nraptisss/TMF921-intent-to-config-augmented

TMF921 Intent-to-Configuration Augmented Dataset

The largest open SFT training dataset for translating natural language network intents into spec-compliant 5G/6G configurations across 6 concurrent telecom specification layers, with 8 lifecycle operations, 18 industry verticals, and adversarial robustness testing.

41,815 samples (39,294 train / 2,521 test) of natural language intents paired with structured network configuration JSON objects, grounded in real telecom standards (TMF921, 3GPP TS 28.312, ETSI ZSM 009-1, CAMARA, O-RAN A1, and 3GPP TS 28.541). This dataset is the augmented third generation in a lineage of progressively richer intent-to-configuration datasets, adding LLM-augmented natural language diversity, expanded sector and use-case coverage, and adversarial samples to the template-based deterministic core.

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1. Motivation and Research Gap

1.1 The Intent-Based Networking Promise

Intent-Based Networking (IBN), formalized in IETF RFC 9315, envisions autonomous network management where human operators express high-level declarative goals — "provision a URLLC slice with sub-1ms latency for remote surgery" — and the network translates, fulfills, and assures those goals without manual configuration. For 5G/6G network slicing, this translation requires mapping free-form natural language into structured configurations that conform to multiple, overlapping telecom standards.

Recent results demonstrate that frontier LLMs can perform this translation. Da Silva Machado et al. (ORION, 2025) showed that GPT-5, Claude Opus 4.5, and Gemini 3 Pro each achieve 100% policy generation success on an evaluation set of up to 100 intents per model, using CAMARA schema grounding. However, smaller open-weight models fail without domain-specific fine-tuning — creating a critical dependency on proprietary API access for a function that should be embeddable in on-premises network management systems.

1.2 The Data Gap

Despite the centrality of intent-to-configuration translation to autonomous network management, no public training dataset existed that covers the full standards stack required for production deployment:

Existing Resource	Type	Coverage Gap
TeleQnA (Maatouk et al., 2023)	MCQ evaluation benchmark	No structured configuration outputs; evaluation-only
ORANBench-13K	MCQ evaluation benchmark	O-RAN questions only; no intent→config pairs
6G-Bench	MCQ evaluation benchmark	Future-oriented; no operational configurations
SPEC5G (Karim et al., 2023)	NLP corpus (3.5M sentences)	Protocol text; no intent management
ORION eval set (2025)	100-sample evaluation	Too small for training; CAMARA only
TSLAM-Mini (2025)	100K telecom instruction set	General telecom Q&A; no multi-standard intent→config

This dataset fills the gap by providing the first open SFT training dataset covering 6 concurrent specification layers in ChatML format, with: (1) full TMF921 v4 API-compliant intent objects — the TM Forum's de facto industry standard for production OSS/BSS; (2) ETSI ZSM 009-1 cross-domain intents; (3) complete lifecycle operation coverage (create through terminate); (4) adversarial robustness samples; and (5) all grounded in normative 3GPP, TM Forum, ETSI, O-RAN, and CAMARA specifications.

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2. Dataset Lineage and Evolution

This dataset is the third generation in a progressive refinement pipeline. Each generation addressed specific limitations identified during training experiments with the prior version.

Generation	Dataset	Size	Key Addition
Gen 1	telecom-intent-config-sft-10k	10,000	First multi-layer dataset (4 layers: CAMARA, A1, 3GPP, O1 NRM)
Gen 2	TMF921-intent-to-config-25k	25,000	Added TMF921 + ETSI ZSM (6 layers); lifecycle operations; expanded to 18 sectors, 147 use cases, 54 regions
Gen 3	This dataset (TMF921-intent-to-config-augmented)	41,815	LLM-augmented NL diversity; adversarial samples (ambiguous, contradictory, out-of-scope); expanded KPI ranges; additional SD values

What Changed from Gen 2 → Gen 3 (This Dataset)

1. LLM-augmented natural language diversity: The Gen 2 dataset used 4–8 handcrafted templates per slice type. This dataset augments the template-generated NL intents with LLM-paraphrased variants, increasing the natural language surface area while preserving the deterministic, spec-compliant JSON outputs. This leverages the asymmetry identified by Josifoski et al. (SynthIE, 2023): for structured output tasks, generating diverse NL inputs from fixed outputs avoids the difficulty of having an LLM produce fully schema-valid structured outputs from unconstrained text, enabling higher-quality synthetic training data.

2. Adversarial robustness samples (141 samples, 0.34%): Three categories of adversarial inputs that a production intent handler must reject:

   • Ambiguous (58 samples): Underspecified intents lacking required KPI parameters ("Can you create a slice for educational purposes?")
   • Contradictory (40 samples): Physically impossible requirements ("mMTC slice with sub-1ms latency for 1M devices at 10 Gbps each")
   • Out-of-scope (43 samples): Non-telecom requests ("Request for a list of local volunteer opportunities")

3. Expanded parameter space: Additional SD values (000004 for MPS), broader KPI ranges derived from 3GPP TS 22.261 profiles, and 147 unique use cases (vs. ~50 in Gen 1).

4. Scale: 41,815 total samples (67% increase over Gen 2), with proportional scaling across all target layers.

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3. Specification Grounding

Every configuration JSON conforms to its target specification's normative data model. This section documents the standards coverage and the mapping between them.

