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Model: lightblue/suzume-llama-3-8B-multilingual Source: Original Platform
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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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[[Paper](https://arxiv.org/abs/2405.12612)] [[Dataset](https://huggingface.co/datasets/lightblue/tagengo-gpt4)]
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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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# 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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config.json
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{
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"_name_or_path": "/workspace/llm_training/axolotl/llama3-multilingual/output_tagengo_openchat_megagon_8B_llama3",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.38.2",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128009
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],
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"transformers_version": "4.38.2"
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}
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3
pytorch_model.bin
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3
pytorch_model.bin
Normal file
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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{
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410503
tokenizer.json
Normal file
410503
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user