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 telecom-focused data curated by 100+ domain experts. Each source class was contributed by a specific institutional partner:

Source	Contributor
arXiv telecom papers, 3GPP standards, telecom Wikipedia, telecom Common Crawl	Yale University
GSMA Permanent Reference Documents, Discover portal	GSMA
IETF RFC series	NetoAI
Industry whitepapers	Khalifa University
O-RAN specifications (working groups 1, 2, 4, 5, 6, 7, 8, 9, 10)	University of Leeds
O-RAN documents across working groups	The University of Texas at Dallas

Released datasets: OTel-LLM, OTel-Embedding, OTel-Reranker, OTel-Safety.

Intended Use

The OTel model family is designed to power end-to-end Retrieval-Augmented Generation (RAG) pipelines for telecommunications. The three model types serve complementary roles:

1. Embedding — Retrieve relevant chunks from telecom specifications, standards, and documentation.
2. Reranker — Re-score and prioritize the retrieved chunks for relevance.
3. LLM — Generate accurate responses grounded in the retrieved context.

Users can deploy the full pipeline or use individual models independently based on their needs.

Note: The LLMs include abstention training — if the model does not receive sufficient context, it will decline to answer rather than hallucinate. This means the models are optimized for context-grounded generation, not open-ended question answering.

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: AMD and NVIDIA GPUs.

Project Resources

• Project page: https://huggingface.co/farbodtavakkoli
• Code: https://github.com/farbodtavakkoli/OTel
• Media coverage list: https://github.com/farbodtavakkoli/OTel/blob/main/docs/media_coverage.md

Citation

@misc{otel_models_2026,
  title  = {OTel: Open Telco AI Datasets, Benchmarks, and Models},
  author = {Tavakkoli, Farbod and others},
  year   = {2026},
  note   = {Open Telco (OTel) model release},
  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