llm-jp-3-172b-alpha2-instruct

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.

Model Variants
llm-jp-3-172b-alpha1
llm-jp-3-172b-alpha1-instruct
llm-jp-3-172b-alpha2
llm-jp-3-172b-alpha2-instruct
llm-jp-3-172b-beta1
llm-jp-3-172b-beta1-instruct

Checkpoints format: Hugging Face Transformers

Caution!: While it has been confirmed that the performance of LLM-jp-3 172B alpha1 and alpha2 is significantly lower than previously released models, we believe they can still be useful for research purposes and are making them available to the public. For more information, please visit this link.

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

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3-172b-alpha2-instruct")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-172b-alpha2-instruct", device_map="auto", torch_dtype=torch.bfloat16)
chat = [
    {"role": "system", "content": "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。"},
    {"role": "user", "content": "自然言語処理とは何か"},
]
tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, 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))

Model Details

Model type: Transformer-based Language Model
Total seen tokens::
- alpha1: 0.7T
- alpha2: 1.4T
- beta1: 0.7T

Params	Layers	Hidden size	Heads	Context length
172b	96	12288	96	4096

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.

Language	Dataset	Tokens
Japanese	Wikipedia	2.6B
	Common Crawl	762.8B
	WARP/PDF	282.1B
	WARP/HTML	2.7B
	Kaken	1.8B
English	Wikipedia	4.7B
	Dolma/CC-head	608.5B
	Dolma/C4	181.6B
	Dolma/Reddit	83.1B
	Dolma/PeS2o	62.9B
	Dolma/Gutenberg	5.5B
	Dolma/Wiki	3.9B
Code	The Stack	114.1B
Chinese	Wikipedia	0.8B
Korean	Wikipedia	0.3B

Instruction tuning

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

Language	Dataset	description
Japanese	ichikara-instruction-004-002	A manually constructed Japanese instruction dataset
	answer-carefully-001	A manually constructed Japanese instruction dataset focusing on LLMs' safety
	databricks-dolly-15k-ja	databricks-dolly-15k translated into Japanese using DeepL
	oasst1-21k-ja	A subset of oasst1 translated into Japanese using DeepL
	oasst2-33k-ja	A subset of oasst2 translated into Japanese using DeepL
	aya-dataset-ja	A Japanese subset of aya_dataset
	ichikara-instruction-format	A small amount of instruction dataset edited from ichikara-instruction, with some constraints on the output format.
English	databricks-dolly-15k	-
	oasst1-21k-en	A subset of oasst1
	oasst2-33k-en	A subset of oasst2
	Daring-Anteater	-
	FLAN	We used sampled one.

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.