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推論用コード
Hugging Faceにアップロードしたモデルを用いてELYZA-tasks-100-TVの出力を得るためのコードです。
LoRA_template_20241127.ipynbで学習したLoRAアダプタとDPOtemplate_20241207.ipynbで学習したDPOアダプタを用いる想定です。
このコードで生成されたjsonlファイルは課題の成果として提出可能なフォーマットになっております。
!pip install -U ipywidgets
!pip install transformers==4.46.3
!pip install -U bitsandbytes
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft==0.13.2
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json
Hugging Faceで取得したTokenをこちらに貼る。
HF_TOKEN = "Hugging Face Token"
ベースとなるモデルと学習したLoRAのアダプタ。
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。
model_id = "models/models--llm-jp--llm-jp-3-13b/snapshots/cd3823f4c1fcbb0ad2e2af46036ab1b0ca13192a"
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"
adapter_id = "yu3733/llm-jp-3-13b-it__gradient_2_vfinal_MATH_lora" # こちらにアップロードしたLoRAアダプタのHugging FaceのIDを指定してください。
adapter_dpo_id = "yu3733/llm-jp-3-13b-it__gradient_2_vfinal_MATH_lora-DPO_2000f" # こちらにアップロードしたDPOアダプタのHugging FaceのIDを指定してください。
QLoRA config
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
Load model
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb_config,
device_map="auto",
token = HF_TOKEN
)
Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
元のモデルにLoRAのアダプタを統合。
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
LoRAのモデルにDPOのアダプタを統合。
model = PeftModel.from_pretrained(model, adapter_dpo_id, token = HF_TOKEN)
データセットの読み込み。
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。
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 = ""
llmjp
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})
こちらで生成されたjsolを提出してください。
本コードではinputも含んでいますが、なくても問題ありません。
必須なのはtask_idとoutputとなります。
import re
jsonl_id = re.sub(".*/", "", adapter_dpo_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')
