llm-jp-3-13b-finetune

Model Card for Model ID

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以下のように推論を行いました。

# python 3.10.12
!pip install -U pip
!pip install -U transformers
!pip install -U bitsandbytes
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
!pip install -U trl
!pip install -U wandb
!pip install ipywidgets --upgrade

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TrainingArguments,
    logging,
)
from peft import (
    LoraConfig,
    PeftModel,
    get_peft_model,
)
import os, torch, gc
from datasets import load_dataset
import bitsandbytes as bnb
from trl import SFTTrainer

# Hugging Face Token
HF_TOKEN = "your_hf_token"

# モデルを読み込み。
# llm-jp-3 1.8B, 3.7B, 13Bのsnapshotをダウンロード済みでmodelsディレクトリに格納してあります。
# base_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。
# その他のモデルは取得に承諾が必要なため、各自でダウンロードお願いします。
base_model_id = "models/models--llm-jp--llm-jp-3-13b/snapshots/cd3823f4c1fcbb0ad2e2af46036ab1b0ca13192a" #Fine-Tuningするベースモデル
# 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" 
new_model_id = "llm-jp-3-13b-finetune" #Fine-Tuningしたモデルにつけたい名前

"""
bnb_config: 量子化の設定

  - load_in_4bit:
      - 4bit量子化形式でモデルをロード

  - bnb_4bit_quant_type:
      - 量子化の形式を指定

  - bnb_4bit_compute_dtype:
      - 量子化された重みを用いて計算する際のデータ型

"""

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4", # nf4は通常のINT4より精度が高く、ニューラルネットワークの分布に最適です
    bnb_4bit_compute_dtype=torch.bfloat16,
)

"""
model: モデル

  - base_model:
      - 読み込むベースモデル (事前に定義したもの)

  - quantization_config:
      - bnb_configで設定した量子化設定

  - device_map:
      - モデルを割り当てるデバイス (CPU/GPU) "auto"で自動に割り当てられます。

tokenizer: トークナイザー

  - base_model:
      - 読み込むベースモデル (事前に定義したもの)

  - trust_remote_code:
      - リモートコードの実行を許可 (カスタムモデルなど)
"""
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)

"""
find_all_linear_names: モデル内の4bit量子化線形層を探します。
"""

def find_all_linear_names(model):
    cls = bnb.nn.Linear4bit # 4bit量子化線形層クラスを指定
    lora_module_names = set() # ここに取得した線形層を保持します。

    # モデル内の全てのモジュールを探索します
    for name, module in model.named_modules():
        if isinstance(module, cls): # モジュールが4bit量子化線形層の場合
            names = name.split('.') # モジュールの名前を分割 (ネストされてる際などに対処)
            lora_module_names.add(names[0] if len(names) == 1 else names[-1]) # 最下層の名前をlora_module_namesに追加

    # 'lm_head' は16ビット演算の際に除外する必要があるため、lora_module_namesから削除
    if 'lm_head' in lora_module_names:
        lora_module_names.remove('lm_head')

    return list(lora_module_names) # lora_module_namesをリストに変換して返します。

modules = find_all_linear_names(model)

"""
peft_config: PEFTの構成設定

  - r
      - LoRA のランク (4, 8, 16 ,32...)
      - 増やすほど学習が捗るが, 過学習のリスクも高まるので注意

  - lora_alpha
      - LoRAのスケーリング係数

  - lora_dropout
      - ドロップアウト率（過学習を防ぐための割合）

  - bias
      - バイアス項の扱い ("none"の場合、LoRAはバイアスを学習しない)

  - task_type
      - タスクタイプ

  - target_modules
      - LoRAを適用するターゲットモジュール (前のコードで特定した層)
"""

peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=modules,
)

model = get_peft_model(model, peft_config)


dataset = load_dataset("json", data_files="your_training_data")

# 学習時のプロンプトフォーマットの定義
prompt = """### 指示
{}
### 回答
{}"""


"""
formatting_prompts_func: 各データをプロンプトに合わせた形式に合わせる
"""
EOS_TOKEN = tokenizer.eos_token # トークナイザーのEOSトークン（文末トークン）
def formatting_prompts_func(examples):
    input = examples["text"] # 入力データ
    output = examples["output"] # 出力データ
    text = prompt.format(input, output) + EOS_TOKEN # プロンプトの作成
    return { "formatted_text" : text, } # 新しいフィールド "formatted_text" を返す
pass

# # 各データにフォーマットを適用
dataset = dataset.map(
    formatting_prompts_func,
    num_proc= 4, # 並列処理数を指定
)

training_arguments = TrainingArguments(
    output_dir=new_model_id,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=2,
    optim="paged_adamw_32bit",
    num_train_epochs=1,
    logging_strategy="steps",
    logging_steps=10,
    warmup_steps=10,
    save_steps=100,
    save_total_limit = 2,
    max_steps = -1,
    learning_rate=5e-5,
    fp16=False,
    bf16=False,
    seed = 3407,
    group_by_length=True,
    report_to="none"
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    peft_config=peft_config,
    max_seq_length= 512,
    dataset_text_field="formatted_text",
    tokenizer=tokenizer,
    args=training_arguments,
    packing= False,
)

model.config.use_cache = False # キャッシュ機能を無効化
trainer.train() # トレーニングを実行

# タスクとなるデータの読み込み。
# 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。
import json
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 = ""

# モデルによるタスクの推論。
from tqdm import tqdm

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=100,
          do_sample=False,
          repetition_penalty=1.2,
          pad_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})

import re
jsonl_id = re.sub(".*/", "", new_model_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')

# モデルとトークナイザーをHugging Faceにアップロード
model.push_to_hub(new_model_id, token=HF_TOKEN, private=True) # Online saving
tokenizer.push_to_hub(new_model_id, token=HF_TOKEN, private=True) # Online saving

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