大規模言語モデル2024講座（東京大学松尾・岩澤研究室）で実施しているものです。

Model_Inference_Template_unsloth_20241127.ipynbとelyza-tasks-100-TV_0.jsonlを使用して出力します。

model_id = "llm-jp/llm-jp-3-13b"

adapter_id = "Chiaki111/llm-jp-3-13b-it4_lora"

Pythonコード

%%capture !pip install unsloth !pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install -U torch !pip install -U peft

from unsloth import FastLanguageModel from peft import PeftModel import torch import json from tqdm import tqdm import re

model_id = "llm-jp/llm-jp-3-13b" adapter_id = "Chiaki111/llm-jp-3-13b-it4_lora"

HF_TOKEN = "トークンを記入"

dtype = None # Noneにしておけば自動で設定 load_in_4bit = True # 今回は13Bモデルを扱うためTrue

model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_id, dtype=dtype, load_in_4bit=load_in_4bit, trust_remote_code=True, )

model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)

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 = ""

FastLanguageModel.for_inference(model)

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})

json_file_id = "llm-jp-3-13b-it4_lora任意のファイル名"

with open(f"/content/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) f.write('\n')

base_model: llm-jp/llm-jp-3-13b tags:

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.