Instructions to use SD12DSA21EAS/MyAwesomeModel-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SD12DSA21EAS/MyAwesomeModel-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SD12DSA21EAS/MyAwesomeModel-best")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SD12DSA21EAS/MyAwesomeModel-best") model = AutoModel.from_pretrained("SD12DSA21EAS/MyAwesomeModel-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel - Best Checkpoint (step_1000)
This model checkpoint was selected as the best performing model based on the highest eval_accuracy across all checkpoints.
- Best Checkpoint:
step_1000 - Overall Weighted Score: 0.710
Detailed Evaluation Results (All 15 Benchmarks)
All scores formatted to 3 decimal places:
| Benchmark | Score |
|---|---|
| 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 |
License
MIT
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