abeiler/NumAndAlphaInstruct-75-25
The abeiler/NumAndAlphaInstruct-75-25 model is a fine-tuned version of Meta's Llama-2-7b-hf architecture. This model was trained using QLORA with specific hyperparameters including a learning rate of 0.0001 and 1 epoch. Its primary differentiation and specific use cases are not detailed in the available information, suggesting it may be a foundational fine-tune or experimental model.
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Model Overview
The abeiler/NumAndAlphaInstruct-75-25 is a fine-tuned language model based on the meta-llama/Llama-2-7b-hf architecture. It was trained using the QLORA method, indicating an efficient fine-tuning approach.
Training Details
The model underwent training with the following key hyperparameters:
- Learning Rate: 0.0001
- Batch Sizes:
train_batch_sizeof 4,eval_batch_sizeof 8 - Optimizer: Adam with default betas and epsilon
- Scheduler: Linear learning rate scheduler
- Epochs: 1
Key Capabilities
- Base Model: Leverages the robust capabilities of Llama-2-7b-hf.
- QLORA Fine-tuning: Suggests efficient adaptation from the base model.
Limitations and Intended Uses
Specific details regarding the dataset used for fine-tuning, the model's intended uses, and its limitations are not provided in the available documentation. Users should exercise caution and conduct further evaluation to determine its suitability for specific applications, as its unique differentiators or optimized performance areas are currently undefined.