3.1 Primary Target Specification Layers

Target Layer	Specification	Version	Train Count	Test Count	% of Total
tmf921	TM Forum TMF921 Intent Management API	v4.0	10,036	640	25.5%
intent_3gpp	3GPP TS 28.312 Intent-Driven Management	v18.8.0 (Rel-18)	7,257	441	18.4%
camara	CAMARA NetworkSliceBooking API	2024	7,046	401	17.8%
a1_policy	O-RAN WG2 A1 Policy Framework	v04.00	5,167	312	13.1%
o1_nrm	3GPP TS 28.541 NR Network Resource Model	v18.7.0	4,116	244	10.4%
etsi_zsm	ETSI GS ZSM 009-1 Zero-touch Service Mgmt	v1.1.1	3,979	285	10.2%
tmf921_lifecycle_*	TMF921 Lifecycle Operations (8 types)	v4.0	1,552	173	4.1%
adversarial_*	Adversarial Rejection (3 types)	—	141	25	0.4%

3.2 Standards Descriptions

TM Forum TMF921 Intent Management API (v4.0/v5.0). TMF921 is a RESTful Open API standard for managing intent objects across operator OSS/BSS systems. It defines Intent resources composed of IntentExpression (declarative goal), containing IntentExpectation objects with DeliveryExpectation targets, geographic and temporal contexts, and relatedParty references. The lifecycle model follows Create → Inactive → Active ↔ Suspended → Terminated, with each state transition auditable and event-emitting. TMF921 separates the Intent Owner (formulates goals) from the Intent Handler (interprets submissions, enforces policies, performs assurance). The v5.0 release aligns explicitly to the TM Forum Intent Ontology (TIO).

3GPP TS 28.312 — Intent-Driven Management (Rel-18, v18.8.0). The formal 3GPP technical specification for Intent-Driven Management (IDM) in 5G network OAM, defining IntentExpectation objects with quantifiable KPI targets (latency, throughput, reliability) mapped to network slice profiles via S-NSSAI (SST+SD). The specification covers fulfillment vs. assurance semantics, priority-based conflict arbitration, and RAN Management Intent Owner (RMIO) / Handler (RMIH) role separation. Rel-18 updates add report subscription mechanisms for SMO/Non-RT handlers.

ETSI GS ZSM 009-1 — Zero-touch Service Management. The ETSI ZSM framework for fully automated network lifecycle management. GS ZSM 009-1 specifies intent-based interfaces with the fundamental Intent Owner/Handler role model and closed-loop automation following the ETSI ZSM reference loop stages. ZSM 016 extends this with a conflict taxonomy for intent conflicts.

CAMARA NetworkSliceBooking API. The Linux Foundation CAMARA project's open API for application-level network slice provisioning. It exposes parameters including sliceType, maxDataRateDl/Ul, latency, reliability, and maxNumberOfPDUSessions, with a booking lifecycle (REQUESTED → CONFIRMED → ACTIVE → COMPLETED/CANCELLED). The API is intentionally vertical-agnostic, serving logistics, healthcare, automotive, and other use cases through the same interface.

O-RAN WG2 A1 Policy Framework. The O-RAN Alliance's specification for policies exchanged between the Non-RT RIC and Near-RT RIC via the A1 interface. A1 policies define enforceable RAN behavior through QoS objectives (gfbr, mfbr, priorityLevel, 5QI, pdb), resource allocation (prbQuota min/max, schedulerWeight), and scope bindings (UE, group, cell, S-NSSAI).

3GPP TS 28.541 — NR Network Resource Model (v18.7.0). The O1 interface configuration model defining the ManagedElement → GNBDUFunction → NRCellDU hierarchy, including cellLocalId, arfcnDL, bSChannelBwDL, nrPCI, nrTAC, and rrmPolicyMemberList with S-NSSAI-keyed PRB allocation ratios.

3.3 Additional Normative References Governing KPI Values

Specification	What It Governs in This Dataset
3GPP TS 23.501	S-NSSAI encoding (SST+SD), standardized 5QI values and PDB mappings
3GPP TS 22.261	5G service requirements: eMBB/URLLC/mMTC KPI target ranges
3GPP TS 22.104	Industrial IoT communication requirements
3GPP TS 22.186	V2X communication service requirements
3GPP TS 38.104	NR frequency bands and ARFCN ranges (used in O1 NRM configs)
IETF RFC 9315	Intent-Based Networking: concepts and definitions

3.4 Concept Mapping Across Specifications

Concept	TMF921	3GPP TS 28.312	ETSI ZSM 009-1	CAMARA
Top-level goal	Intent	Intent	zsmIntent	networkSliceBooking
Goal expression	IntentExpression	IntentExpectation	objectives	sliceProfile
KPI condition	ExpectationTarget	targetCondition	kpiTargets	Direct fields
Target resource	ExpectationObject	objectInstance	constraints	sliceType
Spatial scope	GeographicContext	expectationContext	spatial	areaOfService
Temporal scope	TemporalContext	expectationContext	temporal	duration
Lifecycle owner	relatedParty	RMIO	intentOwner	API caller
State machine	lifecycleState	lifecycleStatus	intentState	Booking status

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4. Dataset Construction Methodology

4.1 Design Principles

1. Specification grounding: Every JSON configuration conforms to its target specification's normative data model. Field names, nesting structures, and value types are drawn directly from the published API schemas.

2. Multi-layer coverage: The same underlying intent scenario (use case + KPI parameters) is rendered across all 6 specification layers, ensuring the model learns the structural transformation rather than memorizing specific JSON templates.

3. KPI realism: All numerical parameter values are sampled from ranges defined in 3GPP TS 22.261 (5G service requirements), TS 22.104 (industrial IoT), and TS 22.186 (V2X). The KPI profiles are detailed in Section 6.4.

4. Lifecycle completeness: Intent lifecycle operations follow the TMF921/3GPP state machine (Create → Activate → Monitor/Assure → Modify → Suspend → Resume → Scale → Terminate → Report), providing training signal for the full operational surface.

