WonseokJayJung/dama-aibrain
dama-aibrain is a 5.1 billion parameter language model, fine-tuned and converted to GGUF format by WonseokJayJung using Unsloth. This model is available in both text-only and multimodal (vision-capable) variants, supporting a 32768 token context length. It is optimized for efficient deployment and use with tools like llama-cli and Ollama, particularly for applications requiring multimodal input.
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Overview
dama-aibrain is a 5.1 billion parameter language model developed by WonseokJayJung. It has been fine-tuned and converted into the GGUF format using Unsloth, which facilitated a 2x faster training process. The model supports a substantial context length of 32768 tokens and is provided in various quantized GGUF files, including gemma-4-e2b-it.Q8_0.gguf and gemma-4-e2b-it.F16-mmproj.gguf for multimodal capabilities.
Key Capabilities
- Efficient Deployment: Optimized for use with
llama-clifor text-only tasks andllama-mtmd-clifor multimodal applications. - Multimodal Support: Includes a vision-capable variant (
F16-mmproj.gguf) for processing image inputs, though specific integration steps are required for platforms like Ollama due to current limitations with separatemmprojfiles. - GGUF Format: Provides broad compatibility with various inference engines and hardware.
Good For
- Developers seeking an efficient, fine-tuned model for text generation.
- Applications requiring multimodal input, with specific setup for vision models on platforms like Ollama.
- Users looking for models optimized for faster training and conversion via Unsloth.