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--- |
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datasets: |
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- kinokokoro/ichikara-instruction-003 |
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language: |
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- ja |
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base_model: |
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- llm-jp/llm-jp-3-13b |
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--- |
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elyza-tasks-100-TV_0.jsonl の回答モデルの作成のためのコードです。 |
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サンプルコードに対して以下の変更を行いスコア改善を試みました。 |
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- データセットを ichikara-instruction-003 の全てのファイルを利用するよう変更 |
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- 学習率(learning_rate) を 2e-5へ変更 |
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- 累積勾配(gradient_accumulation_steps) を 4 に変更 |
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- RoRAのRANK(LoraConfig r)を 32 に変更 |
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自宅のPC(RTX3090) でコードを実行し、解答を出力しました。 |
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```python |
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import wandb |
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import os |
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WANDB_API_KEY = "my-token" |
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wandb.login(key=WANDB_API_KEY) |
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wandb.init(project='llm2024-competition') |
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HF_TOKEN = "my-token" |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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BitsAndBytesConfig, |
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TrainingArguments, |
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logging, |
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) |
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from peft import ( |
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LoraConfig, |
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PeftModel, |
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get_peft_model, |
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) |
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import os, torch, gc |
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from datasets import load_dataset |
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import bitsandbytes as bnb |
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from trl import SFTTrainer |
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SEED_VALUE = 42 |
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base_model_id = "llm-jp/llm-jp-3-13b" |
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new_model_id = "llm-jp-3-13b-finetune" #Fine-Tuningしたモデルにつけたい名前 |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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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 |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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base_model_id, |
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quantization_config=bnb_config, |
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device_map="cuda:0" #auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True) |
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def find_all_linear_names(model): |
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cls = bnb.nn.Linear4bit # 4bit量子化線形層クラスを指定 |
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lora_module_names = set() # ここに取得した線形層を保持します。 |
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# モデル内の全てのモジュールを探索します |
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for name, module in model.named_modules(): |
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if isinstance(module, cls): # モジュールが4bit量子化線形層の場合 |
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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) |
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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 |
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# '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 |
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if 'lm_head' in lora_module_names: |
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lora_module_names.remove('lm_head') |
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return list(lora_module_names) # lora_module_namesをリストに変換して返します。 |
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modules = find_all_linear_names(model) |
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peft_config = LoraConfig( |
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r=32, #16, |
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lora_alpha=32, |
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lora_dropout=0.05, |
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bias="none", |
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task_type="CAUSAL_LM", |
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target_modules=modules, |
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) |
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model = get_peft_model(model, peft_config) |
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from datasets import concatenate_datasets, DatasetDict |
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# 全てのデータセットを読み込み |
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dataset0 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-001-1.json") |
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dataset1 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-001-1.json") |
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dataset2 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-001-2.2.json") |
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dataset3 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-001-5.2.json") |
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dataset4 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-001-2.1.json") |
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dataset5 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-001-5.1.json") |
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dataset6 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-002-1.json") |
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dataset7 = load_dataset("json", data_files="./Distribution20241221_all/ichikara-instruction-003-003-1.json") |
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datasets_to_concatenate = [ |
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dataset0["train"], |
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dataset1["train"], |
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dataset2["train"], |
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dataset3["train"], |
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dataset4["train"], |
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dataset5["train"], |
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dataset6["train"], |
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dataset7["train"] |
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] |
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concatenated_train_dataset = concatenate_datasets(datasets_to_concatenate) |
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dataset_all = DatasetDict({ |
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"train": concatenated_train_dataset |
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}) |
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# 結合したデータを使用 |
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dataset=dataset_all |
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# 学習時のプロンプトフォーマットの定義 |
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prompt = """### 指示 |
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{} |
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### 回答 |
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{}""" |
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""" |
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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 |
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""" |
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EOS_TOKEN = tokenizer.eos_token # トークナイザーのEOSトークン（文末トークン） |
