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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Evaluation
1from unsloth import FastLanguageModel
2from peft import PeftModel
3import torch
4import json
5from tqdm import tqdm
6import re
7
8model_id = "atlimited/llm-jp-3-13b-it"
9
10# unslothのFastLanguageModelで元のモデルをロード。
11dtype = None # Noneにしておけば自動で設定
12load_in_4bit = True # 今回は13Bモデルを扱うためTrue
13
14model, tokenizer = FastLanguageModel.from_pretrained(
15 model_name=model_id,
16 dtype=dtype,
17 load_in_4bit=load_in_4bit,
18 trust_remote_code=True,
19)
20
21# タスクとなるデータの読み込み。
22# 事前にデータをアップロードしてください。
23datasets = []
24with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
25 item = ""
26 for line in f:
27 line = line.strip()
28 item += line
29 if item.endswith("}"):
30 datasets.append(json.loads(item))
31 item = ""
32
33# 推論するためにモデルのモードを変更
34FastLanguageModel.for_inference(model)
35
36results = []
37for dt in tqdm(datasets):
38 input = dt["input"]
39
40 prompt = f"""### 指示\n{input}\n### 回答\n"""
41
42 inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
43
44 outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
45 prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
46
47 results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
48
49jsonl_id = re.sub(".*/", "", adapter_id)
50with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
51 for result in results:
52 json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters
53 f.write('\n')
54
55
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Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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