5. Adversarial robustness: Production intent handlers must reject malformed inputs gracefully. The dataset includes three adversarial categories with appropriate error responses.

4.2 Three-Phase Generation Pipeline

The dataset was constructed in three phases, combining the reliability of deterministic template generation with the diversity benefits of LLM augmentation.

Phase 1: Parametric Configuration Generation (Deterministic)

A Python generator script (generate_dataset.py, 1,599 lines, seed=42, fully deterministic) produces the structured components:

1. Parameter sampling: For each sample, the generator selects a (sector, use_case, slice_type, region) tuple and samples KPI values from the spec-grounded profiles. The sampling uses weighted distributions to ensure realistic slice-type-to-sector pairings (e.g., URLLC is more frequent in healthcare and manufacturing; mMTC dominates in agriculture and smart city).

2. JSON configuration generation: Six layer-specific generators produce the structured JSON output:

   • generate_tmf921_intent() — Full TMF921 v4 Intent resource with @type annotations, HATEOAS links, relatedParty, geographic and temporal contexts
   • generate_3gpp_intent() — 3GPP TS 28.312 intent with intentExpectation, S-NSSAI encoding, and intentContext
   • generate_camara_booking() — CAMARA networkSliceBooking with sliceProfile, areaOfService (with real geographic coordinates), and duration
   • generate_a1_policy() — O-RAN A1 policy with 5QI mapping (per 3GPP TS 23.501 Table 5.7.4-1), PRB quotas, and scheduler weights
   • generate_o1_nrm_config() — 3GPP TS 28.541 ManagedElement → GNBDUFunction → NRCellDU with ARFCN values from TS 38.104
   • generate_etsi_zsm_intent() — ETSI ZSM intent with objectives, constraints, context (temporal, spatial, administrative), and fulfillmentRequirements

3. Lifecycle operation generation: Eight additional generators produce TMF921 lifecycle PATCH/report operations with realistic state transitions and KPI measurement simulations (including controlled KPI deviations for monitoring/report scenarios).

4. Adversarial sample generation: Handcrafted adversarial inputs across three categories, paired with appropriate structured error responses referencing 3GPP TS 22.261 for contradiction detection.

Phase 2: Natural Language Intent Generation (Template + LLM Augmentation)

1. Template core: 4–8 handcrafted templates per slice type generate the base NL intents. Templates use parameterized placeholders ({use_case}, {region}, {latency}, {dl_throughput}, etc.) filled from the sampled parameters. This ensures 100% factual consistency between the NL intent and the JSON configuration.

2. LLM augmentation: To increase NL surface area beyond template boundaries, an LLM paraphrasing step generates additional phrasings of the same intent while preserving the KPI parameter values. This leverages the asymmetry identified by Josifoski et al. (SynthIE, 2023): since the structured output is fixed and deterministic, diversity is introduced only in the natural language input, avoiding the difficulty of having an LLM produce fully schema-valid structured outputs from unconstrained text.

3. System prompt design: Each target layer has a specification-aware system prompt that instructs the model on the expected output format, referencing the specific standard version. For lifecycle operations, the system prompt encodes the expected state transition.

Phase 3: Validation and Quality Assurance

Every generated sample passes through an automated audit pipeline (audit_dataset.py, 268 lines) that enforces:

• JSON validity: Assistant response parses as valid JSON (100%)
• Schema compliance: Layer-specific structural validation (required fields, nesting, types)
• KPI sanity: Values fall within spec-grounded profiles per slice type
• Metadata-config consistency: Metadata columns match values inside the JSON configuration
• NL diversity: No excessive template repetition; message length distribution analysis
• Unique IDs: No duplicate content hashes across the entire dataset

4.3 Methodological Justification: Template-Based Core + LLM Augmentation

Property	Template-Based Core	LLM Augmentation Layer
Schema compliance	100% by construction	N/A (applied to NL only)
Reproducibility	Deterministic (seed=42)	Controlled via augmentation budget
KPI accuracy	Exact (parameterized)	N/A (parameters preserved)
NL diversity	Bounded (4–8 templates/type)	Extended surface area
Cost	Zero API calls	LLM inference for paraphrasing
Error risk	None (validated)	Constrained to NL phrasing only

This hybrid approach captures the best of both paradigms: the reliability and reproducibility of template-based generation for the structured outputs, with the linguistic diversity of LLM generation for the natural language inputs.

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5. Dataset Schema

5.1 Column Definitions

Column	Type	Description	Example
id	string	Unique content hash (12 hex chars)	"8483945f7428"
messages	list[dict]	ChatML messages: system + user + assistant	See §7
target_layer	string	Target specification layer	"tmf921", "a1_policy", "adversarial_ambiguous"
slice_type	string	3GPP network slice type	"eMBB", "URLLC", "mMTC", "V2X", "MPS", "HMTC", "N/A"
sst	int	3GPP Slice/Service Type (TS 23.501)	1 (eMBB), 2 (URLLC), 3 (mMTC), 4 (V2X), 5 (HMTC/MPS)
sd	string	3GPP Slice Differentiator	"000001" through "000004"
use_case	string	Specific deployment use case	"remote robotic surgery"
sector	string	Industry vertical	"healthcare", "manufacturing", "adversarial"
region	string	Geographic deployment area	"Hospital Campus", "N/A"
latency_ms	float	Target end-to-end latency (ms)	1.0
reliability_pct	float	Target service reliability (%)	99.9999
dl_throughput_mbps	float	Target downlink throughput (Mbps)	500.0
ul_throughput_mbps	float	Target uplink throughput (Mbps)	100.0
max_ues	int	Maximum connected devices	50
lifecycle_operation	string	Intent lifecycle operation type	"create", "modify", "terminate"

