DCAgent2/stack-bugs-undr7030
DCAgent2/stack-bugs-undr7030 is an 8 billion parameter language model developed by DCAgent2. This model was trained from scratch, utilizing a cosine learning rate scheduler and AdamW_TORCH_FUSED optimizer. Specific details regarding its primary differentiators, intended uses, and training dataset are not provided in the available documentation.
Loading preview...
Model Overview
The DCAgent2/stack-bugs-undr7030 is an 8 billion parameter language model, developed by DCAgent2. It was trained from scratch, though the specific dataset used for its training is not detailed in the available information. The model's architecture and core capabilities are not explicitly outlined, suggesting it may be a foundational or general-purpose model.
Training Details
The training process for stack-bugs-undr7030 involved several key hyperparameters:
- Learning Rate: 4e-05
- Optimizer: AdamW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08
- Scheduler: Cosine learning rate scheduler with a warmup ratio of 0.1
- Batch Size: A total training batch size of 16 (1 per device across 8 GPUs with 2 gradient accumulation steps) and an evaluation batch size of 64.
- Epochs: Trained for 7.0 epochs.
Limitations and Intended Uses
Specific intended uses, limitations, and evaluation results for stack-bugs-undr7030 are not provided in the current documentation. Users should exercise caution and conduct their own evaluations to determine suitability for particular applications, as its unique strengths or specialized applications are not detailed.