billahaiml/qwen3-vl-8b-bangla
VISIONPricing:Input $0.727 / Output $5.405Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 6, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The billahaiml/qwen3-vl-8b-bangla is an 8 billion parameter Qwen3-VL model developed by billahaiml, fine-tuned from unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit. This vision-language model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for tasks requiring multimodal understanding, combining visual and textual inputs, and is particularly optimized for applications involving the Bengali language.
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Model Overview
The billahaiml/qwen3-vl-8b-bangla is an 8 billion parameter vision-language (VL) model developed by billahaiml. It is a fine-tuned variant of the unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen3-VL architecture.
Key Capabilities
- Multimodal Understanding: As a vision-language model, it processes both visual and textual inputs, enabling tasks that require comprehension across modalities.
- Optimized Training: The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Bengali Language Focus: While the base model is general-purpose, the
banglasuffix suggests a specialization or optimization for tasks involving the Bengali language, making it suitable for region-specific applications.
Good For
- Applications requiring multimodal AI with a focus on visual and textual data.
- Use cases where faster training methodologies are beneficial for model development.
- Projects specifically targeting Bengali language processing in a multimodal context.