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