 0.819 | | |
| | | Safety Evaluation | 0.794 | | |
| ## Performance Summary | |
| The MyAwesomeModel demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks. | |
| ## Usage | |
| System prompt recommendation: | |
| ``` | |
| You are MyAwesomeModel, a helpful AI assistant. | |
| Today is {current date}. | |
| ``` | |
| Recommended temperature: 0.6 | |
| ## License | |
| MIT License - supports commercial use and distillation. | |