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llm-jp-3-172b-alpha1

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.

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-alpha1")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-172b-alpha1", 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::
    • 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.

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