dkingtutcd/gemma-E4B-mt-b-split3
VISIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 17, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The dkingtutcd/gemma-E4B-mt-b-split3 is a 7.9 billion parameter Gemma-based causal language model developed by dkingtutcd, featuring a 32768 token context length. This model is a finetuned iteration, specifically optimized for faster training using Unsloth and Huggingface's TRL library. It is designed for general language generation tasks, leveraging its efficient training methodology for improved performance.
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Model Overview
The dkingtutcd/gemma-E4B-mt-b-split3 is a 7.9 billion parameter language model, finetuned by dkingtutcd. It is based on the Gemma architecture and features a substantial context length of 32768 tokens, making it suitable for processing longer sequences of text.
Key Characteristics
- Efficient Training: This model was finetuned using Unsloth and Huggingface's TRL library, enabling a 2x faster training process compared to conventional methods. This efficiency can translate to quicker iteration cycles and reduced computational costs for further adaptation.
- Finetuned from Previous Iteration: It is a direct finetune of the
dkingtutcd/gemma-E4B-mt-b-split2model, indicating a progressive development approach. - Apache-2.0 License: The model is released under the Apache-2.0 license, providing flexibility for various applications.
Potential Use Cases
- General Text Generation: Its large parameter count and context window make it suitable for a wide range of text generation tasks, from creative writing to summarization.
- Research and Development: The model's efficient training methodology makes it an interesting candidate for researchers exploring faster finetuning techniques for large language models.
- Applications requiring long context: The 32768 token context length allows for handling extensive documents or conversations, which is beneficial for tasks like document analysis or complex dialogue systems.