nabinkhair/genui-vl-2b-merged
The nabinkhair/genui-vl-2b-merged is a 2 billion parameter Qwen3-VL model developed by nabinkhair. This instruction-tuned model was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for visual language tasks, leveraging its Qwen3-VL architecture to process both visual and textual inputs. The model is suitable for applications requiring efficient visual language understanding and generation.
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Model Overview
The nabinkhair/genui-vl-2b-merged is a 2 billion parameter visual language model, developed by nabinkhair. It is an instruction-tuned variant of the Qwen3-VL architecture, specifically finetuned from unsloth/Qwen3-VL-2B-Instruct-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Based on the Qwen3-VL family, indicating its capability to handle both visual and linguistic data.
- Parameter Count: A compact 2 billion parameters, making it efficient for deployment and inference.
- Training Efficiency: The model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 32768 tokens, allowing for processing of substantial input sequences.
Intended Use Cases
This model is well-suited for applications that require:
- Visual Language Understanding: Tasks involving the interpretation of images combined with text.
- Efficient Deployment: Its smaller parameter count makes it a good candidate for resource-constrained environments or applications where speed is critical.
- Instruction Following: As an instruction-tuned model, it is designed to respond effectively to user prompts and instructions in a visual language context.