🐟 EvoLLM-JP-A-v1-7B
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EvoLLM-JP-A-v1-7B is an experimental general-purpose Japanese LLM. This model was created using the Evolutionary Model Merge method. Please refer to our report and blog for more details. This model was produced by merging the following models. We are grateful to the developers of the source models.
Usage
Use the code below to get started with the model.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# 1. load model
device = "cuda" if torch.cuda.is_available() else "CPU"
repo_id = "SakanaAI/EvoLLM-JP-A-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)
Model Details
- Developed by: Sakana AI
- Model type: Autoregressive Language Model
- Language(s): Japanese
- License: Apache License, Version 2.0
- Repository: SakanaAI/evolutionary-model-merge
- Paper: https://arxiv.org/abs/2403.13187
- Blog: https://sakana.ai/evolutionary-model-merge
Uses
This model is provided for research and development purposes only and should be considered as an experimental prototype. It is not intended for commercial use or deployment in mission-critical environments. Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed. Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained. Users must fully understand the risks associated with the use of this model and use it at their own discretion.
Acknowledgement
We would like to thank the developers of the source models for their contributions and for making their work available.
Citation
@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 = {2403.13187},
archivePrefix = {arXiv},
primaryClass = {cs.NE}
}