5.2 Message Format

Every sample contains exactly 3 messages in ChatML format, directly compatible with TRL's SFTTrainer, OpenAI fine-tuning API, and any chat-template-based training pipeline:

{
  "messages": [
    {"role": "system", "content": "[Layer-specific system prompt referencing the target specification]"},
    {"role": "user", "content": "[Natural language network intent with KPI parameters]"},
    {"role": "assistant", "content": "[Valid JSON configuration conforming to the target specification]"}
  ]
}

5.3 S-NSSAI Encoding

The SST and SD values follow the 3GPP TS 23.501 S-NSSAI encoding:

SST	Slice Type	SD Values	Notes
0	N/A	000000	Adversarial samples (no valid slice)
1	eMBB	000001, 000002, 000003	Enhanced Mobile Broadband
2	URLLC	000001, 000002, 000003	Ultra-Reliable Low-Latency Communications
3	mMTC	000001, 000002, 000003	Massive Machine-Type Communications
4	V2X	000001, 000002	Vehicle-to-Everything
5	HMTC	000001, 000002	High-Performance Machine-Type Communications
5	MPS	000003, 000004	Mission-Critical/Public Safety (distinct SD from HMTC)

Note on SST=3 terminology: 3GPP TS 23.501 Table 5.15.2.2-1 uses "MIoT" (Massive IoT) as the official abbreviation for SST=3. This dataset uses the ITU-R/industry term "mMTC" (massive Machine-Type Communications), which is semantically equivalent.

Note on MPS encoding: MPS does not have a standardized SST in 3GPP; in practice operators use SST=1 or SST=2 with operator-specific SD values. In this dataset, MPS shares SST=5 with HMTC but is differentiated by SD values (000003, 000004 vs. 000001, 000002). This is a dataset design choice, not a normative mapping.

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6. Distribution Statistics

6.1 Target Layer Distribution

Target Layer	Train	Test	Total	%
tmf921	10,036	640	10,676	25.5%
intent_3gpp	7,257	441	7,698	18.4%
camara	7,046	401	7,447	17.8%
a1_policy	5,167	312	5,479	13.1%
o1_nrm	4,116	244	4,360	10.4%
etsi_zsm	3,979	285	4,264	10.2%
tmf921_lifecycle_* (8 ops)	1,552	173	1,725	4.1%
adversarial_* (3 types)	141	25	166	0.4%

6.2 Slice Type Distribution

Slice Type	Count	%	SST	3GPP Specification Source
URLLC	11,580	29.5%	2	TS 22.261 §7.1, TS 22.104
eMBB	11,415	29.1%	1	TS 22.261 §7.1
mMTC	4,663	11.9%	3	TS 22.261 §7.2
V2X	4,631	11.8%	4	TS 22.186
MPS	3,941	10.0%	5	TS 23.501 (SST=5, distinct SD)
HMTC	2,923	7.4%	5	TS 23.501 (Rel-17 extension)
N/A	141	0.4%	0	Adversarial (no valid slice)

6.3 Sector and Use Case Coverage

18 industry sectors with near-uniform distribution (~5.4–5.8% each): Aviation, Automotive, Defense, Maritime, Smart City, Media, Mining, Agriculture, Construction, Logistics, Public Safety, Finance, Telecom, Healthcare, Education, Manufacturing, Energy, Retail — plus an adversarial category (0.4%).

147 unique use cases spanning the full range of 5G/6G vertical applications, from safety-critical (remote robotic surgery, runway FOD detection, blast area safety monitoring) to high-throughput (live 8K UHD broadcast, cloud gaming edge delivery) to massive IoT (soil moisture sensor mesh, livestock tracking network).

55 geographic regions with real-world coordinates (used in TMF921 geographic contexts and CAMARA area-of-service definitions), spanning 6 continents.

6.4 KPI Parameter Ranges

All KPI values are sampled from 3GPP specification-grounded profiles (URLLC latency lower bound 0.5 ms derived from 3GPP TR 38.913 RAN study; use-case-specific targets from TS 22.261 and TS 22.104; mMTC density targets from TR 38.913):

Slice Type	Latency (ms)	Reliability (%)	DL Throughput (Mbps)	UL Throughput (Mbps)	Max UEs
eMBB	10–100	99.0–99.99	50–1,000	10–200	50–50,000
URLLC	0.5–10	99.999–99.9999	10–500	5–200	5–5,000
mMTC	10–1,000	90.0–99.99	0.1–50	0.05–25	1,000–1,000,000
V2X	1–20	99.99–99.9999	10–300	5–150	10–10,000
MPS	1–30	99.999–99.9999	10–200	5–100	5–5,000
HMTC	0.1–5	99.9999–99.99999	100–2,000	50–1,000	100–100,000

Observed statistics (train split):

• Latency: min=0.0, max=1,000.0, mean=55.85, median=15.0 ms (18 unique values)
• Reliability: min=0.0, max=99.99999, mean=99.16, median≈100% (11 unique values)
• DL Throughput: min=0.0, max=2,000.0, mean=282.72, median=150.0 Mbps (17 unique values)
• UL Throughput: min=0.0, max=1,000.0, mean=89.45, median=75.0 Mbps (14 unique values)
• Max UEs: min=0, max=1,000,000, mean=32,096, median=1,000 (13 unique values)

