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---
base_model: JunxiongWang/llama3_0_875_mamba2_sft
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- HuggingFaceH4/orca_dpo_pairs
- JunxiongWang/llama3-ultrafeedback-armorm
model-index:
- name: JunxiongWang/Mamba2InLlama_0_875
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Please check [here](https://github.com/jxiw/MambaInLlama/tree/main) for details.
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/junxiong12/huggingface/runs/58mrdgq8)
# JunxiongWang/Mamba2InLlama_0_875
This model is a fine-tuned version of [JunxiongWang/llama3_0_875_mamba2_sft](https://huggingface.co/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](arxiv.org/abs/2408.15237)
```
@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}
}
``` |