初始化项目，由ModelHub XC社区提供模型

Model: farbodtavakkoli/OTel-LLM-1.2B-IT
Source: Original Platform

README.md

@@ -0,0 +1,89 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- LiquidAI/LFM2.5-1.2B-Instruct
+tags:
+- telecom
+- telecommunications
+- gsma
+- fine-tuned
+pipeline_tag: text-generation
+---
+
+# OTel-LLM-1.2B-IT
+
+**OTel-LLM-1.2B-IT** is a telecom-specialized language model fine-tuned on telecommunications domain data. It is part of the [OTel Family of Models](https://huggingface.co/collections/farbodtavakkoli/otel-llm), an open-source initiative to build industry-standard AI models for the global telecommunications sector.
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) |
+| **Parameters** | 1.2B |
+| **Training Method** | Full parameter fine-tuning |
+| **Language** | English |
+| **License** | Apache 2.0 |
+
+## Training Data
+
+The model was trained on high-quality telecom-focused data curated by 100+ domain experts from organizations including AT&T, Microsoft, AMD, GSMA, RelationalAI, Essential AI, Purdue University, Khalifa University, University of Leeds, Yale University, The University of Texas at Dallas, NetoAI, and MantisNLP.
+
+**Data Sources:**
+- GSMA Permanent Reference Documents
+- 3GPP Specifications
+- O-RAN Documentation
+- RFC Series
+- eSIM, terminals, security, networks, roaming, APIs
+- Industry whitepapers and telecom academic papers
+
+## Intended Use
+ 
+The OTel model family is designed to power end-to-end Retrieval-Augmented Generation (RAG) pipelines for telecommunications. The three model types serve complementary roles:
+ 
+1. **Embedding** — Retrieve relevant chunks from telecom specifications, standards, and documentation.
+2. **Reranker** — Re-score and prioritize the retrieved chunks for relevance.
+3. **LLM** — Generate accurate responses grounded in the retrieved context.
+ 
+Users can deploy the full pipeline or use individual models independently based on their needs.
+ 
+**Note:** The LLMs include abstention training — if the model does not receive sufficient context, it will decline to answer rather than hallucinate. This means the models are optimized for context-grounded generation, not open-ended question answering.
+
+## Related Models
+
+### Language Models
+- [OTel LLM Collection](https://huggingface.co/collections/farbodtavakkoli/otel-llm)
+
+### Embedding Models
+- [OTel Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-embedding)
+
+### Reranker Models
+- [OTel Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-reranker)
+
+## Related Datasets
+
+- [OTel-Embedding](https://huggingface.co/datasets/farbodtavakkoli/OTel-Embedding)
+- [OTel-Safety](https://huggingface.co/datasets/farbodtavakkoli/OTel-Safety)
+- [OTel-LLM](https://huggingface.co/datasets/farbodtavakkoli/OTel-LLM)
+- [OTel-Reranker](https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker)
+
+## Training Infrastructure
+
+- **Framework**: ScalarLM (GPU-agnostic)
+- **Compute**: AMD and NVIDIA GPUs.
+
+## Citation
+
+```bibtex
+@misc{otel2026,
+  title={OTel: Open Telco AI Models},
+  author={Tavakkoli, Farbod and Diamos, Gregory and Paulk, Roderic and Terrazas, Jorden},
+  year={2026},
+  url={https://huggingface.co/farbodtavakkoli}
+}
+```
+
+## Contact
+
+If you have any technical questions, please feel free to reach out to farbod.tavakkoli@att.com or farbodtavakoli@gmail.com