nraptisss
/

tmf921-intent-training

@@ -1,310 +0,0 @@
----
-license: apache-2.0
-tags:
-- qlora
-- sft
-- trl
-- peft
-- qwen3
-- tmf921
-- intent-based-networking
-- network-slicing
-- rtx-6000-ada
-- ml-intern
-base_model:
-- Qwen/Qwen3-8B
-datasets:
-- nraptisss/TMF921-intent-to-config-research-sota
----
-# TMF921 Intent-to-Config Training + Evaluation
-Training and evaluation repo for [`nraptisss/TMF921-intent-to-config-research-sota`](https://huggingface.co/datasets/nraptisss/TMF921-intent-to-config-research-sota) on a single **RTX 6000 Ada 48/50GB** server.
-The default recipe is **Qwen3-8B + QLoRA NF4 + TRL SFTTrainer + PEFT LoRA**.
-## Why this recipe
-- Dataset rows were audited with `Qwen/Qwen3-8B` chat-template tokenization.
-- Source max length: **1,316 tokens**, p99: **1,300**, so `max_length=2048` is safe.
-- QLoRA NF4 + double quant follows the QLoRA recipe for fitting large models on one 48GB-class GPU.
-- LoRA uses `target_modules="all-linear"`, recommended for QLoRA-style training.
-- `assistant_only_loss=True` trains only the JSON/config response tokens.
-- Evaluation is split by in-distribution and OOD splits; do not report only a single merged score.
-## Hardware target
-Recommended server:
-- GPU: NVIDIA RTX 6000 Ada, 48GB/50GB VRAM
-- RAM: 64GB+
-- Disk: 200GB+ free
-- CUDA-compatible PyTorch
-Default effective batch size:
-```text
-per_device_train_batch_size = 2
-gradient_accumulation_steps = 8
-effective batch size = 16
-max_length = 2048
-```
-If OOM occurs, preserve the effective batch size by changing:
-```yaml
-per_device_train_batch_size: 1
-gradient_accumulation_steps: 16
-```
-Do **not** reduce `max_length` unless you intentionally want a different training task.
-## Quick start with nohup, unique run dirs, and resumable checkpoints
-```bash
-git clone https://huggingface.co/nraptisss/tmf921-intent-training
-cd tmf921-intent-training
-python -m venv .venv
-source .venv/bin/activate
-python -m pip install -U pip
-bash scripts/install_rtx6000ada.sh
-python scripts/check_gpu.py
-export HF_TOKEN=hf_...
-export CUDA_VISIBLE_DEVICES=0
-export PYTHONPATH="$PWD/src"
-export TOKENIZERS_PARALLELISM=false
-# Optional Trackio dashboard
-# export TRACKIO_SPACE_ID=nraptisss/tmf921-trackio
-bash scripts/nohup_new_run.sh
-```
-The helper creates a fresh run directory every time:
-```text
-runs/qwen3-8b-qlora-YYYYMMDD-HHMMSS/
-  configs/config.yaml
-  logs/train.log
-  outputs/adapter/checkpoint-*/
-  eval/
-```
-Monitor:
-```bash
-RUN_DIR=runs/qwen3-8b-qlora-YYYYMMDD-HHMMSS
-bash scripts/status_run.sh "$RUN_DIR"
-tail -f "$RUN_DIR/logs/train.log"
-watch -n 2 nvidia-smi
-```
-Resume after crash/reboot:
-```bash
-cd tmf921-intent-training
-source .venv/bin/activate
-export HF_TOKEN=hf_...
-export CUDA_VISIBLE_DEVICES=0
-export PYTHONPATH="$PWD/src"
-bash scripts/nohup_resume.sh runs/qwen3-8b-qlora-YYYYMMDD-HHMMSS
-```
-Evaluate after training:
-```bash
-bash scripts/nohup_eval.sh runs/qwen3-8b-qlora-YYYYMMDD-HHMMSS
-```
-Manual training command, if you do not want nohup:
-```bash
-python scripts/train_qlora.py \
-  --config configs/rtx6000ada_qwen3_8b_qlora.yaml
-```
-## Optional Trackio monitoring
-The training script uses the native Transformers/TRL Trackio integration when `project` is set in the config.
-Set a Trackio Space if desired:
-```bash
-export TRACKIO_SPACE_ID=nraptisss/tmf921-trackio
-```
-Or edit:
-```yaml
-project: tmf921-intent-sft
-trackio_space_id: nraptisss/tmf921-trackio
-```
-The trainer logs plain-text loss lines with:
-```python
-disable_tqdm=True
-logging_strategy="steps"
-logging_first_step=True
-report_to="trackio"
-```
-A callback emits Trackio alerts for NaN/Inf loss, high gradient norm, and high eval loss.
