Update README.md

README.md

@@ -11,7 +11,8 @@ language:
 - en
 ---
 
-# Uploaded  model
+# morizon/llm-jp-3-13b-instruct2-grpo-0215_lora
+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。
 
 - **Developed by:** morizon
 - **License:** apache-2.0
@@ -20,3 +21,189 @@ language:
 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)
+
+## Sample Use
+
+
+```python
+
+%%capture
+# Skip restarting message in Colab
+import sys; modules = list(sys.modules.keys())
+for x in modules: sys.modules.pop(x) if "PIL" in x or "google" in x else None
+
+!pip install unsloth vllm
+!pip install --upgrade pillow
+# If you are running this notebook on local, you need to install `diffusers` too
+# !pip install diffusers
+# Temporarily install a specific TRL nightly version
+!pip install git+https://github.com/huggingface/trl.git@e95f9fb74a3c3647b86f251b7e230ec51c64b72b
+```
+
+```python
+from unsloth import FastLanguageModel, PatchFastRL
+PatchFastRL("GRPO", FastLanguageModel)
+
+import re
+import torch
+from datasets import load_dataset, Dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import LoraConfig
+from trl import GRPOConfig, GRPOTrainer
+from unsloth import is_bfloat16_supported
+```
+
+```python
+model_id="llm-jp/llm-jp-3-13b-instruct2"
+adpter_id="morizon/llm-jp-3-13b-instruct2-grpo-MATH-lighteval_step1000_lora"
+
+# --- モデルの読み込みと LoRA 適用 ---
+max_seq_length = 1024  # 推論トレースの最大長
+lora_rank = 64         # LoRA のランク（推奨値：64）
+
+# FastLanguageModel 経由でモデルとトークナイザーを読み込み
+# ※ モデル名は使用するものに合わせてください
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_id,
+    max_seq_length=max_seq_length,
+    load_in_4bit=True,      # 4bit量子化（LoRAファインチューニング時は設定に注意）
+    fast_inference=True,    # vLLM 高速推論を有効化
+    max_lora_rank=lora_rank,
+    gpu_memory_utilization=0.7,
+)
+
+# LoRA (PEFT) を適用
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=lora_rank,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_alpha=lora_rank,
+    use_gradient_checkpointing="unsloth",
+    random_state=3407,
+)
+```
+
+```python
+# --- プロンプトとデータセットの準備 ---
+# 推奨：システムプロンプトを排除し、ユーザープロンプトに全指示を統合
+USER_INSTRUCTION = (
+    "Please ensure your response begins with \"<reasoning>\n\". "
+    "Please reason step by step, and put your final answer within \\boxed{}. "
+)
+
+# テストデータの例（リスト形式）
+test_data = [
+    {"id": 0, "text": "$x^{-1}>x$を満たす正の整数$x$の個数を求めなさい。", "gold": "0", "response": "", "type": "Algebra", "level": "Level 2"},
+    ##評価したいテストデータを入力してください
+]
+
+def extract_boxed_answer_rev(text: str) -> str:
+    """
+    テキスト中から最初の \boxed{...} の中身（ネストを考慮）を抽出する。
+    例: r"\boxed{\frac{\pi}{6}}" -> "\frac{\pi}{6}"
+    """
+    key = r"\boxed{"
+    start_idx = text.find(key)
+    if start_idx == -1:
+        return ""
+    # \boxed{ の直後の位置を開始位置とする
+    start_idx += len(key)
+    brace_count = 1  # 最初の { を既にカウント
+    i = start_idx
+    while i < len(text) and brace_count > 0:
+        if text[i] == "{":
+            brace_count += 1
+        elif text[i] == "}":
+            brace_count -= 1
+        i += 1
+    # i-1 が閉じ括弧に対応する位置
+    return text[start_idx:i-1].strip()
+
+from vllm import SamplingParams
+
+correct = 0
+total = len(test_data)
+
+# 正解ケースと誤答ケースを記録するリスト
+correct_cases = []
+incorrect_cases = []
+
+for item in test_data:
+    # プロンプト生成（USER_INSTRUCTION を先頭に追加）
+    prompt = USER_INSTRUCTION + item["text"]
+    text = tokenizer.apply_chat_template([
+        {"role": "user", "content": prompt},
+    ], tokenize=False, add_generation_prompt=True)
+
+    # 推論実行
+    sampling_params = SamplingParams(
+        temperature=0.6,
+        max_tokens=2048,
+    )
+    output = model.fast_generate(
+        text,
+        sampling_params=sampling_params,
+        lora_request = model.load_lora(adpter_id),
+        # lora_request = model.load_lora("grpo_saved_lora"),
+    )[0].outputs[0].text
+
+    # \boxed{...} の中身を抽出する関数で回答を取得
+    boxed_answer = extract_boxed_answer_rev(output)
+
+    # 結果の表示用
+    print("\n----------Test ID:", item["id"], "----------")
+    print("Prompt:")
+    print(prompt)
+    print("\nLLM Output:")
+    print(output)
+    print("\nExtracted Answer:")
+    print(boxed_answer)
+    print("Gold Answer:", item["gold"])
+
+    # 抽出回答と gold の一致で正解判定
+    if boxed_answer == item["gold"]:
+        correct += 1
+        correct_cases.append({
+            "id": item["id"],
+            "prompt": prompt,
+            "LLM_output": output,
+            "extracted_answer": boxed_answer,
+            "gold": item["gold"]
+        })
+    else:
+        incorrect_cases.append({
+            "id": item["id"],
+            "prompt": prompt,
+            "LLM_output": output,
+            "extracted_answer": boxed_answer,
+            "gold": item["gold"]
+        })
+
+# 正解ケースの表示
+print("\n========== 正解ケース ==========")
+for case in correct_cases:
+    print("\nTest ID:", case["id"])
+    print("Prompt:")
+    print(case["prompt"])
+    print("LLM Output:")
+    print(case["LLM_output"])
+    print("Extracted Answer:", case["extracted_answer"])
+    print("Gold Answer:", case["gold"])
+    print("-" * 40)
+
+# 誤答ケースの表示
+print("\n========== 誤答ケース ==========")
+for case in incorrect_cases:
+    print("\nTest ID:", case["id"])
+    print("Prompt:")
+    print(case["prompt"])
+    print("LLM Output:")
+    print(case["LLM_output"])
+    print("Extracted Answer:", case["extracted_answer"])
+    print("Gold Answer:", case["gold"])
+    print("-" * 40)
+
+accuracy = correct / total * 100
+print("\nOverall Accuracy: {}/{} ({:.2f}%)".format(correct, total, accuracy))
+
+```