Upload merge.py

merge.py

@@ -0,0 +1,56 @@
+import torch
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from datasets import load_dataset
+from peft import LoraConfig, PeftModel
+
+device_map = "auto"
+model = AutoModelForCausalLM.from_pretrained(
+    "/path/to/meta-llama3-8b",
+    #low_cpu_mem_usage=True,
+    return_dict=True,
+    torch_dtype=torch.float16,
+    device_map=device_map,
+)
+
+model = PeftModel.from_pretrained(model, "/path/to/llama3-8b-adapter", device_map=device_map)
+model = model.merge_and_unload()
+
+tokenizer = AutoTokenizer.from_pretrained("/path/to/meta-llama3-8b", trust_remote_code=True)
+tokenizer.pad_token_id = 18610
+
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=4096, do_sample=False)
+print("Padding side:",tokenizer.padding_side)
+val_dataset = load_dataset("csv", data_files={'val':'/path/to/actseq-val-new.csv'})["val"]
+test_dataset = load_dataset("csv", data_files={'test':'/path/to/actseq-test-new.csv'})["test"]
+
+
+def formatting_prompts_func(example):
+     output_texts = []
+     for i in range(len(example['dial_with_actions'])):
+         text = f"Predict the action sequence (AS) for the Minecraft excerpt:\n {example['dial_with_actions'][i]}\n ### AS:"
+         output_texts.append(text)
+     return output_texts
+
+
+val_texts = formatting_prompts_func(val_dataset)
+test_texts = formatting_prompts_func(test_dataset)
+
+print("Val Length:", len(val_texts))
+print("Test Length:", len(test_texts))
+
+f = open("/path/to/val-output-file","w")
+
+for text in val_texts:
+    print(text)
+    print(pipe(text)[0]["generated_text"], file=f)
+
+f.close()
+
+f = open("/path/to/test-output-file","w")
+
+for text in test_texts:
+    print(text)
+    print(pipe(text)[0]["generated_text"], file=f)
+
+f.close()