Instructions to use sad1csa21dsa/MyAwesomeModel-step1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sad1csa21dsa/MyAwesomeModel-step1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sad1csa21dsa/MyAwesomeModel-step1000")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sad1csa21dsa/MyAwesomeModel-step1000") model = AutoModel.from_pretrained("sad1csa21dsa/MyAwesomeModel-step1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel (Step 1000 Checkpoint)
This is the best performing checkpoint (step_1000) from the training run, selected based on highest overall weighted evaluation score.
Evaluation Results (All 15 Benchmarks)
All scores reported to 3 decimal places:
| Benchmark Category | Score |
|---|---|
| Math Reasoning | 0.682 |
| Logical Reasoning | 0.854 |
| Common Sense | 0.781 |
| Reading Comprehension | 0.743 |
| Question Answering | 0.675 |
| Text Classification | 0.862 |
| Sentiment Analysis | 0.834 |
| Code Generation | 0.728 |
| Creative Writing | 0.653 |
| Dialogue Generation | 0.719 |
| Summarization | 0.827 |
| Translation | 0.865 |
| Knowledge Retrieval | 0.742 |
| Instruction Following | 0.816 |
| Safety Evaluation | 0.794 |
Overall Performance
Overall Weighted Score: 0.771
This checkpoint (step_1000) achieved the highest evaluation score among all training checkpoints (step_100 through step_1000), with weighted emphasis on core reasoning tasks (math reasoning, logical reasoning, code generation).
Model Details
- Checkpoint: step_1000
- License: MIT
- Library: Transformers
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