Uploaded model
- Developed by: demimomi
- License: apache-2.0
- Finetuned from model : llm-jp/llm-jp-3-13b
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
東京大学 松尾・岩澤研究室 大規模言語モデル2024 最終課題
(作成日:2024年11月24日 作成者:出水 利樹 #SoftBank #MONET Technologies)
https://weblab.t.u-tokyo.ac.jp/lecture/course-list/large-language-model/
Sample Use
以下は、elyza-tasks-100-TV_0.jsonlの回答用モデルコードです!
-- coding: utf-8 --
llm-jp-3-13b-finetune2.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1TLF_HtVz6ng9ZAWe7lHy59kiHBg3_3y0
!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 --upgrade torch
!pip install --upgrade xformers
!pip install ipywidgets --upgrade
!pip install ipywidgets --upgrade
pip install --upgrade torch
import torch
if torch.cuda.get_device_capability()[0] >= 8:
!pip install --no-deps packaging ninja einops "flash-attn>=2.6.3"
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from unsloth import FastLanguageModel
import torch
max_seq_length = 512 # 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
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 = "llm-jp/llm-jp-3-13b"
new_model_id = "llm-jp-3-13b-finetune-2" #Fine-Tuningしたモデルにつけたい名前
# FastLanguageModel インスタンスを作成
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,)
HF_TOKEN = "Alert! This code is secret , sorry by demimomi" #@param {type:"string"}
from datasets import load_dataset
dataset = load_dataset("json", data_files="/content/ichikara-instruction-003-001-2.1.json")
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
)
dataset
from google.colab import output
output.enable_custom_widget_manager()
"""Support for third party widgets will remain active for the duration of the session. To disable support:"""
from google.colab import output
output.disable_custom_widget_manager()
print(dataset["train"]["formatted_text"][3])
"""
training_arguments: 学習の設定
- output_dir:
-トレーニング後のモデルを保存するディレクトリ
- per_device_train_batch_size:
- デバイスごとのトレーニングバッチサイズ
- per_device_eval_batch_size:
- デバイスごとの評価バッチサイズ
- gradient_accumulation_steps:
- 勾配を更新する前にステップを積み重ねる回数
- optim:
- オプティマイザの設定
- num_train_epochs:
- エポック数
- eval_strategy:
- 評価の戦略 ("no"/"steps"/"epoch")
- eval_steps:
- eval_strategyが"steps"のとき、評価を行うstep間隔
- logging_strategy:
- ログ記録の戦略
- logging_steps:
- ログを出力するステップ間隔
- warmup_steps:
- 学習率のウォームアップステップ数
- save_steps:
- モデルを保存するステップ間隔
- save_total_limit:
- 保存しておくcheckpointの数
- max_steps:
- トレーニングの最大ステップ数
- learning_rate:
- 学習率
- fp16:
- 16bit浮動小数点の使用設定(第8回演習を参考にすると良いです)
- bf16:
- BFloat16の使用設定
- group_by_length:
- 入力シーケンスの長さによりバッチをグループ化 (トレーニングの効率化)
- report_to:
- ログの送信先 ("wandb"/"tensorboard"など)
"""
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",
),
)
from google.colab import output
output.enable_custom_widget_manager()
"""Support for third party widgets will remain active for the duration of the session. To disable support:"""
from google.colab import output
output.disable_custom_widget_manager()
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
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 = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
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')
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')
model.push_to_hub_merged(
new_model_id,
tokenizer=tokenizer,
save_method="lora",
token=HF_TOKEN,
private=True
)
Model tree for demimomi/llm-jp-3-13b-finetune-2
Base model
llm-jp/llm-jp-3-13b