bill-95/medgemma-4b-ft
VISIONConcurrency Cost:1Model Size:4.3BQuant:BF16Ctx Length:32kPublished:Feb 22, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The bill-95/medgemma-4b-ft is a 4.3 billion parameter Gemma 3 model, fine-tuned by bill-95. This model was trained using Unsloth and Huggingface's TRL library, achieving a 2x speedup in the fine-tuning process. It is optimized for efficient deployment and performance, making it suitable for applications requiring a compact yet capable language model.
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Model Overview
The bill-95/medgemma-4b-ft is a 4.3 billion parameter language model, fine-tuned by bill-95. It is based on the Gemma 3 architecture and was developed with a focus on efficient training and deployment.
Key Characteristics
- Architecture: Fine-tuned from
unsloth/medgemma-4b-it-unsloth-bnb-4bit, leveraging the Gemma 3 base model. - Efficient Training: The model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
- Parameter Count: Features 4.3 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens, allowing for processing of substantial input sequences.
Use Cases
This model is particularly well-suited for applications where:
- Resource Efficiency is Critical: Its optimized training and compact size make it ideal for environments with limited computational resources.
- Rapid Prototyping: The faster fine-tuning process can accelerate development cycles for specific tasks.
- General Language Understanding: As a Gemma 3 variant, it can handle a wide range of natural language processing tasks.