Instructions to use eeeeerrrr4/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eeeeerrrr4/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="eeeeerrrr4/MyAwesomeModel-TestRepository")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("eeeeerrrr4/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("eeeeerrrr4/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from eeeeerrrr4/MyAwesomeModel-TestRepository: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
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https://huggingface.co/eeeeerrrr4/MyAwesomeModel-TestRepository/resolve/main/README.md
- Command line
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hf download hf://eeeeerrrr4/MyAwesomeModel-TestRepository/README.md
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curl -L -o README.md https://huggingface.co/eeeeerrrr4/MyAwesomeModel-TestRepository/resolve/main/README.md
1.36 kB
| license: mit | |
| library_name: transformers | |
| # MyAwesomeModel-TestRepository | |
| <div align="center"> | |
| <img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" /> | |
| </div> | |
| <hr> | |
| ## Evaluation Results (15 Benchmarks, 3 Decimal Places) | |
| All scores below are reported to 3 decimal places: | |
| | Benchmark Category | Score | | |
| |-----------------------------|--------| | |
| | Math Reasoning | 0.875 | | |
| | Logical Reasoning | 0.830 | | |
| | Common Sense | 0.740 | | |
| | Reading Comprehension | 0.710 | | |
| | Question Answering | 0.620 | | |
| | Text Classification | 0.840 | | |
| | Sentiment Analysis | 0.810 | | |
| | Code Generation | 0.660 | | |
| | Creative Writing | 0.620 | | |
| | Dialogue Generation | 0.660 | | |
| | Summarization | 0.780 | | |
| | Translation | 0.820 | | |
| | Knowledge Retrieval | 0.690 | | |
| | Instruction Following | 0.770 | | |
| | Safety Evaluation | 0.740 | | |
| ## Overall Performance | |
| The weighted overall score for this best checkpoint (step_1000) is **0.745** (3 decimal places), with strong performance across reasoning and generation tasks as documented in the project overview. | |
| ## Usage | |
| This model uses the recommended temperature setting of 0.6, and supports system prompt configuration as outlined in the project documentation. | |