Farbod Tavakkoli of AT&T’s Chief Data Office has published the OTel 2.0 checkpoint on Hugging Face under the model ID farbodtavakkoli/OTel-2.0-LLM-31B-IT-QLoRA. The release opens the OTel 2.0 family, a telecom-model project involving AT&T, MLCommons, Microsoft, AMD, Dell, GSMA, Red Hat, and Pleias.

The instruction-tuned model starts from Gemma 4 31B-IT and adds telecom knowledge through approximately 440 billion post-training tokens. Its Apache 2.0 license, merged bf16 weights, and support from Microsoft Foundry, Featherless AI, and Red Hat give developers several options for testing and deployment.

The release in six details

QLoRA training without QLoRA runtime dependencies

The team trained a LoRA adapter against a 4-bit NF4 quantization of Gemma 4 31B-IT. It then dequantized the base weights to bf16 and folded the adapter deltas into them, producing a standard Gemma4ForConditionalGeneration checkpoint.

The released artifact therefore uses bf16 weights despite the QLoRA suffix in its name. Inference does not require bitsandbytes, PEFT, or Unsloth. A sibling checkpoint, OTel-2.0-LLM-31B-IT, provides the project’s full-parameter alternative for teams comparing adaptation methods and deployment trade-offs.

The adapter configuration uses rank 256, alpha 512, zero dropout, and every linear layer as a target. Those settings give the adapter substantially more trainable capacity than smaller LoRA configurations, helping it absorb a large domain corpus while retaining the memory savings of quantized training.