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nraptisss/
telecom-intent-pipeline

Name: telecom-intent-pipeline
Availability: InStock

2603.03667

2506.08551

2305.14314

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Telecom Intent-to-Config Pipeline

Complete, zero-cost pipeline for fine-tuning and evaluating LLMs that translate natural language network intents into 5G/6G standard-compliant configurations.

What's Inside

File	Purpose
`train.py`	QLoRA fine-tuning on your intent dataset (Kaggle T4x2 optimized)
`inference.py`	Generate configs from natural language intents (interactive or batch)
`merge_and_push.py`	Merge LoRA adapters + push merged model to Hugging Face Hub
`benchmark.py`	Evaluate on test set: JSON validity, schema compliance, semantic similarity
`kaggle_notebook.ipynb`	Ready-to-run Kaggle notebook (download scripts → train → test → benchmark)
`requirements.txt`	Python dependencies

Supported Datasets (Your Own)

nraptisss/TMF921-intent-to-config-augmented (~35K, multi-layer)
nraptisss/TMF921-intent-to-config-25k (~25K, multi-layer)
nraptisss/telecom-intent-config-sft-10k (~10K, multi-layer)

All datasets use ChatML messages format with system/user/assistant roles.

Supported Target Layers

Layer	Standard	Output Format
`tmf921`	TM Forum Intent Management API v5.0.0	JSON intent FVO
`intent_3gpp`	3GPP TS 28.312 Rel-18	JSON intent payload
`camara`	CAMARA NetworkSliceBooking	OpenAPI JSON
`etsi_zsm`	ETSI ZSM GS 009-1	JSON service profile
`a1_policy`	O-RAN WG2 A1 Interface	JSON policy
`o1_nrm`	3GPP TS 28.541 NR NRM	YANG/XML-style JSON

Quick Start: Kaggle (Free T4x2 GPU)

Option A — Notebook (easiest, 5 minutes setup)

Go to Kaggle → New Notebook
Add GPU T4 x2 accelerator (right panel)
Paste the contents of kaggle_notebook.ipynb into the first cell
Run all cells top to bottom

Option B — Terminal

bash

pip install -q transformers trl peft accelerate bitsandbytes datasets sentence-transformers huggingface-hub

wget https://huggingface.co/nraptisss/telecom-intent-pipeline/resolve/main/train.py
wget https://huggingface.co/nraptisss/telecom-intent-pipeline/resolve/main/inference.py
wget https://huggingface.co/nraptisss/telecom-intent-pipeline/resolve/main/merge_and_push.py
wget https://huggingface.co/nraptisss/telecom-intent-pipeline/resolve/main/benchmark.py

python train.py                    # 2-3 hours on T4x2
python inference.py --intent "Deploy URLLC slice for factory automation with 1ms latency"
python benchmark.py --max_samples 100
python merge_and_push.py           # pushes to your hub

Hyperparameters (T4x2 Optimized)

Parameter	Value	Rationale
Model	Qwen2.5-7B-Instruct	Fits 16GB VRAM, strong reasoning
Quantization	4-bit NF4	QLoRA, enables 7B on T4
LoRA rank	64	Balances capacity and memory
LoRA alpha	16	Standard α = r/4
Batch size	1 per GPU	Fits T4 VRAM
Gradient accumulation	4	Effective batch = 4
Learning rate	2e-4	10× base for LoRA (TRL recommendation)
Max length	512	Covers most intent→config pairs
Epochs	3	Sufficient for ~35K samples
Liger kernel	Disabled	Crashes on T4 with gradient checkpointing
FP16	True	T4 has no bf16 support

Note: liger-kernel is intentionally disabled by default (use_liger_kernel=False). It causes Triton crashes on T4 GPUs when combined with gradient checkpointing. Only enable it if training on A100 / L40 / H100.

Expected Results

After 3 epochs on TMF921-intent-to-config-augmented:

Training time: ~2–3 hours on Kaggle T4x2
JSON validity: >90% on test set
Schema compliance: >85% (key presence check)
Model size: ~14GB merged (FP16), ~200MB LoRA adapters

Customization

Use a different dataset

Edit train.py:

python

DATASET_NAME = "your-username/your-dataset"
DATASET_CONFIG = "default"
TRAIN_SPLIT = "train"

Use a different base model

Edit train.py:

MODEL_NAME = "meta-llama/Llama-3.1-8B-Instruct"

Change LoRA parameters (if OOM)

Edit train.py:

python

LORA_R = 32          # lower = less memory, less capacity
LORA_ALPHA = 8
MAX_LENGTH = 256     # shorter sequences

Push to your own hub

Edit train.py and merge_and_push.py:

HUB_MODEL_ID = "your-username/your-model-name"

Enable Liger kernel (A100/H100 only)

Edit train.py:

use_liger_kernel=True # ONLY on A100 / L40 / H100. WILL crash on T4.

And install: pip install liger-kernel>=0.5.0

Architecture Diagram

┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐
│  Natural Lang   │────▶│   LoRA LLM   │────▶│  JSON Config    │
│  Intent         │     │  (7B params) │     │  (TMF921/3GPP   │
│  "Deploy URLLC  │     │  fine-tuned  │     │  /CAMARA/etc)   │
│   slice..."     │     │  on telecom  │     │                 │
└─────────────────┘     └──────────────┘     └─────────────────┘
                              │
                              ▼
                        ┌──────────────┐
                        │ 4-bit NF4    │
                        │ QLoRA (r=64) │
                        └──────────────┘

Troubleshooting

Problem	Fix
`CUDA OOM` during training	Lower `LORA_R` to 32, reduce `MAX_LENGTH` to 256, or set `per_device_train_batch_size=1`
`liger-kernel` / Triton crash	Already disabled by default. Do NOT install or enable on T4.
`ValueError: Can't find adapter_config.json`	Training didn't finish or save. Check train.py output for errors.
JSON parsing errors in inference	Check `--temperature` (lower = more deterministic), or re-train with more epochs
T4 doesn't support bf16	Already handled: `fp16=True, bf16=False` in config
Slow training	Enable T4x2 (not T4x1) on Kaggle for ~2x speedup
`HFValidationError` on local paths	Use absolute paths or ensure adapter directory exists

Known Issues

Liger kernel on T4: liger_kernel Triton operators crash with gradient checkpointing on T4 GPUs. This is a known incompatibility. The fix is disabling liger (use_liger_kernel=False), which is the default.
Manual gradient checkpointing: Do not call model.gradient_checkpointing_enable() manually when using SFTTrainer. The trainer handles this automatically via gradient_checkpointing=True in SFTConfig. Manual enablement conflicts with PEFT + 4-bit quantization.

Citation

If you use this pipeline, cite:

bibtex

@misc{nraptisss2026telecom,
  title={Telecom Intent-to-Config Pipeline},
  author={Raptis, Nikos},
  year={2026},
  url={https://huggingface.co/nraptisss/telecom-intent-pipeline}
}

References

ORION: arXiv:2603.03667 — Intent-aware orchestration in O-RAN
DeepForm: arXiv:2506.08551 — Reasoning LLM for communication formulation
QLoRA: arXiv:2305.14314 — 4-bit quantization for fine-tuning
TRL: https://github.com/huggingface/trl

License

Apache-2.0