# 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")Quick Links
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.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="afdasfaa/MyAwesomeModel")