---
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 a Japanese Math LLM by Evolutionary Model Merge. 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
**EvoLLM-JP-v1-7B** is a Japanese Math LLM, merged the following source models in the Parameter Space (PS) by Evolutionary Model Merge.

- **Developed by:** [Sakana AI](https://sakana.ai/)
- **Model type:** Autoregressive Language Model
- **Language(s):** Japanese
- **License:** [MICROSOFT RESEARCH LICENSE TERMS](./LICENSE)
- **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)
 
### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge)
- **Paper:** TODO
- **Blog:** TODO


## Usage

Use the code below to get started with the model.


```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)
```

## Evaluation

We present the results on the [MGSM-JA](https://huggingface.co/datasets/juletxara/mgsm) test set that compares the performance of the our evolved LLMs compared to the source LLMs. 
For details on the evaluation, please refer to Section 4.1 of the paper. 
If you want to reproduce the results, please see [our Github repository](https://github.com/SakanaAI/evolutionary-model-merge).

| Id. | Model | Type | Params | MGSM-JA (acc &uarr; ) |
| :--: | :-- | :-- | --: | --: |
| 1 | [Shisa Gamma 7B v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1) | JA general | 7B |9.6 |
| 2 | [WizardMath 7B V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) | EN math | 7B | 18.4 |
| 3 | [Abel 7B 002](https://huggingface.co/GAIR/Abel-7B-002) | EN math | 7B | 30.0 |
| 4 | [Arithmo2 Mistral 7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) | EN math | 7B | 24.0 |
| 5 | [EvoLLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoLLM-JP-v1-7B) | 1+2+3 | 7B | **52.0** |
| 6 | [EvoLLM-JP-A-v1-7B](https://huggingface.co/SakanaAI/EvoLLM-JP-A-v1-7B) | 1+3+4 | 7B | **52.4** |
| 7 | [EvoLLM-JP-v1-10B](https://huggingface.co/SakanaAI/EvoLLM-JP-v1-10B) | 1 + 5 | 10B | **55.6** |

## 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}
}
```