Qwen3-8B TMF921 Intent-to-Configuration QLoRA Adapter

This is the primary stage-1 QLoRA adapter for multi-standard telecom intent-to-configuration translation.

Base model:

Qwen/Qwen3-8B

Training dataset:

nraptisss/TMF921-intent-to-config-research-sota

Training/evaluation repository:

nraptisss/tmf921-intent-training

Intended task

The model translates natural-language 5G/6G telecom/network-slicing intents into structured JSON configuration-like outputs across target families including:

TMF921 intent objects,
3GPP intent-style objects,
ETSI ZSM intent-style objects,
CAMARA network-slice booking-style objects,
O-RAN A1 policy-style objects,
O1 NRM-style objects,
TMF921 lifecycle operations,
adversarial/rejection responses.

This is a research baseline, not a production-certified network-management system.

Training recipe

Item	Value
Base model	`Qwen/Qwen3-8B`
Method	QLoRA SFT
Quantization	4-bit NF4 + double quantization
LoRA target modules	`all-linear`
LoRA rank	64
LoRA alpha	16
LoRA dropout	0.05
Max length	2048
Loss	assistant-only SFT loss
Training split	`train_sota`
Learning rate	2e-4
Scheduler	constant
Optimizer	paged AdamW 32-bit
Hardware	NVIDIA RTX 6000 Ada 48/50GB
Framework	TRL `SFTTrainer` + PEFT

The dataset was audited with the Qwen3 chat template; all source rows fit within 2048 tokens.

Main evaluation results

Stage-1 raw metrics:

Split	JSON parse	Exact match	Field F1	KPI presence
`test_in_distribution`	1.0000	0.0227	0.6868	0.7973
`test_template_ood`	1.0000	0.0014	0.6790	0.8062
`test_use_case_ood`	0.9998	0.0122	0.6825	0.7883
`test_sector_ood`	1.0000	0.0166	0.6610	0.7733
`test_adversarial`	1.0000	0.9697	0.9697	1.0000

Stage-1 normalized metrics:

Split	JSON parse	Normalized field F1	Normalized key F1	Normalized exact
`test_in_distribution`	1.0000	0.7956	0.9811	0.0351
`test_template_ood`	1.0000	0.7865	0.9801	0.0177
`test_use_case_ood`	0.9998	0.7907	0.9805	0.0253
`test_sector_ood`	1.0000	0.7697	0.9818	0.0293
`test_adversarial`	1.0000	0.9697	1.0000	0.9697

Normalization removes volatile/generated fields such as IDs, hrefs, timestamps, schema links, descriptions, and generated identifiers before computing field/key metrics.

Layer-level findings

Strong layers:

tmf921: normalized field F1 around 0.93–0.94.
camara: normalized field F1 around 0.81–0.87.
intent_3gpp: normalized field F1 around 0.80–0.82.
etsi_zsm: normalized field F1 around 0.75–0.79.

Weak layers:

o1_nrm: normalized field F1 around 0.39–0.40.
a1_policy: normalized field F1 around 0.67–0.68.
tmf921_lifecycle_report: normalized field F1 around 0.15–0.18.
tmf921_lifecycle_monitor: normalized field F1 around 0.39–0.52.

A stage-2 weak-layer continuation experiment was performed but is not promoted because it did not materially improve O1/A1 and slightly reduced adversarial robustness. Stage 1 remains the primary model.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen3-8B"
adapter = "nraptisss/Qwen3-8B-TMF921-Intent-QLoRA-qwen3-8b-qlora-20260501-083834"

tokenizer = AutoTokenizer.from_pretrained(adapter, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter)
model.eval()

messages = [
    {"role": "system", "content": "You are a telecom intent-to-configuration assistant. Return valid JSON only."},
    {"role": "user", "content": "Create a URLLC slice for remote robotic surgery in Hospital Campus with 1 ms latency, 99.9999% reliability, 100 Mbps downlink, 50 Mbps uplink, and 20 UEs."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=1536, do_sample=False)
print(tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Limitations

This model is trained on synthetic research data, not real operator logs.
It is not certified against official TMF921/3GPP/ETSI/CAMARA/O-RAN schemas.
It should not be used to deploy real network configurations without expert review and validators.
O1 NRM and A1 policy value fidelity remain open challenges.
Raw exact match is low because many outputs contain volatile/generated fields.
Normalized metrics are a research proxy, not proof of production standards compliance.

Recommended citation

@model{raptis_qwen3_tmf921_qlora_2026,
  title = {Qwen3-8B TMF921 Intent-to-Configuration QLoRA Adapter},
  author = {Raptis, Nikolaos},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/nraptisss/Qwen3-8B-TMF921-Intent-QLoRA-qwen3-8b-qlora-20260501-083834}
}