transformer-lm-japanese-0.1b

This is a JAX/Flax-based transformer language model trained on a Japanese dataset. It is based on the official Flax example code (lm1b).

Update Log

• 2024/05/13 FlaxAutoModelForCausalLM is now supported with custom model code added.

Source Code

We've modified Flax's 'lm1b' example to train on Japanese dataset. You can find the code on Github.

• transformer-lm-japanese

Our Blog Post

• 【0.1Bから作るLLM】 JAX/Flaxで作るTransformer言語モデル

Model Details

Model	Params	Layers	Dim	Heads	PPL	Dataset	Training time
lm1b-default	0.05B	6	512	8	22.67	lm1b	0.5 days
transformer-lm-japanese-default	0.05B	6	512	8	66.38	cc100/ja	0.5 days
transformer-lm-japanese-0.1b	0.1B	12	768	12	35.22	wiki40b/ja	1.5 days

Usage: FlaxAutoModel

Requirements:

pip install transformers>=4.39.0
pip install jax==0.4.13
pip install flax==0.6.11
pip install sentencepiece==0.1.99

# For CPU
pip install jax[cpu]==0.4.13

# For GPU
pip install --upgrade "jax[cuda12_pip]==0.4.13" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Note: Set trust_remote_code=True to load our custom model.

from transformers import AutoTokenizer, FlaxAutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fukugawa/transformer-lm-japanese-0.1b", trust_remote_code=True)
model = FlaxAutoModelForCausalLM.from_pretrained("fukugawa/transformer-lm-japanese-0.1b", trust_remote_code=True)

text = "日本の首都は、"
token_ids = tokenizer.encode(text, return_tensors="jax", add_special_tokens=False)

output_ids = model.generate(
  token_ids,
  do_sample=True,
  temperature=0.6,
  top_k=20,
  max_new_tokens=100
)

output = tokenizer.decode(output_ids[0][0], skip_special_tokens=True)
print(output)

We tested text generation in a Python 3.10 environment on GCP as follows

• Machine Type: c2-standard-4 (4 CPUs, 16GB Memory)
• Disk: 100GB (Standard Persistent Disk)
• OS: Ubuntu 22.04 LTS x86/64

Dataset

• wiki40b/ja

Tokenization

• sentencepiece

Author

Ryoichi Fukugawa