# 必要なライブラリをインストール
%%capture
!pip install unsloth
!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

# 必要なライブラリを読み込み
from unsloth import FastLanguageModel
import torch
import json

model_name = "Mahiro0698/llm-jp-3-13b-it_MN_v2"

max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_name,
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    token = "your token",
)
FastLanguageModel.for_inference(model)

# データセットの読み込み。以elyza-tasks-100-TV_0.jsonlを同じディレクトリに配置します。
# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行します。
datasets = []
with open("/content/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 = ""

from tqdm import tqdm

# 推論　推論が完了するとデフォルトではllm-jp-3-13b-it_MN_v2_output.jsonlに回答が保存されます。
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})

with open(f"/content/llm-jp-3-13b-it_MN_v2_output.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')