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llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0

This repository provides large language models developed by LLM-jp, a collaborative project launched in Japan.

Pre-trained models
llm-jp-13b-v2.0

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-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0", 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: 256B
Model Params Layers Hidden size Heads Context length
13b model 13b 40 5120 40 4096

Training

Tokenizer

The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model. The vocabulary entries were converted from llm-jp-tokenizer v2.2 (100k: code20K_en40K_ja60K.ver2.2). Please refer to README.md of llm-ja-tokenizer for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).

  • Model: Hugging Face Fast Tokenizer using Unigram byte-fallback model
  • Training algorithm: Marging Code/English/Japanese vocabularies constructed with SentencePiece Unigram byte-fallback and reestimating scores with the EM-algorithm.
  • Training data: A subset of the datasets for model pre-training
  • Vocabulary size: 96,867 (mixed vocabulary of Japanese, English, and source code)
    • The acutal size of vocabulary in the pretrained model is 97,024 due to round-up to multiples of 256.

Datasets

Pre-training

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

Language Dataset Tokens
Japanese Wikipedia 1.4B
Common Crawl 130.7B
English Wikipedia 4.7B
The Pile 110.3B
Codes The Stack 8.7B

Instruction tuning

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

Language Dataset description
Japanese ichikara-instruction-004-001 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
English databricks-dolly-15k -
oasst1-21k-en A subset of oasst1
oasst2-33k-en A subset of oasst2

Evaluation

You can view the evaluation results of several LLMs on this leaderboard. We used llm-jp-eval (v1.3.0) for the evaluation.

Besides, we used LLM-as-a-judge frameworks, Japanese Vicuna QA Benchmark and Japanese MT Bench, for evaluation. For details, please refer to our technical blog (in Japanese).

Risks and Limitations

The models released here are still 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.

Namgi Han, Tatsuya Hiraoka, Hirokazu Kiyomaru, Takashi Kodama, and Hiroshi Matsuda.

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Datasets used to train llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0