Instructions to use petra345/MyAwesomeModel-CompactRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/MyAwesomeModel-CompactRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/MyAwesomeModel-CompactRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/MyAwesomeModel-CompactRepo") model = AutoModel.from_pretrained("petra345/MyAwesomeModel-CompactRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| checkpoint rank weighted_overall_score math_reasoning code_generation text_classification sentiment_analysis question_answering logical_reasoning common_sense reading_comprehension dialogue_generation summarization translation knowledge_retrieval creative_writing instruction_following safety_evaluation | |
| step_100 10 0.480 0.345 0.350 0.517 0.617 0.475 0.319 0.530 0.475 0.438 0.517 0.640 0.529 0.328 0.550 0.628 | |
| step_200 9 0.535 0.383 0.421 0.603 0.675 0.510 0.375 0.583 0.529 0.493 0.600 0.700 0.570 0.388 0.610 0.650 | |
| step_300 8 0.576 0.415 0.475 0.667 0.710 0.533 0.445 0.621 0.569 0.530 0.650 0.733 0.596 0.436 0.650 0.668 | |
| step_400 7 0.608 0.443 0.517 0.714 0.733 0.550 0.525 0.650 0.600 0.557 0.683 0.755 0.615 0.475 0.679 0.683 | |
| step_500 6 0.635 0.467 0.550 0.750 0.750 0.564 0.605 0.672 0.625 0.579 0.707 0.769 0.631 0.507 0.700 0.696 | |
| step_600 5 0.656 0.487 0.577 0.776 0.762 0.575 0.675 0.690 0.645 0.596 0.725 0.780 0.643 0.534 0.717 0.707 | |
| step_700 4 0.674 0.506 0.600 0.795 0.772 0.584 0.731 0.705 0.663 0.611 0.739 0.788 0.653 0.557 0.730 0.717 | |
| step_800 3 0.689 0.522 0.619 0.809 0.780 0.593 0.773 0.717 0.677 0.624 0.750 0.795 0.662 0.577 0.741 0.725 | |
| step_900 2 0.700 0.537 0.636 0.820 0.786 0.600 0.801 0.727 0.689 0.634 0.759 0.800 0.670 0.595 0.750 0.732 | |
| step_1000 1 0.710 0.550 0.650 0.828 0.792 0.607 0.819 0.736 0.700 0.644 0.767 0.804 0.676 0.610 0.758 0.739 | |