chlee10/T3Q-ko-solar-sft-v3.0 is a supervised fine-tuned (SFT) language model developed by Chihoon Lee (chlee10) and T3Q. It is based on the chihoonlee10/T3Q-ko-solar-dpo-v3.0 model. This iteration focuses on refining performance through specific training hyperparameters, including a cutoff length of 4096 and a cosine learning rate scheduler. It is designed for tasks benefiting from its fine-tuned capabilities.
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Model Overview
The chlee10/T3Q-ko-solar-sft-v3.0 model is a supervised fine-tuned (SFT) version, building upon the chihoonlee10/T3Q-ko-solar-dpo-v3.0 base model. Developed by Chihoon Lee (chlee10) and T3Q, this model has undergone specific training to enhance its performance for particular applications.
Training Details
The fine-tuning process utilized a set of carefully selected hyperparameters to optimize the model's learning and generalization. Key training parameters include:
- Batch Size: 16
- Number of Epochs: 1
- Cutoff Length: 4096 tokens, defining the maximum sequence length for training.
- Learning Rate: 5e-5, managed by a 'cosine' scheduler with a warmup ratio of 0.06.
- Optimizer: 'paged_adamw_32bit' for efficient memory usage.
- LoRA Configuration: Employed with
r=16,alpha=16, anddropout=0.05, targeting projection layers (k_proj,v_proj,gate_proj,down_proj,up_proj) to efficiently adapt the model. - NEFTune: Applied with a noise alpha of 5 to potentially improve robustness and generalization.
Intended Use
This model is suitable for applications requiring a language model that has been specifically refined through supervised fine-tuning, leveraging the architectural strengths of its base model and the detailed training regimen.