README.md

---
license: apache-2.0
---
# Dataset

Japanese subset of the mC4 dataset

# Training

Trained for 3000 steps on top of the MPT 7b checkpoint mosaicml/mpt-7b

# How to load

Before running this model, please install the following pip package:

```bash
pip install einops
```

To run this model, you may need to load it in a lower precision in order for it to fit onto your GPU. We found for a T4 GPU, it requires loading the model in 8-bit precision. To load the model in 8-bit or 4-bit, please install the following pip packages:

```bash
pip install bitsandbytes accelerate
```

Caution - you will also need enough RAM to load the model. We estimate loading this model requires ~30GB.

<details>
<summary><b>Auto type</b></summary>



```python
from transformers import AutoModelForCausalLM

model_name = "lightblue/japanese-mpt-7b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    trust_remote_code=True
)
```

</details>
<details>
<summary><b>In 8 bit</b></summary>



```python
from transformers import AutoModelForCausalLM

model_name = "lightblue/japanese-mpt-7b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    load_in_8bit=True,
    trust_remote_code=True
)
```

</details>

<details>
<summary><b>In 4 bit</b></summary>



```python
from transformers import AutoModelForCausalLM

model_name = "lightblue/japanese-mpt-7b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    load_in_4bit=True,
    trust_remote_code=True
)
```

</details>


# How to use
```python
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("こんにちは", temperature=0, do_sample=False, return_full_text=False, max_new_tokens=32)
```