kkoglee/dama-aibrain
The kkoglee/dama-aibrain is a 5.1 billion parameter vision-language model, finetuned and converted to GGUF format using Unsloth, with a context length of 32768 tokens. It is designed for both text-only and multimodal applications, providing optimized performance for local deployment. This model is particularly notable for its efficient training, achieved twice as fast with Unsloth, making it suitable for developers seeking performant and easily deployable multimodal capabilities.
Loading preview...
Overview
kkoglee/dama-aibrain is a 5.1 billion parameter vision-language model, finetuned and converted into the GGUF format, making it suitable for local inference with tools like llama.cpp and Ollama. The model leverages Unsloth for efficient training, achieving a 2x speedup during its finetuning process. It supports a substantial context length of 32768 tokens, enabling processing of longer inputs for both textual and multimodal tasks.
Key Capabilities
- Multimodal Processing: Supports both text-only and vision-language tasks, indicated by the availability of
BF16-mmproj.gguffiles. - GGUF Format: Optimized for local deployment and compatibility with
llama.cppand similar inference engines. - Efficient Training: Finetuned with Unsloth, resulting in significantly faster training times.
- Flexible Deployment: Provides clear instructions for creating Ollama-compatible models from the vision model, addressing current limitations in Ollama's support for separate
mmprojfiles.
Use Cases
- Local AI Applications: Ideal for developers building applications that require a multimodal LLM to run efficiently on local hardware.
- Multimodal Chatbots: Can be integrated into chatbots that need to understand and respond to both text and image inputs.
- Research and Development: Suitable for researchers exploring efficient finetuning and deployment of large language models, especially those with multimodal capabilities.