imleadingmylife/AI-Consulting-Gemma-4-v1
The AI-Consulting-Gemma-4-v1 model by imleadingmylife is a 5.1 billion parameter Gemma-based language model, fine-tuned and converted to GGUF format using Unsloth. It features a 32768 token context length and is available in quantized (Q4_K_M) and full-precision (F16) versions, including a multimodal variant. This model is optimized for deployment in environments supporting GGUF, with specific considerations for Ollama vision model integration.
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AI-Consulting-Gemma-4-v1 Overview
This model, developed by imleadingmylife, is a 5.1 billion parameter variant of the Gemma architecture. It has been specifically fine-tuned and converted into the GGUF format using the Unsloth framework, which facilitated a 2x faster training process. The model supports a substantial context length of 32768 tokens, making it suitable for processing longer inputs.
Key Capabilities & Features
- Gemma Architecture: Built upon the Gemma foundation, offering robust language understanding and generation capabilities.
- GGUF Format: Provided in GGUF, enabling efficient deployment and compatibility with various inference engines like
llama-cli. - Quantized and Full-Precision Options: Available in
Q4_K_M.gguffor optimized performance andF16-mmproj.gguffor higher precision, including multimodal support. - Multimodal Support: The
F16-mmproj.gguffile indicates support for multimodal inputs, though specific integration steps are required for platforms like Ollama. - Unsloth Optimization: Benefits from training optimizations provided by Unsloth, leading to faster development cycles.
Good For
- GGUF-compatible Inference: Ideal for users and applications requiring models in the GGUF format.
- Long Context Applications: Suitable for tasks that benefit from a 32768 token context window.
- Multimodal Use Cases: The
F16-mmproj.ggufvariant is designed for applications integrating vision capabilities, with a note on specific setup for Ollama. - Efficient Deployment: The availability of quantized versions makes it suitable for resource-constrained environments.