> **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.
A SegFormer-B4 model fine-tuned for human parsing with 18 semantic classes, optimized for fashion and virtual try-on applications. This model segments human images into 18 semantic categories including body parts (face, hair, arms, hands, legs, feet, torso), clothing items (top, dress, skirt, pants, belt, scarf), and accessories (bag, hat, glasses, jewelry). - **Architecture**: SegFormer-B4 (MIT-B4 encoder + MLP decoder) - **Input Size**: 384 x 576 (width x height) - **Output**: 18-class semantic segmentation mask - **Base Model**: nvidia/mit-b4
