llm-jp-3-150m
LLM-jp-3 is the series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics.
This repository provides llm-jp-3-150m model. For an overview of the LLM-jp-3 models across different parameter sizes, please refer to:
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-3-150m")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3-150m", 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 |
|---|---|---|---|---|---|---|
| 150M | 12 | 512 | 8 | 4096 | 101,874,688 | 50,344,448 |
| 440M | 16 | 1024 | 8 | 4096 | 203,749,376 | 243,303,424 |
| 980M | 20 | 1536 | 8 | 4096 | 305,624,064 | 684,258,816 |
| 1.8b | 24 | 2048 | 16 | 4096 | 407,498,752 | 1,459,718,144 |
| 3.7b | 28 | 3072 | 24 | 4096 | 611,248,128 | 3,171,068,928 |
| 7.2b | 32 | 4096 | 32 | 4096 | 814,997,504 | 6,476,271,616 |
| 13b | 40 | 5120 | 40 | 4096 | 1,018,746,880 | 12,688,184,320 |
| 172b | 96 | 12288 | 96 | 4096 | 2,444,992,512 | 169,947,181,056 |
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 |
Evaluation
Detailed evaluation results are reported in this blog.
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
Model Card Authors
The names are listed in alphabetical order.
Hirokazu Kiyomaru and Takashi Kodama.