AI is happening. The future is updating.OTel-2.0-LLM-31B-IT
> **Versioning notice:** This card describes the current OTel 2.0 OSFT checkpoint. The model weights may be updated over time. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag rather than relying on the floating latest version. **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 direct telecom QnA, abstention, RAG, base-model-style telecom data, and general-purpose instruction-following and tool-calling examples. The current training mixture does not include telecommunications-specific MCP, tool-calling, or instruction-following examples.
> **Versioning notice:** This card describes the current OTel 2.0 OSFT checkpoint. The model weights may be updated over time. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag rather than relying on the floating latest version. **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 direct telecom QnA, abstention, RAG, base-model-style telecom data, and general-purpose instruction-following and tool-calling examples. The current training mixture does not include telecommunications-specific MCP, tool-calling, or instruction-following examples.