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Model: sbintuitions/sarashina2.2-0.5b-instruct-v0.1 Source: Original Platform
2026-06-03 05:44:12 +08:00
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+---
+license: mit
+language:
+- ja
+pipeline_tag: text-generation
+base_model:
+  - sbintuitions/sarashina2.2-0.5b
+---
+
+# sbintuitions/sarashina2.2-0.5b-instruct-v0.1
+
+## Model Summary
+
+This repository provides Japanese language models trained by [SB Intuitions](https://www.sbintuitions.co.jp/).
+
+## Model Details
+
+- Model type: Autoregressive Language Model
+- Language(s): Japanese
+
+## Evaluation in Japanese and English Tasks
+
+| Model                                                                                             | Elyza-tasks-100 | Japanese MT Bench | English MT Bench |
+| ------------------------------------------------------------------------------------------------- | --------------- | ----------------- | ---------------- |
+| [Qwen/Qwen2.5-0.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)                   | 1.53            | 2.95              | 4.98             |
+| **sarashina2.2-0.5B-instruct-v0.1**                                                               | **2.38**        | **4.55**          | **5.09**         |
+|                                                                                                   |                 |                   |                  |
+| [Rakuten/RakutenAI-2.0-mini-instruct](https://huggingface.co/Rakuten/RakutenAI-2.0-mini-instruct) | 2.41            | 4.49              | 5.13             |
+| [SakanaAI/TinySwallow-1.5B-Instruct](https://huggingface.co/SakanaAI/TinySwallow-1.5B-Instruct)   | 2.81            | **5.24**          | 6.31             |
+| [Qwen/Qwen2.5-1.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)                   | 2.28            | 4.06              | **6.99**         |
+| [llm-jp/llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3)           | 2.53            | 4.62              | 4.83             |
+| **sarashina2.2-1B-instruct-v0.1**                                                                 | **2.88**        | 5.09              | 6.46             |
+|                                                                                                   |                 |                   |                  |
+| [google/gemma-2-2b-jpn-it](https://huggingface.co/google/gemma-2-2b-jpn-it)                       | 3.02            | 5.19              | 7.56             |
+| [Qwen/Qwen2.5-3B-instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)                       | 2.99            | 5.68              | **7.88**         |
+| [llm-jp/llm-jp-3-3.7b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct3)           | 2.79            | 4.98              | 5.44             |
+| **sarashina2.2-3B-instruct-v0.1**                                                                 | **3.75**        | **6.51**          | 7.71             |
+
+## How to Use
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
+
+# モデルのロード
+model_name = "sbintuitions/sarashina2.2-0.5b-instruct-v0.1"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+chat_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+set_seed(123)
+
+# ユーザーの入力
+user_input = [{"role": "user", "content": "こんにちは。あなたの名前を教えて"}]
+
+# モデルによる応答生成
+responses = chat_pipeline(
+    user_input,
+    max_length=50,
+    do_sample=True,
+    num_return_sequences=3,
+)
+
+# 応答を表示
+for i, response in enumerate(responses, 1):
+    print(f"Response {i}: {response['generated_text']}")
+
+# Response 1: [{'role': 'user', 'content': 'こんにちは。あなたの名前を教えて'}, {'role': 'assistant', 'content': 'Sarashina2と言います。本日のご要件を教えて下さい。'}]
+# Response 2: [{'role': 'user', 'content': 'こんにちは。あなたの名前を教えて'}, {'role': 'assistant', 'content': 'こんにちは！私の名前はSarashina2です。今日はどうしましたか？'}]
+# Response 3: [{'role': 'user', 'content': 'こんにちは。あなたの名前を教えて'}, {'role': 'assistant', 'content': 'Sarashina2と言います。本日のご要件を教えて下さい。'}]
+```
+
+## Limitations
+
+This model has limited safety training.
+Therefore, it might generate some meaningless sequences, some inaccurate instances, or biased/objectionable outputs.
+Before using it, we would like developers to tune models based on human preferences and safety considerations.
+
+## License
+
+MIT License