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b48ac1a094
* average loss over batches and accumulated steps for tracking * fix layernorm weight decay * use AdamW from Pytorch instead of Transformers * add shuffling of sequences inside the batches * add shuffling of sequences inside the batches * add logging dir and reformat code * fix lr tracking * remove Mistral scaling * keep Mistral scaling * reformat code * fix error * fix error * use shuffling function from Pytorch * remove argument for shuffling batch sequences as it isn't optional * update package versions and install accelerate from source * remove unused package * Update loss average over accumulated steps Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * Update loss average over accumulated steps Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * use one shuffle buffer argument * compute avg_loss in one line Co-authored-by: Loubna ben allal <loubnabenallal@gmail.com> Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com>
27 lines
892 B
Python
27 lines
892 B
Python
from arguments import InitializationArguments
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
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# Configuration
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parser = HfArgumentParser(InitializationArguments)
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args = parser.parse_args()
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# Load codeparrot tokenizer trained for Python code tokenization
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)
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# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
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config_kwargs = {
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"vocab_size": len(tokenizer),
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"scale_attn_by_inverse_layer_idx": True,
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"reorder_and_upcast_attn": True,
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}
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# Load model config (GPT-2 large in this case)
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config = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
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# Initialize new model with config
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model = AutoModelForCausalLM.from_config(config)
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# Save model to the hub
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model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
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