OTel-2.0-LLM-31B-IT

Instructions to use msukhar/OTel-2.0-LLM-31B-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use msukhar/OTel-2.0-LLM-31B-IT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="msukhar/OTel-2.0-LLM-31B-IT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("msukhar/OTel-2.0-LLM-31B-IT")
model = AutoModelForCausalLM.from_pretrained("msukhar/OTel-2.0-LLM-31B-IT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use msukhar/OTel-2.0-LLM-31B-IT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "msukhar/OTel-2.0-LLM-31B-IT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "msukhar/OTel-2.0-LLM-31B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/msukhar/OTel-2.0-LLM-31B-IT

SGLang

How to use msukhar/OTel-2.0-LLM-31B-IT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "msukhar/OTel-2.0-LLM-31B-IT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "msukhar/OTel-2.0-LLM-31B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "msukhar/OTel-2.0-LLM-31B-IT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "msukhar/OTel-2.0-LLM-31B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use msukhar/OTel-2.0-LLM-31B-IT with Docker Model Runner:
```
docker model run hf.co/msukhar/OTel-2.0-LLM-31B-IT
```

OTel-2.0-LLM-31B-IT

Checkpoint update notice: The current checkpoint is expected to be updated within the next few hours. After that initial refresh, OTel 2.0 checkpoints are expected to continue receiving weekly weight updates. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag.

OTel-2.0-LLM-31B-IT is a telecom-specialized instruction model post-trained from Gemma 4 31B-IT on approximately 440 billion telecom training tokens. It is the first release in the OTel 2.0 family and is designed to support telco-grade AI workflows across network operations, standards interpretation, product development, network configuration assistance, RAG, and telecom-specific question answering.

OTel 2.0 extends the original OTel effort from a RAG-oriented telecom fine-tuning release into a larger domain-adapted training program. The model was trained from a much larger standards and telecom corpus, with new data preparation coverage for direct telecom QnA, abstention, RAG, base-model-style telecom data, and general-purpose instruction-following and tool-calling examples. The current training mixture does not include telecommunications-specific MCP, tool-calling, or instruction-following examples.

Release Status

OTel 2.0 models are expected to continue training after release, and weights may be updated weekly. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag rather than relying on the floating latest weights.

Model Details

Attribute	Value
Base model	Gemma 4 31B-IT
Parameters	31B
Model family	OTel 2.0
Training method	Telecom domain post-training / instruction tuning
Raw telecom corpus	~15B tokens from GSMA through Open Telco AI
Processed data volume	>1T tokens processed using Red Hat's open-source Synthetic Data Generation Hub (SDG Hub)
Training tokens	~440B
Compute for data processing	~530 GPUs through Microsoft Azure Managed Compute, primarily AMD MI300X
Model training infrastructure	On-premises AMD MI355X GPUs with Dell Technologies infrastructure and servers
Day 0 inference availability	Microsoft Foundry, Featherless AI, and Red Hat
Supported interaction	English-language text only

Model Lineage

Gemma 4 31B-IT -> OTel 2.0 telecom data processing and post-training -> OTel-2.0-LLM-31B-IT

What Changed From OTel 1.0

Category	Metric	OTel 1.0	OTel 2.0
Tokens	Raw documents	~600M	~15B
Tokens	Training	~1B	~440B
Data preparation	General-purpose agentic tool calling	No	Yes
Data preparation	Knowledge / fact direct QnA	No	Yes
Data preparation	RAG	Yes	Yes
Data preparation	Abstention	Yes	Yes
Data preparation	General-purpose instruction following	No	Yes
Data preparation	Base model training set	No	Yes

Relative to OTel 1.0, OTel 2.0 increases raw source coverage by roughly 25x and training-token volume by roughly 440x. More importantly, the data mixture is broader: OTel 1.0 focused primarily on context-grounded RAG and abstention, while OTel 2.0 adds direct telecom knowledge QnA, general-purpose instruction-following and tool-use-oriented examples, and base-model-style telecom training data. Telecommunications-specific MCP, tool-calling, and instruction-following examples are not part of the current training mixture.

Training Data

The starting corpus contains approximately 15 billion raw tokens provided by GSMA through Open Telco AI. The corpus includes telecom standards and technical material from seven standards development and industry organizations:

Source family	Examples
Cellular standards	3GPP
Telecom standards and specifications	ETSI, ITU
Industry and operator materials	GSMA
Network API specifications	CAMARA
Open RAN specifications	O-RAN
Telecom business and operations frameworks	TM Forum

Dense technical specifications from these sources were converted into material suitable for model training. The raw corpus was processed into over 1 trillion tokens using Red Hat's open-source Synthetic Data Generation Hub (SDG Hub) on Microsoft Azure Managed Compute, using approximately 530 GPUs, primarily AMD MI300X. From this processed pool, approximately 440 billion training tokens were generated for OTel 2.0 post-training. Model training ran on on-premises AMD MI355X GPUs with Dell Technologies infrastructure and servers.

