ersindemirel94/gemma_4_e2b-bnb-4bit
VISIONConcurrent Unit Cost:1Model Size:5.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The ersindemirel94/gemma_4_e2b-bnb-4bit is a 5.1 billion parameter Gemma-4 based causal language model developed by ersindemirel94. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for efficient deployment and inference due to its 4-bit BNB quantization, making it suitable for resource-constrained environments.
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Model Overview
The ersindemirel94/gemma_4_e2b-bnb-4bit is a 5.1 billion parameter language model, fine-tuned by ersindemirel94. It is based on the Gemma-4 architecture and utilizes 4-bit BitsAndBytes (BNB) quantization for efficient performance.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/gemma-4-E2B-it-unsloth-bnb-4bit. - Efficient Training: Leverages Unsloth and Huggingface's TRL library, resulting in a 2x speedup during the fine-tuning process.
- Quantization: Implements 4-bit BNB quantization, which significantly reduces memory footprint and speeds up inference.
Use Cases
This model is particularly well-suited for applications requiring:
- Resource-constrained environments: Its 4-bit quantization makes it efficient for deployment on devices with limited memory or computational power.
- Fast inference: The optimized training and quantization contribute to quicker response times.
- General language generation tasks: As a Gemma-4 based model, it can handle a variety of text generation and understanding tasks.