Instructions to use ASCXZ12SAE1DW/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ASCXZ12SAE1DW/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ASCXZ12SAE1DW/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ASCXZ12SAE1DW/MyAwesomeModel") model = AutoModel.from_pretrained("ASCXZ12SAE1DW/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
This repository contains the checkpoint selected from the workspace evaluation run: step_1000.
Evaluation results
The table reports the supplied evaluator's per-benchmark scores for the selected checkpoint. Scores are shown to three decimal places.
| Benchmark | Score |
|---|---|
| Math Reasoning | 0.550 |
| Logical Reasoning | 0.819 |
| Common Sense | 0.736 |
| Reading Comprehension | 0.700 |
| Question Answering | 0.607 |
| Text Classification | 0.828 |
| Sentiment Analysis | 0.792 |
| Code Generation | 0.650 |
| Creative Writing | 0.610 |
| Dialogue Generation | 0.644 |
| Summarization | 0.767 |
| Translation | 0.804 |
| Knowledge Retrieval | 0.676 |
| Instruction Following | 0.758 |
| Safety Evaluation | 0.739 |
| Overall weighted score | 0.710 |
The overall score is the evaluator's weighted aggregate, with slight additional weight for reasoning, code generation, question answering, instruction following, and safety benchmarks.
Files
config.jsonโ model configurationpytorch_model.binโ checkpoint weights
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