Suzume
This Suzume 8B, a Japanese finetune of Llama 3.
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 almost 3,000 Japanese conversations meaning that this model has the smarts 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!
How to use
You can use the GGUF using LM Studio
LM Studioで簡単に使えます!こちらは使い方を説明します。 LM Studioで「lightblue/suzume-llama-3-8B-japanese-gguf」を検索して下さい。
Evaluation scores
We find that this is the best performing model in the 7/8B class of LLMs on a multitude of Japanese language benchmarks.
Training data
We train on three sources of data to create this model
- megagonlabs/instruction_ja - 669 conversations
- A hand-edited dataset of nearly 700 conversations taken originally from translations of the kunishou/hh-rlhf-49k-ja dataset.
- openchat/openchat_sharegpt4_dataset (Japanese conversations only) - 167 conversations
- Conversations taken from humans talking to GPT-4
- lightblue/tagengo-gpt4 (Japanese prompts only) (Link coming soon!) - 2,482 conversations
- Almost 2,500 diverse Japanese prompts sampled from lmsys/lmsys-chat-1m and then used to prompt
gpt-4-0125-preview
- Almost 2,500 diverse Japanese prompts sampled from lmsys/lmsys-chat-1m and then used to prompt
Training config
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: /workspace/llm_training/axolotl/llama3-ja/openchat_megagon_lbgpt4_ja.json
ds_type: json # see other options below
type: sharegpt
conversation: llama-3
dataset_prepared_path: /workspace/llm_training/axolotl/llama3-ja/prepared_openchat_megagon_lbgpt4_ja
val_set_size: 0.01
output_dir: /workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: False
use_wandb: true
wandb_project: axolotl
wandb_entity: peterd
wandb_name: openchat_megagon_lbgpt4_ja_8B_instruct
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- total_eval_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.303 | 0.08 | 1 | 1.2664 |
1.4231 | 0.23 | 3 | 1.2409 |
1.1007 | 0.46 | 6 | 1.0264 |
1.0635 | 0.69 | 9 | 1.0154 |
1.0221 | 0.92 | 12 | 0.9555 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
How to cite
Please cite this paper when referencing this model.
@article{devine2024tagengo,
title={Tagengo: A Multilingual Chat Dataset},
author={Devine, Peter},
journal={arXiv preprint arXiv:2405.12612},
year={2024}
}
Developer
Peter Devine - (ptrdvn)
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Model tree for lightblue/suzume-llama-3-8B-japanese-gguf
Base model
meta-llama/Meta-Llama-3-8B-Instruct