OTel-LLM-1.2B-IT/README.md

---
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