more fixes
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@@ -184,7 +184,8 @@ def load_model(
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for k, v in cfg.tokens.items():
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tokenizer.add_special_tokens({k: v})
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model.resize_token_embeddings(len(tokenizer))
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# this should only be needed if you are messing with new tokens in the vocab
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# model.resize_token_embeddings(len(tokenizer))
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if cfg.adapter and load_in_8bit and not cfg.load_4bit:
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logging.info("converting PEFT model w/ prepare_model_for_int8_training")
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@@ -207,7 +208,10 @@ def load_model(
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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if torch.cuda.device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) > 1:
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if torch.cuda.device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) > 1 and cfg.load_4bit:
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# llama is PROBABLY model parallelizable, but the default isn't that it is
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# so let's only set it for the 4bit, see
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# https://github.com/johnsmith0031/alpaca_lora_4bit/blob/08b3fca4a4a9e0d3945be1bab4529f100a428636/finetune.py#L130-L133
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model.is_parallelizable = True
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model.model_parallel = True
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