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--- |
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thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png |
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license: llama2 |
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language: |
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- ja |
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- en |
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inference: false |
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datasets: |
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- databricks/databricks-dolly-15k |
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- kunishou/databricks-dolly-15k-ja |
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- izumi-lab/llm-japanese-dataset |
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--- |
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# `rinna/youri-7b-chat-gptq` |
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# Overview |
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`rinna/youri-7b-chat-gptq` is the quantized model for [`rinna/youri-7b-chat`](https://huggingface.co./rinna/youri-7b-chat) using AutoGPTQ. The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference. |
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* **Model architecture** |
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Refer to the [original model](https://huggingface.co./rinna/youri-7b-chat) for architecture details. |
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* **Fine-tuning** |
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Refer to the [original model](https://huggingface.co./rinna/youri-7b-chat) for fine-tuning details. |
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* **Authors** |
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- [Toshiaki Wakatsuki](https://huggingface.co./t-w) |
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- [Tianyu Zhao](https://huggingface.co./tianyuz) |
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- [Kei Sawada](https://huggingface.co./keisawada) |
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--- |
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# Benchmarking |
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Our evaluation experiments show that the quantization yields slight performance degradation on downstream tasks. |
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Results will be updated soon. |
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--- |
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# How to use the model |
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~~~~python |
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import torch |
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from transformers import AutoTokenizer |
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from auto_gptq import AutoGPTQForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("rinna/youri-7b-chat-gptq") |
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model = AutoGPTQForCausalLM.from_quantized("rinna/youri-7b-chat-gptq", use_safetensors=True) |
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instruction = "次の日本語を英語に翻訳してください。" |
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input = "自然言語による指示に基づきタスクが解けるよう学習させることを Instruction tuning と呼びます。" |
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context = [ |
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{ |
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"speaker": "設定", |
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"text": instruction |
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}, |
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{ |
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"speaker": "ユーザー", |
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"text": input |
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} |
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] |
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prompt = [ |
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f"{uttr['speaker']}: {uttr['text']}" |
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for uttr in context |
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] |
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prompt = "\n".join(prompt) |
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prompt = ( |
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prompt |
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+ "\n" |
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+ "システム: " |
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) |
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token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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with torch.no_grad(): |
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output_ids = model.generate( |
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input_ids=token_ids.to(model.device), |
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max_new_tokens=200, |
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do_sample=True, |
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temperature=0.5, |
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pad_token_id=tokenizer.pad_token_id, |
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bos_token_id=tokenizer.bos_token_id, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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output = tokenizer.decode(output_ids.tolist()[0]) |
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print(output) |
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output = output[len(prompt):-len("</s>")].strip() |
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input = "大規模言語モデル（だいきぼげんごモデル、英: large language model、LLM）は、多数のパラメータ（数千万から数十億）を持つ人工ニューラルネットワークで構成されるコンピュータ言語モデルで、膨大なラベルなしテキストを使用して自己教師あり学習または半教師あり学習によって訓練が行われる。" |
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context.extend([ |
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{ |
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"speaker": "システム", |
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"text": output |
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}, |
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{ |
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"speaker": "ユーザー", |
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"text": input |
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} |
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]) |
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prompt = [ |
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f"{uttr['speaker']}: {uttr['text']}" |
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for uttr in context |
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] |
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prompt = "\n".join(prompt) |
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prompt = ( |
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prompt |
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+ "\n" |
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+ "システム: " |
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) |
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token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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with torch.no_grad(): |
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output_ids = model.generate( |
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input_ids=token_ids.to(model.device), |
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max_new_tokens=200, |
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do_sample=True, |
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temperature=0.5, |
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pad_token_id=tokenizer.pad_token_id, |
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bos_token_id=tokenizer.bos_token_id, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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output = tokenizer.decode(output_ids.tolist()[0]) |
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print(output) |
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~~~~ |
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--- |
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# Tokenization |
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The model uses the original llama-2 tokenizer. |
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--- |
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# How to cite |
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~~~ |
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@misc{RinnaYouri7bChatGPTQ, |
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url={https://huggingface.co./rinna/youri-7b-chat-gptq}, |
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title={rinna/youri-7b-chat-gptq}, |
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author={Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei} |
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} |
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~~~ |
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--- |
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# License |
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[The llama2 license](https://ai.meta.com/llama/license/) |
