Armo R M Llama3 8 B v0.1

Introduction

The ArmoRM-Llama3-8B-v0.1 model focuses on multi-objective reward modeling using a Mixture-of-Experts (MoE) approach. Developed by a team including Haoxiang Wang, Wei Xiong, and others, this model is designed for text classification and inference tasks, particularly in providing interpretable preference scoring.

Architecture

The architecture employs a Mixture-of-Experts (MoE) aggregation for combining multiple reward objectives, enabling the model to produce a preference score. This approach integrates various objectives to address different aspects of a given task, such as correctness, coherence, and safety.

Training

The model is finetuned from the FsfairX-LLaMA3-RM-v0.1 base model. It utilizes a multi-objective reward modeling approach, which aggregates reward signals using a gating mechanism to achieve a comprehensive evaluation of task performance.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and PyTorch installed. It's recommended to use a virtual environment.
2. Install Transformers:

   pip install transformers
   

3. Load Model and Tokenizer:

   from transformers import AutoModelForSequenceClassification, AutoTokenizer
   model = AutoModelForSequenceClassification.from_pretrained('RLHFlow/ArmoRM-Llama3-8B-v0.1')
   tokenizer = AutoTokenizer.from_pretrained('RLHFlow/ArmoRM-Llama3-8B-v0.1', use_fast=True)
   

4. Inference: Use the provided demo code to apply the model to your data.
5. GPU Recommendation: For improved performance, especially with larger models, consider using cloud GPUs such as those offered by AWS or Google Cloud.

License

The model is released under the llama3 license. Be sure to review the license terms before use to ensure compliance with any restrictions or obligations.