

# Model Card for Better Base LM

![model image](https://agwarbliu.s3.amazonaws.com/logo.png)

![model image](https://agwarbliu.s3.amazonaws.com/model_select_base.png)


**Efficient, Effective, and Stable alternative of RLHF!**

**Instead of training an additional reward model that is likely to be gamed, we directly train the model on the social games!** 🕹️ 🎲 🎮

Full details on simulation and training can be found [here](https://github.com/agi-templar/Stable-Alignment).

# Training Procedure

This is the very beginning of Stable Alignment project, which is an enhanced instruction tuning model based on LLaMA.

We improve:

- Instruction tuning data quality, by using [AlpacaDataCleaned](https://github.com/gururise/AlpacaDataCleaned), which fixes many errors in original Alpaca dataset.

- Code pretraining with [codealpaca](https://github.com/sahil280114/codealpaca).

We use the [Alpaca fine-tuning script](https://github.com/tatsu-lab/stanford_alpaca) to train this model.


# Bias, Risks, and Limitations

Although this project aims to better align current LMs with social norms, inappropriate content and inherent biases in the training data will still impair the alignment of the model.

The model should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

# Citation

Please cite our paper if you use the data or code in this repo:

```bibtex
@misc{liu2023sociallyaligned,
      title={Training Socially Aligned Language Models in Simulated Human Society},
      author={Ruibo Liu and Ruixin Yang and Chenyan Jia and Ge Zhang and Denny Zhou and Andrew M. Dai and Diyi Yang and Soroush Vosoughi},
      year={2023},
      eprint={2305.16960},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["rlhf", "alignment", "simulation", "computational social science"], "datasets": ["alexl83/AlpacaDataCleaned", "sahil2801/CodeAlpaca-20k"], "pipeline_tag": "text-generation"}
transformers pytorch llama text-generation rlhf alignment simulation computational social science en dataset:alexl83/AlpacaDataCleaned dataset:sahil2801/CodeAlpaca-20k arxiv:2305.16960 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #rlhf #alignment #simulation #computational social science #en #dataset-alexl83/AlpacaDataCleaned #dataset-sahil2801/CodeAlpaca-20k #arxiv-2305.16960 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



# Model Card for Better Base LM

!model image

!model image


Efficient, Effective, and Stable alternative of RLHF!

Instead of training an additional reward model that is likely to be gamed, we directly train the model on the social games! ️  

Full details on simulation and training can be found here.

# Training Procedure

This is the very beginning of Stable Alignment project, which is an enhanced instruction tuning model based on LLaMA.

We improve:

- Instruction tuning data quality, by using AlpacaDataCleaned, which fixes many errors in original Alpaca dataset.

- Code pretraining with codealpaca.

We use the Alpaca fine-tuning script to train this model.


# Bias, Risks, and Limitations

Although this project aims to better align current LMs with social norms, inappropriate content and inherent biases in the training data will still impair the alignment of the model.

The model should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Please cite our paper if you use the data or code in this repo:


# Model Card for Better Base LM

!model image

!model image


Efficient, Effective, and Stable alternative of RLHF!

Instead of training an additional reward model that is likely to be gamed, we directly train the model on the social games! ️  

Full details on simulation and training can be found here. # Training Procedure

This is the very beginning of Stable Alignment project, which is an enhanced instruction tuning model based on LLaMA.

We improve:

- Instruction tuning data quality, by using AlpacaDataCleaned, which fixes many errors in original Alpaca dataset.

- Code pretraining with codealpaca.

We use the Alpaca fine-tuning script to train this model. # Bias, Risks, and Limitations

Although this project aims to better align current LMs with social norms, inappropriate content and inherent biases in the training data will still impair the alignment of the model.

The model should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Please cite our paper if you use the data or code in this repo:
TAGS
#transformers #pytorch #llama #text-generation #rlhf #alignment #simulation #computational social science #en #dataset-alexl83/AlpacaDataCleaned #dataset-sahil2801/CodeAlpaca-20k #arxiv-2305.16960 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for Better Base LM

!model image

!model image


Efficient, Effective, and Stable alternative of RLHF!

Instead of training an additional reward model that is likely to be gamed, we directly train the model on the social games! ️  

Full details on simulation and training can be found here. # Training Procedure

This is the very beginning of Stable Alignment project, which is an enhanced instruction tuning model based on LLaMA.

We improve:

- Instruction tuning data quality, by using AlpacaDataCleaned, which fixes many errors in original Alpaca dataset.

- Code pretraining with codealpaca.

We use the Alpaca fine-tuning script to train this model. # Bias, Risks, and Limitations

Although this project aims to better align current LMs with social norms, inappropriate content and inherent biases in the training data will still impair the alignment of the model.

The model should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Please cite our paper if you use the data or code in this repo: