Llama 3 has exhibited excellent performance on many English language benchmarks.
However, it also seemingly been finetuned on mostly English data, meaning that it will respond in English, even if prompted in Japanese.
We have fine-tuned Llama 3 on more than 3,000 Japanese conversations meaning that this model has the intelligence of Llama 3 but has the added ability to chat in Japanese.
Please feel free to comment on this model and give us feedback in the Community tab!
We will release a paper in the future describing how we made the training data, the model, and the evaluations we have conducted of it.
How to use
You can use the original trained model with vLLM like so:
We also compare our Japanese model to our multilingual model using our multilingual_mt_bench repo.
lightblue/suzume-llama-3-8B-japanese
lightblue/suzume-llama-3-8B-multilingual
Nexusflow/Starling-LM-7B-beta
gpt-3.5-turbo
Japanese 🇯🇵
6.24
6.56
6.22
7.84
Here, we find that our multilingual model outperforms our Japanese model on the Japanese MT-Bench benchmark, indicating that our multilingual model was able to generalize better to the Japanese MT-Bench benchmark from training on more data, even if that added data was not in Japanese.
Note - the discrepancy between the MT-Bench scores of the first and second evaluation of lightblue/suzume-llama-3-8B-japanese are due to the difference in system message of the two evaluation harnesses. The former's system message is in Japanese while the latter's is in English.
Training data
We train on three sources of data to create this model
