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
Download quant_metadata.json from wxsys/spark-servitor: direct link, hf CLI and curl.
- Browser
- Download file 278 Bytes
-
https://huggingface.co/wxsys/spark-servitor/resolve/main/quant_metadata.json
- Command line
-
hf download hf://wxsys/spark-servitor/quant_metadata.json
-
curl -L -o quant_metadata.json https://huggingface.co/wxsys/spark-servitor/resolve/main/quant_metadata.json
278 Bytes
| { | |
| "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 | |
| } |