Update README.md

README.md

@@ -29,8 +29,7 @@ import json
 
 
 HF_TOKEN = "Hugging Face Token"
-model_id = "llm-jp/llm-jp-3-13b" 
-adapter_id = ""
+model_name = "hagifly/llm-jp-3-13b-finetune"
 
 bnb_config = BitsAndBytesConfig(
     load_in_4bit=True,
@@ -39,15 +38,13 @@ bnb_config = BitsAndBytesConfig(
 )
 
 model = AutoModelForCausalLM.from_pretrained(
-    model_id,
+    model_name,
     quantization_config=bnb_config,
     device_map="auto",
     token = HF_TOKEN
 )
 
-tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
-
-model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token = HF_TOKEN)
 
 datasets = []
 with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
@@ -85,7 +82,7 @@ for data in tqdm(datasets):
   results.append({"task_id": data["task_id"], "input": input, "output": output})
 
 import re
-jsonl_id = re.sub(".*/", "", adapter_id)
+jsonl_id = re.sub(".*/", "", model_name)
 with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
     for result in results:
         json.dump(result, f, ensure_ascii=False)  # ensure_ascii=False for handling non-ASCII characters