llm-jp-3-13b-finetune-baseline

Inference Code

実行環境はGoogle Colab(GPU:T4) llm-jp-3-13bをSFTしたモデルで、elyza-tasks-100-TVの推論を行うコードです。

環境構築

!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets

notebookでインタラクティブな表示を可能とする（ただし、うまく動かない場合あり）

!pip install ipywidgets --upgrade

from google.colab import output
output.enable_custom_widget_manager()

モデルの準備

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
import torch
from tqdm import tqdm
import json

HF_TOKENはHugging Faceで各自でtokenを取得してください

HF_TOKEN = " "
model_name = "ryo0529/llm-jp-3-13b-finetune-baseline"

# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=False,
)
)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    token = HF_TOKEN
)

データセットの読み込み

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 = ""

"""

推論実行

results = []
for data in tqdm(datasets):

  input = data["input"]

  prompt = f"""以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい
  ### 指示
  {input}
  ### 回答：
  """

  tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
  with torch.no_grad():
      outputs = model.generate(
          tokenized_input,
          max_new_tokens=100,
          do_sample=False,
          repetition_penalty=1.2
      )[0]
  output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)

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

結果をjsonlで保存

import re
model_name = re.sub(".*/", "", model_name)
with open(f"./{model_name}-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
        f.write('\n')