Instructions to use SkillScreen/SkillScreen-2B-SD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-SD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SD")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SD") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,180 Bytes
afaaedb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"passed": true,
"run_id": "direct-sd-s42",
"seed": 42,
"selected_update": 470,
"precision": "bfloat16",
"threshold": 0.5,
"validation_records": 833,
"longest_validation_tokens": 8192,
"original_adapter_replay_max_logit_difference": 0.0625,
"original_adapter_replay_max_probability_difference": 0.007315270195471513,
"original_adapter_replay_decision_differences_at_0_5": 0,
"fresh_native_reload_max_logit_difference": 0.0,
"trained_classification_head_preserved": true,
"merge_max_logit_difference": 0.1875,
"merge_max_probability_difference": 0.05210707294269523,
"decision_differences_at_0_5": 0,
"adapter_validation_at_0_5": {
"tp": 649,
"fn": 15,
"fp": 6,
"tn": 163,
"recall": 0.9774096385542169,
"fpr": 0.03550295857988166
},
"merged_validation_at_0_5": {
"tp": 649,
"fn": 15,
"fp": 6,
"tn": 163,
"recall": 0.9774096385542169,
"fpr": 0.03550295857988166
},
"peak_gpu_allocated_gib": 5.09755802154541,
"peak_gpu_reserved_gib": 5.21875,
"seconds": 126.86285077797947,
"public_scoring_helper": {
"passed": true,
"probability_difference": 0.0,
"used_tokens": 514
}
}
|