Update README.md

README.md

@@ -20,3 +20,60 @@ 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)
+
+# Instruction tuning
+
+The models have been fine-tuned using the following datasets.
+ 
+| Language | Dataset | description | 
+|:---|:---|:---|
+|Japanese|[ichikara-instruction-003-001-1.json](https://liat-aip.sakura.ne.jp/wp/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF%E4%BD%9C%E6%88%90/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF-%E5%85%AC%E9%96%8B/)| A manually constructed instruction dataset |
+
+データセット作成チーム：
+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)
+
+# Usage
+```python
+from unsloth import FastLanguageModel
+import torch
+import json
+from tqdm import tqdm
+# config
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
+model_name = "https://huggingface.co/kaitoto/llm-jp-3-13b-finetune-2"
+# モデルの読み込み
+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。
+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 = ""
+# 推論
+results = []
+for dt in tqdm(data):
+  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": data["task_id"], "input": input, "output": output})
+# 保存
+with open(f"/content/{model_name}_output.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)
+        f.write('\n')
+```