OTel-LLM-8B-A1B-IT

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, 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. Community Use 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. Model Details | Attribute | Value | |---|---| | Base model | LiquidAI/LFM2.5-8B-A1B | | Parameters | 8B | | OTel training dataset | OTel-LLM | | Dataset fields | `prompt`, `completion`, `abstention`, chunk-count metadata, token-count metadata | | Training method | Full-parameter post-training / fine-tuning | | Language | English | | OTel release license | Apache 2.0 | Model Lineage `LiquidAI/LFM2.5-8B-A1B` -> `OTel-LLM` full-parameter post-training -> `farbodtavakkoli/OTel-LLM-8B-A1B-IT` OTel vs. Base Model | Metric | Base model | OTel fine-tuned | Delta | Evaluation split | |---|---:|---:|---:|---| | LLM-as-judge correctness | 81.2% | 88.4% +/- 0.5 | +7.2 pp | OTel-LLM held-out 10% | 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. Evaluation Caveats 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. Trainin