-## Configs
-### Recommended
-[`configs/rtx6000ada_qwen3_8b_qlora.yaml`](configs/rtx6000ada_qwen3_8b_qlora.yaml)
-Key settings:
-```yaml
-model_name_or_path: Qwen/Qwen3-8B
-dataset_name: nraptisss/TMF921-intent-to-config-research-sota
-train_split: train_sota
-eval_split: validation
-max_length: 2048
-assistant_only_loss: true
-load_in_4bit: true
-lora_r: 64
-lora_alpha: 16
-lora_target_modules: all-linear
-learning_rate: 0.0002
-optim: paged_adamw_32bit
-bf16: true
-push_to_hub: true
-```
-### Experimental 14B
-[`configs/rtx6000ada_qwen3_14b_qlora_experimental.yaml`](configs/rtx6000ada_qwen3_14b_qlora_experimental.yaml)
-Use only after the 8B run, and expect tighter memory.
-## Evaluation
-After training adapters:
-```bash
-python scripts/evaluate_model.py \
-  --model Qwen/Qwen3-8B \
-  --adapter outputs/qwen3-8b-tmf921-qlora \
-  --dataset nraptisss/TMF921-intent-to-config-research-sota \
-  --output_dir outputs/qwen3-8b-tmf921-qlora/eval \
-  --load_in_4bit
-```
-Evaluated splits by default:
-- `test_in_distribution`
-- `test_template_ood`
-- `test_use_case_ood`
-- `test_sector_ood`
-- `test_adversarial`
-Metrics:
-- JSON parse rate
-- canonical JSON exact match
-- field precision / recall / F1
-- slice/SST diagnostic pass
-- KPI text-presence diagnostic pass
-- adversarial status pass
-- stratified metrics by `target_layer`, `slice_type`, and `lifecycle_operation`
-Outputs:
-```text
-outputs/.../eval/all_metrics.json
-outputs/.../eval/<split>/metrics.json
-outputs/.../eval/<split>/predictions.json
-```
-## Merge adapter for deployment
-```bash
-python scripts/merge_adapter.py \
-  --base_model Qwen/Qwen3-8B \
-  --adapter outputs/qwen3-8b-tmf921-qlora \
-  --output_dir outputs/qwen3-8b-tmf921-merged
-```
-Push merged model:
-```bash
-python scripts/merge_adapter.py \
-  --base_model Qwen/Qwen3-8B \
-  --adapter nraptisss/Qwen3-8B-TMF921-Intent-QLoRA-ResearchSOTA \
-  --output_dir outputs/merged \
-  --push_to_hub \
-  --hub_model_id nraptisss/Qwen3-8B-TMF921-Intent-Merged
-```
-## Scientific reporting protocol
-For research papers/reports, report at least:
-1. validation loss,
-2. `test_in_distribution` metrics,
-3. `test_template_ood` metrics,
-4. `test_use_case_ood` metrics,
-5. `test_sector_ood` metrics,
-6. `test_adversarial` metrics,
-7. per-target-layer field F1,
-8. JSON parse rate,
-9. exact-match rate,
-10. rare-class metrics for lifecycle operations and adversarial categories.
-Do **not** claim production standards compliance from JSON validity alone. Official TMF921/3GPP/ETSI/CAMARA/O-RAN validators are still needed for schema-level certification.
-## Files
-```text
-configs/
-  rtx6000ada_qwen3_8b_qlora.yaml
-  rtx6000ada_qwen3_14b_qlora_experimental.yaml
-scripts/
-  train_qlora.py
-  evaluate_model.py
-  merge_adapter.py
-  run_rtx6000ada.sh
-  nohup_new_run.sh
-  nohup_resume.sh
-  nohup_eval.sh
-  status_run.sh
-src/tmf921_train/
-  utils.py
-requirements.txt
-```
-## References
-- QLoRA: https://huggingface.co/papers/2305.14314
-- LoRA: https://huggingface.co/papers/2106.09685
-- TRL SFTTrainer docs: https://huggingface.co/docs/trl/sft_trainer
-- TRL PEFT integration: https://huggingface.co/docs/trl/peft_integration
-- Source dataset: https://huggingface.co/datasets/nraptisss/TMF921-intent-to-config-research-sota
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
-## Usage
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = 'nraptisss/tmf921-intent-training'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.