This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

!pip uninstall unsloth -y !pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install --upgrade torch !pip install --upgrade xformers

!pip install ipywidgets --upgrade

import torch if torch.cuda.get_device_capability()[0] >= 8: !pip install --no-deps packaging ninja einops "flash-attn>=2.6.3"

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from unsloth import FastLanguageModel import torch max_seq_length = 2048 # unslothではRoPEをサポートしているのでコンテキスト長は自由に設定可能 Original 512 dtype = None # Noneにしておけば自動で設定 load_in_4bit = True # 今回は8Bクラスのモデルを扱うためTrue

model_id = "hiroshij/llm-jp-3-13b-finetune-joga-20241202" new_model_id = "llm-jp-3-13b-finetune-joga-20241202-2" #Fine-Tuningしたモデルにつけたい名前

model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_id, dtype=dtype, load_in_4bit=load_in_4bit, trust_remote_code=True, )

model = FastLanguageModel.get_peft_model( model, r = 32, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 32, lora_dropout = 0.05, bias = "none", use_gradient_checkpointing = "unsloth", random_state = 3407, use_rslora = False, loftq_config = None, max_seq_length = max_seq_length, )

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from unsloth import FastLanguageModel import torch max_seq_length = 2048 # unslothではRoPEをサポートしているのでコンテキスト長は自由に設定可能 Original 512 dtype = None # Noneにしておけば自動で設定 load_in_4bit = True # 今回は8Bクラスのモデルを扱うためTrue

model_id = "hiroshij/llm-jp-3-13b-finetune-joga-20241202" new_model_id = "llm-jp-3-13b-finetune-joga-20241202-2" #Fine-Tuningしたモデルにつけたい名前

model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_id, dtype=dtype, load_in_4bit=load_in_4bit, trust_remote_code=True, )

model = FastLanguageModel.get_peft_model( model, r = 32, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 32, lora_dropout = 0.05, bias = "none", use_gradient_checkpointing = "unsloth", random_state = 3407, use_rslora = False, loftq_config = None, max_seq_length = max_seq_length,"My_HF_TOKEN" #@param {type:"string"}

from datasets import load_dataset

dataset = load_dataset("json", data_files="/content/complementary-tasks-3.jsonl")

prompt = """### 指示 {}

{}"""

""" 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

dataset = dataset.map( formatting_prompts_func, num_proc= 4, # 並列処理数を指定 )

dataset

print(dataset["train"]["formatted_text"][3])

from trl import SFTTrainer from transformers import TrainingArguments from unsloth import is_bfloat16_supported

trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset=dataset["train"], max_seq_length = max_seq_length, dataset_text_field="formatted_text", packing = False, args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, num_train_epochs = 1, logging_steps = 10, warmup_steps = 10, save_steps=100, save_total_limit=2, max_steps=-1, learning_rate = 2e-4, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), group_by_length=True, seed = 3407, output_dir = "outputs", report_to = "none", ), )

#@title 学習実行 trainer_stats = trainer.train()

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 = ""

from tqdm import tqdm

FastLanguageModel.for_inference(model)

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 = 1024, use_cache = True, do_sample=False, repetition_penalty=1.2) #Original 512 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"{new_model_id}_output.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) f.write('\n')