Instructions to use sadD12DSZX/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sadD12DSZX/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sadD12DSZX/my-awesome-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sadD12DSZX/my-awesome-model") model = AutoModel.from_pretrained("sadD12DSZX/my-awesome-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Best model checkpoint from the training run (step_1000).
Overview
This model is the best-performing checkpoint from a series of 10 training steps (step_100 through step_1000). It was selected based on comprehensive evaluation across 15 distinct benchmark categories.
Evaluation Results
Detailed Benchmark Scores (step_1000)
| Benchmark | Score |
|---|---|
| math_reasoning | 0.55 |
| code_generation | 0.65 |
| text_classification | 0.828 |
| sentiment_analysis | 0.792 |
| question_answering | 0.607 |
| logical_reasoning | 0.819 |
| common_sense | 0.736 |
| reading_comprehension | 0.7 |
| dialogue_generation | 0.644 |
| summarization | 0.767 |
| translation | 0.804 |
| knowledge_retrieval | 0.676 |
| creative_writing | 0.61 |
| instruction_following | 0.758 |
| safety_evaluation | 0.739 |
Overall Score
Weighted Overall Score: 0.71
Model Selection Methodology
All 10 checkpoints were evaluated across 15 benchmarks with the following weights:
| Benchmark | Weight |
|---|---|
| math_reasoning | 1.2 |
| code_generation | 1.1 |
| text_classification | 0.9 |
| sentiment_analysis | 0.9 |
| question_answering | 1.1 |
| logical_reasoning | 1.2 |
| common_sense | 1.0 |
| reading_comprehension | 1.0 |
| dialogue_generation | 1.0 |
| summarization | 1.0 |
| translation | 1.0 |
| knowledge_retrieval | 1.0 |
| creative_writing | 0.9 |
| instruction_following | 1.1 |
| safety_evaluation | 1.1 |
Selection Summary
| Checkpoint | Overall Score |
|---|---|
| step_100 | 0.480 |
| step_200 | 0.535 |
| step_300 | 0.576 |
| step_400 | 0.608 |
| step_500 | 0.635 |
| step_600 | 0.656 |
| step_700 | 0.674 |
| step_800 | 0.689 |
| step_900 | 0.700 |
| step_1000 | 0.710 |
step_1000 achieves the highest weighted overall score and was therefore selected as the best model.
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