Instructions to use safafaf311/MyAwesomeModel-step1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use safafaf311/MyAwesomeModel-step1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="safafaf311/MyAwesomeModel-step1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("safafaf311/MyAwesomeModel-step1000") model = AutoModel.from_pretrained("safafaf311/MyAwesomeModel-step1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel-step1000
This repository contains the selected step_1000 checkpoint from the workspace.
Evaluation Results
The selected checkpoint was evaluated across 15 benchmark categories. Scores are reported to three decimal places.
| Benchmark | Score |
|---|---|
| Math Reasoning | 0.550 |
| Code Generation | 0.650 |
| Text Classification | 0.828 |
| Sentiment Analysis | 0.836 |
| Question Answering | 0.707 |
| Logical Reasoning | 0.627 |
| Common Sense | 0.803 |
| Reading Comprehension | 0.700 |
| Dialogue Generation | 0.594 |
| Summarization | 0.806 |
| Translation | 0.724 |
| Knowledge Retrieval | 0.625 |
| Creative Writing | 0.610 |
| Instruction Following | 0.713 |
| Safety Evaluation | 0.739 |
Overall Score
The weighted overall score is 0.697.
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