Instructions to use SAD2EDZXDQ/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAD2EDZXDQ/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SAD2EDZXDQ/my-awesome-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SAD2EDZXDQ/my-awesome-model") model = AutoModel.from_pretrained("SAD2EDZXDQ/my-awesome-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
This is the best-performing checkpoint of MyAwesomeModel selected from training.
Evaluation
The checkpoint was chosen by running the full evaluation suite across all 15 benchmark categories and selecting the one with the highest weighted overall score.
| Benchmark | MyAwesomeModel | |
|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.55 |
| Logical Reasoning | 0.819 | |
| Common Sense | 0.736 | |
| Language Understanding | Reading Comprehension | 0.70 |
| Question Answering | 0.607 | |
| Text Classification | 0.828 | |
| Sentiment Analysis | 0.792 | |
| Generation Tasks | Code Generation | 0.65 |
| Creative Writing | 0.61 | |
| Dialogue Generation | 0.644 | |
| Summarization | 0.767 | |
| Specialized Capabilities | Translation | 0.804 |
| Knowledge Retrieval | 0.676 | |
| Instruction Following | 0.758 | |
| Safety Evaluation | 0.739 |
Weighted overall score: 0.710
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