OTel-2.0-LLM-31B-IT

> **Checkpoint update notice:** The current checkpoint is expected to be updated within the next few hours. After that initial refresh, OTel 2.0 checkpoints are expected to continue receiving weekly weight updates. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag. **OTel-2.0-LLM-31B-IT** is a telecom-specialized instruction model post-trained from **Gemma 4 31B-IT** on approximately **440 billion telecom training tokens**. It is the first release in the OTel 2.0 family and is designed to support telco-grade AI workflows across network operations, standards interpretation, product development, network configuration assistance, RAG, and telecom-specific question answering. OTel 2.0 extends the original OTel effort from a RAG-oriented telecom fine-tuning release into a larger domain-adapted training program. The model was trained from a much larger standards and telecom corpus, with new data preparation coverage for agentic tool calling, direct telecom QnA, instruction following, abstention, RAG, and base-model-style training data. OTel 2.0 models are expected to continue training after release, and weights may be updated weekly. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag rather than relying on the floating latest weights. `Gemma 4 31B-IT` -> OTel 2.0 telecom data processing and post-training -> `OTel-2.0-LLM-31B-IT` Relative to OTel 1.0, OTel 2.0 increases raw source coverage by roughly 25x and training-token volume by roughly 440x. More importantly, the data mixture is broader: OTel 1.0 focused primarily on context-grounded RAG and abstention, while OTel 2.0 adds direct telecom knowledge QnA, instruction following, tool-use-oriented examples, and base-model-style telecom training data. The starting corpus contains approximately **15 billion raw tokens** provided by GSMA through Open Telco AI. The corpus includes telecom standards and technical material from seven standards development and industry organizations: The raw corpus was processed into approximately **1 trillion tokens** using **Red Hat SDG Hub** on **Azure Managed Compute**, using approximately **530 GPUs**, primarily **AMD MI300X**. From this processed pool, approximately **440 billion training tokens** were generated for OTel 2.0 post-training. OTel 2.0 includes a broader supervised and synthetic-data mixture than OTel 1.0: OTel-2.0-LLM-31B-IT is intended for telecom-focused applications where domain knowledge, standards familiarity, and deployment control matter. Suitable use cases include: - Retrieval-Augmented Generation over telecom standards, specifications, and internal technical documentation. - Standards interpretation and summarization for 3GPP, ETSI, GSMA, CAMARA, ITU, O-RAN, and TM Forum materials. - Product development, network configuration assistance, and engineering support tasks. - Telecom-specific direct QnA where the model has been separately evaluated for the target benchmark or application. - Agentic workflows where a larger system provides verified tools, retrieval, validation, and audit logging. For high-impact operational use, the model should be deployed with retrieval, source attribution, validation checks, and human review appropriate to the system risk. - OTel 2.0 is domain-specific to telecommunications and should not be treated as a general-purpose model for unrelated fields. - Telecom standards evolve over time; answers should be checked against the relevant document version and release. - Agentic tool calling requires an external tool runtime, tool schema validation, permissioning, logging, and safeguards. The model alone does not guarantee safe tool execution. - RAG quality depends on document ingestion, chunking, retrieval, reranking, prompt design, and source freshness. - Direct QnA behavior should be evaluated separately from RAG behavior; strong performance in one setting does not imply strong performance in the other. - [OTel LLM Collection](https://huggingface.co/collections/farbodtavakkoli/otel-llm) - [OTel Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-embedding) - [OTel Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-reranker) - Project page: https://huggingface.co/farbodtavakkoli - Code: https://github.com/farbodtavakkoli/OTel - Media coverage list: https://github.com/farbodtavakkoli/OTel/blob/main/docs/media_coverage.md **The contributor list will be updated soon to reflect all members involved in the project.** Organizations involved in the OTel 2.0 release and supporting infrastructure include AT&T, Microsoft, Dell, AMD, Red Hat, and GSMA. @misc{otel_2_models_2026, title = {OTel 2.0: Open Telco AI Datasets, Benchmarks, and Models}, author = {Tavakkoli, Farbod and Diamos, Gregory and Church, Kenneth, Terrazas, Jorden and Paulk, Roderic and others}, year = {2026}, note = {Open Telco (OTel 2.0) model release}, url = {https://huggingface.co/farbodtavakkoli}, organization = {AT\&T, Microsoft, Dell, AMD, Red Hat, GSMA} } For technical questions, contact farbod.tavakkoli@att.com or farbodtavakoli@gmail.com.