farbodtavakkoli/OTel-LLM-3B-IT

Hugging Face
TEXT GENERATIONPricing:Input $0.08 / Cached $0.016 / Output $0.4Concurrent Unit Cost:1Model Size:3BQuant:BF16Context Size:32kPublished:Feb 12, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

OTel-LLM-3B-IT by farbodtavakkoli is a 3 billion parameter, instruction-tuned language model specifically fine-tuned on OTel telecommunications data. Built upon the mistralai/Ministral-3-3B-Instruct-2512 base model, it is designed for context-grounded telecom answer generation within Retrieval-Augmented Generation (RAG) pipelines. This model excels at providing accurate responses to telecom-specific queries when provided with relevant context, demonstrating a +7.0 percentage point improvement in LLM-as-judge correctness over its base model on held-out OTel evaluation data.

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OTel-LLM-3B-IT: Specialized Telecom Language Model

OTel-LLM-3B-IT is a 3 billion parameter language model developed by farbodtavakkoli, specifically fine-tuned for the telecommunications sector. It is built on the mistralai/Ministral-3-3B-Instruct-2512 base model and is part of the OTel Family of Models, an open-source initiative to provide AI resources for global telecommunications. This model undergoes full-parameter fine-tuning on a curated OTel telecommunications dataset, enhancing its ability to generate context-grounded answers for telecom-specific queries.

Key Capabilities

  • Domain-Specific Expertise: Optimized for telecommunications data, including arXiv telecom papers, 3GPP standards, GSMA documents, IETF RFCs, and O-RAN specifications.
  • Context-Grounded Generation: Designed to provide accurate answers when supplied with retrieved telecom context, making it ideal for RAG pipelines.
  • Improved Correctness: Achieves a +7.0 percentage point improvement in LLM-as-judge correctness over its base model on held-out OTel evaluation data.
  • Robust Training: Trained on a dataset of 326,767 high-confidence examples curated by over 100 domain experts.

Good for

  • Retrieval-Augmented Generation (RAG) Systems: Excellent for applications requiring precise, context-grounded answers to telecom questions.
  • Telecom-Specific QA: Ideal for use cases where accurate information retrieval and generation within the telecommunications domain are critical.
  • Developers in Telecom: Provides a specialized tool for building AI solutions tailored to the unique demands of the telecom industry.