Mamba2-In-Llama3
Collection
Mamba2 distilled from Llama3 8B instruct. The Mamba in the Llama: Distilling and Accelerating Hybrid Models (https://arxiv.org/abs/2408.15237).
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4 items
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Updated
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2
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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:
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The following hyperparameters were used during training:
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
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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 |
@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}
}