aashish093/qwen3-vl-4b-scheme-extract
VISIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:May 11, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The aashish093/qwen3-vl-4b-scheme-extract model is a 4 billion parameter Qwen3-VL architecture, fine-tuned by aashish093. This model was optimized for training speed using Unsloth and Huggingface's TRL library, building upon the unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit base. It is designed for visual language tasks, leveraging its Qwen3-VL foundation for multimodal understanding and generation.
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Model Overview
The aashish093/qwen3-vl-4b-scheme-extract is a 4 billion parameter visual language model, fine-tuned by aashish093. It is based on the Qwen3-VL architecture, specifically building upon the unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit model.
Key Characteristics
- Architecture: Qwen3-VL, indicating its capability for multimodal tasks involving both visual and linguistic inputs.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Optimization: The fine-tuning process for this model was accelerated using Unsloth and Huggingface's TRL library, resulting in a 2x faster training time compared to standard methods.
- License: Distributed under the Apache-2.0 license.
Potential Use Cases
This model is suitable for applications requiring:
- Visual Language Understanding: Tasks that involve interpreting and generating responses based on both images and text.
- Efficient Deployment: Its 4B parameter size and optimized training suggest it could be a good candidate for scenarios where faster inference or reduced resource consumption is beneficial.
- Further Fine-tuning: Developers looking for a Qwen3-VL base that has undergone optimized fine-tuning might find this model a good starting point for specific downstream tasks.