Instructions to use ayanami-kitasan/code-pruner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayanami-kitasan/code-pruner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ayanami-kitasan/code-pruner")# Load model directly from transformers import SwePrunerForCodeCompression model = SwePrunerForCodeCompression.from_pretrained("ayanami-kitasan/code-pruner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 463 Bytes
9f502c5 2ce9736 9f502c5 75a77bc 9f502c5 c5a550f 9f502c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"architectures": [
"SwePrunerForCodeCompression"
],
"backbone_model_name_or_path": "Qwen/Qwen3-Reranker-0.6B",
"bottleneck": 256,
"compression_head_type": "crf",
"compression_loss_type": "focal",
"dropout": 0.4,
"early_layer_ratio": 0.25,
"middle_layer_ratio": 0.5,
"model_type": "swepruner",
"num_fusion_layers": 1,
"num_heads": 8,
"torch_dtype": "bfloat16",
"transformers_version": "4.55.0",
"use_multi_layer_fusion": true
}
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