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1from transformers import (
2 AutoModelForCausalLM,
3 AutoTokenizer,
4 BitsAndBytesConfig,
5)
6from peft import PeftModel
7import torch
8from tqdm import tqdm
9import json
10
11
12HF_TOKEN = "your_token"
13model_id = "Ki0427/llm-jp-3-13b-ft"
14
15# QLoRA config
16bnb_config = BitsAndBytesConfig(
17 load_in_4bit=True,
18 bnb_4bit_quant_type="nf4",
19 bnb_4bit_compute_dtype=torch.bfloat16,
20)
21
22# Load model
23model = AutoModelForCausalLM.from_pretrained(
24 model_id,
25 quantization_config=bnb_config,
26 device_map="auto",
27 token = HF_TOKEN
28)
29
30# Load tokenizer
31tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
32
33# 元のモデルにLoRAのアダプタを統合。
34model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
35
36datasets = []
37with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
38 item = ""
39 for line in f:
40 line = line.strip()
41 item += line
42 if item.endswith("}"):
43 datasets.append(json.loads(item))
44 item = ""
45
46results = []
47for data in tqdm(datasets):
48
49 input = data["input"]
50 prompt = f"""### 指示
51 {input}
52 ### 回答
53 """
54
55 input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
56 outputs = model.generate(**input_ids, max_new_tokens=512, do_sample=False, repetition_penalty=1.2,)
57 output = tokenizer.decode(outputs[0][input_ids.input_ids.size(1):], skip_special_tokens=True)
58
59 results.append({"task_id": data["task_id"], "input": input, "output": output})
60
61results = []
62for data in tqdm(datasets):
63
64 input = data["input"]
65
66 prompt = f"""### 指示
67 あなたは優秀で正確な回答者です。下記の指示に対して適切な回答を行なってください。
68 {input}
69 ### 回答
70 """
71
72 tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
73 attention_mask = torch.ones_like(tokenized_input)
74 with torch.no_grad():
75 outputs = model.generate(
76 tokenized_input,
77 attention_mask=attention_mask,
78 max_new_tokens=100,
79 do_sample=False,
80 repetition_penalty=1.2,
81 pad_token_id=tokenizer.eos_token_id
82 )[0]
83 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
84
85 results.append({"task_id": data["task_id"], "input": input, "output": output})
86
87import re
88jsonl_id = re.sub(".*/", "", new_model_id)
89with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
90 for result in results:
91 json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters
92 f.write('\n')
93