metadata

library_name: transformers
license: other
language:
  - ja

🐟 EvoLLM-JP-v1-7B

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EvoLLM-JP-v1-7B is a Japanese Math LLM by Evolutionary Model Merge.

Model Details

Model Description

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
Model type: Autoregressive Language Model
Language(s): Japanese
License: MICROSOFT RESEARCH LICENSE TERMS
Source models:

Model Sources

Repository: SakanaAI/evolutionary-model-merge
Paper: TODO
Blog: TODO

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-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 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.

Id.	Model	Type	Params	MGSM-JA (acc ↑ )
1	Shisa Gamma 7B v1	JA general	7B	9.6
2	WizardMath 7B V1.1	EN math	7B	18.4
3	Abel 7B 002	EN math	7B	30.0
4	Arithmo2 Mistral 7B	EN math	7B	24.0
5	EvoLLM-JP-v1-7B	1+2+3	7B	52.0
6	EvoLLM-JP-A-v1-7B	1+3+4	7B	52.4
7	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

@misc{Sakana2024EvolutionaryModelMerge,
      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}
}