zaddyzaddy/Soro-GPT
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Jun 11, 2024License:apache-2.0Architecture:Transformer Open Weights Cold
Soro-GPT is an 8 billion parameter Llama-3 based causal language model developed by zaddyzaddy. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. This model is designed for general language generation tasks, leveraging the efficiency gains from its optimized training process.
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Soro-GPT: An Efficiently Trained Llama-3 Model
Soro-GPT is an 8 billion parameter language model developed by zaddyzaddy. It is built upon the Llama-3 architecture and has been fine-tuned using a combination of Unsloth and Huggingface's TRL library. This specific training methodology allowed for a significantly faster fine-tuning process, reportedly twice as fast compared to standard methods.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/llama-3-8b-bnb-4bit. - Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Leverages Unsloth for accelerated training, making it a potentially attractive option for developers seeking quicker iteration cycles.
- License: Distributed under the Apache-2.0 license, providing broad usage permissions.
Potential Use Cases
- General Text Generation: Suitable for a wide array of tasks requiring coherent and contextually relevant text output.
- Experimentation: Its efficient training process makes it a good candidate for rapid prototyping and experimentation with Llama-3 based models.
- Resource-Conscious Applications: The 8B parameter size, combined with optimized training, suggests it could be effective in environments where computational resources are a consideration.