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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 json
10
11HF_TOKEN = "Hugging Face Token" #Write権限のHFトークンを設定
12base_model_id = "llm-jp/llm-jp-3-13b"
13adapter_id = "shiki07/llm-jp-3-13b-it_lora"
14eval_data_path = "./elyza-tasks-100-TV_0.jsonl" # elyza-tasks-100-TVのパスを指定1# QLoRA config
2bnb_config = BitsAndBytesConfig(
3 load_in_4bit=True,
4 bnb_4bit_quant_type="nf4",
5 bnb_4bit_compute_dtype=torch.bfloat16,
6)
7
8# Load model
9model = AutoModelForCausalLM.from_pretrained(
10 base_model_id,
11 quantization_config=bnb_config,
12 device_map="auto",
13 token = HF_TOKEN
14)
15
16tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, token = HF_TOKEN)
17model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)1# データセットの読み込み。
2datasets = []
3with open(eval_data_path, "r") as f:
4 item = ""
5 for line in f:
6 line = line.strip()
7 item += line
8 if item.endswith("}"):
9 datasets.append(json.loads(item))
10 item = ""
11
12# llmjp
13results = []
14for data in tqdm(datasets):
15
16 input = data["input"]
17
18 prompt = f"""### 指示
19 {input}
20 ### 回答
21 """
22
23 tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
24 attention_mask = torch.ones_like(tokenized_input)
25 with torch.no_grad():
26 outputs = model.generate(
27 tokenized_input,
28 attention_mask=attention_mask,
29 max_new_tokens=100,
30 do_sample=False,
31 repetition_penalty=1.2,
32 pad_token_id=tokenizer.eos_token_id
33 )[0]
34 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
35
36 results.append({"task_id": data["task_id"], "input": input, "output": output})
37
38import re
39jsonl_id = re.sub(".*/", "", adapter_id)
40with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
41 for result in results:
42 json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters
43 f.write('\n')