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76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
"""Minimal training demo for advanced PEFT adapters in Ludwig.
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Run with: python train_example.py --adapter pissa
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"""
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from __future__ import annotations
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import argparse
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import logging
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import yaml
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from ludwig.api import LudwigModel
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ADAPTER_CONFIGS = {
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"lora": {"type": "lora", "r": 8, "alpha": 16},
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"pissa": {"type": "lora", "r": 8, "alpha": 16, "init_lora_weights": "pissa"},
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"corda": {"type": "lora", "r": 8, "alpha": 16, "init_lora_weights": "corda"},
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"rslora": {"type": "lora", "r": 8, "alpha": 16, "use_rslora": True},
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"dora": {"type": "lora", "r": 8, "alpha": 16, "use_dora": True},
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"tinylora": {"type": "tinylora", "r": 2, "u": 64},
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"oft": {"type": "oft", "oft_block_size": 32},
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"hra": {"type": "hra", "r": 8},
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"ln_tuning": {"type": "ln_tuning"},
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"vblora": {"type": "vblora", "r": 4, "num_vectors": 256, "vector_length": 768, "topk": 2},
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"adalora": {"type": "adalora", "r": 8, "target_r": 4, "init_r": 12, "total_step": 1000},
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"ia3": {"type": "ia3"},
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"vera": {"type": "vera", "r": 256},
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}
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BASE_CONFIG = """
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model_type: llm
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base_model: sshleifer/tiny-gpt2
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input_features:
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- name: text
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type: text
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output_features:
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- name: label
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type: text
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trainer:
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type: finetune
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learning_rate: 0.0001
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epochs: 1
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batch_size: 4
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"""
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def main():
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parser = argparse.ArgumentParser(description="Train with a specific PEFT adapter")
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parser.add_argument("--adapter", default="pissa", choices=list(ADAPTER_CONFIGS.keys()))
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parser.add_argument("--dataset", default=None, help="Path to dataset CSV (optional)")
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args = parser.parse_args()
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config = yaml.safe_load(BASE_CONFIG)
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config["adapter"] = ADAPTER_CONFIGS[args.adapter]
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print(f"Training with adapter: {args.adapter}")
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print(f"Adapter config: {config['adapter']}")
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print()
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model = LudwigModel(config=config, logging_level=logging.INFO)
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if args.dataset:
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stats, _, output_dir = model.train(dataset=args.dataset)
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print(f"Training complete. Results in: {output_dir}")
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else:
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print("No dataset provided — config validated successfully.")
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print("Pass --dataset <path> to run training.")
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if __name__ == "__main__":
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main()
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