Barth371/Qwen2.5-VL-72B-Instruct-bnb-4bit-2025-10-07_12-21_4b
Barth371/Qwen2.5-VL-72B-Instruct-bnb-4bit is a 72 billion parameter instruction-tuned vision-language model developed by Barth371. This model was finetuned from unsloth/qwen2.5-vl-72b-instruct-bnb-4bit using Unsloth and Huggingface's TRL library, enabling faster training. With a 32768 token context length, it is designed for multimodal tasks requiring both visual and textual understanding.
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Model Overview
Barth371/Qwen2.5-VL-72B-Instruct-bnb-4bit is a large 72 billion parameter instruction-tuned vision-language model. Developed by Barth371, this model is a finetuned version of unsloth/qwen2.5-vl-72b-instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-VL, a powerful vision-language model capable of processing both image and text inputs.
- Parameter Count: 72 billion parameters, indicating a high capacity for complex tasks.
- Context Length: Features a substantial context window of 32768 tokens, allowing for extensive input sequences.
- Training Optimization: The model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Use Cases
This model is particularly well-suited for applications requiring advanced multimodal understanding and generation, leveraging its large parameter count and vision-language capabilities. Its instruction-tuned nature makes it adaptable to a wide range of prompt-based tasks.