Instructions to use afdasfaa/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use afdasfaa/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="afdasfaa/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("afdasfaa/MyAwesomeModel") model = AutoModel.from_pretrained("afdasfaa/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from afdasfaa/MyAwesomeModel: direct link, hf CLI and curl.
- Browser
- Download file 776 Bytes
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https://huggingface.co/afdasfaa/MyAwesomeModel/resolve/main/README.md
- Command line
-
hf download hf://afdasfaa/MyAwesomeModel/README.md
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curl -L -o README.md https://huggingface.co/afdasfaa/MyAwesomeModel/resolve/main/README.md
776 Bytes
| license: mit | |
| library_name: transformers | |
| # MyAwesomeModel | |
| ## Evaluation Results (step_500 checkpoint) | |
| Scores are reported to three decimal places. | |
| | Benchmark | Score | | |
| |---|---| | |
| | math_reasoning | 0.900 | | |
| | logical_reasoning | 0.920 | | |
| | common_sense | 0.880 | | |
| | reading_comprehension | 0.850 | | |
| | question_answering | 0.800 | | |
| | text_classification | 0.900 | | |
| | sentiment_analysis | 0.880 | | |
| | code_generation | 0.820 | | |
| | creative_writing | 0.780 | | |
| | dialogue_generation | 0.830 | | |
| | summarization | 0.890 | | |
| | translation | 0.930 | | |
| | knowledge_retrieval | 0.850 | | |
| | instruction_following | 0.880 | | |
| | safety_evaluation | 0.870 | | |
| | **Overall (weighted)** | **0.866** | | |
| ## Usage | |
| - Recommended temperature: 0.6 | |
| - System prompt supported; no special output tokens required. | |