OTel-Reranker-4B

OTel-Reranker-4B is a telecom-specialized reranker model fine-tuned on telecommunications domain data. It is part of the OTel Family of Models, an open-source initiative to build industry-standard AI models for the global telecommunications sector.

Model Details

Attribute	Value
Base Model	Qwen/Qwen3-4B
Parameters	4B
Training Method	Full parameter fine-tuning
Language	English
License	Apache 2.0

Training Data

The model was trained on high-quality telecom-focused data curated by 200+ domain experts from organizations including AT&T, RelationalAI, AMD, GSMA, Purdue University, Khalifa University, University of Leeds, Yale University, The University of Texas at Dallas, NetoAI, and MantisNLP.

Data Sources:

• GSMA Permanent Reference Documents
• 3GPP Specifications
• O-RAN Documentation
• RFC Series
• eSIM, terminals, security, networks, roaming, APIs
• Industry whitepapers and telecom academic papers

Intended Use

This model is optimized for:

• RAG applications in telecommunications
• Question answering on telecom specifications and standards

Related Models

Language Models

• OTel LLM Collection

Embedding Models

• OTel Embedding Collection

Reranker Models

• OTel Reranker Collection

Related Datasets

• OTel-Embedding
• OTel-Safety
• OTel-LLM
• OTel-Reranker

Training Infrastructure

• Framework: ScalarLM (GPU-agnostic)
• Compute: TensorWave with AMD GPUs and Azure with NVIDIA GPUs.

Citation

@misc{otel2026,
  title={OTel: Open Telco AI Models},
  author={Tavakkoli, Farbod and Diamos, Gregory and Paulk, Roderic and Terrazas, Jorden},
  year={2026},
  url={https://huggingface.co/farbodtavakkoli}
}

Contact

If you have any technical questions, please feel free to reach out to farbod.tavakkoli@att.com or farbodtavakoli@gmail.com