llm-jp-3-13b-finetune

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Model Details

Model Description

このモデルはllm-jp/llm-jp-3-13bを、ichikara-instruction-003-001-1.jsonデータセットを用いて、 ELYZA-tasks-100という日本語能力を測るベンチマークの改変版、ELYZA-tasks-100-TVで高い推論能力を出すためにファインチューニングしたモデルである。

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [Takayuki Shiki (@shixxtaxx)]
• Language(s) (NLP): [ja]
• Finetuned from model [optional]: [llm-jp/llm-jp-3-13b]
• Training Data: 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/Distribution20241221_all/ichikara-instruction-003-001-1.json

How to Get Started with the Model

Use the code below to get started with the model.

以下は本モデルを用いてELYZA-tasks-100-TVデータセットからの推論出力を得るためのコードです。

前提条件として以下が必要

・Python環境 ・Hugging Faceのアクセストークン

ライブラリのインストール

!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft

# notebookでインタラクティブな表示を可能とする（ただし、うまく動かない場合あり）
!pip install ipywidgets --upgrade

モデル、トークナイザーの読み込み

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json

# Hugging Faceで取得したTokenをこちらに貼る。
HF_TOKEN = "Hugging Face Token"

# ベースとなるモデルと学習したLoRAのアダプタ。
# model_idの値はomnicampusの環境におけるモデルのパスを表しており、それ以外の環境で実行する場合は変更の必要があります。
model_id = "models/models--llm-jp--llm-jp-3-13b/snapshots/cd3823f4c1fcbb0ad2e2af46036ab1b0ca13192a"
# omnicampus以外の環境をご利用の方は以下をご利用ください。
# base_model_id = "llm-jp/llm-jp-3-13b" 
adapter_id = "shixxtaxx/llm-jp-3-13b-finetune"

# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token = HF_TOKEN
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)

# 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)

データセットの読み込み

# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行。
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 = ""

推論

results = []
for data in tqdm(datasets):

  input = data["input"]

  prompt = f"""### 指示
  {input}
  ### 回答
  """

  tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
  attention_mask = torch.ones_like(tokenized_input)
  with torch.no_grad():
      outputs = model.generate(
          tokenized_input,
          attention_mask=attention_mask,
          max_new_tokens=100,
          do_sample=False,
          repetition_penalty=1.2,
          pad_token_id=tokenizer.eos_token_id
      )[0]
  output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)

  results.append({"task_id": data["task_id"], "input": input, "output": output})

出力のjsonlファイルを生成。

import re
jsonl_id = re.sub(".*/", "", adapter_id)
with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)  # ensure_ascii=False for handling non-ASCII characters
        f.write('\n')

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

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Summary

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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