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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 813 Bytes
4bb5747 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"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"
]
}
|