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RichardErkhov/
Rakuten_-_RakutenAI-7B-chat-gguf

Name: Rakuten_-_RakutenAI-7B-chat-gguf
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RakutenAI-7B-chat - GGUF

Model creator: https://huggingface.co/Rakuten/
Original model: https://huggingface.co/Rakuten/RakutenAI-7B-chat/

Name	Quant method	Size
RakutenAI-7B-chat.Q2_K.gguf	Q2_K	2.6GB
RakutenAI-7B-chat.IQ3_XS.gguf	IQ3_XS	2.89GB
RakutenAI-7B-chat.IQ3_S.gguf	IQ3_S	3.04GB
RakutenAI-7B-chat.Q3_K_S.gguf	Q3_K_S	3.02GB
RakutenAI-7B-chat.IQ3_M.gguf	IQ3_M	3.14GB
RakutenAI-7B-chat.Q3_K.gguf	Q3_K	3.35GB
RakutenAI-7B-chat.Q3_K_M.gguf	Q3_K_M	3.35GB
RakutenAI-7B-chat.Q3_K_L.gguf	Q3_K_L	3.64GB
RakutenAI-7B-chat.IQ4_XS.gguf	IQ4_XS	3.76GB
RakutenAI-7B-chat.Q4_0.gguf	Q4_0	3.91GB
RakutenAI-7B-chat.IQ4_NL.gguf	IQ4_NL	3.95GB
RakutenAI-7B-chat.Q4_K_S.gguf	Q4_K_S	3.94GB
RakutenAI-7B-chat.Q4_K.gguf	Q4_K	4.15GB
RakutenAI-7B-chat.Q4_K_M.gguf	Q4_K_M	4.15GB
RakutenAI-7B-chat.Q4_1.gguf	Q4_1	4.33GB
RakutenAI-7B-chat.Q5_0.gguf	Q5_0	4.75GB
RakutenAI-7B-chat.Q5_K_S.gguf	Q5_K_S	4.75GB
RakutenAI-7B-chat.Q5_K.gguf	Q5_K	4.87GB
RakutenAI-7B-chat.Q5_K_M.gguf	Q5_K_M	4.87GB
RakutenAI-7B-chat.Q5_1.gguf	Q5_1	5.16GB
RakutenAI-7B-chat.Q6_K.gguf	Q6_K	5.63GB
RakutenAI-7B-chat.Q8_0.gguf	Q8_0	7.3GB

Original model description:

license: apache-2.0

RakutenAI-7B-chat

Model Description

RakutenAI-7B is a systematic initiative that brings the latest technologies to the world of Japanese LLMs. RakutenAI-7B achieves the best scores on the Japanese language understanding benchmarks while maintaining a competitive performance on the English test sets among similar models such as OpenCalm, Elyza, Youri, Nekomata and Swallow. RakutenAI-7B leverages the Mistral model architecture and is based on Mistral-7B-v0.1 pre-trained checkpoint, exemplifying a successful retrofitting of the pre-trained model weights. Moreover, we extend Mistral's vocabulary from 32k to 48k to offer a better character-per-token rate for Japanese.

The technical report can be accessed at arXiv.

If you are looking for a foundation model, check RakutenAI-7B.

If you are looking for an instruction-tuned model, check RakutenAI-7B-instruct.

An independent evaluation by Kamata et.al. for Nejumi LLMリーダーボード Neo using a weighted average of llm-jp-eval and Japanese MT-bench also confirms the highest performance of chat/instruct versions of RakutenAI-7B among Open LLMs of similar sizes, with a score of 0.393/0.331 respectively, as of 22nd March 2024.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Rakuten/RakutenAI-7B-chat"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
model.eval()

requests = [
    "「馬が合う」はどう言う意味ですか",
    "How to make an authentic Spanish Omelette?",
]

system_message = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user_input} ASSISTANT:"

for req in requests:
    input_req = system_message.format(user_input=req)
    input_ids = tokenizer.encode(input_req, return_tensors="pt").to(device=model.device)
    tokens = model.generate(
        input_ids,
        max_new_tokens=1024,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )
    out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True)
    print("USER:\n" + req)
    print("ASSISTANT:\n" + out)
    print()
    print()

Model Details

Developed by: Rakuten Group, Inc.
Language(s): Japanese, English
License: This model is licensed under Apache License, Version 2.0.
Instruction-Tuning Dataset: We fine-tune our foundation model to create RakutenAI-7B-instruct and RakutenAI-7B-chat using a mix of open source and internally hand-crafted datasets. We use train part of the following datasets (CC by-SA License) for instruction-tuned and chat-tuned models:
- JSNLI
- RTE
- KUCI
- BELEBELE
- JCS
- JNLI
- Dolly-15K
- OpenAssistant1

Limitations and Bias

The suite of RakutenAI-7B models is capable of generating human-like text on a wide range of topics. However, like all LLMs, they have limitations and can produce biased, inaccurate, or unsafe outputs. Please exercise caution and judgement while interacting with them.

Citation

For citing our work on the suite of RakutenAI-7B models, please use:

@misc{rakutengroup2024rakutenai7b,
      title={RakutenAI-7B: Extending Large Language Models for Japanese}, 
      author={{Rakuten Group, Inc.} and Aaron Levine and Connie Huang and Chenguang Wang and Eduardo Batista and Ewa Szymanska and Hongyi Ding and Hou Wei Chou and Jean-François Pessiot and Johanes Effendi and Justin Chiu and Kai Torben Ohlhus and Karan Chopra and Keiji Shinzato and Koji Murakami and Lee Xiong and Lei Chen and Maki Kubota and Maksim Tkachenko and Miroku Lee and Naoki Takahashi and Prathyusha Jwalapuram and Ryutaro Tatsushima and Saurabh Jain and Sunil Kumar Yadav and Ting Cai and Wei-Te Chen and Yandi Xia and Yuki Nakayama and Yutaka Higashiyama},
      year={2024},
      eprint={2403.15484},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf