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	---
	license: cc-by-nc-4.0
	tags:
	- generated_from_trainer
	base_model: lightblue/suzume-llama-3-8B-multilingual
	model-index:
	- name: workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda
	results: []
	---
	# Suzume ORPO

	<p align="center">
	<img width=500 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kWQSu02YfgYdUQqv4s5lq.png" alt="Suzume with Mitsu - a Japanese tree sparrow with honey on it"/>
	</p>

	[[Paper]](https://arxiv.org/abs/2405.18952) [[Dataset]](https://huggingface.co/datasets/lightblue/mitsu)

	This is Suzume ORPO, an ORPO trained fine-tune of the [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) model using our [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset.

	We have trained several versions of this model using ORPO and so recommend that you use the best performing model from our tests, [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half).

	Note that this model has a non-commerical license as we used the Command R and Command R+ models to generate our training data for this model ([lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu)).

	We are currently working on a developing a commerically usable model, so stay tuned for that!

	# Model list

	We have ORPO trained the following models using different proportions of the [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset:
	* Trained on the top/bottom responses of all prompts in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full)
	* Trained on the top/bottom responses of the prompts of the 75\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75)
	* Trained on the top/bottom responses of the prompts of the 50\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half)
	* Trained on the top/bottom responses of the prompts of the 25\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25)

	# Model results

	We compare the MT-Bench scores across 6 languages for our 4 ORPO trained models, as well as some baselines:

	* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - The foundation model that our models are ultimately built upon
	* [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) - The highest performing open model on the Chatbot arena that is of a similar size to ours
	* gpt-3.5-turbo - A fairly high quality (although not state-of-the-art) proprietary LLM
	* [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) - The base model which we train our ORPO finetunes from

	\| MT-Bench language \| meta-llama/Meta-Llama-3-8B-Instruct \| Nexusflow/Starling-LM-7B-beta \| gpt-3.5-turbo \| lightblue/suzume-llama-3-8B-multilingual \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75 \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half \| lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25 \|
	\|-----------------------\|-----------------------------------------\|-----------------------------------\|-------------------\|----------------------------------------------\|--------------------------------------------------------------\|---------------------------------------------------------------\|--------------------------------------------------------------\|---------------------------------------------------------------\|
	\| Chinese 🇨🇳 \| NaN \| 6.97 \| 7.55 \| 7.11 \| 7.65 \| 7.77 \| 7.74 \| 7.44 \|
	\| English 🇺🇸 \| 7.98 \| 7.92 \| 8.26 \| 7.73 \| 7.98 \| 7.94 \| 7.98 \| 8.22 \|
	\| French 🇫🇷 \| NaN \| 7.29 \| 7.74 \| 7.66 \| 7.84 \| 7.46 \| 7.78 \| 7.81 \|
	\| German 🇩🇪 \| NaN \| 6.99 \| 7.68 \| 7.26 \| 7.28 \| 7.64 \| 7.7 \| 7.71 \|
	\| Japanese 🇯🇵 \| NaN \| 6.22 \| 7.84 \| 6.56 \| 7.2 \| 7.12 \| 7.34 \| 7.04 \|
	\| Russian 🇷🇺 \| NaN \| 8.28 \| 7.94 \| 8.19 \| 8.3 \| 8.74 \| 8.94 \| 8.81 \|

	We can see noticable improvement on most languages compared to the base model. We also find that our ORPO models achieve the highest score out of all the models we evaluated for a number of languages.

	# Training data

	We trained this model using the [lightblue/mitsu_full_borda](https://huggingface.co/datasets/lightblue/mitsu_full_borda) dataset.

	# Training configuration
	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	[<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)
	<details><summary>See axolotl config</summary>

	axolotl version: `0.4.0`
	```yaml
	base_model: lightblue/suzume-llama-3-8B-multilingual
	model_type: LlamaForCausalLM
	tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast

	load_in_8bit: false
	load_in_4bit: false
	strict: false

	rl: orpo
	orpo_alpha: 0.1
	remove_unused_columns: false

	chat_template: chatml
	datasets:
	- path: lightblue/mitsu_tophalf_borda
	type: orpo.chat_template
	conversation: llama-3
	dataset_prepared_path: /workspace/llm_training/axolotl/llama3-multilingual-orpo/prepared_mitsu_half_borda
	val_set_size: 0.02
	output_dir: /workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda

	sequence_len: 8192
	sample_packing: false
	pad_to_sequence_len: true

	use_wandb: true
	wandb_project: axolotl
	wandb_entity: peterd
	wandb_name: mitsu_half_borda

	gradient_accumulation_steps: 8
	micro_batch_size: 1
	num_epochs: 1
	optimizer: paged_adamw_8bit
	lr_scheduler: cosine
	learning_rate: 8e-6

	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: 20
	eval_table_size:
	saves_per_epoch: 1
	debug:
	deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
	weight_decay: 0.0
	special_tokens:
	pad_token: <\|end_of_text\|>
	```

	</details><br>

	# workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_half_borda

	This model is a fine-tuned version of [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0935

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 8e-06
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 4
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 32
	- total_eval_batch_size: 4
	- 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 7.6299 \| 0.02 \| 1 \| 7.7014 \|
	\| 7.041 \| 0.07 \| 3 \| 3.9786 \|
	\| 0.6089 \| 0.15 \| 6 \| 0.1393 \|
	\| 0.1308 \| 0.22 \| 9 \| 0.1244 \|
	\| 0.1051 \| 0.29 \| 12 \| 0.1112 \|
	\| 0.1021 \| 0.36 \| 15 \| 0.1063 \|
	\| 0.0861 \| 0.44 \| 18 \| 0.1026 \|
	\| 0.1031 \| 0.51 \| 21 \| 0.0979 \|
	\| 0.0996 \| 0.58 \| 24 \| 0.0967 \|
	\| 0.0923 \| 0.65 \| 27 \| 0.0960 \|
	\| 0.1025 \| 0.73 \| 30 \| 0.0944 \|
	\| 0.1103 \| 0.8 \| 33 \| 0.0939 \|
	\| 0.0919 \| 0.87 \| 36 \| 0.0937 \|
	\| 0.104 \| 0.94 \| 39 \| 0.0935 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.0

	# How to cite

	```tex
	@article{devine2024sure,
	title={Are You Sure? Rank Them Again: Repeated Ranking For Better Preference Datasets},
	author={Devine, Peter},
	journal={arXiv preprint arXiv:2405.18952},
	year={2024}
	}
	```

	# Developer

	Peter Devine - ([ptrdvn](https://huggingface.co/ptrdvn))