dkingtutcd/gemma-E4B-mt-b-split2
VISIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 16, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
dkingtutcd/gemma-E4B-mt-b-split2 is a 7.9 billion parameter language model developed by dkingtutcd, fine-tuned from dkingtutcd/gemma-E4B-mt-b-split1. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general language generation tasks, leveraging its efficient training methodology to provide a capable and optimized base for further applications.
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Model Overview
dkingtutcd/gemma-E4B-mt-b-split2 is a 7.9 billion parameter language model, fine-tuned by dkingtutcd from the dkingtutcd/gemma-E4B-mt-b-split1 base model. This iteration focuses on training efficiency, utilizing the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Efficient Training: Achieved 2x faster training speeds compared to traditional methods, thanks to the integration of Unsloth.
- Base Model: Fine-tuned from a gemma4 architecture, indicating a strong foundation for various language tasks.
- Parameter Count: Features 7.9 billion parameters, offering a balance between performance and computational requirements.
Good For
- Rapid Prototyping: The efficient training methodology makes it suitable for developers looking to quickly iterate on fine-tuning experiments.
- General Language Generation: Can be applied to a wide range of natural language processing tasks, leveraging its gemma-based architecture.
- Resource-Conscious Development: Its optimized training process suggests potential benefits for projects with limited computational resources or time constraints.