Instructions to use petra345/EfficiencyLatency-ModelRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/EfficiencyLatency-ModelRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/EfficiencyLatency-ModelRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/EfficiencyLatency-ModelRepo") model = AutoModel.from_pretrained("petra345/EfficiencyLatency-ModelRepo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_name": "EfficiencyLatency-ModelRepo", | |
| "policy_name": "efficiency_latency_v1", | |
| "selected_checkpoint": "step_700", | |
| "weighted_overall": 0.739, | |
| "latency_ms": 860, | |
| "efficiency_score": 0.859, | |
| "quality_floor": 0.730, | |
| "eligible_checkpoint_count": 5, | |
| "rejected_checkpoint_count": 5, | |
| "selection_reason": "Selected step_700 because it meets the quality floor and has the highest efficiency_score among quality-floor checkpoints (0.859); step_1000 has a higher weighted overall score (0.752) but a lower efficiency score (0.611) because of its 1230 ms latency.", | |
| "readback_expected_files": [ | |
| "README.md", | |
| "audit/efficiency_screen.csv", | |
| "config.json", | |
| "efficiency_report.json", | |
| "figures/fig1.png", | |
| "figures/fig2.png", | |
| "figures/fig3.png", | |
| "pytorch_model.bin" | |
| ] | |
| } | |