|
--- |
|
base_model: JunxiongWang/mamba_0_5_sft |
|
tags: |
|
- mamba |
|
- alignment-handbook |
|
- generated_from_trainer |
|
datasets: |
|
- HuggingFaceH4/ultrafeedback_binarized |
|
model-index: |
|
- name: mamba_0_5_dpo_ep3 |
|
results: [] |
|
--- |
|
|
|
Please check [here](https://github.com/jxiw/MambaInLlama/tree/main) for details. |
|
|
|
<!-- 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. --> |
|
|
|
# mamba_0_5_dpo_ep3 |
|
|
|
This model is a fine-tuned version of [JunxiongWang/mamba_0_5_dpo_ep3](https://huggingface.co/JunxiongWang/mamba_0_5_sft) on the HuggingFaceH4/ultrafeedback_binarized dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.7141 |
|
- Rewards/chosen: -5.3346 |
|
- Rewards/rejected: -8.3118 |
|
- Rewards/accuracies: 0.7891 |
|
- Rewards/margins: 2.9772 |
|
- Logps/rejected: -337.4994 |
|
- Logps/chosen: -304.9619 |
|
- Logits/rejected: -2.7812 |
|
- Logits/chosen: -2.8272 |
|
|
|
## 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: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
|
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
|
| 0.1171 | 1.0466 | 2000 | 0.5329 | -1.4521 | -2.9272 | 0.7734 | 1.4750 | -283.6535 | -266.1376 | -2.8897 | -2.9362 | |
|
| 0.0086 | 2.0931 | 4000 | 0.7141 | -5.3346 | -8.3118 | 0.7891 | 2.9772 | -337.4994 | -304.9619 | -2.7812 | -2.8272 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.1.0+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} |
|
} |
|
``` |
|
|