a85efffbef
* bump transformers==4.52.4 * don't use hf offline for qwen tokenizer * increase timeout * don't use methodtype * increase timeout * better assertion logging * upgrade deepspeed version too
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
"""
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E2E tests for reward model lora llama
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"""
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import unittest
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from axolotl.common.datasets import load_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
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class TestRewardModelLoraSmolLM2(unittest.TestCase):
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"""
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Test case for Llama reward models using LoRA
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"""
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@with_temp_dir
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def test_rm_lora(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"model_type": "AutoModelForSequenceClassification",
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"num_labels": 1,
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"chat_template": "alpaca",
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"reward_model": True,
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"sequence_len": 2048,
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"pad_to_sequence_len": True,
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.0,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "argilla/distilabel-intel-orca-dpo-pairs",
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"type": "bradley_terry.chat_template",
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"split": "train[:10%]",
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},
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],
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"lora_modules_to_save": ["embed_tokens", "lm_head"],
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"remove_unused_columns": False,
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"max_steps": 10,
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"num_epochs": 1,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"gradient_checkpointing": True,
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"warmup_ratio": 0.1,
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"use_tensorboard": True,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss (%s) is too high"
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)
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check_model_output_exists(temp_dir, cfg)
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