Llm Jp 3 13b

🚀 llm-jp-3-13b

This repository offers large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. The development received partial support from GENIAC.

🚀 Quick Start

This repository provides large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. The development was partially supported by GENIAC.

Here are the available model variants:

The checkpoints are in the Hugging Face Transformers format.

📦 Installation

Required Libraries and Their Versions

• torch>=2.3.0
• transformers>=4.40.1
• tokenizers>=0.19.1
• accelerate>=0.29.3
• flash-attn>=2.5.8

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-13b")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-13b", device_map="auto", torch_dtype=torch.bfloat16)
text = "自然言語処理とは何か"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=100,
        do_sample=True,
        top_p=0.95,
        temperature=0.7,
        repetition_penalty=1.05,
    )[0]
print(tokenizer.decode(output))

📚 Documentation

Model Details

Tokenizer

The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model. The vocabulary entries were converted from llm-jp-tokenizer v3.0. Please refer to README.md of llm-jp-tokenizer for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).

Datasets

Pre-training

The models have been pre-trained using a blend of the following datasets:

Instruction tuning

The models have been fine-tuned on the following datasets:

Evaluation

llm-jp-eval (v1.3.1)

We evaluated the models using 100 examples from the dev split.

Japanese MT Bench

We evaluated the models using gpt-4-0613. Please see the codes for details.

Risks and Limitations

The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.

Send Questions to

llm-jp(at)nii.ac.jp

📄 License

Apache License, Version 2.0

Model Card Authors

The names are listed in alphabetical order.

Hirokazu Kiyomaru and Takashi Kodama.