# 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")Quick Links
MyAwesomeModel-TestRepository
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.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="eeeeerrrr4/MyAwesomeModel-TestRepository")