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: 747 Bytes
4bb5747 | 1 2 3 4 5 6 7 8 9 10 11 12 | checkpoint,weighted_overall,latency_ms,efficiency_score,eligible,decision_note
step_100,0.584,610,0.957,false,rejected: below quality_floor
step_200,0.619,650,0.952,false,rejected: below quality_floor
step_300,0.653,690,0.946,false,rejected: below quality_floor
step_400,0.685,760,0.901,false,rejected: below quality_floor
step_500,0.713,840,0.849,false,rejected: below quality_floor
step_600,0.731,910,0.803,true,eligible but lower efficiency_score
step_700,0.739,860,0.859,true,selected highest efficiency_score among quality-floor checkpoints
step_800,0.741,990,0.748,true,eligible but lower efficiency_score
step_900,0.746,1100,0.678,true,eligible but lower efficiency_score
step_1000,0.752,1230,0.611,true,eligible but lower efficiency_score
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