llm-jp-3-172b-instruct3

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

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-instruct3")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-172b-instruct3", 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::
  • llm-jp-3-1.8b: 2.1T
  • llm-jp-3-3.7b: 2.1T
  • llm-jp-3-13b: 2.1T
  • llm-jp-3-172b-beta1: 0.7T
  • llm-jp-3-172b-beta2: 1.4T
  • llm-jp-3-172b: 2.1T

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.

Post-training

We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.

Supervised Fine-tuning

The datasets used for supervised fine-tuning are as follows:

Direct Preference Optimization

We used synthetic preference data to improve both the helpfulness and harmlessness of the LLM. The datasets will be made available soon.

Evaluation

llm-jp-eval (v1.4.1)

We evaluated the models using 100 examples from the dev split. Note that we skipped the CG (Code Generation) task.

Japanese MT Bench

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

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

See the LICENSE file.

Model Card Authors

The names are listed in alphabetical order.

Hirokazu Kiyomaru and Takashi Kodama.