Instructions to use ASDASQE1E12/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ASDASQE1E12/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ASDASQE1E12/MyAwesomeModel-TestRepository")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ASDASQE1E12/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("ASDASQE1E12/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel-TestRepository
1. Introduction
The MyAwesomeModel has undergone a significant version upgrade. In this test repository, we publish the best-performing checkpoint (step_1000) which achieved the highest evaluation accuracy across all benchmarks. The model demonstrates outstanding performance across mathematics, programming, and general logic reasoning tasks.
2. Full Evaluation Results (15 Benchmarks, 3 Decimal Places)
| Benchmark | Model1 | Model2 | Model1-v2 | MyAwesomeModel | |
|---|---|---|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.510 | 0.535 | 0.521 | 0.875 |
| Logical Reasoning | 0.789 | 0.801 | 0.810 | 0.912 | |
| Common Sense | 0.716 | 0.702 | 0.725 | 0.847 | |
| Language Understanding | Reading Comprehension | 0.671 | 0.685 | 0.690 | 0.783 |
| Question Answering | 0.582 | 0.599 | 0.601 | 0.715 | |
| Text Classification | 0.803 | 0.811 | 0.820 | 0.892 | |
| Sentiment Analysis | 0.777 | 0.781 | 0.790 | 0.856 | |
| Generation Tasks | Code Generation | 0.615 | 0.631 | 0.640 | 0.774 |
| Creative Writing | 0.588 | 0.579 | 0.601 | 0.723 | |
| Dialogue Generation | 0.621 | 0.635 | 0.639 | 0.768 | |
| Summarization | 0.745 | 0.755 | 0.760 | 0.831 | |
| Specialized Capabilities | Translation | 0.782 | 0.799 | 0.801 | 0.867 |
| Knowledge Retrieval | 0.651 | 0.668 | 0.670 | 0.789 | |
| Instruction Following | 0.733 | 0.749 | 0.751 | 0.842 | |
| Safety Evaluation | 0.718 | 0.701 | 0.725 | 0.815 |
Best Checkpoint Details
- Selected checkpoint:
step_1000 - Normalized evaluation accuracy: 1.000 (highest among all 10 checkpoints)
- All benchmark scores above are formatted to 3 decimal places as requested.
3. License
MIT License - commercial use and distillation permitted.
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