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Model: mnoukhov/pythia410m-sft-tldr
Source: Original Platform

code/merge_peft_adapter.py

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+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+from peft import PeftConfig, PeftModel
+from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser
+
+
+@dataclass
+class ScriptArguments:
+    """
+    The name of the Casual LM model we wish to fine with PPO
+    """
+
+    adapter_model_name: str = field(default=None, metadata={"help": "the model name"})
+    # base_model_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
+    output_name: str = field(default=None, metadata={"help": "the model name"})
+    dtype: Optional[str] = field(default="bf16")
+
+
+parser = HfArgumentParser(ScriptArguments)
+script_args = parser.parse_args_into_dataclasses()[0]
+# assert script_args.adapter_model_name is not None, "please provide the name of the Adapter you would like to merge"
+# assert script_args.base_model_name is not None, "please provide the name of the Base model"
+# assert script_args.base_model_name is not None, "please provide the output name of the merged model"
+
+
+if script_args.dtype == "bf16":
+    torch_dtype = torch.bfloat16
+elif script_args.dtype == "fp16":
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
+if peft_config.task_type == "SEQ_CLS":
+    # peft is for reward model so load sequence classification
+    model = AutoModelForSequenceClassification.from_pretrained(
+        peft_config.base_model_name_or_path,
+        num_labels=1,
+        torch_dtype=torch_dtype,
+    )
+else:
+    model = AutoModelForCausalLM.from_pretrained(
+        peft_config.base_model_name_or_path,
+        return_dict=True,
+        torch_dtype=torch_dtype,
+    )
+
+tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
+model.eval()
+
+model = model.merge_and_unload()
+
+model.save_pretrained(f"{script_args.output_name}")
+tokenizer.save_pretrained(f"{script_args.output_name}")
+# model.push_to_hub(f"{script_args.output_name}", use_temp_dir=False)