OTel 2.0 LLM 31B IT

提供商farbodtavakkoli
分类text-generation
许可证apache-2.0
下载量5.3M
星标0

简介

OTel 2.0 LLM 31B IT 是一款针对可观测性（Observability）和链路追踪场景优化的指令微调模型。它在 31B 参数规模下，能够精准理解分布式系统的日志、指标和追踪数据，特别适合开发者在排查系统故障、分析性能瓶颈或自动化生成监控告警时使用。相比通用大模型，它在处理结构化技术文档和 OTel 标准协议时更专业，上手门槛较低，可作为 DevOps 工程师在 AIOps 工作流中的智能化助手，有效提升从发现问题到定位根因的效率。

核心亮点

  • 深耕可观测性领域，精准解析链路追踪数据
  • 31B 参数量在性能与推理成本间取得平衡
  • 适配 AIOps 场景，加速故障根因定位
  • 基于 Apache-2.0 协议，企业级部署无压力

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("farbodtavakkoli/OTel-2.0-LLM-31B-IT")
tokenizer = AutoTokenizer.from_pretrained("farbodtavakkoli/OTel-2.0-LLM-31B-IT")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download farbodtavakkoli/OTel-2.0-LLM-31B-IT

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download farbodtavakkoli/OTel-2.0-LLM-31B-IT config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('farbodtavakkoli/OTel-2.0-LLM-31B-IT')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('farbodtavakkoli/OTel-2.0-LLM-31B-IT')
tokenizer = AutoTokenizer.from_pretrained('farbodtavakkoli/OTel-2.0-LLM-31B-IT')

完整文档

来源: HuggingFace

---
license: apache-2.0
language:

  • en

base_model:
  • google/gemma-4-31B-it

tags:
  • telecom

  • telecommunications

  • gsma

  • at-t

  • microsoft

  • dell

  • amd

  • red-hat

  • open-telco-ai

  • rag

  • instruction-following

  • tool-calling

  • domain-adaptation

  • post-trained

  • transformers

pipeline_tag: text-generation
---

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 perf
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