metadata

base_model: JunxiongWang/mamba_0_75_sft
tags:
  - alignment-handbook
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: mamba_0_75_dpo_ep3
    results: []

Please check here for details.

mamba_0_75_dpo_ep3

This model is a fine-tuned version of JunxiongWang/mamba_0_75_sft on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.7077
Rewards/chosen: -4.3611
Rewards/rejected: -7.0013
Rewards/accuracies: 0.7812
Rewards/margins: 2.6403
Logps/rejected: -333.1784
Logps/chosen: -302.4903
Logits/rejected: -2.8351
Logits/chosen: -2.8752

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
0.1188	1.0466	2000	0.5385	-1.3137	-2.8323	0.7852	1.5186	-291.4879	-272.0161	-2.9057	-2.9472
0.0093	2.0931	4000	0.7077	-4.3611	-7.0013	0.7812	2.6403	-333.1784	-302.4903	-2.8351	-2.8752

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}
}