metadata
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
tags:
- telecom
- telecommunications
- gsma
- fine-tuned
pipeline_tag: text-classification
OTel-Reranker-0.6B
OTel-Reranker-0.6B 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/Qwen3-0.6B |
| Parameters | 0.6B |
| 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
Embedding Models
Reranker Models
Related Datasets
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