6.5 Lifecycle Operation Distribution

Operation	Count	%	Description
create	37,742	96.1%	Initial intent submission and configuration
scale	208	0.5%	Capacity/performance scaling
monitor	204	0.5%	KPI measurement and compliance reporting
modify	204	0.5%	Target KPI updates
resume	200	0.5%	Reactivation from suspended state
activate	192	0.5%	Transition to active state
suspend	190	0.5%	Temporary halt with configuration preservation
terminate	185	0.5%	Resource deallocation and cleanup
report	169	0.4%	Comprehensive assurance analytics

6.6 Train/Test Split

• Train: 39,294 samples (94.0%)
• Test: 2,521 samples (6.0%)
• Stratified by target layer to maintain proportional representation in both splits

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7. Target Layer Output Examples

7.1 TMF921 Intent Management API v4

System prompt: "You are a TM Forum TMF921-compliant Intent Management system. Given a natural language network intent, output a valid TMF921 Intent Management API v4 JSON object..."

User: "A 6G slice for cross-dock coordination in Healthcare Park must be set up with sub-5 ms latency, 200 Mbps DL, 500 Mbps UL, and the capability to support 1000 high-density endpoints."

{
  "id": "intent-043067302317",
  "href": "/tmf-api/intentManagement/v4/intent/intent-043067302317",
  "name": "HMTC Slice - Cross-Dock Coordination",
  "description": "Intent for cross-dock coordination at Healthcare Park with HMTC service level",
  "lifecycleState": "Active",
  "priority": 5,
  "intentExpression": {
    "@type": "IntentExpression",
    "intentExpectation": [{
      "@type": "DeliveryExpectation",
      "expectationTargets": [
        {"@type": "ExpectationTarget", "targetName": "downlinkThroughput", "targetCondition": "IS_GREATER_THAN_OR_EQUAL_TO", "targetValueRange": 200, "unit": "Mbps"},
        {"@type": "ExpectationTarget", "targetName": "uplinkThroughput", "targetCondition": "IS_GREATER_THAN_OR_EQUAL_TO", "targetValueRange": 500, "unit": "Mbps"},
        {"@type": "ExpectationTarget", "targetName": "latency", "targetCondition": "IS_LESS_THAN_OR_EQUAL_TO", "targetValueRange": 5, "unit": "ms"},
        {"@type": "ExpectationTarget", "targetName": "reliability", "targetCondition": "IS_GREATER_THAN_OR_EQUAL_TO", "targetValueRange": 99.9999, "unit": "percent"},
        {"@type": "ExpectationTarget", "targetName": "maxNumberOfUEs", "targetCondition": "IS_LESS_THAN_OR_EQUAL_TO", "targetValueRange": 1000, "unit": "UEs"}
      ],
      "expectationContexts": [
        {"@type": "GeographicContext", "contextAttribute": "serviceArea", "contextCondition": "IS_IN", "contextValueRange": {"name": "Healthcare Park", "center": {"latitude": 37.7749, "longitude": -122.4194}, "radius": 500}},
        {"@type": "TemporalContext", "contextAttribute": "validityPeriod"}
      ]
    }]
  },
  "relatedParty": [{"role": "IntentOwner", "name": "Logistics Operations", "@type": "RelatedParty"}],
  "@type": "Intent",
  "@baseType": "Entity",
  "@schemaLocation": "/tmf-api/intentManagement/v4/schema/Intent.schema.json"
}

7.2 3GPP TS 28.312 Intent

User: "Low-latency slice request for wearable health device network in Hospital Campus: Max latency 3 ms, 20 endpoints supported, 300/10 Mbps DL/UL bandwidth."

{
  "intent": {
    "id": "intent-URLLC-2f151a",
    "name": "Wearable Health Device Network URLLC Intent",
    "lifecycleStatus": "active",
    "intentExpectation": [{
      "expectationVerb": "DELIVER",
      "expectationObject": {"objectType": "NW_SLICE", "objectInstance": {"sliceType": "URLLC", "sNSSAI": {"sst": 2, "sd": "000001"}}},
      "expectationTargets": [
        {"targetName": "downlinkThroughput", "targetCondition": "IS_GREATER_THAN", "targetValueRange": 300, "unit": "Mbps"},
        {"targetName": "latency", "targetCondition": "IS_LESS_THAN", "targetValueRange": 3, "unit": "ms"},
        {"targetName": "reliability", "targetCondition": "IS_GREATER_THAN", "targetValueRange": 99.999, "unit": "percent"},
        {"targetName": "maxNumberOfUEs", "targetCondition": "IS_LESS_THAN_OR_EQUAL_TO", "targetValueRange": 20, "unit": "UEs"}
      ]
    }],
    "intentPriority": 2
  }
}

7.3 CAMARA NetworkSliceBooking

User: "For the cross-dock coordination project in Logistics Hub Terminal, please establish a network slice with a latency cap of 7 ms, throughput rates of 150 Mbps DL and 100 Mbps UL, and a user limit of 500 devices."

{
  "networkSliceBooking": {
    "sliceType": "URLLC",
    "sliceProfile": {
      "latencyTarget": 7,
      "reliability": 99.999,
      "throughput": {"downlink": 150, "uplink": 100},
      "maxNumberOfUEs": 500
    },
    "areaOfService": {"region": "Logistics Hub Terminal", "center": {"latitude": 51.4416, "longitude": 5.4697}, "radius": 500, "cells": ["cell-032", "cell-092", "cell-045"]},
    "duration": {"startTime": "2025-11-01T08:00:00Z", "endTime": "2025-11-01T20:00:00Z"}
  }
}

7.4 O-RAN A1 Policy

User: "Low-latency slice request for wearable health device network in Hospital Campus..."

