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Load trained model for interpreting trained tokens #5

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Load trained model for interpreting trained tokens #5

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7 changes: 3 additions & 4 deletions peft/interpret/interpret.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import json

from transformers import AutoModelForCausalLM
from peft import get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType
from peft import PeftModel, get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType
import torch

from numpy import array
Expand All @@ -10,9 +10,7 @@


def load_model():
# NOTE: Currently loading untrained model, will push changes for trained model loading
model_name_or_path = "meta-llama/Llama-2-7b-chat-hf"

peft_config = PromptTuningConfig(
task_type=TaskType.CAUSAL_LM,
prompt_tuning_init=PromptTuningInit.TEXT,
Expand All @@ -22,7 +20,8 @@ def load_model():
tokenizer_name_or_path=model_name_or_path,
)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map='cpu', torch_dtype=torch.float16)
model = get_peft_model(model, peft_config) # add PEFT pieces to the LLM
#model = get_peft_model(model, peft_config) # add PEFT pieces to the LLM
model = PeftModel.from_pretrained(model, 'meyceoz/prompt-llama-2')
return model


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