first update

README.md

@@ -20,3 +20,92 @@ 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 transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+import torch
+from tqdm import tqdm
+import json
+
+HF_TOKEN = "your-token"
+model_name = "qcube/llm-jp-3-13b-finetune2"
+
+# QLoRA config
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=False,
+)
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto",
+    token=HF_TOKEN,
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    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 = ""
+
+# llmjp
+results = []
+for data in tqdm(datasets):
+
+    input = data["input"]
+
+    prompt = f"""### 指示
+    {input}
+    ### 回答：
+    """
+
+    tokenized_input = tokenizer.encode(
+        prompt, add_special_tokens=False, return_tensors="pt"
+    ).to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            tokenized_input, max_new_tokens=100, do_sample=False, repetition_penalty=1.2
+        )[0]
+    output = tokenizer.decode(
+        outputs[tokenized_input.size(1) :], skip_special_tokens=True
+    )
+
+    results.append({"task_id": data["task_id"], "input": input, "output": output})
+
+
+import re
+
+model_name = re.sub(".*/", "", model_name)
+with open(f"./{model_name}-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")
+
+```