---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
datasets:
- farbodtavakkoli/OTel-Reranker
tags:
- telecom
- telecommunications
- gsma
- rag
- full-parameter-fine-tuning
- fine-tuned
pipeline_tag: text-classification
---

# OTel-Reranker-0.6B

**OTel-Reranker-0.6B** is a telecom reranker model full-parameter fine-tuned on OTel telecommunications data. It is part of the [OTel Family of Models](https://huggingface.co/collections/farbodtavakkoli/otel-10-reranker), an open-source initiative to build reference AI resources for the global telecommunications sector.

Across the 3 OTel reranker baselines, OTel fine-tuning improves MRR@10 by +0.530 to +0.592 over the base checkpoints.

## 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

| Attribute | Value |
|---|---|
| Base model | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
| Parameters | 0.6B |
| OTel training dataset | [OTel-Reranker](https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker) |
| Dataset fields | `sentence_0`, `sentence_1`, `label` |
| Training method | Full-parameter post-training / fine-tuning |
| Language | English |
| OTel release license | Apache 2.0 |

## Model Lineage

`Qwen/Qwen3-0.6B` -> `OTel-Reranker` full-parameter post-training -> `farbodtavakkoli/OTel-Reranker-0.6B`

## OTel vs. Base Model

| Metric | Base model | OTel fine-tuned | Delta | Evaluation split |
|---|---:|---:|---:|---|
| MRR@10 | 0.346 | 0.938 +/- 0.007 | +0.592 | OTel-Reranker held-out 5% |

Standard errors are computed with bootstrap resampling (`n=1000`) over the held-out OTel evaluation partition. MRR@10 measures how quickly the first truly relevant telecom passage is promoted near the top of the reranked list.

## Evaluation Caveats

- Reranker results measure held-out OTel reranking partitions.
- Reported standard errors come from bootstrap resampling over the held-out evaluation partitions.
- Reranking quality depends on the candidate passages supplied by the upstream retriever.
- 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.

| Source | Contributor |
|---|---|
| arXiv telecom papers, 3GPP standards, telecom Wikipedia, telecom Common Crawl | Yale University |
| GSMA Permanent Reference Documents, Discover portal | GSMA |
| IETF RFC series | NetoAI |
| Industry whitepapers | Khalifa University |
| O-RAN specifications (working groups 1, 2, 4, 5, 6, 7, 8, 9, 10) | University of Leeds |
| O-RAN documents across working groups | Purdue University and The University of Texas at Dallas |

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.

## Representative Training Row

`OTel-Reranker` rows are pointwise cross-encoder relevance examples.

```json
{
  "sentence_0": "The Fronthaul Gateway can translate FH protocol from an O-DUx with split option 7-2 to an O-RUy with split option 8.",
  "sentence_1": "Fronthaul Gateway that can translate FH protocol from an O-DUx with split option x to an O-RUy with split option y, with currently available option 7-2 to 8.",
  "label": 1.0
}
```

## Intended Use

This model is intended to re-score telecom query-passage pairs after an initial retrieval step. It is designed for the reranking stage of a telecom RAG pipeline, where the goal is to promote the most relevant retrieved passages before answer generation.

## Training Recipe

| Item | Value |
|---|---|
| Framework | ScalarLM |
| Optimizer | AdamW, 8-bit |
| Learning-rate schedule | Cosine decay with warmup |
| Weight decay | 0.01 |
| Warmup steps | 100 |
| Random seed | 42 |
| Maximum sequence length | 1500 tokens |
| Precision | BF16 |
| Attention | Flash Attention 2 |
| Distributed training | Fully Sharded Data Parallel |
| Gradient checkpointing | Enabled |
| Epochs | 3 for LLM/embedding models; 2 for rerankers |
| Compute | AMD MI300X/MI325X/MI355X and NVIDIA A100/H100 GPUs |

## Usage

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_name = "farbodtavakkoli/OTel-Reranker-0.6B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    trust_remote_code=True,
)

query = "What is the F1 interface?"
documents = [
    "The F1 interface connects O-DU to O-CU in O-RAN architecture.",
    "5G networks use millimeter wave frequencies.",
]

pairs = [[query, doc] for doc in documents]
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    scores = model(**inputs).logits.squeeze()
print(scores)
```

## 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.

## Related Models

### Language Models
- [OTel LLM 2.0 Collection](https://huggingface.co/collections/farbodtavakkoli/otel-20-llm)
- [OTel LLM 1.0 Collection](https://huggingface.co/collections/farbodtavakkoli/otel-10-llm)

### Embedding Models
- [OTel Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-10-embedding)

### Reranker Models
- [OTel Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-10-reranker)

## Resources

- [Project Page](https://huggingface.co/farbodtavakkoli)
- [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}
}
```

## 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)