---
language:
- ja
license: apache-2.0
tags:
- ja
- japanese
- text-generation
- lm
- jax
- flax
- lm1b
datasets:
- wiki40b
---
# 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](https://github.com/google/flax/tree/main/examples/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](https://github.com/FookieMonster/transformer-lm-japanese)

## Our Blog Post

* [【0.1Bから作るLLM】 JAX/Flaxで作るTransformer言語モデル](https://zenn.dev/fukugawa/articles/4446573ec0f697)

## 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 |

![tensor-board](./tensorboard-v1.png)

## 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.

~~~~python
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](https://github.com/google/sentencepiece)

## Author

[Ryoichi Fukugawa](https://zenn.dev/fukugawa)