morizon
/

llm-jp-3-13b_mix_30000_1209

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-library_name: transformers
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 ---
+language: ja
+license: apache-2.0
+tags:
+  - text-generation
+  - transformers
+  - lora
+model_name: llm-jp-3-13b_mix_30000_1209
+base_model: llm-jp/llm-jp-3-13b
+adapter_model: morizon/llm-jp-3-13b_mix_30000_1209
+inference_framework: transformers
 ---
+# モデル名
+このモデルは日本語テキスト生成タスク向けに最適化されたLoRAアダプタ付きのモデルです。
+## 概要
+#Sample Use
+以下は、elyza-tasks-100-TV_0.jsonl回答のためのコードです。
+```python
+!pip install -U bitsandbytes
+!pip install -U transformers
+!pip install -U accelerate
+!pip install -U datasets
+!pip install -U peft
+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 = "your_token"
+# ベースとなるモデルと学習したLoRAのアダプタ。
+model_id = "llm-jp/llm-jp-3-13b"
+adapter_id = "morizon/llm-jp-3-13b_mix_30000_1209"
+# 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)
+model.eval()
+# 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 = ""
+# システムプロンプト（固定の指示）
+system_prompt = """
+あなたはユーザが知りたいことを正確に把握し、的確に回答するアシスタントです。
+1. 指示に従う際は、必ずその内容を完全に理解し、結論を優先的に考慮するように心掛けてください。
+2. 問題の解答となる根拠は、常に文章内から探し出すようにして下さい。
+3. 指示から主要な情報と詳細を抽出し、要点を漏らさず回答することを重視して下さい。
+4. 回答のトーンやスタイルは、与えられたテーマや質問に合わせて柔軟に調整して下さい。
+5. 回答を作成した後は、必ず推敲を行い、誤りや曖昧さがないかどうか確認して下さい。
+"""
+results = []
+# データセットの処理
+for data in tqdm(datasets):
+    input_text = data["input"]
+    # プロンプトの構築（システムプロンプト + ユーザー入力）
+    prompt = f"""### 指示
+{system_prompt}
+{input_text}
+### 回答
+"""
+    # トークナイズ
+    tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+    attention_mask = torch.ones_like(tokenized_input)
+    # 推論パラメータの設定
+    max_new_tokens = 1024
+    do_sample = True
+    top_p = 0.95
+    temperature = 0.7
+    repetition_penalty = 1.05
+    # 推論実行
+    with torch.no_grad():
+        outputs = model.generate(
+            tokenized_input,
+            attention_mask=attention_mask,  # attention_maskを明示的に指定
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            top_p=top_p,
+            temperature=temperature,
+            repetition_penalty=repetition_penalty,
+            pad_token_id=tokenizer.eos_token_id
+        )[0]
+    # 出力の整形
+    output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+    # 結果を表示
+    print(f"Task ID: {data['task_id']}\nInput: {input_text}\nOutput: {output}\n{'-'*50}")
+    # 結果の保存
+    results.append({"task_id": data["task_id"], "input": input_text, "output": output})
+import re
+jsonl_id = re.sub(".*/", "", adapter_id)
+with open(f"./{jsonl_id}-outputs_rev.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')
+```