metadata

base_model: JunxiongWang/mamba_0_875_sft
tags:
  - mamba
  - alignment-handbook
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: mamba_0_875_dpo_ep3
    results: []

mamba_0_875_dpo_ep3

This model is a fine-tuned version of JunxiongWang/mamba_0_875_sft on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.6922
Rewards/chosen: -3.9752
Rewards/rejected: -6.3998
Rewards/accuracies: 0.7852
Rewards/margins: 2.4245
Logps/rejected: -333.8416
Logps/chosen: -307.0094
Logits/rejected: -2.4971
Logits/chosen: -2.5509

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: 5e-07
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 32
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.1219	1.0466	2000	0.5598	-1.2751	-2.5954	0.7539	1.3204	-295.7982	-280.0076	-2.6264	-2.6813
0.0099	2.0931	4000	0.6922	-3.9752	-6.3998	0.7852	2.4245	-333.8416	-307.0094	-2.4971	-2.5509

Framework versions

Transformers 4.41.2
Pytorch 2.1.0+cu118
Datasets 2.20.0
Tokenizers 0.19.1

MambaInLlama

@article{junxiongdaniele2024mambainllama,
  title   = {The Mamba in the Llama: Distilling and Accelerating Hybrid Models},
  author  = {Junxiong Wang and Daniele Paliotta and Avner May and Alexander M. Rush and Tri Dao},
  journal = {arXiv preprint arXiv:2408.15237},
  year    = {2024}
}