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LLMstudy/
gemma-2-27b-it-241217-2epoch-Llama_lora

Name: gemma-2-27b-it-241217-2epoch-Llama_lora
Availability: InStock

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Sample Use

Google Colab 上でモデルを読み込み、elyza-tasks-100-TV_0.jsonl のinput からoutput を出力し、jsonl 形式で保存するコードは以下の通りである。

python

# 必要なライブラリをインストール
%%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

# 必要なライブラリを読み込み
from unsloth import FastLanguageModel
from peft import PeftModel
import torch
import json
from tqdm import tqdm
import re
import pandas as pd
from datasets import load_dataset
import time

from google.colab import userdata
HF_TOKEN=userdata.get('HF_TOKEN')

# ベースとなるモデルと学習したLoRAのアダプタ（Hugging FaceのIDを指定）。
model_id = "./gemma-2-27b"
adapter_id = "LLMstudy/gemma-2-27b-it-241217-2epoch-Llama_lora"

# 入出力ファイルの設定
test_file_path = './elyza-tasks-100-TV_0.jsonl'
output_file_path = f'./answer_gemma-2-27b-it-241217.jsonl'

# プロンプトフォーマットの定義
prompt = """以下は、タスクを説明する指示です。指示を適切に満たす回答を書いてください。


### 指示:
{}


### 回答:
{}"""

# モデルの読み込み
!huggingface-cli login --token $HF_TOKEN
!huggingface-cli download google/gemma-2-27b --local-dir gemma-2-27b/

# model parameters
max_seq_length = 2048
dtype = None
load_in_4bit = True

# FastLanguageModel インスタンスを作成
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_id,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    trust_remote_code=True,
)

# 元のモデルにLoRAのアダプタを統合。
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)

# 推論
# データセットの読み込み
datasets = []
with open(test_file_path, "r") as f:
    item = ""
    for line in f:
      line = line.strip()
      item += line
      if item.endswith("}"):
        datasets.append(json.loads(item))
        item = ""

# 学習したモデルを用いてタスクを実行
FastLanguageModel.for_inference(model)
start_time = time.time()
results = []
for dt in tqdm(datasets):
  input = dt["input"]
  instruction = prompt.format(input, "")
  inputs = tokenizer([instruction], return_tensors = "pt").to(model.device)

  outputs = model.generate(**inputs, max_new_tokens = 2048, use_cache = True, do_sample=False, repetition_penalty=1.2)
  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答:\n')[-1]

  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
  print(f"task_id: {dt['task_id']}")
  print(f"prompt: {instruction}")
  print(f"output: {prediction}")
  print("-" * 50)
end_time = time.time()
print(f"Execution Time: {end_time - start_time} seconds")

# jsonlで保存
with open(output_file_path, 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')

License

This model is distributed under the following terms:

Gemma Terms of Use (see attached Gemma_Terms_of_Use.txt or https://ai.google.dev/gemma/terms)
Additional Restrictions:
- Use of this model, its derivatives, or its outputs is strictly prohibited for any purpose, including research, commercial, or educational purposes, without the explicit permission of the creator.
- Redistribution of this model or its derivatives in any form is prohibited without the explicit permission of the creator.

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