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intent-translation-training
nraptisss/intent-translation-training
intent-translation-training is a machine learning model from nraptisss. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
The first open-source fine-tuning pipeline for translating natural language network intents into spec-compliant 5G/6G configurations across 6 telecom standards.
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Updated Apr 30, 2026
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At a glance
- Access
- Public
- Created
- Apr 27, 2026
- Updated
- Apr 30, 2026
- SHA
- a2801bbc
Model card
From the Hugging Face model README
TMF921 Intent-to-Configuration Translation — Training Pipeline
The first open-source fine-tuning pipeline for translating natural language network intents into spec-compliant 5G/6G configurations across 6 telecom standards.
What This Does
Fine-tunes an LLM (default: Qwen3-8B) via 4-bit QLoRA on the TMF921-intent-to-config-augmented dataset (41.8K samples) to translate natural language network intents into structured JSON configurations for:
| Target Standard | Description |
|---|---|
| TMF921 | TM Forum Intent Management API v4 |
| 3GPP TS 28.312 | Intent-Driven Management (Rel-18) |
| CAMARA | GSMA Open Gateway NetworkSliceBooking |
| ETSI ZSM | Zero-touch Service Management intents |
| O-RAN A1 Policy | Non-RT RIC A1 policy objects |
| 3GPP O1 NRM | O1 interface NR Network Resource Model |
Plus TMF921 lifecycle operations (activate, modify, suspend, resume, terminate, scale, monitor, report) and adversarial rejection (ambiguous, out-of-scope, contradictory intents).
Quick Start
1. Clone & Install
git clone https://huggingface.co/nraptisss/intent-translation-training
cd intent-translation-training
pip install -r requirements.txt
2. Train (defaults — RTX 6000 Ada 50GB)
python train.py
This will:
- Load Qwen3-8B in 4-bit NF4 quantization
- Apply QLoRA (r=32, alpha=64) to all linear layers
- Train for 3 epochs with cosine LR schedule
- Save checkpoints and best model to
./output/
3. Evaluate
python evaluate.py --adapter_path ./output --num_samples 200
4. One-command pipeline
chmod +x run.sh
./run.sh
Training Configuration
| Parameter | Default | Notes |
|---|---|---|
| Base model | Qwen/Qwen3-8B | Any HF causal LM works |
| Quantization | 4-bit NF4 + double quant | ~4.5 GB VRAM for weights |
| LoRA rank | 32 | target_modules="all-linear" |
| LoRA alpha | 64 | 2× rank |
| Batch size | 4 × 8 grad_accum = 32 effective | |
| Learning rate | 1e-4 | 10× base for LoRA |
| Epochs | 3 | ~3,700 steps total |
| Max length | 4096 tokens | Covers 99%+ of samples |
| Loss | assistant_only_loss=True | Train only on config outputs |
| Precision | bf16 | |
| Flash attention | Yes (flash_attention_2) | |
| Gradient checkpointing | Yes | Saves ~40% VRAM |
VRAM Estimate (RTX 6000 Ada 50GB)
| Component | VRAM |
|---|---|
| Model weights (4-bit) | ~4.5 GB |
| LoRA adapters | ~0.5 GB |
| Activations + gradients (checkpointed) | ~12 GB |
| Optimizer states (AdamW, LoRA params only) | ~1 GB |
| KV cache / batch | ~8 GB |
| Total estimated | ~26 GB |
Leaves ~24 GB headroom. You can increase batch_size to 8 if desired.
Customization
Different base model
python train.py --base_model Qwen/Qwen2.5-7B-Instruct
python train.py --base_model meta-llama/Llama-3.1-8B-Instruct
python train.py --base_model microsoft/phi-4
Push to Hugging Face Hub
export HF_TOKEN="hf_..."
python train.py --push_to_hub --hub_model_id your-username/model-name
Adjust hyperparameters
python train.py --lr 2e-4 --epochs 5 --lora_r 64 --lora_alpha 128 --batch_size 2 --grad_accum 16
No flash attention (older GPUs)
python train.py --no_flash_attn
Evaluation Metrics
The evaluation script measures:
| Metric | Description |
|---|---|
| JSON Validity Rate | % of outputs that are valid JSON |
| Structure Correctness | % with correct root keys per standard |
| KPI Field Accuracy | % containing correct latency, throughput, reliability, UE values |
| All KPIs Correct | % where ALL 5 KPI fields match |
| Adversarial Accuracy | % of bad intents correctly rejected |
Results are broken down per target layer (TMF921, 3GPP, CAMARA, etc.).
File Structure
├── train.py # QLoRA fine-tuning script
├── evaluate.py # Evaluation with per-layer metrics
├── run.sh # One-command train+eval pipeline
├── requirements.txt # Python dependencies
└── README.md # This file
Dataset
nraptisss/TMF921-intent-to-config-augmented — 41,815 samples:
- 39,153 standard intent→config pairs across 6 target layers
- 1,552 lifecycle operation samples (8 TMF921 operations)
- 141 adversarial samples (ambiguous, out-of-scope, contradictory)
- 18 sectors, 147 use cases, 55 regions
- 6 slice types: eMBB, URLLC, mMTC, V2X, MPS, HMTC
References
- ORION: Intent-Aware Orchestration in Open RAN
- TelecomGPT
- ORANSight-2.0
- NEFMind
- TMF921 Intent Management API v5.0
- 3GPP TS 28.312 Rel-18
License
Apache 2.0
- Created
- Apr 27, 2026
- Updated
- Apr 30, 2026