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Hyperparameter tuning skill for TTC training using Optuna. Use when optimizing learning rate, batch size, LoRA rank, and other training parameters.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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TTC-aware training skill for Qwen models. Use when training models with Test-Time Compute awareness, including CPT → SFT → RLVR → TTC pipeline stages. Supports Kaggle and HF Jobs backends.
| name | ttc-hparam |
| description | Hyperparameter tuning skill for TTC training using Optuna. Use when optimizing learning rate, batch size, LoRA rank, and other training parameters. |
| compatibility | Requires Optuna installed and compute backend |
Automated hyperparameter search for TTC training pipelines using Optuna.
pip install optuna optuna-integration
| Parameter | Range | Default |
|---|---|---|
learning_rate | 1e-6 to 1e-3 | 2e-5 |
batch_size | 1, 2, 4, 8, 16 | 4 |
lora_r | 4, 8, 16, 32, 64 | 16 |
lora_alpha | 8, 16, 32 | 32 |
warmup_ratio | 0.0 to 0.2 | 0.1 |
weight_decay | 0.0 to 0.1 | 0.01 |
python scripts/optuna_tune.py \
--model Qwen/Qwen3.5-0.8B \
--dataset path/to/data \
--n-trials 20 \
--backend kaggle \
--metric eval_loss
python scripts/optuna_tune.py \
--model Qwen/Qwen3.5-0.8B \
--dataset path/to/data \
--n-trials 100 \
--backend hf-jobs \
--metric eval_loss \
--pruner median \
--sampler tpe
import optuna
def objective(trial):
lr = trial.suggest_float("learning_rate", 1e-6, 1e-3, log=True)
batch_size = trial.suggest_categorical("batch_size", [1, 2, 4, 8])
lora_r = trial.suggest_categorical("lora_r", [4, 8, 16, 32])
# Run training with these params
loss = train_and_evaluate(lr, batch_size, lora_r)
return loss
After tuning:
optuna_study.db - SQLite database with all trialsbest_params.json - Optimal hyperparametersoptuna_dashboard.html - Visualization reportUser: Tune hyperparameters for my SFT training on Qwen 0.8B
Agent will:
1. Define search space
2. Launch Optuna study
3. Run trials on backend
4. Report best parameters
5. Save study results