OTel-LLM-8B-A1B-IT

**OTel-LLM-8B-A1B-IT** is a context-grounded telecom language model full-parameter fine-tuned on OTel telecommunications data. It is part of the [OTel Family of Models](https://huggingface.co/collections/farbodtavakkoli/otel-llm), an open-source initiative to build reference AI resources for the global telecommunications sector. Across the core OTel LLM baselines, OTel fine-tuning improves context-grounded correctness over the base checkpoints by +3.7 to +10.0 percentage points. As of June 23, 2026, the released OTel models had more than 18 million downloads, and the Open Telco AI project had received 157+ pieces of media coverage worldwide. `LiquidAI/LFM2.5-8B-A1B` -> `OTel-LLM` full-parameter post-training -> `farbodtavakkoli/OTel-LLM-8B-A1B-IT` Standard errors are computed with bootstrap resampling (`n=10`) over the held-out OTel evaluation partition. LLM correctness is judged by GPT-4o mini using the retrieved context and reference answer. - LLM results measure context-grounded answer generation from retrieved context, not unrestricted context-free telecom QA. - Reported standard errors come from bootstrap resampling over the held-out evaluation partitions. - Answer quality depends on the retriever, reranker, context window, and prompt policy around the model. - External benchmark transfer, multilingual performance, and per-subdomain performance should be evaluated separately for production settings. 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](https://huggingface.co/datasets/farbodtavakkoli/OTel-LLM), [OTel-Embedding](https://huggingface.co/datasets/farbodtavakkoli/OTel-Embedding), [OTel-Reranker](https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker), and [OTel-Safety](https://huggingface.co/datasets/farbodtavakkoli/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. `OTel-LLM` rows pair a context-grounded telecom RAG prompt with a reference completion. { "anchor": "How can a cell be considered to be operating in MBSFN mode for 3.84/7.68 Mcps TDD?", "completion": "A cell shall be considered to be operating in MBSFN mode when individual scrambling codes are assigned to all timeslots via the IE \"TDD MBSFN Information\".", "abstention": false, "n_positive_chunks": 1, "n_negative_chunks": 4 } This model is intended for context-grounded telecom answer generation in Retrieval-Augmented Generation (RAG) pipelines. It should receive retrieved telecom context and generate an answer grounded in that context. The model is not optimized for unrestricted context-free question answering. For questions where the retrieved context is missing or insufficient, use an abstention-aware prompt or one of the dedicated `-Safety` variants. from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "farbodtavakkoli/OTel-LLM-8B-A1B-IT" 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)) - 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. - For unrestricted telecom QA without retrieved context, use a separately evaluated context-free QnA model rather than assuming this RAG-oriented checkpoint will behave optimally. - [OTel LLM Collection](https://huggingface.co/collections/farbodtavakkoli/otel-llm) - [OTel Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-embedding) - [OTel Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-reranker) - Project page: https://huggingface.co/farbodtavakkoli - Code: https://github.com/farbodtavakkoli/OTel - Media coverage list: https://github.com/farbodtavakkoli/OTel/blob/main/docs/media_coverage.md @misc{otel_models_2026, title = {OTel: Open Telco AI Datasets, Benchmarks, and Models}, author = {Tavakkoli, Farbod and others}, year = {2026}, note = {Open Telco (OTel) model release}, url = {https://huggingface.co/farbodtavakkoli} } For technical questions, contact farbod.tavakkoli@att.com or farbodtavakoli@gmail.com.