---
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
* 2024/05/20 Added JGLUE 4-task benchmark scores.
* 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 | Dataset | Dataset size | Training time | PPL |
|-|-|-|-|-|-|-|-|-|
| transformer-lm-japanese-0.1b | 0.1B | 12 | 768 | 12 | wiki40b/ja | 2.19GB | 1.5 days | 35.22 |

## Benchmarking

* **JGLUE 4-task (2024/05/22)**

    - *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness) library for evaluation.*
    - *We modified the harness to work with the FlaxAutoModel for evaluating JAX/Flax models. See the code [here](https://github.com/FookieMonster/lm-evaluation-harness).*
    - *We evaluated four tasks: JCommonsenseQA-1.1, JNLI-1.3, MARC-ja-1.1, and JSQuAD-1.1.*
    - *All evaluations used version 0.3 (Alpaca) of the prompt template in a zero-shot setting.*
    - *The revision of the custom model used: [here](https://huggingface.co/fukugawa/transformer-lm-japanese-0.1b/commit/fe82d0f1366af71df8f8b383bf8de9ab6b0030be).*
   
    | Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD |
    | :-- | :-- | :-- | :-- | :-- | :-- |
    | transformer-lm-japanese-0.1b | 41.41 | 35.21 | 43.59 | 78.63 | 8.24 |
    | Reference: rinna/japanese-gpt-neox-small | 40.75 | 40.39 | 29.13 | 85.48 | 8.02 |

## Usage: FlaxAutoModel

#### Requirements:

```
pip install transformers>=4.39.0
pip install jax==0.4.31
pip install flax==0.8.3
pip install sentencepiece==0.1.99

# For CPU
pip install -U "jax[cpu]==0.4.31"

# For GPU
pip install -U "jax[cuda12]==0.4.31"
```

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
* OS: Ubuntu 22.04 LTS x86/64

## Dataset

* [wiki40b/ja](https://www.tensorflow.org/datasets/catalog/wiki40b?hl=ja#wiki40bja) (2.19GB)
 
## Tokenization

* [sentencepiece](https://github.com/google/sentencepiece)

## Author

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