5 Compact Hugging Face Models for Running Locally

Deepsandhya Shukla 24 Apr, 2024 • 4 min read


In the world of machine learning, the trend toward smaller, more efficient models has grown significantly. These compact models are crucial for developers and researchers who need to run applications locally on devices with limited resources. Not only do they require less computational power, but they also allow for rapid deployment and agile testing. This capability is particularly valuable in scenarios where quick decision-making and real-time analytics are required. Let’s explore how small models on platforms like Hugging Face are making significant strides in making AI more accessible and versatile.

5 Compact Hugging Face Models for Running Locally

Compact Hugging Face Models for Running Locally

1. TrOCR: Handwriting Recognition Simplified

Model Size: TrOCR-base-handwritten, despite its extensive capabilities, has a modest size of 1.33 GB.

HuggingFace Link

Description: This model integrates seamlessly into applications requiring text extraction from various handwritten sources.

Practical Applications in Local Environments

TrOCR’s efficiency and compact size make it perfect for applications in environments with constrained computing resources. For instance, it can be employed in educational software for digitizing handwritten assignments or in healthcare settings to convert doctors’ notes into digital records. Its quick processing times enable real-time transcription, facilitating workflows that rely on immediate digital data availability.

2. ViT-GPT2: Efficient Image Captioning

Model Size: The ViT-GPT2 is under 1 GB (~982 MB) model in size, making it suitable for running on local machines without high-end GPUs.

HuggingFace Link

Description: This model uniquely combines Vision Transformer (ViT) and GPT-2 architectures to interpret and describe images accurately. It’s designed to understand the context within images and generate corresponding textual descriptions, a task that typically requires substantial computational resources.

Usage Scenarios for Image-to-Text Conversion

ViT-GPT2 excels in scenarios where quick image understanding is crucial, such as in content moderation for social media platforms or in aiding visually impaired individuals by providing real-time descriptions of their surroundings. Additionally, it can be utilized in educational technology to create interactive learning tools that describe images or diagrams automatically.

3. LCM-LoRA: Accelerating Stable Diffusion

Model Size: LCM-LoRA is an adapter module designed to be lightweight and efficient. It is just 135 MB in size, which is perfect for enhancing performance without bulk.

HuggingFace Link

Description: The Latent Consistency Model with Localized Random Attention (LCM-LoRA) significantly speeds up the inference process of the larger Stable Diffusion models. It strategically modifies key components to reduce computational demands while maintaining high-quality output, making it ideal for creative applications requiring rapid generation of visuals.

Benefits for Creative Tasks on Local Setups

LCM-LoRA’s acceleration capabilities make it invaluable for graphic designers, digital artists, and content creators working on local machines. Users can integrate this model into graphic design software to quickly generate detailed images, concept art, or even prototypes for client projects. Its fast processing enables real-time adjustments and iterations, streamlining creative workflows significantly.

4. DETR-ResNet-50: Object Detection Made Accessible

Model Size: DETR-ResNet-50 offers a harmonious balance between size and detection efficacy and is just 167 MB,  designed to be manageable for local deployment.

HuggingFace Link

Description: DETR (Detection Transformer) harnesses the power of the transformer architecture combined with a ResNet-50 backbone to process images for object detection tasks efficiently. This model simplifies the detection pipeline, eliminating the need for many hand-engineered components by learning to predict object boundaries directly from the full image context.

Applicability for Quick Object Detection Tasks

The DETR model is particularly suited for applications like surveillance systems where real-time object detection can provide immediate feedback, such as identifying unauthorized access or monitoring crowded areas. It’s also useful in retail environments for shelf auditing and inventory management, providing precise and quick analysis without the need for cloud computing resources.

5. YOLOv8s: Real-Time Stock Market Pattern Detection

Model Size: YOLOv8s maintains a lean architecture with a size of 134 MB, enabling it to deliver high-speed performance while being compact enough for local use.

HuggingFace Link

Description: Tailored specifically for the finance sector, YOLOv8s leverages the latest advancements in the YOLO object detection framework to identify and classify stock market patterns from video data. This model can detect complex trading patterns in real time, aiding traders and analysts by providing actionable insights promptly.

Implementing YOLOv8s for Live Trading Insights

Integrating YOLOv8s into trading platforms can revolutionize the way market data is analyzed. Traders can use this model to automatically detect and respond to emerging patterns, reducing the reaction time and allowing for quicker decision-making based on visual cues from live trading videos. This capability is crucial for high-frequency trading environments where speed translates into competitive advantage.

These small yet powerful models demonstrate that advanced AI capabilities can be effectively downsized and optimized for local applications, opening up new possibilities across various industries.


Compact models from Hugging Face exemplify the democratization of artificial intelligence, making advanced AI accessible for local deployment across various industries. These models optimize performance with reduced computational demands, enabling rapid deployment, agile testing, and real-time analytics on devices with limited resources. When selecting the right model, it’s crucial to consider the specific requirements of the task to leverage AI efficiently. The integration of these models into local applications paves the way for broader, more inclusive use of technology, transforming industries by enhancing speed and decision-making capabilities.

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