abeiler/goatV10-QLORA
The abeiler/goatV10-QLORA model is a fine-tuned version of Meta's Llama-2-7b-hf, a 7 billion parameter causal language model. This QLORA fine-tune was trained with a learning rate of 0.0001 over one epoch, achieving a validation loss of 0.3861. While specific differentiators and intended uses are not detailed, its base architecture suggests general language understanding and generation capabilities.
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Model Overview
The abeiler/goatV10-QLORA is a fine-tuned language model based on Meta's Llama-2-7b-hf architecture. This model was trained using QLORA, a parameter-efficient fine-tuning method, over a single epoch. The training process involved a learning rate of 0.0001 and resulted in a final validation loss of 0.3861.
Training Details
- Base Model:
meta-llama/Llama-2-7b-hf - Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Learning Rate: 0.0001
- Batch Sizes: Training batch size of 4, evaluation batch size of 8
- Epochs: 1
- Frameworks: Transformers 4.33.1, Pytorch 2.0.0, Datasets 2.12.0, Tokenizers 0.13.3
Limitations and Further Information
The model card indicates that more information is needed regarding its specific description, intended uses, limitations, and the dataset used for training and evaluation. Users should be aware that without these details, the precise capabilities and optimal applications of this fine-tuned model are not fully clear. The reported loss of 0.3860 on the evaluation set provides a quantitative measure of its performance during training.