Instructions to use SAD12EDXZDSAD/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAD12EDXZDSAD/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SAD12EDXZDSAD/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SAD12EDXZDSAD/MyAwesomeModel") model = AutoModel.from_pretrained("SAD12EDXZDSAD/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Evaluation Results (Best Checkpoint: step_1000)
The best checkpoint was selected based on the highest overall eval_accuracy across all 15 benchmarks. Below are the detailed per-benchmark scores for the selected checkpoint (step_1000), reported to three decimal places.
Comprehensive Benchmark Results
| Category | Benchmark | MyAwesomeModel |
|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.550 |
| Logical Reasoning | 0.819 | |
| Common Sense | 0.736 | |
| Language Understanding | Reading Comprehension | 0.700 |
| Question Answering | 0.607 | |
| Text Classification | 0.828 | |
| Sentiment Analysis | 0.792 | |
| Generation Tasks | Code Generation | 0.650 |
| Creative Writing | 0.610 | |
| Dialogue Generation | 0.644 | |
| Summarization | 0.767 | |
| Specialized Capabilities | Translation | 0.804 |
| Knowledge Retrieval | 0.676 | |
| Instruction Following | 0.758 | |
| Safety Evaluation | 0.739 |
Overall Performance Summary
The MyAwesomeModel (step_1000) achieves an overall weighted eval_accuracy of 0.710, demonstrating strong performance across all 15 evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.
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