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def formatting_prompts_func(examples): |
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input = examples["text"] # 入力データ |
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output = examples["output"] # 出力データ |
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text = prompt.format(input, output) + EOS_TOKEN # プロンプトの作成 |
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return { "formatted_text" : text, } # 新しいフィールド "formatted_text" を返す |
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pass |
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# # 各データにフォーマットを適用 |
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dataset = dataset.map( |
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formatting_prompts_func, |
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num_proc= 4, # 並列処理数を指定 |
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) |
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# データをtrainデータとtestデータに分割 (test_sizeの比率に) |
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dataset = dataset["train"].train_test_split(test_size=0.1, seed=SEED_VALUE) |
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training_arguments = TrainingArguments( |
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output_dir=new_model_id, |
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per_device_train_batch_size=1, # |
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gradient_accumulation_steps=4, # def: 2 |
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optim="paged_adamw_32bit", |
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num_train_epochs=1, # def: 1 |
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logging_strategy="steps", |
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logging_steps=10, |
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warmup_steps=10, |
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save_steps=100, |
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save_total_limit = 2, |
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max_steps = -1, # def:-1 |
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learning_rate=2e-5, # def:5e-5, |
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fp16= False, |
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bf16= False, |
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seed = SEED_VALUE, |
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group_by_length=True, |
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report_to="wandb" |
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) |
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trainer = SFTTrainer( |
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model=model, |
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train_dataset=dataset["train"], |
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peft_config=peft_config, |
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max_seq_length= 512, |
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dataset_text_field="formatted_text", |
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tokenizer=tokenizer, |
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args=training_arguments, |
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packing= False, |
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) |
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model.config.use_cache = False # キャッシュ機能を無効化 |
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trainer.train() # トレーニングを実行 |
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from datetime import datetime |
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# 現在の日時を取得 |
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now = datetime.now() |
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# フォーマットを指定して日時を文字列に変換 |
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formatted_date = now.strftime("%Y%m%d_%H%M%S") # 例: "20241214_153045" |
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print(formatted_date) |
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# タスクとなるデータの読み込み。 |
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# 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。 |
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import json |
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datasets = [] |
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with open("./elyza-tasks-100-TV_0.jsonl", "r") as f: |
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item = "" |
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for line in f: |
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line = line.strip() |
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item += line |
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if item.endswith("}"): |
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datasets.append(json.loads(item)) |
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item = "" |
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# モデルによるタスクの推論。 |
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from tqdm import tqdm |
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results = [] |
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for data in tqdm(datasets): |
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input = data["input"] |
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prompt = f"""### 指示 |
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{input} |
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### 回答 |
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""" |
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tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device) |
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attention_mask = torch.ones_like(tokenized_input) |
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with torch.no_grad(): |
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outputs = model.generate( |
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tokenized_input, |
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attention_mask=attention_mask, |
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max_new_tokens=100, |
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do_sample=False, |
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repetition_penalty=1.2, |
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pad_token_id=tokenizer.eos_token_id |
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)[0] |
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output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True) |
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results.append({"task_id": data["task_id"], "input": input, "output": output}) |
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# こちらで生成されたjsolを提出してください。 |
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# 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。 |
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# 必須なのはtask_idとoutputとなります。 |
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import re |
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jsonl_id = re.sub(".*/", "", new_model_id) |
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with open(f"./{jsonl_id}-outputs-{formatted_date}.jsonl", 'w', encoding='utf-8') as f: |
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for result in results: |
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json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters |
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f.write('\n') |
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# モデルとトークナイザーをHugging Faceにアップロード |
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model.push_to_hub(new_model_id, token=HF_TOKEN, private=True) # Online saving |
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tokenizer.push_to_hub(new_model_id, token=HF_TOKEN, private=True) # Online saving |
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``` |
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--- |
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library_name: transformers |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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### Model Sources [optional] |
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- **Repository:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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