Update README.md

README.md

@@ -33,6 +33,24 @@ The evaluation suite curates 7 telecom-domain benchmarks from academic and indus
 | **ORANBench** | 200 | O-RAN architecture and specifications |
 | **SRSRANBench** | 300 | srsRAN open-source network stack |
 
+### Models
+
+- **[OTel-Emeddings](https://huggingface.co/farbodtavakkoli/models)** -  Emeddings on different architectures and sizes to improve RAG on telco docuements  *Contirbuted by the Open Telco Ai project and trained by ATT.*
+- **[OTel-LLM](https://huggingface.co/farbodtavakkoli/models)** - Bases LLMs on different architectures and sizes improve RAG on telco docuements. *Contirbuted by the Open Telco Ai project and trained by ATT.*
+- **[AdaptKey-Nemotron-30b](https://huggingface.co/AdaptKey/AdaptKey-Nemotron-30b)** — NVIDIA Nemotron 3 Nano fine-tuned by AdaptKey for telecom. *Contributed by NVIDIA and AdaptKey.*
+
+### Datasets
+
+- **[telecom-kg-rel19](https://huggingface.co/datasets/GSMA/telecom-kg-rel19)** — Large-scale telecom knowledge graph built from 3GPP Release 19 specifications, with text chunks for retrieval-augmented generation (RAG) and LLM reasoning over standards
+- **[oran_spec_knowledge_graph](https://huggingface.co/datasets/GSMA/oran_spec_knowledge_graph)** A knowelge graph of 25,103 nodes and 98,679 relationships extracted from official O-RAN Alliance specification documents using OpenAI GPT-4.1
+- **[AdaptKey Nemotron 30B Training Data](https://huggingface.co/AdaptKey/AdaptKey-Nemotron-30b/tree/main/training_data)** — Dataset used to fine-tune Nemotron 3 Nano for telecom. *Contributed by NVIDIA and AdaptKey.*
+
+### Resources
+
+- **[NVIDIA Blueprint: AI Agent for Telecom Network Configuration Planning](https://build.nvidia.com/nvidia/telco-network-configuration)** — Agentic blueprint for RAN configuration. *Contributed by NVIDIA (in collaboration with BubbleRAN).*
+- **[NVIDIA Blueprint: Intent Driven RAN Energy Efficiency](https://build.nvidia.com/viavi/intent-driven-ran-energy-efficiency)** — Agentic blueprint for RAN energy saving with simulation. *Contributed by NVIDIA (in collaboration with VIAVI).*
+- **[NVIDIA Guide: Teaching a Model to Reason Over Telecom Network Incidents](https://nvidia-nemo.github.io/Skills/tutorials/2026/02/27/teaching-a-model-to-reason-over-telecom-network-incidents/)** — Guide on how to build NOC reasoning agents. *Contributed by NVIDIA (in collaboration with Tech Mahindra).*
+
 ## Satellite — Eval Runner
 
 [**Satellite**](https://github.com/gsma-labs/evals) provides telecom-focused evaluation operations built on Inspect AI. Run the full Open Telco benchmark suite locally within your own infrastructure with a single command.
@@ -45,13 +63,8 @@ Purpose-built sandbox environments that place AI agents inside live telecom netw
 
 - **[5gs-sandbox](https://github.com/gsma-labs/5gs-sandbox)** — Run AI agent evaluations inside a complete 5G Standalone network. A full 5G SA deployment with 15 Docker containers (Open5GS + UERANSIM), enabling agents to configure, diagnose, and optimize real 5G network functions with actual performance measurement.
 
-## Datasets and Knowledge Resources
-
-- **[telecom-kg-rel19](https://huggingface.co/datasets/GSMA/telecom-kg-rel19)** — Large-scale telecom knowledge graph built from 3GPP Release 19 specifications, with text chunks for retrieval-augmented generation (RAG) and LLM reasoning over standards
-- **[oran_spec_knowledge_graph](https://huggingface.co/datasets/GSMA/oran_spec_knowledge_graph)** A knowelge graph of 25,103 nodes and 98,679 relationships extracted from official O-RAN Alliance specification documents using OpenAI GPT-4.1
-
 ## Research and Community
 
-- **GSMA AI Initiatives**: https://www.gsma.com/solutions-and-impact/technologies/artificial-intelligence/
+- **GSMA AI Initiatives**: https://www.gsma.com/solutions-and-impact/technologies/artificial-intelligence/open-telco.ai/
 - **Open Gateway**: https://www.gsma.com/solutions-and-impact/gsma-open-gateway/
 - **MWC (Mobile World Congress)**: https://www.mwcbarcelona.com/