Update README

README.md

@@ -20,3 +20,76 @@ 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
+
+以下は、elyza-tasks-100-TV_0.jsonl の回答のためのコードです。
+
+```python
+from unsloth import FastLanguageModel
+import torch
+import json
+
+
+HF_TOKEN = "your-token"
+model_name = "qcube/llm-jp-3-13b-finetune6"
+
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+    token=HF_TOKEN,
+)
+FastLanguageModel.for_inference(model)
+
+# 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 = ""
+
+
+from tqdm import tqdm
+
+# 推論
+results = []
+for dt in tqdm(datasets):
+    input = dt["input"]
+
+    prompt = f"""### 指示\n{input}\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})
+
+
+with open(f"./llm-jp-3-13b-finetune6-outputs-3.jsonl", "w", encoding="utf-8") as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)
+        f.write("\n")
+```