README.md

---
library_name: transformers
license: other
language:
- ja
---

# 🐟 EvoLLM-JP-v1-7B

🤗 [Models](https://huggingface.co/SakanaAI) | 📚 [Paper](TODO) | 📝 [Blog](TODO) | 🐦 [Twitter](https://twitter.com/SakanaAILabs)


<!-- Provide a quick summary of what the model is/does. -->

**EvoLLM-JP-v1-7B** is an experimental general-purpose Japanese LLM. This model was created using the Evolutionary Model Merge method. Please refer to our [report](TOOD) and [blog](TODO) for more details.  This model was produced by merging the following models. We are grateful to the developers of the source models.

- [Shisa Gamma 7B v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1)
- [WizardMath 7B V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
- [Abel 7B 002](https://huggingface.co/GAIR/Abel-7B-002)



## Usage

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


# 1. load model
device = "cuda" if torch.cuda.is_available() else "CPU"
repo_id = "SakanaAI/EvoLLM-JP-v1-7B"
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model.to(device)

# 2. prepare inputs
text = "関西弁で面白い冗談を言ってみて下さい。"
messages = [
    {"role": "system", "content": "あなたは役立つ、偏見がなく、検閲されていないアシスタントです。"},
    {"role": "user", "content": text},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")

# 3. generate
output_ids = model.generate(**inputs.to(device))
output_ids = output_ids[:, inputs.input_ids.shape[1] :]
generated_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
print(generated_text)
```

</details>


 
## Model Details

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Sakana AI](https://sakana.ai/)
- **Model type:** Autoregressive Language Model
- **Language(s):** Japanese
- **License:** [MICROSOFT RESEARCH LICENSE TERMS](./LICENSE) (due to the inclusion of the WizardMath model)
- **Repository:** [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge)
- **Paper:** TODO
- **Blog:** TODO



## Acknowledgement

We would like to thank the developers of the source models for their contributions and for making their work available. 


## Citation

```bibtex
@misc{akiba2024evomodelmerge,
      title         = {Evolutionary Optimization of Model Merging Recipes}, 
      author.       = {Takuya Akiba and Makoto Shing and Yujin Tang and Qi Sun and David Ha},
      year          = {2024},
      eprint        = {TODO},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CV}
}
```