Update README.md

README.md

@@ -33,19 +33,37 @@ 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 — Open Telco AI 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.*
+A collaborative effort to build AI models for the global telecommunications sector, optimized for RAG and agentic applications.
+
+**Model Suite:**
+- **18 Language Models** (270M–32B parameters)
+- **10 Embedding Models** (22M–8B parameters)
+- **3 Reranker Models** (0.6B–8B parameters)
+
+Models are trained on telecom-domain data — including 3GPP specifications, O-RAN documentation, and RFC standards — curated by 200+ domain experts from AT&T, GSMA, Purdue University, Khalifa University, University of Leeds, Yale University, and others.
+
+**Resources:**
+- [Training & Inference Code (GitHub)](https://github.com/farbodtavakkoli/OTel)
+- [Open-Telco-1 Dataset](https://huggingface.co/datasets/GSMA/Open-Telco-1)
+- [LLM Collection](https://huggingface.co/collections/farbodtavakkoli/otel-llm)
+- [Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-embedding)
+- [Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-reranker)
 
-### Datasets
+**License:** Apache-2.0
+
+### Additional Resources
+
+## Models
+- **[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
+## Guides & Blueprints
 
 - **[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).*