Data Preparation Capabilities

OTel 2.0 includes a broader supervised and synthetic-data mixture than OTel 1.0:

Capability	Description
RAG	Context-grounded telecom answer generation from retrieved standards and technical documents
Abstention	Training examples that teach the model to avoid answering when context is missing, irrelevant, or insufficient
Direct telecom QnA	Knowledge and factual question-answer pairs for standards, protocols, services, and network concepts
Instruction following	General-purpose instruction-following examples; the current mixture does not include telecommunications-specific instruction-following examples
Agentic tool calling	General-purpose tool-calling examples; the current mixture does not include telecommunications-specific MCP or tool-calling examples

Intended Use

OTel-2.0-LLM-31B-IT is intended for telecom-focused applications where domain knowledge, standards familiarity, and deployment control matter. Suitable use cases include:

Retrieval-Augmented Generation over telecom standards, specifications, and internal technical documentation.
Standards interpretation and summarization for 3GPP, ETSI, GSMA, CAMARA, ITU, O-RAN, and TM Forum materials.
Product development, network configuration assistance, and engineering support tasks.
Telecom-specific direct QnA where the model has been separately evaluated for the target benchmark or application.
Agentic workflows where a larger system provides verified tools, retrieval, validation, and audit logging.

For high-impact operational use, the model should be deployed with retrieval, source attribution, validation checks, and human review appropriate to the system risk.

Limitations and Responsible Use

Supported Language and Modality

OTel 2.0 currently supports English-language, text-only interactions. It has not been established as a multilingual or multimodal model and should not be assumed to support images, audio, video, or other non-text modalities.

Tool Use and Instruction Following

The training data includes general-purpose tool-calling and instruction-following examples, but it does not currently include:

Telecommunications-specific MCP examples.
Telecommunications-specific tool-calling examples.
Telecommunications-specific instruction-following examples.

General-purpose tool-use training should not be interpreted as readiness to operate telecommunications tools autonomously. Agentic deployment requires an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards, and human review appropriate to the risk. The model alone does not guarantee correct or safe tool execution.

Telecommunications Data Not Included in Training

The current training mixture does not include the following classes of operational telecommunications data:

Event data: Elasticsearch records covering user activity, anomalies, failures, IMS events, and RADIUS authentication.
Network-performance data: KPIs, 5G performance metrics, and Passive Intermodulation (PIM) interference data.
RF and spectral data: Field-test results, antenna-port metrics, signal diagnostics, heatmaps, and noise or interference measurements.
5G core data: Control-plane and inter-Network Function (NF) signaling data.

Standards knowledge should not be interpreted as experience with live operator telemetry, private network records, or these excluded operational datasets. Applications involving these data types require separate task-specific evaluation, grounding, and validation.

General Reliability and Deployment Limitations

OTel 2.0 is domain-specific to telecommunications and should not be treated as a general-purpose model for unrelated fields.
Telecom standards evolve over time; answers should be checked against the relevant document version and release.
RAG quality depends on document ingestion, chunking, retrieval, reranking, prompt design, and source freshness.
Direct QnA behavior should be evaluated separately from RAG behavior; strong performance in one setting does not imply strong performance in the other.
For high-impact operational use, deploy the model with retrieval, source attribution, validation checks, and human review appropriate to the system risk.

Related Models

Project Resources

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

Contributors and Organizations

Contributors to the OTel 2.0 release and supporting infrastructure include:

Organization	Contributors
AT&T	Farbod Tavakkoli, Jorden Terrazas, Roderic Paulk, Sharath Japa, Tzvi Chumash, Miguel Armenta, Pavan Tagirisa, Kostikey Mustakas, Mark Austin, Andy Markus
MLCommons	Gregory Diamos, David Kanter, Kenneth Church
Microsoft	Chunyu Li, Gulsimo Osimi, Rick Lievano, Inayat Wali, Manoj Bableshwar, Marie-Louise Onga Nana, Naomi Moneypenny, Osi Otugo, Rahul Kumar, SeokJin Han, Steve Sweetman, Trinidad Salazar, Ven Kumar, Vivek Ramaswamy
AMD	Alexander Finn, Andy Allred, Andrey Ivannikov, Antti-Ville Suni, Mark van Heeswijk, Kumaran Siva, Curt Wortman, Mehrvash Poole, Eric Lynn
Dell	Brian Sweeney, Suzanne Randall, Patrick Allen, Matt Currie, Justin Wilson, Jason Kane, Keith Napoleon, Sarah Lake, Mike Hess, Randy Tornes
GSMA	Louis Powell, Zeinab Nezami, Enrique Molero
Red Hat	Aditi Saluja, William Caban, Shivchander Sudalairaj, Kai Xu, Ravi Sharma, Hanen Garcia, Joe Crispo, Eshwar Sivaramakrishnan
Pleias	Anastasia Stasenko, Pierre-Carl Langlais, Mohamed Chenene, Carlos Rosas, Yannick Detrois

Organizations involved in the OTel 2.0 release and supporting infrastructure include AT&T, MLCommons, Microsoft, AMD, Dell, GSMA, Red Hat, and Pleias.

Citation

@misc{otel_2_models_2026,
  title  = {OTel 2.0: Open Telco AI Datasets, Benchmarks, and Models},
  author = {Tavakkoli, Farbod and others},
  year   = {2026},
  note   = {Open Telco (OTel 2.0) model release},
  url    = {https://huggingface.co/farbodtavakkoli},
  organization = {AT\&T, MLCommons, Microsoft, AMD, Dell, GSMA, Red Hat, Pleias}
}

Contact

For technical questions, contact farbod.tavakkoli@att.com or farbodtavakoli@gmail.com.

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32B params

Tensor type

BF16

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