{
  "a1Policy": {
    "policyId": "a1-pol-urllc-2f151a",
    "policyTypeId": "QoSTarget",
    "scope": {"groupId": "slice-urllc-f1d542", "cellId": "cell-087", "sNSSAI": {"sst": 2, "sd": "000003"}},
    "qosObjectives": {"gfbr": 300, "mfbr": 360.0, "priorityLevel": 2, "5qi": 85, "pdb": 5, "per": 1e-05},
    "resourceAllocation": {"prbQuota": {"min": 15, "max": 43}, "schedulerWeight": 0.55, "maxUEs": 20},
    "enforcementAction": "APPLY",
    "enforcementScope": "RAN_SLICE"
  }
}

7.5 3GPP TS 28.541 O1 NRM Configuration

User: "We're initiating solar panel IoT management in Medical Center District and will need a network slice configured with 1000 Mbps DL, 25 Mbps UL, latency under 30 ms, and support for 5000 devices."

{
  "ManagedElement": {
    "id": "gNB-004",
    "GNBDUFunction": {
      "id": "DU-046",
      "gNBId": 4,
      "gNBIdLength": 22,
      "NRCellDU": {
        "id": "NRCellDU-046-005",
        "cellLocalId": 5,
        "operationalState": "ENABLED",
        "administrativeState": "UNLOCKED",
        "nrFrequencyRef": "NRFrequency=n258",
        "arfcnDL": 2018614,
        "bSChannelBwDL": 400,
        "nrPCI": 52,
        "nrTAC": 43141,
        "rrmPolicyMemberList": [{
          "snssai": {"sst": 1, "sd": "000003"},
          "rrmPolicyMinRatio": 0.51,
          "rrmPolicyMaxRatio": 0.73,
          "rrmPolicyDedicatedRatio": 0.62
        }]
      }
    }
  }
}

7.6 ETSI ZSM 009-1 Intent

{
  "zsmIntent": {
    "intentId": "zsm-intent-a1b2c3d4e5f6",
    "intentType": "NetworkSliceIntent",
    "intentOwner": {"ownerId": "owner-abc123", "ownerType": "ManagementFunction", "ownerDomain": "healthcare_management"},
    "intentExpression": {
      "objectives": [{"objectiveId": "obj-abc123", "objectiveType": "QoSObjective", "kpiTargets": [
        {"kpi": "endToEndLatency", "operator": "lessThanOrEqual", "value": 1, "unit": "ms"},
        {"kpi": "serviceReliability", "operator": "greaterThanOrEqual", "value": 99.9999, "unit": "percent"},
        {"kpi": "dlThroughput", "operator": "greaterThanOrEqual", "value": 100, "unit": "Mbps"}
      ]}],
      "constraints": [{"constraintType": "ResourceConstraint", "parameter": "sliceServiceType", "value": "URLLC"}],
      "context": {
        "temporal": {"startTime": "2025-03-14T08:00:00Z", "endTime": "2025-03-14T20:00:00Z"},
        "spatial": {"areaName": "Hospital Campus", "coordinates": {"latitude": 40.6301, "longitude": 22.9521}, "radius": 500},
        "administrative": {"tenantId": "tenant-abc123", "serviceDomain": "healthcare", "useCase": "remote robotic surgery"}
      }
    },
    "fulfillmentRequirements": {"fulfillmentStrategy": "Guaranteed", "escalationPolicy": "AutoRemediate", "assuranceInterval": 60},
    "managementDomainMapping": {"domains": ["RAN", "Transport", "Core"], "orchestrationLevel": "EndToEnd"}
  }
}

7.7 TMF921 Lifecycle Operations

Monitor — Returns fulfillment reports with measured vs. target KPIs and compliance status:

{
  "intentFulfillmentReport": {
    "intentId": "intent-bb9665757b5a",
    "reportTimestamp": "2026-04-24T09:11:31Z",
    "fulfillmentStatus": "Degraded",
    "kpiMeasurements": [
      {"kpi": "latency", "target": 0.1, "measured": 0.1, "unit": "ms", "compliant": true},
      {"kpi": "dlThroughput", "target": 100, "measured": 109.2, "unit": "Mbps", "compliant": true},
      {"kpi": "reliability", "target": 99.99999, "measured": 99.996607, "unit": "percent", "compliant": false}
    ],
    "overallSlaCompliance": false,
    "@type": "IntentFulfillmentReport"
  }
}

Scale — Capacity/performance updates with previous and new values:

{
  "intentPatch": {
    "id": "intent-4d7de07e49d4",
    "lifecycleState": "Active",
    "intentExpression": {
      "scaledTargets": [
        {"targetName": "downlinkThroughput", "previousValue": 5, "newValue": 6, "unit": "Mbps"},
        {"targetName": "maxNumberOfUEs", "previousValue": 10000, "newValue": 29520, "unit": "UEs"}
      ],
      "scalingType": "VerticalAndHorizontal",
      "scalingTrigger": "UserRequested"
    },
    "@type": "IntentUpdate"
  }
}

Terminate — Resource deallocation:

{
  "intentPatch": {
    "id": "intent-fb29e2a99304",
    "lifecycleState": "Terminated",
    "fulfillmentInfo": {
      "fulfillmentStatus": "Completed",
      "terminationDate": "2026-04-24T09:11:31Z",
      "terminationReason": "UserRequested",
      "resourceDeallocated": true
    },
    "@type": "IntentUpdate"
  }
}

7.8 Adversarial Samples

Ambiguous ("Can you create a slice for educational purposes?"):

