Prompt: long neck dog If neck isn’t long enough try increasing the weight (Long neck:1.5) dog The results can be hit or miss. I used a brute force approach for the image above, it took hundreds of tries. Try it yourself and share your results submitted by /u/AnimeDiff [link] [comments]
We’re publishing a paper in Nature introducing Aeneas, the first AI model for contextualizing ancient inscriptions.
Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and introduce mRAKL: a Retrieval-Augmented Generation …
At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this …
Read more “Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization”
Enterprises will not see immediate impact from the AI Action Plan, but it signals wider support for open-source models and evaluations.Read More
The remarks, which came during a keynote speech at a summit hosted by the All-In Podcast, follow President Donald Trump’s newly released AI Action Plan.
A University of Warwick epigraphy expert has collaborated with Google DeepMind to evaluate “Aeneas,” an AI model that reimagines Roman inscriptions.
https://civitai.com/models/1800528?modelVersionId=2037657 https://huggingface.co/reverentelusarca/flux-kontext-zoom-out-lora submitted by /u/sktksm [link] [comments]
In this article, you will learn: • how Scikit-LLM integrates large language models like OpenAI’s GPT with the Scikit-learn framework for text analysis.
This post is divided into five parts; they are: • Preparing the Dataset for Training • Implementing the Seq2Seq Model with LSTM • Training the Seq2Seq Model • Using the Seq2Seq Model • Improving the Seq2Seq Model In