haddock-development/ttc-pi-package/ttc-hparam

pip install optuna optuna-integration

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

User: 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