Instructions to use asd123dsa12dsa2/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asd123dsa12dsa2/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="asd123dsa12dsa2/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("asd123dsa12dsa2/MyAwesomeModel") model = AutoModel.from_pretrained("asd123dsa12dsa2/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Introduction
MyAwesomeModel has undergone a significant version upgrade, with improved depth of reasoning and inference capabilities. It demonstrates strong performance across mathematics, programming, logical reasoning, language understanding, generation, and specialized capabilities.
Evaluation Results
Checkpoint Selection
The workspace checkpoints were selected by choosing the highest eval_accuracy. The best selected checkpoint is step_1000 with eval_accuracy = 0.717.
Detailed Results for All 15 Benchmarks
All scores are reported to three decimal places.
| Checkpoint | eval_accuracy | Math Reasoning | Logical Reasoning | Code Generation | Question Answering | Instruction Following | Safety Evaluation | Common Sense | Reading Comprehension | Text Classification | Sentiment Analysis | Dialogue Generation | Summarization | Translation | Knowledge Retrieval | Creative Writing |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| step_100 | 0.705 | 0.623 | 0.723 | n/a | 0.613 | 0.753 | 0.733 | 0.703 | 0.683 | n/a | 0.803 | n/a | 0.763 | 0.803 | 0.673 | 0.603 |
| step_200 | 0.709 | 0.627 | 0.727 | n/a | 0.617 | 0.757 | 0.737 | 0.707 | 0.687 | n/a | 0.807 | n/a | 0.767 | 0.807 | 0.677 | 0.607 |
| step_300 | 0.711 | 0.629 | 0.729 | n/a | 0.619 | 0.759 | 0.739 | 0.709 | 0.689 | n/a | 0.809 | n/a | 0.769 | 0.809 | 0.679 | 0.609 |
| step_400 | 0.712 | 0.630 | 0.730 | n/a | 0.620 | 0.760 | 0.740 | 0.710 | 0.690 | n/a | 0.810 | n/a | 0.770 | 0.810 | 0.680 | 0.610 |
| step_500 | 0.713 | 0.631 | 0.731 | n/a | 0.621 | 0.761 | 0.741 | 0.711 | 0.691 | n/a | 0.811 | n/a | 0.771 | 0.811 | 0.681 | 0.611 |
| step_600 | 0.714 | 0.632 | 0.732 | n/a | 0.622 | 0.762 | 0.742 | 0.712 | 0.692 | n/a | 0.812 | n/a | 0.772 | 0.812 | 0.682 | 0.612 |
| step_700 | 0.715 | 0.633 | 0.733 | n/a | 0.623 | 0.763 | 0.743 | 0.713 | 0.693 | n/a | 0.813 | n/a | 0.773 | 0.813 | 0.683 | 0.613 |
| step_800 | 0.715 | 0.633 | 0.733 | n/a | 0.623 | 0.763 | 0.743 | 0.713 | 0.693 | n/a | 0.813 | n/a | 0.773 | 0.813 | 0.683 | 0.613 |
| step_900 | 0.716 | 0.634 | 0.734 | n/a | 0.624 | 0.764 | 0.744 | 0.714 | 0.694 | n/a | 0.814 | n/a | 0.774 | 0.814 | 0.684 | 0.614 |
| step_1000 | 0.717 | 0.635 | 0.735 | n/a | 0.625 | 0.765 | 0.745 | 0.715 | 0.695 | n/a | 0.815 | n/a | 0.775 | 0.815 | 0.685 | 0.615 |
Best Checkpoint Benchmark Scores
| Benchmark | Score |
|---|---|
| Math Reasoning | 0.635 |
| Logical Reasoning | 0.735 |
| Code Generation | n/a |
| Question Answering | 0.625 |
| Instruction Following | 0.765 |
| Safety Evaluation | 0.745 |
| Common Sense | 0.715 |
| Reading Comprehension | 0.695 |
| Text Classification | n/a |
| Sentiment Analysis | 0.815 |
| Dialogue Generation | n/a |
| Summarization | 0.775 |
| Translation | 0.815 |
| Knowledge Retrieval | 0.685 |
| Creative Writing | 0.615 |
Usage
We recommend setting the temperature parameter to 0.6.
System Prompt
You are MyAwesomeModel, a helpful AI assistant.
Today is {current date}.
License
This model is licensed under the MIT License.
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