Inference

from unsloth import FastLanguageModel
from peft import PeftModel
import torch
import json
from tqdm import tqdm
import re

model_id = "llm-jp/llm-jp-3-13b"
adapter_id = ""
HF_TOKEN = "" # use your token

dtype = None
load_in_4bit = True # 今回は13Bモデルを扱うためTrue

model, tokenizer = FastLanguageModel.from_pretrained(
 model_name=model_id,
 dtype=dtype,
 load_in_4bit=load_in_4bit,
 trust_remote_code=True,
)

model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)

datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
 item = ""
 for line in f:
 line = line.strip()
 item += line
 if item.endswith("}"):
 datasets.append(json.loads(item))
 item = ""

FastLanguageModel.for_inference(model)

results = []
for dt in tqdm(datasets):
 input = dt["input"]

 prompt = f"""### 指示\n{input}\n### 回答\n"""

 inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)

 outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
 prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]

 results.append({"task_id": dt["task_id"], "input": input, "output": prediction})

json_file_id = re.sub(".*/", "", adapter_id)
with open(f"/content/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f:
 for result in results:
 json.dump(result, f, ensure_ascii=False)
 f.write('\n')

Base model