rlcgn589
/

llm-jp-3-13b-it-12_lora

@@ -1,200 +1,176 @@
 ---
-library_name: transformers
 tags:
 - unsloth
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+最終提出README
 ---
+base_model: llm-jp/llm-jp-3-13b
 tags:
+- text-generation-inference
+- transformers
 - unsloth
+- llama
+- trl
+license: other
+language:
+- en
+- ja
+datasets:
+- Aratako/Magpie-Tanuki-8B—annotated-96k
+- tohoku-nlp/abc-multiple-choice
 ---
+# Uploaded  model
+- **Developed by:** rlcgn589
+- **License:** other
+- **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.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# Usage(Code for inference)
+本モデルを用いてELYZA-tasks-100-TVの出力を得るためのコードです。
+なお、松尾研LLM講座2024の最終コンペ課題の提出物を得ることを目的としたコードです。そのため、コンペの評価用途以外は使用禁止とします。
+このコードで生成されたjsonlファイルは課題の成果として提出可能なフォーマットになっています。
+## 環境構築
+omnicampus環境下で事前にterminalで環境構築の必要があります。
+```
+# conda環境の構築
+wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+# このコマンドではいくつか質問があるので答えて下さい
+bash Miniforge3-$(uname)-$(uname -m).sh
+# 以下、インストール先が/root/miniforge3であることを前提とします
+export PATH=/root/miniforge3/bin:$PATH
+conda init
+# ここで一度、terminalを立ち上げ直す必要があります。
+# 以下のリンク先に従い環境を作ります
+# https://docs.unsloth.ai/get-started/installation/conda-install
+conda create --name unsloth_env python=3.10 pytorch-cuda=12.1 pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers -y
+conda activate unsloth_env
+pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes
+# jupyter notebook用のセットアップ。
+conda install -c conda-forge ipykernel -y
+python -m ipykernel install --user --name=unsloth_env --display-name "Python (unsloth_env)"
+```
+## 推論用コードの実行
+```python
+# 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
+# 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"
+adapter_id = "rlcgn589/llm-jp-3-13b-it-12_lora"
+# Hugging Face Token を指定。
+HF_TOKEN = "your Hugging Face Token"
+# unslothのFastLanguageModelで元のモデルをロード。
+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,
+)
+# 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 = ""
+# モデルを用いてタスクの推論。
+# 推論するためにモデルのモードを変更
+FastLanguageModel.for_inference(model)
+results = []
+for dt in tqdm(datasets):
+  input = dt["input"]
+  prompt = f"""### 指示\n{input}簡潔に回答してください。\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})
+# 結果をjsonlで保存。
+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')
+```
+## Datasets
+### Instruction tuning
+本モデルは以下のデータセットでファインチューニングしました。
+| Language | Dataset | description |
+|:---|:---|:---|
+|Japanese| Aratako/Magpie-Tanuki-8B-annotated-96k | MagpieとTanuki-8B-dpo-v1.0で作成されたデータにcalm3-22bでアノテーションを施した合成データを一部使用 |
+|Japanese| tohoku-nlp/abc-multiple-choice | オリジナルの4択の選択肢問題を一部加工（クイズと回答は変えていない） |
+### Data Source
+ファインチューニング用のデータセット作成に作成者が使用したモデルとツールを示します。データを使わせていただきありがとうございます。
+#### Tanuki-8B-dpo-v1.0
+https://huggingface.co/weblab-GENIAC/Tanuki-8B-dpo-v1.0
+#### Magpie
+```tex
+@misc{xu2024magpiealignmentdatasynthesis,
+      title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
+      author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
+      year={2024},
+      eprint={2406.08464},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2406.08464},
+}
+```
+#### CyberAgentLM3-22B-Chat
+```tex
+@misc{cyberagent-calm3-22b-chat,
+      title={cyberagent/calm3-22b-chat},
+      url={https://huggingface.co/cyberagent/calm3-22b-chat},
+      author={Ryosuke Ishigami},
+      year={2024},
+}
+```
+#### abc-multiple-choice
+https://github.com/cl-tohoku/abc-multiple-choice
+https://jedworkshop.github.io/JLR2024/materials/a-1.pdf
+abc-multiple-choiceのライセンスに関する記載を抜粋して記します。
+- 本データセットのクイズ問題の著作権は abc/EQIDEN 実行委員会 に帰属します。
+- 本データセットは研究目的での利用許諾を得ているものです。商用目的での利用は不可とします。