Based on our record, Hugging Face seems to be a lot more popular than PatternPad. While we know about 296 links to Hugging Face, we've tracked only 3 mentions of PatternPad. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Design beautiful custom patterns effortlessly with PatternPad. - Source: dev.to / 4 months ago
PatternPad: It generates graphical patterns based on a variety of parameters. This results in an endless number of variations. You can choose from popular styles or create your own individual pattern. - Source: dev.to / about 1 year ago
That's an SVG pattern in a CSS background-image property, the exact line of code is here. If memory serves me correctly, I used this site to generate the pattern: https://patternpad.com/. Source: about 3 years ago
Compatibility with standard tools: Functions with OCI-compliant registries such as Docker Hub and integrates with widely-used tools including Hugging Face, ZenML, and Git. - Source: dev.to / 2 days ago
Hugging Face's Transformers: A comprehensive library with access to many open-source LLMs. https://huggingface.co/. - Source: dev.to / 25 days ago
Hugging Face provides licensing for their NLP models, encouraging businesses to deploy AI-powered solutions seamlessly. Learn more here. Actionable Advice: Evaluate your algorithms and determine if they can be productized for licensing. Ensure contracts are clear about usage rights and application fields. - Source: dev.to / 30 days ago
There are lots of open-source models available on HuggingFace that can be used to create vector embeddings. Transformers.js is a module that lets you use machine learning models in JavaScript, both in the browser and Node.js. It uses the ONNX runtime to achieve this; it works with models that have published ONNX weights, of which there are plenty. Some of those models we can use to create vector embeddings. - Source: dev.to / about 1 month ago
From transformers import pipeline Import torch Pipe = pipeline( "image-text-to-text", model="google/gemma-3-4b-it", device="cpu", torch_dtype=torch.bfloat16 ) Messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type":... - Source: dev.to / about 2 months ago
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