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JunxiongWang/Mamba2InLlama_0_75

This model is a fine-tuned version of JunxiongWang/llama3_0_75_mamba2_sft on the HuggingFaceH4/ultrafeedback_binarized, the HuggingFaceH4/orca_dpo_pairs and the JunxiongWang/llama3-ultrafeedback-armorm datasets. It achieves the following results on the evaluation set:

• Loss: 0.4695
• Rewards/chosen: -1.5489
• Rewards/rejected: -2.8730
• Rewards/accuracies: 0.8107
• Rewards/margins: 1.3240
• Logps/rejected: -589.1575
• Logps/chosen: -449.6615
• Logits/rejected: 1.1678
• Logits/chosen: 1.2259

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.494	0.4798	2000	0.4938	-1.4838	-2.6084	0.7911	1.1246	-562.7021	-443.1515	1.1609	1.2167
0.4911	0.9597	4000	0.4695	-1.5489	-2.8730	0.8107	1.3240	-589.1575	-449.6615	1.1678	1.2259

Framework versions

• Transformers 4.43.1
• Pytorch 2.1.1+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}
}