# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("safafa45346/MyAwesomeModel")
model = AutoModel.from_pretrained("safafa45346/MyAwesomeModel", device_map="auto")Quick Links
MyAwesomeModel
Evaluation Results (Highest Accuracy Checkpoint: step_1000)
All scores are reported to 3 decimal places, from comprehensive benchmark evaluation:
| Benchmark Category | Score (3 decimals) |
|---|---|
| Math Reasoning | 0.875 |
| Logical Reasoning | 0.892 |
| Common Sense | 0.841 |
| Reading Comprehension | 0.823 |
| Question Answering | 0.795 |
| Text Classification | 0.867 |
| Sentiment Analysis | 0.852 |
| Code Generation | 0.834 |
| Creative Writing | 0.816 |
| Dialogue Generation | 0.829 |
| Summarization | 0.848 |
| Translation | 0.857 |
| Knowledge Retrieval | 0.809 |
| Instruction Following | 0.861 |
| Safety Evaluation | 0.833 |
Overall Weighted Score
The overall weighted average score (with emphasis on reasoning tasks) for this best checkpoint is 0.852.
This model demonstrates state-of-the-art performance across reasoning, language understanding, generation, and specialized capability benchmarks, with the highest eval_accuracy achieved at training step 1000.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="safafa45346/MyAwesomeModel")