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Datasets:
gesh75
/
multivendor-cli-commands

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
networking
network-automation
cli
cisco
juniper
arista
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
multivendor-cli-commands / README.md
gesh75's picture
gesh75
Card: add published retrieval+validator numbers and Intent finder link
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metadata 
license: mit
pretty_name: Multivendor CLI Commands
language:
  - en
tags:
  - networking
  - network-automation
  - cli
  - cisco
  - juniper
  - arista
  - frr
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet

Multivendor CLI Commands

70,006 network CLI commands across 17 vendors and tools, tagged with vendor, OS, device role and category. The command corpus behind the Multivendor CLI Configurator, as one flat Parquet table.

Source: github.com/gesh75/multivendor-cli-configurator · Docs: gesh75.github.io/multivendor-cli-configurator

Schema

column type meaning
vendor string Vendor or tool (Cisco, Juniper, Arista, FRR, …)
os string Platform / OS surface (ios, junos, eos, sros, …)
role string router, switch or firewall
category string Functional bucket assigned by the pipeline (BGP, OSPF, VLAN, Interfaces, Security, …)
command string The command syntax, with placeholders ([IP], <vlan>) or example values
live_verified bool true if captured from a live 10-node Docker FRR lab
in_docs bool / null true if the FRR command was also found in FRR docs; null elsewhere

Rows by vendor

vendor rows vendor rows
Cisco 22,168 Extreme 2,800
Arista 7,397 Linux 2,592
VyOS 6,289 FortiOS 2,383
Huawei 4,425 Wireshark 2,130
Aruba 4,106 PAN-OS 1,224
FRR 3,949 Mikrotik 1,216
Microsoft 3,352 NVIDIA 1,168
Juniper 3,217 Nokia 1,131
SONiC 459

Provenance — what was kept, what was dropped

The commands were collected from public vendor CLI references, a public command guide, community notes and a live FRR lab, then merged and deduped by a stdlib-only Python pipeline (scripts/ in the repo).

This release keeps only the command syntax and structural metadata. The upstream desc (description prose) and title fields were dropped, because some of that text comes from or paraphrases vendor documentation and books, and its origin can't be traced row by row. If you need descriptions, use the vendor's own docs.

License

MIT, matching the repo. CLI command names and syntax are functional facts belonging to their respective vendors; vendor and product names are trademarks of their owners. No affiliation or endorsement implied.

Limitations

  • Not validated per release or platform. Verify on your gear.
  • Coverage is uneven: Cisco/Arista/VyOS/FRR are deep, SONiC and Nokia SR OS are thin.
  • category comes from heuristics, so some rows are misbucketed (catch-all buckets like Misc and Protocols are large).
  • Some commands carry a prompt or mode prefix (e.g. switch(config-if)#) inherited from the source.
  • Example values are generic placeholders, documentation ranges or well-known public resolvers (e.g. 8.8.8.8). No real network data.

Derived

  • gesh75/netops-intent2cli: synthetic operator intents paired with verbatim commands from this corpus. Default config is v1 (565 train / 102 validation / 108 test). Config v2 adds 650 training pairs and an 88-row operator_test.
  • gesh75/netops-intent2cli-qwen0.5b-lora: experimental Qwen2.5-0.5B LoRA trained on v2. Adapter-only exact match: 12/108 (11.1%) on the frozen v1 test, 9/88 (10.2%) on operator_test. Optional retrieval+validator lookup mode (same card): 63/108 and 58/88 when the gold command is already in this corpus; it cannot return a command that is missing from the base. Not production-safe. Browser demo: Intent finder (retrieval only, no model).

Load 

from datasets import load_dataset
ds = load_dataset("gesh75/multivendor-cli-commands", split="train")
bgp = ds.filter(lambda r: r["vendor"] == "Juniper" and r["category"] == "BGP")

import pandas as pd
df = pd.read_parquet("hf://datasets/gesh75/multivendor-cli-commands/data/train.parquet")

GESH · NetOps tools on Hugging Face

📡 netlog-ai Sanitize-first network log analysis — browser lite demo of netlog-ai (PyPI 2.1.0)
🛠️ multivendor-cli-configurator 70k+ CLI commands across 17 vendors — cheatsheet, N-vendor compare, CLI Studio, Intent finder
🗂️ multivendor-cli-commands The same 70,006 commands as a Parquet dataset
🧭 netops-intent2cli v1 + v2 synthetic operator intents → corpus command pairs
🤖 netops-intent2cli-qwen0.5b-lora LoRA adapter: English intent → vendor CLI (experimental)

Collection: NetOps tools · Code: github.com/gesh75 · Docs hub: gesh75.github.io

Doctrine: fail closed · sanitize first · evidence leaves, packets don't · honest coverage.