Text Classification
Transformers
Safetensors
English
Korean
Japanese
qev_decision_merged
feature-extraction
qev
typed-decision
custom-code
bf16
custom_code
Instructions to use querypieai/QEV-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use querypieai/QEV-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="querypieai/QEV-9B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("querypieai/QEV-9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 646 Bytes
96adfbe | 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 | {
"historical_metrics": {
"mmlu": 0.445068359375,
"belebele": 0.7328405491024287,
"private": 0.8796296296296297,
"composite": 0.6858461793690195,
"dlp_accuracy": 0.9851594568992738,
"dlp_recall": 0.9781420765027322,
"dlp_f2": 0.9795184490306442,
"dlp_fp": 19,
"dlp_fn": 28
},
"new_full_benchmark": false,
"counts": {
"mmlu": 16384,
"belebele": 24622,
"private": 540,
"dlp": 3167
},
"dlp_threshold": 0.5,
"context": {
"state": 384,
"branch": 1024,
"packed": 2048
},
"scope": "Historical unmerged BF16 source only; NOT newly measured merged/INT8 benchmark scores"
}
|