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)
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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wxsys/spark-servitor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 278 Bytes
eb69e29 | 1 2 3 4 5 6 7 8 9 10 11 | {
"quant_algorithm": "AWQ_INT4",
"group_size": 128,
"total_tensors": 444,
"outlier_preserved_tensors": 219,
"quantized_gemm_tensors": 225,
"compression_ratio": 2.34,
"format": "INT4",
"binary_file": "spark-servitor-awq_int4.servitor",
"file_size_mb": 1608.27
} |