🐦 Llama-3.1-8B-Magpie-Align-SFT-v0.1
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
About This Model
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on
It achieves performance comparable with the official Llama-3.1-8B-Instruct Model with SFT only!
- Alpaca Eval 2 (GPT-4-Turbo-1106): 24.79 (LC), 25.05 (WR)
- Arena Hard: 21.0
Other Information
License: Please follow Meta Llama 3 Community License (Data) and Meta Llama 3.1 Community License (Model).
Conversation Template: Please use Llama 3 official chat template for the best performance.
Questions? Please contact Zhangchen by email.
Citation
If you find the model, data, or code useful, please cite our paper:
@article{xu2024magpie,
title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
year={2024},
eprint={2406.08464},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 79
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7863 | 0.0024 | 1 | 0.7710 |
| 0.5422 | 0.2007 | 85 | 0.4937 |
| 0.476 | 0.4014 | 170 | 0.4382 |
| 0.4594 | 0.6021 | 255 | 0.4174 |
| 0.4383 | 0.8028 | 340 | 0.4057 |
| 0.4397 | 1.0035 | 425 | 0.3978 |
| 0.3927 | 1.1845 | 510 | 0.3956 |
| 0.3895 | 1.3852 | 595 | 0.3934 |
| 0.3832 | 1.5859 | 680 | 0.3925 |
| 0.3957 | 1.7866 | 765 | 0.3924 |
Framework versions
- Transformers 4.43.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
axolotl version: 0.4.1
base_model: meta-llama/Meta-Llama-3.1-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Magpie-Align/Magpie-Reasoning-150K
type: sharegpt
conversation: llama3
- path: Magpie-Align/Magpie-Pro-MT-300K-v0.1
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: /data/axolotl_out/Llama-3.1-8B-Mix-SFT
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3.1-8B-Mix-SFT
wandb_log_model:
hub_model_id: Magpie-Align/Llama-3.1-8B-Magpie-Align-SFT-v0.1
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
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_ratio: 0.1
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>