CharlesLi/llama_3_alpaca_per_class_reflect
CharlesLi/llama_3_alpaca_per_class_reflect is an 8 billion parameter language model, fine-tuned from Meta's Llama-3.1-8B-Instruct architecture. This model was trained for 30 steps with a learning rate of 0.0002, achieving a final validation loss of 0.8348. It is intended for general language generation tasks, building upon the capabilities of its Llama-3.1 base.
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Model Overview
CharlesLi/llama_3_alpaca_per_class_reflect is an 8 billion parameter language model, fine-tuned from the meta-llama/Llama-3.1-8B-Instruct base model. This fine-tuning process aimed to adapt the powerful Llama-3.1 architecture for specific tasks, though the exact dataset used for fine-tuning is not specified beyond "None dataset".
Training Details
The model underwent a focused training regimen with the following key hyperparameters:
- Base Model: meta-llama/Llama-3.1-8B-Instruct
- Learning Rate: 0.0002
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Batch Size: 4 (train and eval), with a total distributed batch size of 16
- Training Steps: 30
- Final Validation Loss: 0.8348
Intended Use Cases
Given its foundation on Llama-3.1-8B-Instruct, this model is generally suitable for instruction-following tasks, text generation, and conversational AI. However, specific performance characteristics or specialized applications resulting from its fine-tuning are not detailed in the provided information. Users should evaluate its performance for their particular use case, especially considering the unspecified training data.