chihoonlee10/T3Q-ko-solar-dpo-v1.0
T3Q-ko-solar-dpo-v1.0 is a 10.7 billion parameter language model developed by Chihoon Lee and T3Q. It is a DPO fine-tuned version of the davidkim205/nox-solar-10.7b-v4 model, featuring a 4096 token context length. This model is optimized for enhanced performance through Direct Preference Optimization.
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Overview
T3Q-ko-solar-dpo-v1.0 is a 10.7 billion parameter language model developed by Chihoon Lee and T3Q. This model is built upon the davidkim205/nox-solar-10.7b-v4 architecture and has undergone further fine-tuning using Direct Preference Optimization (DPO). It is designed to leverage the strengths of its base model while incorporating preference-based learning to refine its outputs.
Key Capabilities
- DPO Fine-tuning: Utilizes Direct Preference Optimization to align model behavior with desired outcomes, potentially leading to more coherent and preferred responses.
- Base Model Enhancement: Builds upon the davidkim205/nox-solar-10.7b-v4, suggesting a foundation in general language understanding and generation.
- Context Length: Supports a context window of 4096 tokens, allowing for processing and generating moderately long sequences of text.
Good For
- Applications requiring a model with improved alignment through DPO.
- Tasks where the base davidkim205/nox-solar-10.7b-v4 model is suitable, with an expectation of refined performance.
- Research and development in DPO techniques on existing large language models.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.