OTel-LLM-8.3B-Safety

OTel-LLM-8.3B-Safety is a safety-tuned telecom language model full-parameter fine-tuned on OTel telecommunications data. It is part of the OTel Family of Models, an open-source initiative to build reference AI resources for the global telecommunications sector.

The safety variants are auxiliary models trained to refuse when retrieved context is insufficient or off-topic.

Community Use

As of May 3, 2026, the released OTel models had been downloaded over 16 million times, and the Open Telco AI project had received 157+ pieces of media coverage worldwide.

Model Details

Model Lineage

EssentialAI/rnj-1-instruct -> OTel-Safety full-parameter post-training -> farbodtavakkoli/OTel-LLM-8.3B-Safety

OTel vs. Base Model

This model is an auxiliary safety-tuned variant trained for abstention behavior and is not part of the 30-model core baseline table. It should be evaluated with abstention-focused metrics for the target deployment, such as correct-abstention rate on insufficient-context examples and answer quality on sufficient-context examples.

Evaluation Caveats

• This auxiliary safety model is not part of the 30-model core baseline table.
• Abstention quality depends on the retriever, reranker, context window, and prompt policy around the model.
• Evaluate both correct-abstention rate on insufficient-context examples and answer quality on sufficient-context examples before deployment.
• External benchmark transfer, multilingual performance, and per-subdomain performance should be evaluated separately for production settings.

Training Data

The model was trained on telecom-focused data curated by 100+ domain experts. The raw corpus contained roughly 1.1M training points and was filtered to 326,767 higher-confidence examples.

Released datasets: OTel-LLM, OTel-Embedding, OTel-Reranker, and OTel-Safety.

The OTel datasets release derived QA/retrieval/reranking examples rather than the raw source documents.

Each released dataset includes a dataset card and Croissant metadata with Responsible AI fields for data limitations, biases, sensitive-information considerations, use cases, social impact, synthetic-data status, and provenance.

Representative Training Row

OTel-Safety is schema-compatible with OTel-LLM, but focuses on cases where the retrieved context is insufficient or off-topic and the model should abstain.

{
  "anchor": "How are SCP domains structured and grouped in the SCP trust domain solution?",
  "completion": "I do not have enough information based on the provided context to answer your question.",
  "abstention": true,
  "n_positive_chunks": 0,
  "n_negative_chunks": 5
}

Intended Use

This model is intended for telecom RAG pipelines where the assistant should refuse to answer when retrieved context is missing, irrelevant, or insufficient. It can be used as an abstention-focused generation model or as a reference checkpoint for further safety tuning.

The model should be evaluated in the full target pipeline before deployment, because abstention quality depends on the retriever, reranker, context window, and refusal policy used around the model.

Training Recipe

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "farbodtavakkoli/OTel-LLM-8.3B-Safety"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = """You are a precise telecom assistant in a RAG pipeline.
Use only the retrieved context to answer.

User Question
What is the purpose of the F1 interface in O-RAN?

Retrieved Contexts
CONTEXT 1
The F1 interface connects the O-RAN Distributed Unit (O-DU) to the O-RAN Central Unit (O-CU).

Answer:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations and Responsible Use

• OTel models are domain-specific to telecommunications and should not be treated as general-purpose models.
• The current release is English-only and primarily text-centric.
• The reported OTel performance results use held-out OTel evaluation partitions and should not be interpreted as results from a fully independent external benchmark suite.
• Aggregate scores can hide subdomain variation; collaborator stress tests suggest O-RAN retrieval is comparatively strong, while academic-paper and GSMA PRD examples need further curation.
• Generated telecom content should be verified before operational, customer-facing, regulatory, safety, or network-configuration use.
• Users must comply with both the OTel release license and the upstream base-model license or terms.
• The model may over-abstain or under-abstain depending on retrieval quality and prompt format; tune and measure this behavior before production use.

Related Models

• OTel LLM Collection
• OTel Embedding Collection
• OTel Reranker Collection

Project Resources

• Project page: https://huggingface.co/farbodtavakkoli
• Code: https://github.com/farbodtavakkoli/OTel
• Media coverage list: https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-1.0-media-coverage.md

Citation

@inproceedings{tavakkoli2026otel,
  title     = {OTel: Open Telco AI Datasets, Benchmarks, and Models},
  author    = {Tavakkoli, Farbod and Diamos, Gregory and Church, Kenneth and Kanter, David and Austin, Mark and Karim, Imtiaz and Rahman, Mirza Masfiqur and Debbah, Merouane Abdelkader and Nezami, Zeinab and Maatouk, Ali and Tassiulas, Leandros and Ying, Rex and Sorros, Nick and Powell, Louis and Vasiloglou, Nikolaos and Vaswani, Ashish and Singla, Somanshu and Chaluvaraju, Adarsh},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026},
  url       = {https://github.com/farbodtavakkoli/OTel}
}

Contact

For technical questions, contact farbod.tavakkoli@att.com or farbodtavakoli@gmail.com.