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HomeLLMsOTel LLM 20B Reasoning

OTel LLM 20B Reasoning

by farbodtavakkoli

Open source · 86k downloads · 0 likes

0.0
(0 reviews)ChatAPI & Local
About

OTel LLM 20B Reasoning is a language model specialized in the telecommunications sector, designed to meet the technical and regulatory needs of the industry. Trained on data from recognized sources such as 3GPP specifications, GSMA documents, or RFCs, it excels in analyzing and understanding complex technical documents related to networks, security, or standards. Its key capabilities include assisting in finding precise answers within standards corpora, aiding in the drafting of technical documentation, and supporting problem-solving for telecom infrastructures. This model stands out for its sector-specific expertise, developed in collaboration with over 200 domain experts, and proves particularly useful for applications like Retrieval-Augmented Generation (RAG) or automated question-answering systems. It is aimed at telecom professionals, developers, and researchers seeking to leverage AI to optimize their work with demanding technical standards.

Documentation

OTel-LLM-20B-Reasoning

OTel-LLM-20B-Reasoning is a telecom-specialized language 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

AttributeValue
Base Modelopenai/gpt-oss-20b
Parameters20B
Training MethodFull parameter fine-tuning
LanguageEnglish
LicenseApache 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

Bibtex
@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 [email protected] or [email protected]

Capabilities & Tags
pytorchgpt_osstelecomtelecommunicationsgsmafine-tunedtext-generationconversationalen
Links & Resources
Specifications
CategoryChat
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Parameters20B parameters
Rating
0.0

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