llm-jp-3-13b-adapter

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# # 推論用コード
# Hugging Faceにアップロードしたモデルを用いてELYZA-tasks-100-TVの出力を得るためのコードです。 
# こちらはLoRA_template 
# このコードで生成されたjsonlファイルは課題の成果として提出可能なフォーマットになっております。

# In[1]:


get_ipython().system('pip install -U bitsandbytes')
get_ipython().system('pip install -U transformers')
get_ipython().system('pip install -U accelerate')
get_ipython().system('pip install -U datasets')
get_ipython().system('pip install -U peft')


# In[2]:


# 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）
get_ipython().system('pip install ipywidgets --upgrade')


# In[3]:


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


# In[4]:


# Hugging Faceで取得したTokenをこちらに貼る。
HF_TOKEN = "your_token"


# In[5]:


# ベースとなるモデルと学習したLoRAのアダプタ。
# model_idの値はomnicampusの環境におけるモデルのパスを表しており、それ以外の環境で実行する場合は変更の必要があります。
model_id = "models/models--llm-jp--llm-jp-3-13b/snapshots/cd3823f4c1fcbb0ad2e2af46036ab1b0ca13192a"
# omnicampus以外の環境をご利用の方は以下をご利用ください。
# base_model_id = "llm-jp/llm-jp-3-13b" 
adapter_id = "to-sat/llm-jp-3-13b-adapter" # こちらにアップロードしたHugging FaceのIDを指定してください。


# In[6]:


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


# In[7]:


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

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)


# In[8]:


# 元のモデルにLoRAのアダプタを統合。
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)


# In[9]:


# データセットの読み込み。
# 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。
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 = ""


# In[ ]:


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

  input = data["input"]

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

  tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
  attention_mask = torch.ones_like(tokenized_input)
  with torch.no_grad():
      outputs = model.generate(
          tokenized_input,
          attention_mask=attention_mask,
          max_new_tokens=1024,
          do_sample=False,
          repetition_penalty=1.2,
          pad_token_id=tokenizer.eos_token_id,
          eos_token_id=tokenizer.eos_token_id, 
          forced_eos_token_id=tokenizer.eos_token_id,
      )[0]
  output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)

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


# In[ ]:


# こちらで生成されたjsolを提出してください。
# 本コードではinputとeval_aspectも含んでいますが、なくても問題ありません。
# 必須なのはtask_idとoutputとなります。
import re
jsonl_id = re.sub(".*/", "", adapter_id)
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
        f.write('\n')