farbodtavakkoli/OTel-LLM-3B-IT
OTel-LLM-3B-IT by farbodtavakkoli is a 3 billion parameter language model, fine-tuned from Mistral-3-3B specifically for telecommunications data. This model is optimized for context-grounded answer generation within Retrieval-Augmented Generation (RAG) pipelines, demonstrating a +7.5 percentage point improvement in correctness over its base model on telecom-specific tasks. It is designed to provide precise, domain-specific responses when provided with relevant telecom context.
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OTel-LLM-3B-IT: A Specialized Telecom Language Model
OTel-LLM-3B-IT is a 3 billion parameter language model developed by farbodtavakkoli, derived from mistralai/Mistral-3-3B through full-parameter fine-tuning on the OTel telecommunications dataset. This model is a key component of the OTel Family of Models, an initiative focused on creating open-source AI resources for the global telecom sector.
Key Capabilities & Features
- Domain-Specific Expertise: Fine-tuned on a curated dataset of 326,767 high-confidence telecom examples, including arXiv papers, 3GPP standards, GSMA documents, IETF RFCs, and O-RAN specifications.
- Context-Grounded Correctness: Achieves a +7.5 percentage point improvement in LLM-as-judge correctness (64.4% vs. 56.9%) over its base model for context-grounded telecom QA.
- RAG Optimization: Specifically designed for Retrieval-Augmented Generation (RAG) pipelines, excelling at generating answers based on provided telecom context.
- Apache 2.0 License: Released under a permissive Apache 2.0 license, facilitating broad community use.
Intended Use Cases
- Telecom RAG Systems: Ideal for applications requiring precise, context-grounded answers to telecommunications-related queries.
- Specialized QA: Suitable for question-answering systems where the input includes retrieved telecom-specific documents or information.
Limitations
- Domain-Specific: Not intended as a general-purpose language model; performance outside the telecom domain is not guaranteed.
- English-Only: The current release is text-centric and supports only the English language.
- Context-Dependent: Optimized for context-grounded generation; performance for unrestricted, context-free telecom QA may vary.