## SteerLM: Customizing AI Models for More Personalized Responses
In the dynamic world of artificial intelligence, developers and users face a common challenge – the need for more customized and nuanced responses from large language models like Llama 2. Although these models can generate human-like text, their answers often lack personalization. Existing approaches, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), have limitations that result in mechanical and complex responses.
To tackle this issue, NVIDIA Research introduces SteerLM, an innovative technique that promises to address these challenges head-on. SteerLM offers a novel and user-centric approach to customizing the responses of large language models, giving users more control over the output by allowing them to define key attributes that guide the model’s behavior.
SteerLM operates through a four-step supervised fine-tuning process that simplifies the customization of large language models. First, an Attribute Prediction Model is trained using human-annotated datasets to evaluate qualities like helpfulness, humor, and creativity. Next, this model is used to annotate diverse datasets, enriching the variety of data accessible to the language model. Then, SteerLM employs attribute-conditioned supervised fine-tuning, training the model to generate responses based on specified attributes, such as perceived quality. Finally, the model is refined through bootstrap training, resulting in diverse responses and optimal alignment.
One of the standout features of SteerLM is its real-time adjustability, which allows users to fine-tune attributes during inference, catering to their specific needs on the fly. This flexibility opens up a wide range of potential applications, from gaming and education to accessibility. With SteerLM, companies can serve multiple teams with personalized capabilities from a single model, eliminating the need to rebuild models for each distinct application.
SteerLM’s simplicity and user-friendliness are evident in its metrics and performance. In experiments, SteerLM 43B outperformed existing RLHF models like ChatGPT-3.5 and Llama 30B RLHF on the Vicuna benchmark. By offering a straightforward fine-tuning process that requires minimal changes to infrastructure and code, SteerLM delivers exceptional results with less hassle, making it a significant advancement in AI customization.
NVIDIA takes a significant step forward in democratizing advanced customization by releasing SteerLM as open-source software within its NVIDIA NeMo framework. Developers now have the opportunity to access the code and try out this technique with a customized 13B Llama 2 model, available on platforms like Hugging Face. Detailed instructions are also provided for those interested in training their SteerLM model.
As the field of large language models continues to evolve, solutions like SteerLM become increasingly essential to deliver genuinely helpful and aligned AI systems. SteerLM propels the AI community forward in the pursuit of more customized and adaptable AI systems, ushering in a new era of bespoke artificial intelligence.
To read the full article, check out the reference article [here](https://blogs.nvidia.com/blog/2023/10/11/customize-ai-models-steerlm/?ref=maginative.com).