llm-jp-3-13b-finetune5

このモデルは Hugging Face 上で提供されている llm-jp/llm-jp-3-13b をファインチューニングしたモデルです。 主に日本語の指示応答タスク向けに最適化されています。

モデルの概要

• タイプ: Transformer-based Language Model
• トレーニングデータ: LLM-jp の公開している Ichikara Instruction
  https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/
  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)
• タスク: elyza_100_tv のベンチマークに対する応答

#　必要なライブラリをインストール !pip install -U bitsandbytes !pip install -U transformers !pip install -U accelerate !pip install -U datasets !pip install -U peft

#　必要なライブラリを読み込み from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) from peft import PeftModel import torch from tqdm import tqdm import json

Hugging Face Tokenを指定。

下記の URL から Hugging Face Token を取得できますので下記の HF_TOKEN に入れてください。 https://huggingface.co/settings/tokens

HF_TOKEN = "" #@param {type:"string"}

ベースとなるモデルと学習したLoRAのアダプタ。

model_idの値は適宜変更

model_id = "llm-jp/llm-jp-3-13b" adapter_id = "tarosaii/llm-jp-3-13b-finetune5"

QLoRAの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", use_auth_token=HF_TOKEN )

Load tokenizer

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True,use_auth_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)

タスクとなるデータセットの読み込み。

事前にデータセットをアップロード

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 = ""

モデルを用いてタスクの推論

results = [] for data in tqdm(datasets):

input = data["input"] prompt = f"""### 指示 {input}

回答

"""

トークナイズ（input_idsのみ取得）

inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to(model.device)

出力生成

outputs = model.generate( input_ids=input_ids, max_new_tokens=512, do_sample=False, repetition_penalty=1.2, )

出力をデコード

output_text = tokenizer.decode( outputs[0][input_ids.size(1):], skip_special_tokens=True )

results.append({"task_id": data["task_id"], "input": input, "output": output_text})

結果をjsonlで保存。

import re jsonl_id = re.sub(".*/", "", adapter_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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