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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3# モデルとトークナイザーのロード
4model_id = "deepkick/llm-jp-3-13b-finetune"
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id)
7
8# 推論
9input_text = "日本語での生成タスクの例を示してください。"
10input_ids = tokenizer(input_text, return_tensors="pt").input_ids
11output = model.generate(input_ids, max_new_tokens=50)
12print(tokenizer.decode(output[0], skip_special_tokens=True))1import json
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4# モデルのロード
5model_id = "deepkick/llm-jp-3-13b-finetune"
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForCausalLM.from_pretrained(model_id)
8
9# JSONLファイルの読み込み
10with open("llm-jp-3-13b-finetune-outputs.jsonl", "r") as f:
11 datasets = [json.loads(line) for line in f]
12
13# 推論
14results = []
15for data in datasets:
16 input_text = data["input"]
17 input_ids = tokenizer(input_text, return_tensors="pt").input_ids
18 output = model.generate(input_ids, max_new_tokens=50)
19 output_text = tokenizer.decode(output[0], skip_special_tokens=True)
20 results.append({"task_id": data["task_id"], "output": output_text})
21
22# 結果を保存
23with open("outputs.jsonl", "w") as f:
24 for result in results:
25 f.write(json.dumps(result) + "\n")llm-jp/llm-jp-3-13b1{"task_id": "0", "output": "タスク0の生成結果"}
2{"task_id": "1", "output": "タスク1の生成結果"}