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wxsys
/
spark-servitor

Text Classification
Transformers
English
Indonesian
code
security
code-review
static-analysis
vulnerability-detection
sarif
sliding-window-attention
spark
cpp
avx2
awq
zero-shot
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use wxsys/spark-servitor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use wxsys/spark-servitor with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="wxsys/spark-servitor")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("wxsys/spark-servitor", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
spark-servitor
3.06 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
wxsys's picture
wxsys
weights: add verified compact slim model (1.3 GB)
c75ec49 verified 1 day ago
  • int4
    weights: add outlier-preserved AWQ INT4 binary weights 1 day ago
  • slim
    weights: add verified compact slim model (1.3 GB) 1 day ago
  • .gitattributes
    1.66 kB
    weights: add verified compact slim model (1.3 GB) 1 day ago
  • README.md
    11.4 kB
    docs: add available model variants (Slim 1.3GB & Baseline 1.6GB) 1 day ago
  • quant_metadata.json
    278 Bytes
    config: add quant_metadata.json 1 day ago
  • servitor_config.json
    732 Bytes
    config: add servitor_config.json 1 day ago