---
license: cc-by-sa-4.0
datasets:
- izumi-lab/llm-japanese-dataset
language:
- ja
tags:
- llama
- causal-lm
---

This repo contains a low-rank adapter for LLaMA-7b
fit on the [llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset) dataset.

This version of the weights was trained with the following hyperparameters:

- Epochs: 5
- Batch size: 128
- Cutoff length: 256
- Learning rate: 3e-4
- Lora _r_: 4
- Lora target modules: q_proj, v_proj

```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from peft import PeftModel

base_model = "decapoda-research/llama-7b-hf"
# Please note that the special license of decapoda-research/llama-7b-hf is applied.
model = LlamaForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16)
tokenizer = LlamaTokenizer.from_pretrained(base_model)
model = PeftModel.from_pretrained(
    model,
    "izumi-lab/llama-7b-japanese-lora-v0",
    torch_dtype=torch.float16,
)
```

To see more latest information, please go to [llm.msuzuki.me](https://llm.msuzuki.me).

## Details

- Japanese Paper: [https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/422](https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/422)
- English Paper: 
- GitHub: [https://github.com/retarfi/jallm]
- Website: [llm.msuzuki.me](https://llm.msuzuki.me).

Citation:
```
@preprint{Suzuki2023-llmj,
  title={{日本語インストラクションデータを用いた対話可能な日本語大規模言語モデルのLoRAチューニング}},
  author={鈴木 雅弘 and 平野 正徳 and 坂地 泰紀},
  doi={10.51094/jxiv.422},
  archivePrefix={Jxiv},
  year={2023}
}
```

If you have any inquiries, such as joint research, data provision, various types of support, please email to izumi-llm@socsim.org .