Uploaded model
- Developed by: hzhn
- 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.
Instruction Tuning
The models have been fine-tuned on the following datasets.
Language | Dataset | description |
---|---|---|
Japanese | ichikara-instruction-003-001-1.json | A manually constructed instruction dataset |
データセット作成チーム: 関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)
Usage
以下はElyza-tasks-100-TV_0.jsonlの回答のためのコードです。
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_token"
base_model_id = "llm-jp/llm-jp-3-13b"
new_model_id = "llm-jp-3-13b-it_lora"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
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)
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 = 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="./ichikara-instruction-003-001-1.json")
# 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
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
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')
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llm-jp/llm-jp-3-13b