Singh8898/DiegoCropper
VISIONPricing:Input $0.4 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:32kPublished:May 30, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
DiegoCropper is a 7 billion parameter Qwen2.5-VL model developed by Singh8898, fine-tuned from unsloth/qwen2.5-vl-7b-instruct-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for tasks requiring a Qwen2.5-VL architecture and supports a context length of 32768 tokens.
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Overview
DiegoCropper is a 7 billion parameter Qwen2.5-VL model developed by Singh8898. It has been fine-tuned from the unsloth/qwen2.5-vl-7b-instruct-bnb-4bit base model, leveraging the Unsloth library for accelerated training.
Key Characteristics
- Architecture: Qwen2.5-VL, indicating capabilities in both language and vision tasks.
- Parameter Count: 7 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned 2x faster using Unsloth and Huggingface's TRL library, which can be beneficial for rapid iteration and deployment.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Good For
- Applications requiring a Qwen2.5-VL model with improved training speed.
- Tasks that benefit from a 7B parameter model with a large context window.
- Developers looking for a fine-tuned model based on a Qwen2.5-VL instruction-tuned variant.