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1!pip install -U bitsandbytes
2!pip install -U transformers
3!pip install -U accelerate
4!pip install -U datasets
5!pip install -U peft1from transformers import (
2 AutoModelForCausalLM,
3 AutoTokenizer,
4 BitsAndBytesConfig,
5)
6from peft import PeftModel
7import torch
8from tqdm import tqdm
9import json1base_model_id = "llm-jp/llm-jp-3-13b"
2adapter_id = "arbk/llm_jp_3_13b_finetune_30"1from huggingface_hub import login
2HF_TOKEN = "Your_Token" # 自身のhuggingfaceのトークンを入力
3login(token=HF_TOKEN)
4
5bnb_config = BitsAndBytesConfig(
6 load_in_4bit=True,
7 bnb_4bit_quant_type="nf4",
8 bnb_4bit_compute_dtype=torch.bfloat16,
9)
10
11model = AutoModelForCausalLM.from_pretrained(
12 base_model_id,
13 quantization_config=bnb_config,
14 device_map="auto",
15 token = HF_TOKEN
16)
17
18tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, token = HF_TOKEN)model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)1datasets = []
2with open("elyza-tasks-100-TV_0.jsonl", "r") as f: # openの第一引数ににファイル名を入力。デフォルトではelyza-tasks-100-TV_0.jsonlを例に記載。ファイルは事前に自身のカレントディレクトリにアップロードしてください。
3 item = ""
4 for line in f:
5 line = line.strip()
6 item += line
7 if item.endswith("}"):
8 datasets.append(json.loads(item))
9 item = ""1results = []
2for data in tqdm(datasets):
3
4 input = data["input"]
5
6 prompt = f"""### 指示
7 {input}
8 ### 回答
9 """
10
11 tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
12 attention_mask = torch.ones_like(tokenized_input)
13 with torch.no_grad():
14 outputs = model.generate(
15 tokenized_input,
16 attention_mask=attention_mask,
17 max_new_tokens=500,
18 do_sample=False,
19 repetition_penalty=1.2,
20 pad_token_id=tokenizer.eos_token_id
21 )[0]
22 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
23
24 results.append({"task_id": data["task_id"], "input": input, "output": output})1import re
2jsonl_id = re.sub(".*/", "", adapter_id) # デフォルトではadapter_id用いたファイル名になります。必要に応じて変更してください。
3with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
4 for result in results:
5 json.dump(result, f, ensure_ascii=False)
6 f.write('\n')