The open-source large language model llm-jp-3-1.8b - Supports multiple languages and easily handles natural language processing tasks.

Llm Jp 3 1.8b

Developed by llm-jp

A large language model developed by the National Institute of Informatics in Japan, supporting multiple languages such as Japanese and English, and suitable for natural language processing tasks.

Large Language Model

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Japanese large model #Multilingual support #Instruction fine-tuning

Downloads 1,378

Release Time : 9/23/2024

Model Overview

A large language model based on the Transformer architecture, focusing on natural language processing tasks in Japanese and English, and providing multiple model variants to meet different needs.

Model Features

Multilingual support

Supports multiple languages such as Japanese, English, Chinese, and Korean, suitable for cross - language tasks.

Multiple model variants

Provides model variants with different parameter scales, including 1.8B, 3.7B, 13B, and 172B, to meet the needs of different scenarios.

High - performance tokenizer

A tokenizer based on the Unigram byte - fallback model, supporting efficient text processing.

Model Capabilities

Text generation

Natural language understanding

Multilingual processing

Instruction fine - tuning

Use Cases

Natural language processing

Japanese text generation

Generate high - quality Japanese text, suitable for content creation and automated writing.

The generated text is fluent and grammatically correct.

Multilingual translation

Supports translation tasks between Japanese and other languages.

The translation results are accurate and natural.

Instruction fine - tuning

Instruction response

Generate corresponding responses according to user instructions, suitable for chatbots and virtual assistants.

The responses are accurate and in line with the user's intention.

🚀 llm-jp-3-1.8b

This repository offers large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics, aiming to provide powerful text - generation capabilities.

🚀 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.

Model Variants
llm-jp-3-1.8b
llm-jp-3-1.8b-instruct
llm-jp-3-3.7b
llm-jp-3-3.7b-instruct
llm-jp-3-13b
llm-jp-3-13b-instruct
llm-jp-3-172b-beta1
llm-jp-3-172b-beta1-instruct

Checkpoints format: Hugging Face Transformers

📦 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-3-1.8b")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-1.8b", 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

Model type: Transformer-based Language Model
Total seen tokens: 2.1T

Params	Layers	Hidden size	Heads	Context length	Embedding parameters	Non-embedding parameters
1.8b	24	2048	16	4096	407,896,064	1,459,718,144
3.7b	28	3072	24	4096	611,844,096	3,171,068,928
13b	40	5120	40	4096	1,019,740,160	12,688,184,320

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	237.3B
	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 instruction dataset
	answer-carefully-002	A manually constructed instruction dataset focusing on LLMs' safety
	ichikara-instruction-format	A small amount of instruction dataset edited from ichikara-instruction, with some constraints on the output format.
	AutoMultiTurnByCalm3-22B	A synthetic instruction dataset.
	ramdom-to-fixed-multiturn-Calm3	A synthetic instruction dataset.
	wizardlm8x22b-logical-math-coding-sft_additional-ja	A synthetic instruction dataset.
	Synthetic-JP-EN-Coding-Dataset-567k	A synthetic instruction dataset. We used sampled one.
English	FLAN	We used sampled one.

Evaluation

llm-jp-eval (v1.3.1)

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

Model name	average	EL	FA	HE	MC	MR	MT	NLI	QA	RC
llm-jp-3-1.8b	0.3767	0.3725	0.1948	0.2350	0.2500	0.0900	0.7730	0.3080	0.4629	0.7040
llm-jp-3-1.8b-instruct	0.4596	0.4280	0.1987	0.3250	0.3300	0.4200	0.7900	0.3520	0.4698	0.8224
llm-jp-3-3.7b	0.4231	0.3812	0.2440	0.2200	0.1900	0.3600	0.7947	0.3800	0.4688	0.7694
llm-jp-3-3.7b-instruct	0.5188	0.4191	0.2504	0.3400	0.5000	0.5800	0.8166	0.4500	0.4881	0.8247
llm-jp-3-13b	0.5802	0.5570	0.2593	0.4600	0.7000	0.6300	0.8292	0.3460	0.5937	0.8469
llm-jp-3-13b-instruct	0.6168	0.5408	0.2757	0.4950	0.9200	0.7100	0.8317	0.4640	0.4642	0.8500

Japanese MT Bench

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

Model name	average	coding	extraction	humanities	math	reasoning	roleplay	stem	writing
llm-jp-3-1.8b-instruct	4.93	1.50	4.70	7.80	1.55	2.60	7.80	6.10	7.40
llm-jp-3-3.7b-instruct	5.50	1.95	4.05	8.25	2.25	4.00	8.80	7.25	7.45
llm-jp-3-13b-instruct	6.47	3.15	7.05	9.15	3.75	5.40	8.30	7.50	7.45

🔧 Technical Details

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.

📄 License

Apache License, Version 2.0

Model Card Authors

The names are listed in alphabetical order.

Hirokazu Kiyomaru and Takashi Kodama.

Send Questions to

llm-jp(at)nii.ac.jp

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