# TI Coding Agent Probe Skill

Use this skill when the user wants to run or modify the TI coding-agent SFT probe experiments based on the ModelScope dataset `eigentom/ti_coding_agent_training_probe_20260624`. Hugging Face remains available as a fallback backend.

## Repository Contract

- Work in `/ssd/workspace/yi/ti_coding_agent_probe` on B300 unless the user says otherwise.
- Use the B300 proxy for all network access:
  - `http_proxy=http://100.72.0.101:8888`
  - `https_proxy=http://100.72.0.101:8888`
  - `HF_ENDPOINT=https://hf-mirror.com`
- Do not install Python packages globally. Use the repository-local `.venv` created by `scripts/setup_env.sh`.
- `scripts/setup_env.sh` installs `uv` inside `.venv`, then installs SWIFT from the submodule using SWIFT's documented source-install path: `uv pip install -e third_party/modelscope-swift --torch-backend=auto`.
- Default setup adds training extras: `deepspeed<0.19`, `liger-kernel`, `tensorboard`, and `nvitop`. Disable with `INSTALL_TRAINING_EXTRAS=0`.
- Install SWIFT optional all-dependencies only when explicitly needed: `INSTALL_SWIFT_ALL=1 ./scripts/setup_env.sh`.
- Do not start GPU training before checking GPU occupancy with `nvidia-smi`.
- Do not commit or upload `data/`, `models/`, `outputs/`, `runs/`, or `logs/`.

## Key Commands

Initialize the repo and environment:

```bash
git submodule update --init --recursive
./scripts/setup_env.sh
```

Download the training/eval probe dataset from ModelScope:

```bash
export MODELSCOPE_API_TOKEN=<modelscope-api-key>
./scripts/download_dataset.py
```

The downloader writes:

- `data/raw/training_probe/{train,validation}.jsonl.gz`
- `data/raw/training_probe/{train,validation}.parquet`
- `data/processed/training_probe/{train,validation}.jsonl`
- `data/processed/training_probe/{train,validation}.parquet`

Use Hugging Face fallback only when requested:

```bash
export DATASET_BACKEND=huggingface
export HF_ENDPOINT=https://hf-mirror.com
export HF_DATASET_REPO_ID=<owner>/ti_coding_agent_training_probe_20260624
./scripts/download_dataset.py
```

Download base models:

```bash
./scripts/download_models.sh
```

Run the full ordered experiment:

```bash
./scripts/run_all_experiments.sh
```

Run only one stage:

```bash
./scripts/train_qwen35_9b_lora.sh
./scripts/train_qwen35_9b_full.sh
./scripts/train_qwen36_27b_lora.sh
./scripts/train_qwen36_27b_full.sh
```

Open TensorBoard:

```bash
./scripts/tensorboard.sh
```

## Training Semantics

The dataset uses SWIFT-style chat messages. `system`, `user`, and `tool` messages should remain masked with `loss=false`; only assistant trajectories should contribute to loss. This keeps scaffold prompts and tool outputs as conditioning context rather than targets to memorize.

The default experiment uses:

- 1 epoch
- LoRA rank 32 for LoRA runs
- bf16 full fine-tuning for full runs
- `max_length=262144`
- checkpoint save every 1000 steps
- validation every 1000 steps
- TensorBoard logging under `runs/`

Override model IDs or paths with:

```bash
export QWEN35_9B_MODEL_ID=<hf-id>
export QWEN36_27B_MODEL_ID=<hf-id>
export QWEN35_9B_MODEL_PATH=<local-path>
export QWEN36_27B_MODEL_PATH=<local-path>
```