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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/sarashina2.2-0.5b |
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--- |
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# sbintuitions/sarashina2.2-0.5b-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/sarashina2.2-0.5b-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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