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--- |
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license: apache-2.0 |
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datasets: |
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- graelo/wikipedia |
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- uonlp/CulturaX |
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- HuggingFaceH4/ultrachat_200k |
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language: |
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- ja |
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- en |
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--- |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/3uLNwKHFwEgT2YQ-BGOiH.png" alt="drawing" width="600"/> |
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</p> |
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# How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("lightblue/karasu-7B") |
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model = AutoModelForCausalLM.from_pretrained("lightblue/karasu-7B", torch_dtype=torch.bfloat16, device_map="auto") |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}] |
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messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"}) |
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prompt = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False) |
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pipe(prompt, max_new_tokens=100, do_sample=False, temperature=0.0, return_full_text=False) |
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``` |
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# Base checkpoint |
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augmxnt/shisa-7b-v1 |
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* Mistral-7B base |
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* Pre-trained on 8B of MADLAD-Ja |
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* Finetuned on Japanese instructions |
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* Highest scoring 7B model on conversation benchmark (JA MT-Bench) |
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# Training datasets (total ~7B) |
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* Aozora Bunko |
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* Japanese Law Precedent Dataset |
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* Japanese Wikipedia |
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* .lg.jp, .go.jp, .ac.jp domain webscrapes from CulturaX (Any documents with same first 25 characters were de-duplicated) |
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* English Ultrachat200K-gen (So that it doesn't forget English and chatting ability learned in the base checkpoint) |
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# Developed by |
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<a href="https://www.lightblue-tech.com"> |
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<img src="https://www.lightblue-tech.com/wp-content/uploads/2021/10/LBlogo-scaled.jpg" alt="Lightblue technology logo" width="400"/> |
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</a> |
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### Engineers |
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Peter Devine |
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Sho Higuchi |
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### Advisors |
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Yuuki Yamanaka |
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Atom Sonoda |
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### Dataset evaluator |
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Renju Aoki |
