don't load models in 8bit unless they are using an adapter, also fix tokenizer load in exceptional case
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@@ -109,7 +109,7 @@ def load_model(
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else:
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model = LlamaForCausalLM.from_pretrained(
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base_model,
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load_in_8bit=cfg.load_in_8bit,
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load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
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torch_dtype=torch_dtype,
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device_map=cfg.device_map,
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)
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@@ -117,14 +117,14 @@ def load_model(
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elif model_type:
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model = getattr(transformers, model_type).from_pretrained(
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base_model,
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load_in_8bit=cfg.load_in_8bit,
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load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
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torch_dtype=torch_dtype,
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device_map=cfg.device_map,
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)
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else:
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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load_in_8bit=cfg.load_in_8bit,
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load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
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torch_dtype=torch_dtype,
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device_map=cfg.device_map,
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)
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@@ -135,7 +135,7 @@ def load_model(
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logging.exception(e)
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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load_in_8bit=cfg.load_in_8bit,
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load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
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torch_dtype=torch_dtype,
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device_map=cfg.device_map,
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)
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@@ -147,7 +147,7 @@ def load_model(
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else:
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tokenizer = getattr(transformers, tokenizer_type).from_pretrained(model)
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except:
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tokenizer = AutoTokenizer.from_pretrained(base_model)
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tokenizer = AutoTokenizer.from_pretrained(base_model_config)
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logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
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logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
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