---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: Router
tags:
- trl
- sft
- gemma3
licence: license
datasets:
- d-s-b/synthetic-reasoning-dataset
---

# Model Card for Router

This model is fine-tuned to serve as a router for reasoning tasks, classifying input queries into one of three categories:

  no_reasoning – Direct factual lookup or simple recall (e.g., "What is the capital of France?")
  
  low_reasoning – Requires light reasoning such as simple arithmetic, comparisons, or single logical steps (e.g., "If John has 5 apples and eats 2, how many are left?")
  
  high_reasoning – Requires multi-step reasoning, deep logical chains, or complex problem-solving (e.g., "Prove that the sum of two even numbers is always even").

## Quick start

```python
from transformers import pipeline

pipe = pipeline("text-generation", model="d-s-b/Router")
messages = [
    {"role": "user", "content": "what is capital of india"}
]
pipe(messages)
```

## Training Details

Method: Supervised fine-tuning with SFTTrainer

Objective: Multi-class classification with labels (no_reasoning, low_reasoning, high_reasoning)

Dataset: Custom dataset of queries annotated with reasoning levels.




## Limitations & Bias

May misclassify borderline queries (e.g., between low_reasoning and high_reasoning).

Performance depends on the diversity of training data.

Inherits any biases from the base Gemma 3 270M model.

### Framework versions

- TRL: 0.21.0
- Transformers: 4.55.1
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4

## Citations



    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
@article{gemma_2025,
    title={Gemma 3},
    url={https://arxiv.org/abs/2503.19786},
    publisher={Google DeepMind},
    author={Gemma Team},
    year={2025}
}


```