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llm-jp-3-1.8b

llm-jp / llm-jp-3-1.8b

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Model's Last Updated: September 27 2024
text-generation

Introduction of llm-jp-3-1.8b

Model Details of llm-jp-3-1.8b

llm-jp-3-1.8b

This repository provides large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics .

Checkpoints format: Hugging Face Transformers

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-3-1.8b")
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))
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
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
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.

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