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suzume-llama-3-8B-multilingual-orpo-borda-full

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metadata
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_full_borda
    results: []

Suzume ORPO

Suzume with Mitsu - a Japanese tree sparrow with honey on it

[Paper] [Dataset]

This is Suzume ORPO, an ORPO trained fine-tune of the lightblue/suzume-llama-3-8B-multilingual model using our 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.

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).

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 dataset:

Model results

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

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 dataset.

Training configuration

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

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_full_borda
    type: orpo.chat_template
    conversation: llama-3
dataset_prepared_path: /workspace/llm_training/axolotl/llama3-multilingual-orpo/prepared_mitsu_full_borda
val_set_size: 0.02
output_dir: /workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_full_borda

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true

use_wandb: true
wandb_project: axolotl
wandb_entity: peterd
wandb_name: mitsu_full_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|>

workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_full_borda

This model is a fine-tuned version of lightblue/suzume-llama-3-8B-multilingual on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1005

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
7.6836 0.01 1 7.6268
3.9429 0.05 4 2.3993
0.1193 0.1 8 0.1331
0.1039 0.15 12 0.1209
0.1082 0.2 16 0.1189
0.1209 0.25 20 0.1180
0.1106 0.3 24 0.1157
0.103 0.35 28 0.1156
0.1141 0.41 32 0.1123
0.1156 0.46 36 0.1104
0.0925 0.51 40 0.1089
0.1113 0.56 44 0.1052
0.1146 0.61 48 0.1073
0.1029 0.66 52 0.1086
0.1198 0.71 56 0.1072
0.1205 0.76 60 0.1062
0.1209 0.81 64 0.1041
0.1047 0.86 68 0.1020
0.0798 0.91 72 0.1008
0.1007 0.96 76 0.1005

Framework versions

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

How to cite 

@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)