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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 peft
6
7from transformers import (
8 AutoModelForCausalLM,
9 AutoTokenizer,
10 BitsAndBytesConfig,
11)
12from peft import PeftModel
13import torch
14from tqdm import tqdm
15import json
16
17# Hugging Faceで取得したToken
18HF_TOKEN = "(Your Token)"
19
20# ベースとなるモデルと学習したLoRAのアダプタ。
21model_id = "llm-jp/llm-jp-3-13b"
22adapter_id = "tamai-hgf/llm-jp-3-13b-finetune"
23
24# QLoRA config
25bnb_config = BitsAndBytesConfig(
26 load_in_4bit=True,
27 bnb_4bit_quant_type="nf4",
28 bnb_4bit_compute_dtype=torch.bfloat16,
29)
30
31# Load model
32model = AutoModelForCausalLM.from_pretrained(
33 model_id,
34 quantization_config=bnb_config,
35 device_map="auto",
36 token = HF_TOKEN
37)
38
39# Load tokenizer
40tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
41
42# 元のモデルにLoRAのアダプタを統合。
43model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
44
45# データセットの読み込み。
46datasets = []
47with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
48 item = ""
49 for line in f:
50 line = line.strip()
51 item += line
52 if item.endswith("}"):
53 datasets.append(json.loads(item))
54 item = ""
55
56# 推論結果の作成
57results = []
58for data in tqdm(datasets):
59
60 input = data["input"]
61
62 prompt = f"""### 指示
63 {input}
64 ### 回答
65 """
66
67 tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
68 attention_mask = torch.ones_like(tokenized_input)
69 with torch.no_grad():
70 outputs = model.generate(
71 tokenized_input,
72 attention_mask=attention_mask,
73 max_new_tokens=100,
74 do_sample=False,
75 repetition_penalty=1.2,
76 pad_token_id=tokenizer.eos_token_id
77 )[0]
78 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
79
80 results.append({"task_id": data["task_id"], "input": input, "output": output})
81
82# jsonl出力
83import re
84jsonl_id = re.sub(".*/", "", adapter_id)
85with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
86 for result in results:
87 json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters
88 f.write('\n')