muradil211/AetherSearch
AetherSearch by muradil211 is a 3.1 billion parameter search-augmented language model. It was developed through a multi-stage post-training pipeline incorporating Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and reinforcement learning. This model is designed to enhance language understanding and generation by integrating search capabilities. It is suitable for applications requiring advanced reasoning and context-aware responses.
Loading preview...
AetherSearch: A Search-Augmented Language Model
AetherSearch, developed by muradil211, is a 3.1 billion parameter language model distinguished by its search-augmented capabilities. It has undergone a sophisticated multi-stage post-training process, which includes Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and reinforcement learning. This rigorous training methodology aims to enhance the model's ability to leverage external information, making it more robust and context-aware.
Key Capabilities
- Search Augmentation: Designed to integrate search functionalities, allowing for more informed and accurate responses.
- Advanced Post-Training: Utilizes SFT, DPO, and reinforcement learning for refined performance and alignment.
- Standard Hugging Face Format: Provided in a readily usable format compatible with
AutoModelForCausalLMandAutoTokenizerfor easy integration into existing workflows.
Good For
- Applications requiring models that can incorporate external search results.
- Tasks benefiting from models trained with advanced alignment techniques like DPO and RL.
- Developers looking for a 3.1B parameter model with enhanced reasoning and contextual understanding.