Add README.md & LICENSE

LICENSE

@@ -0,0 +1,41 @@
+# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+
+This work is licensed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License**.
+
+To view a copy of this license, visit [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+---
+
+## **Summary of Terms**
+You are free to:
+- **Share** — copy and redistribute the material in any medium or format.
+- **Adapt** — remix, transform, and build upon the material.
+
+**Under the following terms:**
+- **Attribution (BY):** You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+- **Non-Commercial (NC):** You may not use the material for commercial purposes.
+- **ShareAlike (SA):** If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+**No additional restrictions:** You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+---
+
+## **Attribution Requirements**
+When redistributing or adapting this work, you must include the following attribution in a clear and visible manner:
+
+```
+This work, containing model adapter weights, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
+Original works:
+- Base Model: [https://huggingface.co/llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) (Apache License 2.0)
+- Datasets:
+  - [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (CC BY-SA 4.0)
+  - [ichikara-instruction](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/) (CC BY-NC-SA 4.0)
+This work:
+- Adapter Weights: CC BY-NC-SA 4.0
+- Creator: tokutsu
+```
+
+---
+
+**Disclaimer:**
+The materials are provided \"as is\", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement.

README.md

@@ -6,17 +6,85 @@ tags:
 - unsloth
 - llama
 - trl
-license: apache-2.0
+licenses:
+- Apache-2.0       # Base model
+- CC-BY-NC-SA-4.0  # Adapter & Dataset (ichikara-instruction)
+- CC-BY-SA-4.0     # Dataset (ELYZA-tasks-100)
 language:
-- en
+- ja
+datasets:
+- elyza/ELYZA-tasks-100
+- ichikara-instruction
 ---
 
-# Uploaded  model
+# llm-jp-3-13b-it: A Fine-tuned model for ELYZA-tasks-100
 
-- **Developed by:** tokutsu
-- **License:** apache-2.0
-- **Finetuned from model :** llm-jp/llm-jp-3-13b
+## Overview
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+This is a fine-tuned [`llm-jp-3-13b-it`](https://huggingface.co/tokutsu/llm-jp-3-13b-it) model for [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100). The model was trained on ELYZA-tasks-100 and the [ichikara-instruction dataset](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/).
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## Usage
+
+Load the model and tokenizer with the following code:
+
+```python
+from unsloth import FastLanguageModel
+
+model_id = "tokutsu/llm-jp-3-13b-it"
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_id,
+    dtype=None,
+    load_in_4bit=True,
+    trust_remote_code=True,
+)
+FastLanguageModel.for_inference(model)
+
+prompt = """### 指示
+仕事の熱意を取り戻すためのアイデアを5つ挙げてください。
+
+### 回答
+"""
+
+inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+outputs = model(**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]
+```
+
+## Example Output
+
+Here is an example of what the output would look like:
+
+```plaintext
+1. 仕事に関連する趣味を持つ: 趣味はストレス解消やリラックス効果があり、仕事へのモチベーションアップにもつながります。例えば、ガーデニングが好きならオフィスで観葉植物を育てたり、料理が得意であれば同僚とランチ会をするなど、自分なりの仕事との接点を見つけてみましょう。
+2. 目標設定を行う: 達成可能な目標を立てることで、日々成長していることを実感でき、やりがいも生まれてきます。また、定期的に進捗状況を確認することで、達成感とともにさらなるやる気につながるでしょう。
+3. 同僚たちと交流する: 職場での人間関係は、仕事に対する情熱を維持するために重要です。コミュニケーションをとることで、お互いのことを理解し、助け合うことができます。職場のイベントに参加したり、休憩時間には雑談したりして、積極的に周りの人と関わりましょう。
+4. 新しいスキルを身につける: スキル向上のための勉強や、新しい資格取得などにより、自分の能力を高めることができます。自己啓発的な活動が、自信や向上心へとつながるかもしれません。
+5. 休暇をとってリフレッシュする: 長期休暇をとり、心身ともに休息することは大切なことです。旅行へ行ったり、家族と一緒に過ごしたりすることで気分転換ができ、また新たな気持ちで仕事に取り組むことができるようになります。
+```
+
+## Additional Information
+
+The model was trained using LoRA with the following specifications:
+
+### **Base Model**
+- The training started with the pre-trained language model **`llm-jp/llm-jp-3-13b`**.
+
+### **Datasets**
+- **ELYZA-tasks-100:** A comprehensive dataset covering 100 diverse tasks, enhancing the model's ability to generalize across multiple domains. ([link](https://huggingface.co/datasets/elyza/ELYZA-tasks-100))
+- **ichikara-instruction:** This dataset contains a diverse range of text samples, providing a strong foundation for understanding contextual nuances. ([link](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/))
+
+### **Training Methodology**
+- **PEFT with LoRA:** The training employed **PEFT (Parameter-Efficient Fine-Tuning)** using **LoRA (Low-Rank Adaptation)**, enabling efficient fine-tuning with reduced computational costs while retaining the model's performance. This model was trained with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+## License
+
+This model is licensed under the **CC BY-NC-SA 4.0** License. For more details, see the [LICENSE](https://huggingface.co/tokutsu/llm-jp-3-13b-it/blob/main/LICENSE) file in this repository.
+
+## Acknowledgment
+
+This model was developed as part of the [LLM course 2024](https://weblab.t.u-tokyo.ac.jp/lecture/course-list/large-language-model/) exercises conducted by the Matsuo-Iwasawa Lab at the University of Tokyo.