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---
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:
If you have any inquiries, such as joint research, data provision, various types of support, please email to [email protected] . |