argsearch/llama-7b-sft-float32
The argsearch/llama-7b-sft-float32 is a 7 billion parameter language model, likely based on the Llama architecture, fine-tuned for specific tasks. Trained with a learning rate of 5e-05 over 3 epochs, this model is a foundational SFT (Supervised Fine-Tuning) variant. Its primary use case would involve applications requiring a moderately sized, fine-tuned language model for general text generation or understanding tasks.
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Overview
The argsearch/llama-7b-sft-float32 is a 7 billion parameter language model, likely derived from the Llama architecture, that has undergone Supervised Fine-Tuning (SFT). The model was trained using a learning rate of 5e-05, a batch size of 8, and an Adam optimizer over 3 epochs. While specific details about its training dataset and intended uses are not provided in the available documentation, its SFT nature suggests it has been optimized for specific downstream tasks.
Key Characteristics
- Parameter Count: 7 billion parameters, offering a balance between performance and computational efficiency.
- Training Details: Fine-tuned with a learning rate of 5e-05, utilizing an Adam optimizer with standard beta values and epsilon, over 3 epochs.
- Frameworks: Developed using Transformers 4.35.2, Pytorch 2.1.1+cu121, Datasets 2.15.0, and Tokenizers 0.15.0.
Potential Use Cases
Given its SFT nature and 7B parameter size, this model could be suitable for:
- General text generation and completion tasks.
- Applications requiring a fine-tuned model for specific domain understanding, assuming it was trained on relevant data.
- As a base model for further fine-tuning on more specialized datasets.