reasoning-router-v1

Reasoning Router v1

Model Name: AmirMohseni/reasoning-router-v1
Base Model: answerdotai/ModernBERT-large (396M parameters)
Task: Binary classification — decide whether to use reasoning mode for a given text prompt.

📌 Overview

This model routes incoming prompts to one of two categories:

• no_think – Reasoning mode should not be used (fast, fewer tokens, lower cost).
• think – Reasoning mode should be used (slower, more tokens, potentially higher accuracy).

It is designed to help reduce unnecessary reasoning calls in large language model pipelines, saving computation and cost while maintaining quality.

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🚀 Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "AmirMohseni/reasoning-router-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Inference function
def classify_text(text):
    # Tokenize input
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

    # Get logits
    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits
    predicted_class_id = logits.argmax(dim=-1).item()
    predicted_label = model.config.id2label[predicted_class_id]

    return predicted_label, logits.squeeze().tolist()

# Example usage
label, logits = classify_text("This is an example input.")
print("Predicted label:", label)
print("Logits:", logits)

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🏷 Labels

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📄 Model Details

• Base Model: answerdotai/ModernBERT-large — a 396M parameter encoder model optimized for classification.
• Training Objective: Supervised fine-tuning for binary routing classification.
• Intended Use: As part of an LLM routing system to decide whether to enable reasoning mode for a query.
• Languages: English (primary).

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⚠️ Limitations & Bias

• The model is trained primarily on English data — performance may degrade on other languages.
• Predictions are probabilistic; borderline cases may require human validation in high-stakes use cases.
• May reflect biases present in the training data.

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📚 Citation

If you use this model, please cite:

@misc{mohseni2025reasoningrouterv1,
    title={Reasoning Router v1},
    author={Amir Mohseni},
    year={2025},
    howpublished={\url{https://huggingface.co/AmirMohseni/reasoning-router-v1}}
}