# Training Progress

Last updated: 2026-06-27

## Current State

- `training/` contains the Stage 0 / Stage 1 recipe layer plus a tested data-engineering layer.
- `training/scripts/run_sft.py` and `training/scripts/merge_lora.py` provide runnable LoRA/QLoRA training and merge entrypoints.
- `training/scripts/check_training_gates.py` blocks training until the CyberGym baseline and processed JSONL data exist.
- **Data engineering (new):** `datasets.yaml` manifest + `hf_download.py` (probe/download) +
  `sft_adapters.py` (per-source schema adapters) + `build_sft_dataset.py` (adapt → route `<think>` →
  dedup → data card) + `synthesize_think.py` (rejection-sampling reasoning backfill).
  All schemas in the manifest were verified against the live HF datasets-server on 2026-06-27.
- **Eval + decontam (new):** `decontaminate.py` (13-gram + fuzzy collision gate),
  `build_eval_sets.py` (held-out vuln-detection + disjoint knowledge-MCQ sets), and
  `eval_endpoint.py` (reusable scorer for the base baseline and every checkpoint).
  `check_training_gates.py` now hard-requires the decontamination report.
- **Offline test green:** `python training/tests/test_data_pipeline.py` runs adapt → build →
  synthesize(`--mock`) → re-build → split, plus build-eval-sets → eval(`--mock`) → decontaminate
  (54 checks) with no GPU/heavy deps.
- `normalize_sft_jsonl.py` / `split_jsonl.py` remain as the simple ad-hoc data path.
- Local `vastai` CLI is installed and usable.
- Hugging Face API confirms `Qwen/Qwen3.6-27B` exists, is public, Apache-2.0, and reports `model_type=qwen3_5`.
- Docker Hub lists `pytorch/pytorch:2.12.1-cuda12.6-cudnn9-devel`, which is the default image in the Vast.ai create script.
- CyberGym pre-training baseline is now a hard gate before QLoRA or LoRA starts.

## Data findings (2026-06-27)

- PrimeVul / DiverseVul are raw `func`+`target` detection tables → handled by the new
  `detection_func_target` adapter (the generic normalizer rejected them outright).
- `cwe` fields arrive as stringified JSON lists; adapters parse them.
- MegaVul / PrimeVul / DiverseVul have `test` splits → reserved for held-out vuln-detection eval (`--eval`).
- `SecCoderX/Reasoning_Vulnerability_Detection_SFT_Cold_Start` and
  `Leopo1d/OpenVul_Rejection_Sampling_Reasoning_SFT` return **HTTP 401** (gated) → disabled in the
  manifest until `huggingface-cli login` + access approval.

## Next Commands

Safe offer search:

```bash
NUM_GPUS=1 STORAGE_GB=650 bash training/scripts/vastai_search_h100.sh
NUM_GPUS=2 STORAGE_GB=900 bash training/scripts/vastai_search_h100.sh
```

Cost-incurring create step, after choosing an offer:

```bash
CONFIRM_CREATE=1 bash training/scripts/vastai_create_stage1.sh <offer-id>
```

Remote bootstrap:

```bash
git clone https://github.com/ModelsLab/infosec.git
cd infosec
bash training/scripts/vastai_onstart.sh
python training/scripts/phase0_qwen_smoke.py --model Qwen/Qwen3.6-27B --mode config
python training/scripts/phase0_qwen_smoke.py --model Qwen/Qwen3.6-27B --mode load --device-map auto --dtype bfloat16
```

Mandatory benchmark before training:

```bash
sed -n '1,240p' training/recipes/pretraining_cybergym_baseline.md
```

Data prep recipe:

```bash
sed -n '1,240p' training/recipes/data_preparation.md
```

Gate checks:

```bash
python training/scripts/check_training_gates.py --config training/configs/pilot_qlora.yaml
python training/scripts/check_training_gates.py --config training/configs/stage1_lora_sft.yaml
```

Training entrypoints after gates pass:

```bash
torchrun --nproc_per_node=1 training/scripts/run_sft.py --config training/configs/pilot_qlora.yaml
torchrun --nproc_per_node=2 training/scripts/run_sft.py --config training/configs/stage1_lora_sft.yaml
python training/scripts/merge_lora.py --config training/configs/stage1_lora_sft.yaml
```

## Data prep next commands

```bash
python training/scripts/hf_download.py --probe-manifest training/configs/datasets.yaml   # verify schemas
python training/scripts/hf_download.py --all --profile pilot                              # cheap raw pull
python training/scripts/build_sft_dataset.py --profile pilot --mix-name pilot             # build + data card
python training/scripts/synthesize_think.py --input data/think_synthesis/pilot.to_synthesize.jsonl \
    --output data/think_synthesis/pilot.synthesized.jsonl --mock   # swap --mock for a real TEACHER_BASE_URL
python training/scripts/build_sft_dataset.py --profile pilot --mix-name pilot \
    --include-synthesized data/think_synthesis/pilot.synthesized.jsonl
python training/scripts/split_jsonl.py --input data/processed/pilot.ready.normalized.jsonl \
    --train data/processed/pilot_train.chatml.jsonl --val data/processed/pilot_val.chatml.jsonl --val-ratio 0.02
```

## Blockers Before Real Training

- DONE: Hugging Face download/export scripts and per-source schema adapters now exist and are tested.
- DONE: rejection-sampling `<think>` synthesis driver exists (validated with `--mock`; needs a real teacher endpoint).
- DONE: decontamination gate (13-gram + fuzzy; optional embedding) + held-out eval sets + reusable endpoint scorer.
- NEXT (needs GPU host): provision Vast.ai, Phase-0 load gate, serve base vLLM, capture base eval + CyberGym baseline, then train.
- Gated reasoning sets need `huggingface-cli login` + access before they can be enabled.
- Processed SFT JSONL is generated by the pipeline but not committed (gitignored); run the data-prep commands above.
- The Qwen3.6 hybrid architecture must pass the Phase 0 full-load test on the rented image before running SFT.
- CyberGym baseline data/server is not installed on a GPU/Docker host yet.
- Training must not start until the base-model CyberGym smoke and frozen level1 baseline reports exist.