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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
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