metadata
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: llama2
language:
- ja
- en
inference: false
datasets:
- databricks/databricks-dolly-15k
- kunishou/databricks-dolly-15k-ja
- izumi-lab/llm-japanese-dataset
rinna/youri-7b-instruction-gptq
Overview
rinna/youri-7b-instruction-gptq is the quantized model for rinna/youri-7b-instruction using AutoGPTQ. The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference.
Model architecture
Refer to the original model for architecture details.
Fine-tuning
Refer to the original model for fine-tuning details.
Authors
Benchmarking
Please refer to rinna's LM benchmark page.
How to use the model
import torch
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rinna/youri-7b-instruction-gptq")
model = AutoGPTQForCausalLM.from_quantized("rinna/youri-7b-instruction-gptq", use_safetensors=True)
instruction = "次の日本語を英語に翻訳してください。"
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。"
prompt = f"""
以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。
### 指示:
{instruction}
### 入力:
{input}
### 応答:
"""
token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
input_ids=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)
Tokenization
The model uses the original llama-2 tokenizer.
How to cite
@misc{RinnaYouri7bInstructionGPTQ,
url={https://huggingface.co/rinna/youri-7b-instruction-gptq},
title={rinna/youri-7b-instruction-gptq},
author={Wakatsuki, Toshiaki and Zhao, Tianyu and Sawada, Kei}
}