rizkysulaeman/Qwen3-VL-8B-Vision-GRPO-HealthCare
The rizkysulaeman/Qwen3-VL-8B-Vision-GRPO-HealthCare model is an 8 billion parameter multimodal large language model, based on the Qwen3-VL architecture, specifically fine-tuned for healthcare applications. This vision-capable model processes both text and images, offering a context length of 32768 tokens. It was fine-tuned and converted to GGUF format using Unsloth, optimizing it for efficient deployment and use in specialized healthcare contexts.
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Overview
The rizkysulaeman/Qwen3-VL-8B-Vision-GRPO-HealthCare is an 8 billion parameter multimodal large language model (LLM) built upon the Qwen3-VL architecture. This model is specifically fine-tuned for applications within the healthcare domain, enabling it to process and understand both textual and visual information relevant to medical contexts. It supports a substantial context length of 32768 tokens, allowing for comprehensive analysis of complex inputs.
Key Characteristics
- Multimodal Capabilities: Designed to handle both text and image inputs, making it suitable for tasks requiring visual understanding in healthcare.
- Healthcare Specialization: Fine-tuned for specific use cases within the healthcare sector, suggesting enhanced performance on related data.
- Efficient Deployment: The model has been converted to the GGUF format, which is optimized for efficient inference on various hardware, including CPU-only setups, via tools like
llama.cpp. - Unsloth Optimization: The fine-tuning and conversion process leveraged Unsloth, indicating potential benefits in training speed and resource efficiency.
Available Formats
Multiple GGUF quantized versions are provided for flexible deployment:
qwen3-vl-8b-instruct.Q5_K_M.ggufqwen3-vl-8b-instruct.Q8_0.ggufqwen3-vl-8b-instruct.Q4_K_M.ggufqwen3-vl-8b-instruct.F16-mmproj.gguf
Usage
This model can be run using llama.cpp tools. For multimodal interactions, the llama-mtmd-cli is recommended, while llama-cli can be used for text-only operations.