llm-jp-13b-instruct-full-ac_001_16x-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.

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-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-13b-instruct-full-ac_001_16x-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

Training

• Pre-training:

  • Hardware: 128 A100 40GB GPUs (mdx cluster)
  • Software: Megatron-LM

• Instruction tuning:

  • Hardware: 8 A100 40GB GPUs (mdx cluster)
  • Software: TRL and DeepSpeed

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.

Instruction tuning

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

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.