| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: EssentialAI/rnj-1 |
| tags: |
| - classification |
| - telecom |
| - 5g |
| - root-cause-analysis |
| - telelogs |
| datasets: |
| - netop/TeleLogs |
| pipeline_tag: text-classification |
| --- |
| |
| # OTel-LLM-8.3B-Classification |
|
|
| **OTel-LLM-8.3B-Classification** is a telecom-specialized classification model fine-tuned for **5G network root cause analysis (RCA)**, achieving **~99% accuracy** on the [TeleLogs benchmark](https://huggingface.co/datasets/netop/TeleLogs). It is part of the [OTel Family of Models](https://huggingface.co/collections/att/otel-10-llm), an open-source initiative to build industry-standard AI models for the global telecommunications sector. |
|
|
| ## Model Details |
|
|
| | Attribute | Value | |
| |---|---| |
| | **Base Model** | [EssentialAI/rnj-1](https://huggingface.co/EssentialAI/rnj-1) | |
| | **Parameters** | 8.3B | |
| | **Training Method** | Full parameter fine-tuning with classification head | |
| | **Task** | Multi-class sequence classification (8 root cause classes) | |
| | **Language** | English | |
| | **License** | Apache 2.0 | |
|
|
| ## Benchmark Results |
|
|
| This model achieves **~99% accuracy** on the TeleLogs test set, higher than the published baselines in the TeleLogs paper shown below, including the reasoning-augmented models. It is reported as an auxiliary experiment in the OTel paper (NeurIPS 2026) and is not part of the paper's 30-model baseline table. |
|
|
| | Model | Reasoning | Test pass@1 | Test maj@4 | |
| |---|---|---|---| |
| | Qwen2.5-32B-Instruct | ❌ | 18.85% | 19.60% | |
| | DeepSeek-R1-Distill-Llama-70B | ✅ | 29.42% | 34.84% | |
| | QwQ-32B | ✅ | 33.62% | 39.00% | |
| | Qwen3-32B | ✅ | 33.77% | 37.04% | |
| | Qwen2.5-RCA-1.5B | ✅ | 87.56% | 87.73% | |
| | Qwen2.5-RCA-7B | ✅ | 87.01% | 88.89% | |
| | Qwen2.5-RCA-32B | ✅ | 95.86% | 96.18% | |
| | **OTel-LLM-8.3B-Classification** | ❌ | **~99%** | — | |
|
|
| ## TeleLogs Dataset |
|
|
| [TeleLogs](https://huggingface.co/datasets/netop/TeleLogs) is a synthetic dataset designed to advance research on root cause analysis in 5G networks. It simulates drive-test scenarios involving a user equipment (UE) moving through a region covered by multiple 5G base stations (gNodeBs). Each instance includes a symptom (throughput degradation below 600 Mbps) and one or more root causes from 8 predefined classes: |
|
|
| 1. Test vehicle speed exceeds 40 km/h, impacting user throughput |
| 2. Downtilt angle of the serving cell is too large, causing weak coverage at the far end |
| 3. Serving cell coverage distance exceeds 1 km, resulting in poor RSRP |
| 4. Non-colocated co-frequency neighboring cells cause severe interference |
| 5. Neighbor cell and serving cell have the same PCI mod 30, causing reference signal overlap |
| 6. Frequent handovers degrading user performance |
| 7. Misconfigured handover thresholds degrading user performance |
| 8. Average scheduled resource blocks (RBs) of the serving cell are below 160 |
|
|
| For more details, see the [TeleLogs paper](https://arxiv.org/abs/2507.21974). |
|
|
| ## Training Details |
|
|
| ### Approach |
|
|
| The model was trained as a **sequence classification** model. The classification head consists of a **Dropout(0.1) layer followed by a Linear projection** from the model's hidden dimension to the 8 root cause classes. |
|
|
| ### Hyperparameters |
|
|
| | Parameter | Value | |
| |---|---| |
| | **Learning Rate** | 7e-6 | |
| | **LR Scheduler** | Cosine | |
| | **Warmup Steps** | 150 | |
