---
license: mit
library_name: vllm
base_model: Qwen/Qwen2.5-Coder-0.5B
pipeline_tag: token-classification
tags:
- code
- code-pruning
- context-pruning
- vllm
- qwen2
datasets:
- nick007x/github-code-2025
metrics:
- f1
---

# Fast Code Pruner

Task-aware context pruning for coding agents with a 17-layer
Qwen2.5-Coder-0.5B backbone and a native vLLM serving path.

[GitHub repository](https://github.com/rotem154154/fast-code-pruner) ·
[Original SWE-Pruner](https://github.com/Ayanami1314/swe-pruner)

## Validation quality

Evaluation uses the fixed 6,119-example seed-43 validation split.

| Model | Accuracy ↑ | Precision ↑ | Recall ↑ | F1 ↑ |
|---|---:|---:|---:|---:|
| **fast-code-pruner** | **85.94%** | **81.49%** | **83.49%** | **82.48%** |
| code-pruner | 84.07% | 80.02% | 80.91% | 80.46% |

## Architecture

```text
Original Code-Pruner                         Fast Code Pruner

Qwen3 layers 1–28                           Qwen2.5-Coder layers 1–17
frozen backbone                             frozen weights + rank-8 LoRA
          │                                           │
hidden layers 7 + 14 + 28                   normalized hidden layer 17
3 × 1024 → concatenate 3072                 896 → gated PolyNorm → 2432
          │                                           │
1× bidirectional full attention             1× bidirectional full attention
8 heads, width 3072                         16 heads, width 2432
          │                                           │
CRF emissions: 3072 → 256 → 2               CRF emissions: 2432 → 128 → 2
```

Fast Code Pruner uses only the normalized final-layer representation and
physically skips the three removed attention branches. Rank-8 LoRA updates are
merged into dense weights during export. Prefix caching is disabled because
the bidirectional fusion head requires the complete input.

## Serving

Install the native vLLM package directly from this model repository:

```bash
pip install \
  "fast-code-pruner[vllm] @ https://huggingface.co/irotem98/fast-code-pruner/resolve/main/package/fast_code_pruner-0.1.0-py3-none-any.whl"
fast-code-pruner serve
```

Inputs longer than 8,192 tokens are split with overlap and merged
automatically.

### Serving performance

| Model | Backend | Concurrency 1 ↑ | Concurrency 16 ↑ |
|---|---|---:|---:|
| **fast-code-pruner** | vLLM 0.13.0 | **79.0 req/s** | **143.4 req/s** |
| fast-code-pruner | Hugging Face | 16.01 req/s | 16.03 req/s |
| code-pruner | Hugging Face | 9.83 req/s | 10.03 req/s |

The vLLM results are medians of three 1,000-request end-to-end HTTP runs over
the same 100 validation examples on one NVIDIA RTX PRO 6000 Blackwell Server
Edition. Prefix caching was disabled. Median model-input throughput was 56.1k
tokens/s at concurrency 1 and 101.8k tokens/s at concurrency 16.
`fast-code-pruner` Hugging Face results are medians of three 100-request runs
with an 8,192-token model length, matching the serialized legacy endpoint. The
original `code-pruner` row uses its Hugging Face backend benchmark.

## Training

The public repository includes a lightweight, hard-label launcher matching the
released architecture:

```bash
bash train/train_fast_code_pruner.sh
```

It contains no teacher-target generation or knowledge-distillation stage.

## Attribution

This model builds on
[SWE-Pruner](https://github.com/Ayanami1314/swe-pruner) and
[Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B).

```bibtex
@misc{wang2026sweprunerselfadaptivecontextpruning,
  title={SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents},
  author={Yuhang Wang and Yuling Shi and Mo Yang and Rongrui Zhang and
          Shilin He and Heng Lian and Yuting Chen and Siyu Ye and Kai Cai and
          Xiaodong Gu},
  year={2026},
  eprint={2601.16746},
  archivePrefix={arXiv},
  primaryClass={cs.SE}
}
```