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rinna_-_youri-7b-chat-gguf

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youri-7b-chat - GGUF

Name Quant method Size
youri-7b-chat.Q2_K.gguf Q2_K 2.36GB
youri-7b-chat.IQ3_XS.gguf IQ3_XS 2.6GB
youri-7b-chat.IQ3_S.gguf IQ3_S 2.75GB
youri-7b-chat.Q3_K_S.gguf Q3_K_S 2.75GB
youri-7b-chat.IQ3_M.gguf IQ3_M 2.9GB
youri-7b-chat.Q3_K.gguf Q3_K 3.07GB
youri-7b-chat.Q3_K_M.gguf Q3_K_M 3.07GB
youri-7b-chat.Q3_K_L.gguf Q3_K_L 3.35GB
youri-7b-chat.IQ4_XS.gguf IQ4_XS 3.4GB
youri-7b-chat.Q4_0.gguf Q4_0 3.56GB
youri-7b-chat.IQ4_NL.gguf IQ4_NL 3.58GB
youri-7b-chat.Q4_K_S.gguf Q4_K_S 3.59GB
youri-7b-chat.Q4_K.gguf Q4_K 3.8GB
youri-7b-chat.Q4_K_M.gguf Q4_K_M 3.8GB
youri-7b-chat.Q4_1.gguf Q4_1 3.95GB
youri-7b-chat.Q5_0.gguf Q5_0 4.33GB
youri-7b-chat.Q5_K_S.gguf Q5_K_S 4.33GB
youri-7b-chat.Q5_K.gguf Q5_K 4.45GB
youri-7b-chat.Q5_K_M.gguf Q5_K_M 4.45GB
youri-7b-chat.Q5_1.gguf Q5_1 4.72GB
youri-7b-chat.Q6_K.gguf Q6_K 5.15GB
youri-7b-chat.Q8_0.gguf Q8_0 6.67GB

Original model description:

language: - ja - en license: llama2 datasets: - databricks/databricks-dolly-15k - kunishou/databricks-dolly-15k-ja - izumi-lab/llm-japanese-dataset thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png inference: false model-index: - name: youri-7b-chat results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 51.19 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rinna/youri-7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 76.09 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rinna/youri-7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 46.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rinna/youri-7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 41.17 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rinna/youri-7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 75.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rinna/youri-7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rinna/youri-7b-chat name: Open LLM Leaderboard

rinna/youri-7b-chat

rinna-icon

Overview

The model is the instruction-tuned version of rinna/youri-7b. It adopts a chat-style input format.

Benchmarking

Please refer to rinna's LM benchmark page.

How to use the model 

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/youri-7b-chat")
model = AutoModelForCausalLM.from_pretrained("rinna/youri-7b-chat")

if torch.cuda.is_available():
    model = model.to("cuda")

instruction = "次の日本語を英語に翻訳してください。"
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。"

context = [
    {
        "speaker": "設定",
        "text": instruction
    },
    {
        "speaker": "ユーザー",
        "text": input
    }
]
prompt = [
    f"{uttr['speaker']}: {uttr['text']}"
    for uttr in context
]
prompt = "\n".join(prompt)
prompt = (
    prompt
    + "\n"
    + "システム: "
)
token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=200,
        do_sample=True,
        temperature=0.5,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

output = tokenizer.decode(output_ids.tolist()[0])
print(output)
"""
設定: 次の日本語を英語に翻訳してください。
ユーザー: 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。
システム:  Learning to solve tasks based on natural language instructions is called instruction tuning.</s>
"""

output = output[len(prompt):-len("</s>")].strip()
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。"

context.extend([
    {
        "speaker": "システム",
        "text": output
    },
    {
        "speaker": "ユーザー",
        "text": input
    }
])
prompt = [
    f"{uttr['speaker']}: {uttr['text']}"
    for uttr in context
]
prompt = "\n".join(prompt)
prompt = (
    prompt
    + "\n"
    + "システム: "
)
token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=200,
        do_sample=True,
        temperature=0.5,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

output = tokenizer.decode(output_ids.tolist()[0])
print(output)
"""
設定: 次の日本語を英語に翻訳してください。
ユーザー: 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。
システム: Learning to solve tasks based on natural language instructions is called instruction tuning.
ユーザー: 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。
システム:  Large language models (LLMs) are computer language models consisting of a deep artificial neural network with millions to billions of parameters that are trained by self-supervised learning or semi-supervised learning using vast unlabeled text corpora.</s>
"""

Tokenization

The model uses the original llama-2 tokenizer.

How to cite 

@misc{rinna-youri-7b-chat,
    title = {rinna/youri-7b-chat},
    author={Zhao, Tianyu and Sawada, Kei}
    url = {https://huggingface.co/rinna/youri-7b-chat},
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    url = {https://arxiv.org/abs/2404.01657},
}

License

The llama2 license

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 48.51
AI2 Reasoning Challenge (25-Shot) 51.19
HellaSwag (10-Shot) 76.09
MMLU (5-Shot) 46.06
TruthfulQA (0-shot) 41.17
Winogrande (5-shot) 75.06
GSM8k (5-shot) 1.52
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