harataku
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llm-jp-3-13b-finetune

Text Generation

Transformers

Safetensors

Japanese

Inference Endpoints

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-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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-## Uses
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+# llm-jp-3-13b-finetune 使用方法ガイド
+## モデル概要
+このモデルは、llm-jp/llm-jp-3-13bをベースにLoRA (Parameter-Efficient Fine-Tuning)で学習された公開モデルです。
+## 必要な環境
+- Python 3.10以上
+- CUDA対応GPU（推奨）
+- 必要なライブラリ:
+  - transformers
+  - bitsandbytes
+  - accelerate
+  - torch
+  - peft
+## インストール手順
+```bash
+# 必要なライブラリのインストール
+pip install -U pip
+pip install -U transformers
+pip install -U bitsandbytes
+pip install -U accelerate
+pip install -U peft
+pip install -U torch
+```
+## 基本的な使用方法
+### 1. シンプルな使用方法
+```python
+from transformers import pipeline
+# パイプラインの作成
+generator = pipeline(
+    "text-generation",
+    model="harataku/llm-jp-3-13b-finetune",
+    device=0  # GPU使用
+)
+# テキスト生成
+prompt = """### 指示
+好きな食べ物について教えてください
+### 回答
+"""
+response = generator(prompt, max_length=200, num_return_sequences=1)
+print(response[0]['generated_text'])
+```
+### 2. 詳細な設定による使用方法
+```python
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig
+)
+import torch
+# モデルの設定
+model_id = "harataku/llm-jp-3-13b-finetune"
+# 量子化の設定
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+# モデルとトークナイザーの読み込み
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+# 推論用の関数
+def generate_response(input_text):
+    prompt = f"""### 指示
+{input_text}
+### 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]
+    response = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+    return response
+# 使用例
+input_text = "好きな食べ物について教えてください"
+response = generate_response(input_text)
+print(response)
+```
+## パラメータの説明
+### モデル生成時のパラメータ
+- `max_new_tokens`: 生成する最大トークン数（デフォルト: 100）
+- `do_sample`: サンプリングを行うかどうか（デフォルト: False）
+- `repetition_penalty`: 繰り返しを抑制するためのペナルティ（デフォルト: 1.2）
+### 入力フォーマット
+入力は以下の形式で行います：
+```
+### 指示
+[入力テキスト]
+### 回答
+```
+## トラブルシューティング
+1. メモリエラーが発生する場合：
+```python
+# より少ないメモリ使用量の設定
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16  # bfloat16からfloat16に変更
+)
+```
+2. GPUが利用できない場合：
+```python
+# CPUでの実行設定
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cpu",
+    low_cpu_mem_usage=True
+)
+```
+## 注意事項
+- 4bit量子化を使用しているため、メモリ使用量は効率的ですが、推論速度とのトレードオフがあります
+- GPU環境での実行を推奨します
+- 長い入力テキストの場合は、`max_new_tokens`の値を適宜調整してください
+## ライセンス
+本モデルはベースモデル（llm-jp/llm-jp-3-13b）のライセンスを継承しています。また、学習データとして使用したichikara-instructionデータセットのライセンス（CC-BY-NC-SA）も適用されます。
+## 引用
+学習データについて：
+```
+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)
+```
+## 更新履歴
+- 2024/03/XX: モデルをpublic設定に変更
+- 2024/03/XX: READMEを更新し、より簡単な使用方法を追加