| | **Batch Size (per device)** | 4 | |
| | **Gradient Accumulation Steps** | 2 | |
| | **Effective Batch Size** | 64 (4 × 2 × 8 GPUs) | |
| | **Epochs** | 25 | |
| | **Weight Decay** | 0.01 | |
| | **Label Smoothing** | 0.1 | |
| | **Max Gradient Norm** | 0.5 | |
| | **Max Sequence Length** | 5000 tokens | |
| | **Precision** | BF16 | |
| | **Attention** | Flash Attention 2 | |
| | **Distributed Training** | FSDP | |
| | **Gradient Checkpointing** | Enabled | |
|
|
| ### Classification Head |
|
|
| ``` |
| Dropout(0.1) → Linear(hidden_size, 8, bias=False) |
| ``` |
|
|
| Weight initialization: Normal distribution with mean=0.0, std=0.01. |
|
|
| ## Intended Use |
|
|
| This model is optimized for: |
|
|
| - **5G network root cause analysis** — classifying network log data into specific fault categories |
| - **Telecom troubleshooting** — automated diagnosis of throughput degradation causes from drive-test measurements |
|
|
| ## Related Models |
|
|
| ### Language Models |
| - [OTel LLM 2.0 Collection](https://huggingface.co/collections/att/otel-20-llm) |
| - [OTel LLM 1.0 Collection](https://huggingface.co/collections/att/otel-10-llm) |
|
|
| ### Embedding Models |
| - [OTel Embedding Collection](https://huggingface.co/collections/att/otel-10-embedding) |
|
|
| ### Reranker Models |
| - [OTel Reranker Collection](https://huggingface.co/collections/att/otel-10-reranker) |
|
|
| ## Related Datasets |
|
|
| - [TeleLogs](https://huggingface.co/datasets/netop/TeleLogs) — 5G RCA benchmark |
| - [OTel-Embedding](https://huggingface.co/datasets/att/OTel-Embedding) |
| - [OTel-Safety](https://huggingface.co/datasets/att/OTel-Safety) |
| - [OTel-LLM](https://huggingface.co/datasets/att/OTel-LLM) |
| - [OTel-Reranker](https://huggingface.co/datasets/att/OTel-Reranker) |
|
|
| ## Training Infrastructure |
|
|
| - **Framework**: ScalarLM (GPU-agnostic) |
| - **Compute**: TensorWave with AMD GPUs and Azure with NVIDIA GPUs |
| - **Training Code**: [OTel code](https://github.com/farbodtavakkoli/OTel) |
|
|
| ## Resources |
|
|
| - [Project Page](https://huggingface.co/att) |
| - [OTel code](https://github.com/farbodtavakkoli/OTel) |
| - [OTel Selected Coverage](https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-selected-coverage.md) |
| - [OTel Paper](https://arxiv.org/pdf/2610.07766) |
| - [OTel 1.0 Coverage](https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-1.0-coverage.md) |
| - [OTel 2.0 Coverage](https://github.com/farbodtavakkoli/OTel/blob/main/docs/OTel-2.0-coverage.md) |
| - [OTel Docker](https://hub.docker.com/u/farbodatdocker) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{tavakkoli2026otel, |
| title = {OTel: Open Telco AI Datasets, Benchmarks, and Models}, |
| author = {Tavakkoli, Farbod and others}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, |
| year = {2026}, |
| url = {https://github.com/farbodtavakkoli/OTel} |
| } |
| ``` |
|
|
| If you use the TeleLogs dataset, please also cite: |
|
|
| ```bibtex |
| @article{sana2025reasoning, |
| title={{Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks}}, |
| author={Mohamed Sana and Nicola Piovesan and Antonio De Domenico and Yibin Kang and Haozhe Zhang and Merouane Debbah and Fadhel Ayed}, |
| year={2025}, |
| eprint={arXiv:2507.21974}, |
| url={https://arxiv.org/abs/2507.21974} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| If you have any technical questions, please feel free to reach out to [farbod.tavakkoli@att.com](mailto:farbod.tavakkoli@att.com) or [farbodtavakoli@gmail.com](mailto:farbodtavakoli@gmail.com) |
|
|