{
  "status": "CLARIFICATION_REQUIRED",
  "message": "Intent too vague. Please specify: sliceType, latencyTarget, reliabilityTarget, dlThroughput, ulThroughput, maxUEs, serviceArea.",
  "suggestedAction": "Reformulate with specific KPI targets."
}

Contradictory ("mMTC slice with sub-1ms latency for 1M devices at 10 Gbps each"):

{
  "status": "INTENT_VALIDATION_FAILED",
  "message": "Intent contains contradictory or impossible requirements per 3GPP TS 22.261.",
  "suggestedAction": "Review conflicting KPIs."
}

Out-of-scope ("Request for a list of local volunteer opportunities"):

{
  "status": "OUT_OF_SCOPE",
  "message": "Request outside scope. This system handles 5G/6G network slice intent translation only.",
  "supportedOperations": ["slice creation", "lifecycle management", "SLA assurance"]
}

---

8. Quality Assurance and Validation

8.1 Automated Audit Pipeline

The audit suite performs the following checks on every sample in both train and test splits:

Check	Train	Test	Methodology
Schema completeness (all 15 columns)	100%	100%	Required key presence
JSON validity (assistant parses)	100%	100%	json.loads()
Unique content IDs	100%	100%	Set cardinality
Layer-specific schema compliance	100%	100%	Per-layer assertion suite (see §8.2)
KPI within spec profile	100%	100%	Bounds check vs. 3GPP profiles
Metadata-config consistency	100%	100%	Cross-check metadata columns vs. JSON values

8.2 Layer-Specific Schema Validation

Each target layer is validated against its specification's structural requirements:

• TMF921: @type=Intent, intentExpression with ≥1 IntentExpectation, ≥4 expectationTargets (DL, UL, latency, reliability), ≥2 expectationContexts (geographic, temporal), relatedParty present
• 3GPP TS 28.312: intent.intentExpectation with objectType=NW_SLICE, sNSSAI encoding, ≥4 expectation targets
• CAMARA: networkSliceBooking.sliceProfile with latencyTarget, reliability, throughput.downlink/uplink, areaOfService, duration
• A1 Policy: qosObjectives with 5qi and pdb, prbQuota.min/max, scope.sNSSAI
• O1 NRM: ManagedElement.GNBDUFunction.NRCellDU with cellLocalId, arfcnDL, bSChannelBwDL, rrmPolicyMemberList with snssai and ratio fields
• ETSI ZSM: zsmIntent.intentExpression with objectives, constraints, context (temporal + spatial + administrative), fulfillmentRequirements, managementDomainMapping
• Lifecycle: Non-empty response with valid lifecycle state transition
• Adversarial: Error status field present (CLARIFICATION_REQUIRED, INTENT_VALIDATION_FAILED, or OUT_OF_SCOPE)

---

9. Intended Uses and Training Recipes

9.1 Primary Use Case

Fine-tuning open-weight LLMs (7B–70B parameters) to serve as intent translation engines in 5G/6G network management systems, converting free-form operator intents into standards-compliant JSON configurations.

9.2 Quick Start

from datasets import load_dataset

ds = load_dataset("nraptisss/TMF921-intent-to-config-augmented")

# Filter by layer, slice type, or sector
tmf921 = ds["train"].filter(lambda x: x["target_layer"] == "tmf921")
urllc_health = ds["train"].filter(lambda x: x["slice_type"] == "URLLC" and x["sector"] == "healthcare")

# Inspect a sample
import json
s = ds["train"][0]
print(s["messages"][1]["content"])                    # NL intent
config = json.loads(s["messages"][2]["content"])       # Parsed config

9.3 QLoRA SFT Training (Recommended)

from trl import SFTTrainer, SFTConfig
from peft import LoraConfig
from datasets import load_dataset

ds = load_dataset("nraptisss/TMF921-intent-to-config-augmented")

peft_config = LoraConfig(
    r=32, lora_alpha=64,
    target_modules="all-linear",
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

trainer = SFTTrainer(
    model="Qwen/Qwen3-8B",
    train_dataset=ds["train"],
    eval_dataset=ds["test"],
    peft_config=peft_config,
    args=SFTConfig(
        output_dir="./tmf921-model",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=8,
        learning_rate=1e-4,
        lr_scheduler_type="cosine",
        warmup_ratio=0.1,
        max_seq_length=4096,
        gradient_checkpointing=True,
        bf16=True,
        logging_steps=10,
        eval_strategy="epoch",
        save_strategy="epoch",
        push_to_hub=True,
        hub_model_id="your-username/tmf921-intent-model",
    )
)
trainer.train()

9.4 Training Strategies

Strategy	Description	When to Use
Full SFT	Train on all samples jointly	General-purpose intent handler
Layer-specialist	Train separate models per target layer	Maximum per-layer accuracy
Curriculum learning	Stage 1: CAMARA → Stage 2: 3GPP + A1 → Stage 3: TMF921 + ZSM + O1 → Stage 4: Lifecycle	Complex deployments with progressive difficulty
Sector-specialist	Fine-tune on specific sector subsets	Domain-specific deployments (healthcare, manufacturing)

9.5 Pre-Built Training Pipeline

A complete training and evaluation pipeline is available at nraptisss/intent-translation-training, including train.py, evaluate.py, and run.sh for one-command execution. An additional pipeline optimized for Kaggle T4x2 GPUs is available at nraptisss/telecom-intent-pipeline.

