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llama
alignment-handbook
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JunxiongWang/Mamba2InLlama_0_875

This model is a fine-tuned version of JunxiongWang/llama3_0_875_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: 0.4761
  • Rewards/chosen: -1.4040
  • Rewards/rejected: -2.6012
  • Rewards/accuracies: 0.7982
  • Rewards/margins: 1.1973
  • Logps/rejected: -584.9104
  • Logps/chosen: -459.0677
  • Logits/rejected: 0.3408
  • Logits/chosen: 0.3851

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
0.5009 0.4798 2000 0.4998 -1.4973 -2.6147 0.7804 1.1175 -586.2582 -468.3976 0.4682 0.5136
0.4895 0.9597 4000 0.4761 -1.4040 -2.6012 0.7982 1.1973 -584.9104 -459.0677 0.3408 0.3851

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}
}
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