llm-jp-3-13b-finetune

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ここに最終課題のREAD.MEとしての内容を記載します：Model_Inference_Template_20241127 1201DL　をベースに使いました。

!pip install -U bitsandbytes !pip install -U transformers !pip install -U accelerate !pip install -U datasets !pip install -U peft

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 = "HF 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 = "akamei/llm-jp-3-13b-finetune" # こちらにアップロードした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( base_model_id, quantization_config=bnb_config, device_map="auto", token = HF_TOKEN, )

Load tokenizer

tokenizer = AutoTokenizer.from_pretrained(base_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)

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とeval_aspect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_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')