@@ -6,14 +6,14 @@ from llamafactory.hparams import get_train_args
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from llamafactory.model import load_model, load_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-LlamaForCausalLM")
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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TRAINING_ARGS = {
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"stage": "sft",
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"do_train": True,
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"finetuning_type": "freeze",
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"dataset": "llamafactory/tiny_dataset",
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"dataset": "llamafactory/tiny-supervised-dataset",
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"dataset_dir": "ONLINE",
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"template": "llama3",
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"cutoff_len": 1024,
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@@ -25,12 +25,7 @@ TRAINING_ARGS = {
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def test_freeze_all_modules():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{
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"freeze_trainable_layers": 1,
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**TRAINING_ARGS,
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}
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)
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model_args, _, _, finetuning_args, _ = get_train_args({"freeze_trainable_layers": 1, **TRAIN_ARGS})
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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for name, param in model.named_parameters():
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@@ -44,11 +39,7 @@ def test_freeze_all_modules():
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def test_freeze_extra_modules():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{
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"freeze_trainable_layers": 1,
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"freeze_extra_modules": "embed_tokens,lm_head",
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**TRAINING_ARGS,
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}
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{"freeze_trainable_layers": 1, "freeze_extra_modules": "embed_tokens,lm_head", **TRAIN_ARGS}
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)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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||||
|
||||
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Block a user