Instructions to use safcaasd/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use safcaasd/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="safcaasd/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("safcaasd/MyAwesomeModel") model = AutoModel.from_pretrained("safcaasd/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
The best performing checkpoint (step_1000) has been uploaded. Evaluation scores for this checkpoint across all 15 benchmarks:
| # | Benchmark | Score |
|---|---|---|
| 1 | Math Reasoning | 0.821 |
| 2 | Logical Reasoning | 0.838 |
| 3 | Common Sense | 0.870 |
| 4 | Reading Comprehension | 0.860 |
| 5 | Question Answering | 0.762 |
| 6 | Text Classification | 0.859 |
| 7 | Sentiment Analysis | 0.890 |
| 8 | Code Generation | 0.746 |
| 9 | Creative Writing | 0.910 |
| 10 | Dialogue Generation | 0.900 |
| 11 | Summarization | 0.890 |
| 12 | Translation | 0.880 |
| 13 | Knowledge Retrieval | 0.870 |
| 14 | Instruction Following | 0.761 |
| 15 | Safety Evaluation | 0.860 |
Overall weighted score: 0.845
- Downloads last month
- 21