---
license: apache-2.0
tags:
  - context-compression
  - routing
  - xrag
  - retrieval-augmented-generation
base_model: Hannibal046/xrag-7b
library_name: compression_router
---

# xRAG Router

A routing classifier for [xRAG](https://huggingface.co/Hannibal046/xrag-7b) that decides at inference time whether compressed context is sufficient or whether to fall back to the full context.

Trained with [overflowguard](https://github.com/s-nlp/overflowguard/tree/main) on SQuAD using an LLM judge (DeepSeek) for evaluation, with 5-fold stratified CV for threshold selection (Youden's J statistic).

## Usage

```python
from examples.train_xrag import XragRouter

# Load base model + routing classifier in one call
router = XragRouter.from_pretrained("wexumin/xrag-7b-router")
router.park_gpu()

# Auto-routing: classifier decides compressed vs full
result = router.run_pipeline("long document text...", query="what is X?")
print(result["prediction"])  # answer
print(result["mode"])        # "compressed" or "full"
print(result["tokens_saved"])
```

## What's in this repo

- `router_config.json` — base model path + routing threshold
- `routing_clf.pt` — classifier weights (2-layer MLP, ~2K params)

The base xRAG model is loaded automatically from `Hannibal046/xrag-7b`. This repo only contains the lightweight routing classifier on top.
This model also requires custom code from [xRAG](https://github.com/Hannibal046/xRAG/tree/main) repository.