from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
model_id = "chopratejas/technique-router"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

# Classify a query
query = "What brand is the TV?"
inputs = tokenizer(query, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    pred_id = torch.argmax(probs, dim=-1).item()
    confidence = probs[0][pred_id].item()

technique = model.config.id2label[pred_id]
print(f"{query} -> {technique} ({confidence:.0%})")
# Output: What brand is the TV? -> preserve (73%)

With Headroom SDK

python

from headroom.image import TrainedRouter

router = TrainedRouter()
decision = router.classify(image_bytes, "What brand is the TV?")
print(decision.technique)  # Technique.PRESERVE

Intended Use

This model is designed for:

Routing image analysis queries to optimal compression techniques
Reducing token usage in vision-language model applications
Enabling cost-effective image understanding at scale

Limitations

English language only
Optimized for common image understanding queries
May not generalize well to domain-specific terminology

Citation

bibtex

@misc{headroom-technique-router,
  title={Technique Router for Image Token Optimization},
  author={Headroom AI},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/chopratejas/technique-router}
}