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--- |
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license: other |
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license_name: llama-3 |
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license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct |
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results: [] |
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--- |
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<p align="center"> |
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<img width=400 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kg3QjQOde0X743csGJT-f.png" alt="Suzume - a Japanese tree sparrow"/> |
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</p> |
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# Suzume |
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[[Paper](https://arxiv.org/abs/2405.12612)] [[Dataset](https://huggingface.co/datasets/lightblue/tagengo-gpt4)] |
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This Suzume 8B, a Japanese finetune of Llama 3. |
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Llama 3 has exhibited excellent performance on many English language benchmarks. |
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However, it also seemingly been finetuned on mostly English data, meaning that it will respond in English, even if prompted in Japanese. |
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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. |
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Please feel free to comment on this model and give us feedback in the Community tab! |
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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. |
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# How to use |
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You can use the original trained model with vLLM like so: |
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```python |
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from vllm import LLM, SamplingParams |
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95) |
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llm = LLM(model="lightblue/suzume-llama-3-8B-japanese") |
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prompts = [ |
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"東京のおすすめの観光スポットを教えて下さい", |
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] |
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outputs = llm.generate(prompts, sampling_params) |
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for output in outputs: |
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prompt = output.prompt |
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generated_text = output.outputs[0].text |
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") |
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``` |
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# Evaluation scores |
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We find that this is the best performing model in the 7/8B class of LLMs on a multitude of Japanese language benchmarks. |
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We calculate our Japanese evaluation scores using our [lightblue-tech/japanese_llm_eval](https://github.com/lightblue-tech/japanese_llm_eval) repo. |
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We also compare our Japanese model to our multilingual model using our [multilingual_mt_bench](https://github.com/Peter-Devine/multilingual_mt_bench/tree/main/fastchat/llm_judge) repo. |
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| | **lightblue/suzume-llama-3-8B-japanese** | **lightblue/suzume-llama-3-8B-multilingual** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** | |
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|-----------------|------------------------------------------|----------------------------------------------|-----------------------------------|-------------------| |
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| **Japanese 🇯🇵** | 6.24 | 6.56 | 6.22 | 7.84 | |
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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. |
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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. |
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# Training data |
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We train on three sources of data to create this model |
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* [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja) - 669 conversations |
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* A hand-edited dataset of nearly 700 conversations taken originally from translations of the [kunishou/hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) dataset. |
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* [openchat/openchat_sharegpt4_dataset](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json) (Japanese conversations only) - 167 conversations |
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* Conversations taken from humans talking to GPT-4 |
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* lightblue/tagengo-gpt4 (Japanese prompts only) (Link coming soon!) - 2,482 conversations |
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* Almost 2,500 diverse Japanese prompts sampled from [lmsys/lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) and then used to prompt `gpt-4-0125-preview` |
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# Training config |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: /workspace/llm_training/axolotl/llama3-ja/openchat_megagon_lbgpt4_ja.json |
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ds_type: json # see other options below |
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type: sharegpt |
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conversation: llama-3 |
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dataset_prepared_path: /workspace/llm_training/axolotl/llama3-ja/prepared_openchat_megagon_lbgpt4_ja |
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val_set_size: 0.01 |
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output_dir: /workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct |
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sequence_len: 8192 |
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sample_packing: true |
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pad_to_sequence_len: true |
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eval_sample_packing: False |
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use_wandb: true |
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wandb_project: axolotl |
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wandb_entity: peterd |
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wandb_name: openchat_megagon_lbgpt4_ja_8B_instruct |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 2 |
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num_epochs: 1 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 1e-5 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 10 |
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evals_per_epoch: 5 |
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eval_table_size: |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json |
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weight_decay: 0.0 |
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special_tokens: |
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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 3 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 12 |
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- total_eval_batch_size: 6 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.303 | 0.08 | 1 | 1.2664 | |
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| 1.4231 | 0.23 | 3 | 1.2409 | |
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| 1.1007 | 0.46 | 6 | 1.0264 | |
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| 1.0635 | 0.69 | 9 | 1.0154 | |
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| 1.0221 | 0.92 | 12 | 0.9555 | |
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### Framework versions |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.0 |
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# How to cite |
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Please cite [this paper](https://arxiv.org/abs/2405.12612) when referencing this model. |
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```tex |
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@article{devine2024tagengo, |
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title={Tagengo: A Multilingual Chat Dataset}, |
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author={Devine, Peter}, |
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journal={arXiv preprint arXiv:2405.12612}, |
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year={2024} |
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} |
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``` |
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# Developer |
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Peter Devine - ([ptrdvn](https://huggingface.co/ptrdvn)) |
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