| | --- |
| | 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} |
| | } |
| | |
| |
|
| | ``` |
