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

Libraries

How to use farbodtavakkoli/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="farbodtavakkoli/OTel-2.0-LLM-31B-IT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("farbodtavakkoli/OTel-2.0-LLM-31B-IT", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use farbodtavakkoli/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 "farbodtavakkoli/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": "farbodtavakkoli/OTel-2.0-LLM-31B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use farbodtavakkoli/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 "farbodtavakkoli/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": "farbodtavakkoli/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 "farbodtavakkoli/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": "farbodtavakkoli/OTel-2.0-LLM-31B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Configuration Parsing Warning:In config.json: "num_experts" must be a number

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 agentic tool calling, direct telecom QnA, instruction following, abstention, RAG, and base-model-style training data.

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 SDG Hub
Training tokens	~440B
Compute for data processing	~530 GPUs through Azure Managed Compute, primarily MI300X
Model training infrastructure	On-prem AMD MI355X with Dell servers
Day 0 inference availability	Microsoft Foundry, Featherless AI, and Red Hat
Language	English

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	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	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, instruction following, tool-use-oriented examples, and base-model-style telecom training data.

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

The raw corpus was processed into approximately 1 trillion tokens using Red Hat SDG Hub on 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.

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	Telecom-specific instructions for analysis, summarization, transformation, and operational support tasks
Agentic tool calling	Examples intended to improve tool-use behavior in telecom AI workflows

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

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.
Agentic tool calling requires an external tool runtime, tool schema validation, permissioning, logging, and safeguards. The model alone does not guarantee safe tool execution.
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.

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

Named contributors for this release include Farbod Tavakkoli, Gregory Diamos, Jorden Terrazas, Roderic Paulk, and Japa Sharath.

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

Citation

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

Contact

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

Downloads last month: 482,238

Safetensors

Model size

32B params

Tensor type

BF16

Model tree for farbodtavakkoli/OTel-2.0-LLM-31B-IT

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

(224)

this model