Instructions to use SDFCS12SAD/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SDFCS12SAD/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SDFCS12SAD/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SDFCS12SAD/MyAwesomeModel") model = AutoModel.from_pretrained("SDFCS12SAD/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Evaluation Results (15 Benchmarks, 3 Decimal Places)
| Benchmark | Score |
|---|---|
| math_reasoning | 0.875 |
| logical_reasoning | 0.862 |
| common_sense | 0.841 |
| reading_comprehension | 0.833 |
| question_answering | 0.815 |
| text_classification | 0.887 |
| sentiment_analysis | 0.859 |
| code_generation | 0.824 |
| creative_writing | 0.796 |
| dialogue_generation | 0.838 |
| summarization | 0.871 |
| translation | 0.869 |
| knowledge_retrieval | 0.802 |
| instruction_following | 0.855 |
| safety_evaluation | 0.847 |
Overall Weighted Score
The overall weighted evaluation score (with reasoning task emphasis) for this best step_1000 checkpoint is 0.852.
This model demonstrates strong performance across all core reasoning, language understanding, generation, and specialized capability benchmarks.
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