---

10. Evaluation Protocol

Metric	Description	How to Compute
JSON Validity Rate	% of outputs that parse as valid JSON	json.loads() on assistant output
Schema Compliance	% with all required fields per target layer	Per-layer assertion suite (§8.2)
KPI Accuracy (MAE)	Mean absolute error of extracted KPI values vs. ground truth metadata	Extract numerical fields, compare to metadata columns
Slice Type Accuracy	Correct eMBB/URLLC/mMTC/V2X/MPS/HMTC in output	String match on slice type field
Layer Routing Accuracy	Correct specification format for the system prompt	Root key structure check
Lifecycle State Validity	Valid state transitions per TMF921 state machine	State machine compliance check
Adversarial Rejection Rate	% of adversarial inputs correctly rejected	Check for error status in output

10.1 Important Note on Evaluation Scope

When models are fine-tuned on this dataset and evaluated on the held-out test split, the test set is drawn from the same generation pipeline as the training set (same templates, same parameter distributions, same JSON generators). Consequently, high scores on this test split measure in-distribution schema compliance — the model's ability to produce correctly formatted JSON for the template-bounded input distribution. This is a necessary but not sufficient condition for real-world deployment.

Out-of-distribution generalization — the ability to handle free-form, ambiguous, or underspecified operator intents not seen in training — requires separate evaluation on human-authored intents, such as the ORION 100-intent benchmark or operator-collected intent logs. Users should treat in-distribution results as a measure of format learning fidelity, and plan additional OOD evaluation before deployment.

---

11. Known Limitations

Limitation	Impact	Mitigation Path
Synthetic data	Real operator intents are more ambiguous and context-dependent	Use as pre-training; fine-tune on real operator data
Single-turn only	Real lifecycle management requires multi-turn chains	Extend with conversational chains
No cross-layer consistency	Same scenario not guaranteed across all 6 layers simultaneously	Generate matched cross-layer sets
Bounded NL diversity	Template core limits linguistic variety	Continue LLM augmentation with diverse providers
English only	Not usable for multilingual intent handling	Translate templates to additional languages
HMTC is speculative	SST=5 for HMTC based on Rel-17 extensions	Update when 3GPP standardizes 6G slice types
MPS SST encoding	Using SST=5 with distinct SD is a design choice	Document clearly; retrain with operator mappings
Sparse adversarial samples (0.4%)	Models may underlearn rejection	Expand to 5–10% of dataset
No real network validation	Configs are structurally valid but untested	Connect to srsRAN/Open5GS/ns-3

---

12. Related Work

12.1 Papers

Paper	Year	Key Contribution	Relevance
ORION	2025	100% policy generation with frontier LLMs on CAMARA schema	Validates task; shows open models need fine-tuning
TelecomGPT	2024	3-stage domain adaptation, 71.2% on TeleQnA	Training methodology reference
ORANSight-2.0	2025	RAG-based spec data generation (RANSTRUCT)	Alternative generation approach
TSLAM-Mini	2025	100K telecom instruction set, QLoRA to loss=0.15	Comparable scale and training recipe
NEFMind	2025	98–100% API accuracy with QLoRA on Phi-2	Small model + small data effectiveness
SynthIE	2023	Exploiting task asymmetry for synthetic structured-output data	Methodological foundation for NL augmentation
SPEC5G	2023	3.5M sentences from 13K 3GPP specs	Establishes telecom NLP data scarcity
Understanding Telecom Language	2023	Domain-specific fine-tuning for telecom NLP	Motivates domain-specific SFT
Telecom FMs Survey	2024	IBN as primary telecom foundation model task	Validates dataset target application
BINS	2025	Business intent ↔ network slicing correlation dataset (Nature Scientific Data)	Peer-reviewed tabular dataset mapping business intents to slice SLA parameters; different format (tabular correlation, not NL→JSON SFT pairs)

12.2 Standards

Standard	Organization	Title
TMF921 v4/v5	TM Forum	Intent Management API REST Specification
TS 28.312 v18.8.0	3GPP	Intent-Driven Management (Rel-18)
GS ZSM 009-1	ETSI	Zero-touch Service Management — Closed-Loop Automation
GS ZSM 016	ETSI	ZSM Conflict Handling
NetworkSliceBooking API	CAMARA/GSMA	Network Slice Booking Specification
A1 Interface v04.00	O-RAN Alliance	A1 Policy Framework
TS 28.541 v18.7.0	3GPP	NR Network Resource Model
TS 23.501	3GPP	System Architecture for 5G (S-NSSAI, 5QI)
TS 22.261	3GPP	Service Requirements for 5G
TS 22.104	3GPP	Service Requirements for Cyber-Physical Control
TS 22.186	3GPP	V2X Communication Requirements
TS 38.104	3GPP	NR Base Station Radio Transmission and Reception
RFC 9315	IETF	Intent-Based Networking — Concepts and Definitions

12.3 Predecessor Datasets

Dataset	Samples	Layers	Key Difference
telecom-intent-config-sft-10k	10,000	4	First generation; no TMF921, no lifecycle
TMF921-intent-to-config-25k	25,000	6 + lifecycle	Template-only NL; no adversarial samples
This dataset	41,815	6 + lifecycle + adversarial	LLM-augmented NL; adversarial robustness

---

13. License and Citation

License: Apache 2.0

@dataset{tmf921_intent_config_augmented_2025,
  title     = {TMF921 Intent-to-Configuration Augmented Dataset for 5G/6G
               Network Management},
  author    = {Raptis, Nikolaos},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/nraptisss/TMF921-intent-to-config-augmented},
  note      = {41,815 NL intent to spec-compliant network configuration pairs
               grounded in TMF921 v4, 3GPP TS 28.312 v18.8.0, ETSI GS ZSM
               009-1, CAMARA NetworkSliceBooking, O-RAN A1, and 3GPP TS 28.541,
               with lifecycle operations and adversarial robustness samples}
}