--- base_model: llm-jp/llm-jp-3-13b tags: - text-generation-inference - transformers - unsloth - llama - trl license: cc-by-nc-sa-4.0 language: - en --- # 使用データセット (1) https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/ 関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024) # Sample Use '''Python''' from unsloth import FastLanguageModel import torch import json model_name = "YMIMURA/llm-jp-3-13b-finetune-3" max_seq_length = 2048 dtype = None load_in_4bit = True model, tokenizer = FastLanguageModel.from_pretrained( model_name = model_name, max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, token = "YOUR_TOKEN", ) FastLanguageModel.for_inference(model) datasets = [] with open("./elyza-tasks-100-TV_0.jsonl", "r") as f: item = "" for line in f: line = line.strip() item += line if item.endswith("}"): datasets.append(json.loads(item)) item = "" from tqdm import tqdm # 推論 results = [] for dt in tqdm(datasets): input = dt["input"] prompt = f"""### 指示\n{input}\n### 回答\n""" inputs = tokenizer([prompt], return_tensors = "pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2) prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1] results.append({"task_id": dt["task_id"], "input": input, "output": prediction}) with open(f"/content/{model_name}_output.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) f.write('\n') '''''''ここまで'''''' # Uploaded model - **Developed by:** YMIMURA - **License:** apache-2.0 - **Finetuned from model :** llm-jp/llm-jp-3-13b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth)