---
base_model: JunxiongWang/llama3_mamba_0_5_sft
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- HuggingFaceH4/orca_dpo_pairs
- JunxiongWang/llama3-ultrafeedback-armorm
model-index:
- name: JunxiongWang/MambaInLlama_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/d364ppka)
# JunxiongWang/MambaInLlama_0_875

This model is a fine-tuned version of [JunxiongWang/llama3_mamba_0_5_sft](https://huggingface.co/JunxiongWang/llama3_mamba_0_5_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: 0.4393
- Rewards/chosen: -1.7352
- Rewards/rejected: -3.4828
- Rewards/accuracies: 0.8286
- Rewards/margins: 1.7476
- Logps/rejected: -623.4827
- Logps/chosen: -439.4573
- Logits/rejected: -0.6261
- Logits/chosen: -0.6084

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4627        | 0.4798 | 2000 | 0.4641          | -1.4789        | -2.9625          | 0.8036             | 1.4836          | -571.4525      | -413.8262    | -0.7196         | -0.7049       |
| 0.4644        | 0.9597 | 4000 | 0.4393          | -1.7352        | -3.4828          | 0.8286             | 1.7476          | -623.4827      | -439.4573    | -0.6261         | -0.6084       |


### 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}
}
```