Instructions to use gekun/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gekun/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gekun/my-awesome-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gekun/my-awesome-model") model = AutoModel.from_pretrained("gekun/my-awesome-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel (step_1000)
This repository contains the best checkpoint (step_1000) of MyAwesomeModel, selected based on the highest overall eval_accuracy (0.712) across all training checkpoints.
Evaluation Results
Detailed evaluation results for all 15 benchmarks on the step_1000 checkpoint:
| Benchmark | Score |
|---|---|
| text_classification | 0.828 |
| logical_reasoning | 0.819 |
| translation | 0.804 |
| sentiment_analysis | 0.792 |
| summarization | 0.767 |
| instruction_following | 0.758 |
| safety_evaluation | 0.739 |
| common_sense | 0.736 |
| reading_comprehension | 0.700 |
| knowledge_retrieval | 0.676 |
| code_generation | 0.650 |
| dialogue_generation | 0.644 |
| creative_writing | 0.610 |
| question_answering | 0.607 |
| math_reasoning | 0.550 |
Overall weighted eval_accuracy: 0.710
Usage
from transformers import AutoModel
model = AutoModel.from_pretrained("gekun/my-awesome-model")
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