OTel-LLM-14B-IT by farbodtavakkoli is a 14 billion parameter, instruction-tuned language model based on Qwen3-14B, specifically fine-tuned on extensive telecommunications domain data. This model is designed to excel in telecom-specific applications, offering specialized knowledge for RAG systems and question answering on industry standards. It leverages a 32K token context length to process complex telecom specifications and documentation.
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OTel-LLM-14B-IT: A Specialized Telecom Language Model
OTel-LLM-14B-IT, developed by farbodtavakkoli, is a 14 billion parameter language model built upon the Qwen3-14B architecture. It is a key component of the OTel Family of Models, an initiative focused on creating open-source AI for the global telecommunications sector. This model underwent full parameter fine-tuning using a comprehensive dataset curated by over 200 domain experts from leading organizations like AT&T, GSMA, and Purdue University.
Key Capabilities
- Telecom Domain Expertise: Specialized knowledge derived from extensive training on GSMA Permanent Reference Documents, 3GPP Specifications, O-RAN Documentation, RFC Series, and various industry whitepapers.
- Optimized for RAG: Designed to enhance Retrieval Augmented Generation (RAG) applications within the telecommunications industry.
- Precise Question Answering: Excels at answering questions related to complex telecom specifications and standards.
- Robust Training: Leveraged ScalarLM framework and compute resources from TensorWave (AMD GPUs) and Azure (NVIDIA GPUs).
Good For
- Developing RAG systems that require deep understanding of telecommunications data.
- Automating information retrieval from telecom specifications and academic papers.
- Building intelligent assistants for telecom professionals and researchers.
This model is licensed under Apache 2.0 and is part of a broader ecosystem including OTel Embedding and OTel Reranker models, along with related datasets.