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4d20f09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | # 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](https://huggingface.co/datasets/nraptisss/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
```bash
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)
```bash
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
```bash
python evaluate.py --adapter_path ./output --num_samples 200
```
### 4. One-command pipeline
```bash
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
```bash
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
```bash
export HF_TOKEN="hf_..."
python train.py --push_to_hub --hub_model_id your-username/model-name
```
### Adjust hyperparameters
```bash
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)
```bash
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](https://huggingface.co/datasets/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](https://arxiv.org/abs/2603.03667)
- [TelecomGPT](https://arxiv.org/abs/2407.09424)
- [ORANSight-2.0](https://arxiv.org/abs/2503.05200)
- [NEFMind](https://arxiv.org/abs/2508.09240)
- [TMF921 Intent Management API v5.0](https://www.tmforum.org/resources/specification/tmf921-intent-management-api-rest-specification-r21-0-0/)
- [3GPP TS 28.312 Rel-18](https://www.3gpp.org/dynareport/28312.htm)
## License
Apache 2.0
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