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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: lightblue/suzume-llama-3-8B-multilingual |
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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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This Suzume 8B, a multilingual finetune of Llama 3 ([meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)). |
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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 other languages. |
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We have fine-tuned Llama 3 on almost 90,000 multilingual conversations meaning that this model has the smarts of Llama 3 but has the added ability to chat in more languages. |
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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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The easiest way to use this model on your own computer is to use the [GGUF version of this model (lightblue/suzume-llama-3-8B-multilingual-gguf)](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-gguf) using a program such as [jan.ai](https://jan.ai/) or [LM Studio](https://lmstudio.ai/). |
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If you want to use this model directly in Python, we recommend using vLLM for the fastest inference speeds. |
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```python |
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from vllm import LLM, SamplingParams |
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sampling_params = SamplingParams(temperature=0.0, max_tokens=100) |
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llm = LLM(model="lightblue/suzume-llama-3-8B-multilingual") |
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messages = [] |
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messages.append({"role": "user", "content": "Bonjour!"}) |
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prompt = llm.llm_engine.tokenizer.tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False) |
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prompts = [prompt] |
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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 achieve the following MT-Bench scores across 6 languages: |
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| | **meta-llama/Meta-Llama-3-8B-Instruct** | **lightblue/suzume-llama-3-8B-multilingual** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** | |
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|-----------------|-----------------------------------------|----------------------------------------------|-----------------------------------|-------------------| |
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| **German** 🇩🇪 | NaN | 7.26 | 6.99 | 7.68 | |
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| **French** 🇫🇷 | NaN | 7.66 | 7.29 | 7.74 | |
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| **Japanese** 🇯🇵 | NaN | 6.56 | 6.22 | 7.84 | |
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| **Russian** 🇷🇺 * | NaN | 8.19 | 8.28 | 7.94 | |
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| **Chinese** 🇨🇳 | NaN | 7.11 | 6.97 | 7.55 | |
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| **English** 🇺🇸 | 7.98 | 7.73 | 7.92 | 8.26 | |
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\* (Note the Russian scores exclude code, reasoning and math problems due to not having any translated reference answers for these questions.) |
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We observe minimal degredation of Llama 3's English ability while achieving best-in-class multilingual abilities compared to the top rated 7B model ([Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta)) on the [Chatbot Arena Leaderboard](https://chat.lmsys.org/?leaderboard). |
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[Here is our evaluation script.](https://drive.google.com/file/d/15HPn7452t8LbTD9HKSl7ngYYWnsoOG08/view?usp=sharing) |
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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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* [lightblue/tagengo-gpt4](https://huggingface.co/datasets/lightblue/tagengo-gpt4) - 76,338 conversations |
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* A diverse dataset of initial inputs 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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* [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja) - 669 conversations |
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* A hand-edited dataset of nearly 700 Japanese 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) - 6,206 conversations |
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* Multilingual conversations of humans talking to GPT-4. |
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<details><summary>We prepare our data like so:</summary> |
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```python |
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import pandas as pd |
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from datasets import Dataset, load_dataset, concatenate_datasets |
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### Tagengo |
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gpt4_dataset = load_dataset("lightblue/tagengo-gpt4", split="train") |
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gpt4_dataset = gpt4_dataset.filter(lambda x: x["response"][1] == "stop") |
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#### |
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### Megagon |
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megagon_df = pd.read_json( |
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"https://raw.githubusercontent.com/megagonlabs/instruction_ja/main/data/data.jsonl", |
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lines=True, |
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orient="records" |
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) |
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role_map = {"user": "human", "agent": "gpt"} |
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megagon_df["conversations"] = megagon_df.utterances.apply(lambda x: [{"from": role_map[y["name"]], "value": y["text"]} for y in x]) |
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megagon_df["language"] = "Japanese" |
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megagon_df = megagon_df[["conversations", "language"]] |
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megagon_dataset = Dataset.from_pandas(df) |
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### |
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### Openchat |
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openchat_df = pd.read_json("https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json?download=true") |
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openchat_df["conversations"] = openchat_df["items"] |
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openchat_dataset = Dataset.from_pandas(openchat_df) |
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### |
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dataset = concatenate_datasets([gpt4_dataset, megagon_dataset, openchat_dataset]) |
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dataset = dataset.filter(lambda x: not any([y["value"] is None for y in x["conversations"]])) |
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dataset.select_columns(["conversations"]).to_json("/workspace/llm_training/axolotl/llama3-multilingual/tagengo_openchat_megagon.json") |
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``` |
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</details> |
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<br/> |
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# workspace/llm_training/axolotl/llama3-multilingual/output_tagengo_openchat_megagon_8B_llama3 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the above described dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6595 |
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## Training procedure |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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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-multilingual/tagengo_openchat_megagon.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-multilingual/prepared_tagengo_openchat_megagon |
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val_set_size: 0.01 |
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output_dir: /workspace/llm_training/axolotl/llama3-multilingual/output_tagengo_openchat_megagon_8B_llama3 |
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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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use_wandb: true |
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wandb_project: wandb_project |
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wandb_entity: wandb_entity |
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wandb_name: wandb_name |
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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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<details><summary>Note - we added this Llama 3 template to fastchat directly as the Llama 3 chat template was not supported when we trained this model.</summary> |
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```python |
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from fastchat.conversation import Conversation |
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from fastchat.conversation import register_conv_template |
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from fastchat.conversation import SeparatorStyle |
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register_conv_template( |
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Conversation( |
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name="llama-3", |
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system_template="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}", |
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roles=("<|start_header_id|>user<|end_header_id|>\n", "<|start_header_id|>assistant<|end_header_id|>\n"), |
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sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE, |
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sep="<|eot_id|>", |
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stop_token_ids=[128009], |
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stop_str="<|eot_id|>", |
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) |
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) |
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``` |
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</details><br> |
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### Training hyperparameters |
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This model was trained using 4 x A100 (80GB) for roughly 2.5 hours. |
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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: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 8 |
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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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| 1.1894 | 0.0 | 1 | 1.0110 | |
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| 0.8493 | 0.2 | 73 | 0.7057 | |
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| 0.8047 | 0.4 | 146 | 0.6835 | |
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| 0.7644 | 0.6 | 219 | 0.6687 | |
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| 0.7528 | 0.8 | 292 | 0.6615 | |
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| 0.7794 | 1.0 | 365 | 0.6595 | |
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### Framework versions |
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- Transformers 4.38.2 |
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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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# Developer |
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Peter Devine - ([ptrdvn](https://huggingface.co/ptrdvn)) |
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