davanstrien/qwen35-4b-iconclass-vlm
davanstrien/qwen35-4b-iconclass-vlm is a 4.5 billion parameter Qwen3.5-based vision-language model developed by davanstrien. This model was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for tasks requiring visual understanding combined with language processing, leveraging its Qwen3.5 architecture for robust performance.
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Model Overview
davanstrien/qwen35-4b-iconclass-vlm is a 4.5 billion parameter vision-language model (VLM) built upon the Qwen3.5 architecture. Developed by davanstrien, this model was specifically finetuned using the Unsloth library, which facilitated a 2x faster training process, alongside Huggingface's TRL library.
Key Characteristics
- Base Model: Finetuned from unsloth/Qwen3.5-4B-Base, inheriting its foundational capabilities.
- Training Efficiency: Leverages Unsloth for accelerated finetuning, making the training process significantly quicker.
- Parameter Count: Features 4.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context length of 32768 tokens.
Intended Use Cases
This model is particularly well-suited for applications that require the integration of visual information with language understanding. Its finetuning suggests an optimization for specific tasks within the vision-language domain, making it a strong candidate for projects needing a capable VLM with efficient training origins.