🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
🎯The space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
📄PDFs are rendered using the ReportLab software library toolkit.
Last Week in Medical AI: Top Research Papers/Models 🔥 🏅 (December 7 – December 14, 2024)
Medical LLM & Other Models - PediaBench: Chinese Pediatric LLM - Comprehensive pediatric dataset - Advanced benchmarking platform - Chinese healthcare innovation - BiMediX: Bilingual Medical LLM - Multilingual medical expertise - Diverse medical knowledge integration - Cross-cultural healthcare insights - MMedPO: Vision-Language Medical LLM - Clinical multimodal optimization - Advanced medical image understanding - Precision healthcare modeling
Frameworks and Methodologies - TOP-Training: Medical Q&A Framework - Hybrid RAG: Secure Medical Data Management - Zero-Shot ATC Clinical Coding - Chest X-Ray Diagnosis Architecture - Medical Imaging AI Democratization
Benchmarks & Evaluations - KorMedMCQA: Korean Healthcare Licensing Benchmark - Large Language Model Medical Tasks - Clinical T5 Model Performance Study - Radiology Report Quality Assessment - Genomic Analysis Benchmarking
Medical LLM Applications - BRAD: Digital Biology Language Model - TCM-FTP: Herbal Prescription Prediction - LLaSA: Activity Analysis via Sensors - Emergency Department Visit Predictions - Neurodegenerative Disease AI Diagnosis - Kidney Disease Explainable AI Model
Ethical AI & Privacy - Privacy-Preserving LLM Mechanisms - AI-Driven Digital Organism Modeling - Biomedical Research Automation - Multimodality in Medical Practice
Last Week in Medical AI: Top Research Papers/Models 🔥 🏅 (December 2 – December 7, 2024)
Medical LLM & Models - Block MedCare: Blockchain AI & IoT - LLMs4Life: Biomedical Ontology Learning - LLaMA II for Multimodal Diagnosis - Compact LLM for EHR Privacy
Frameworks & Methods - RARE: Retrieval-Augmented Reasoning - STORM: Strategies for Rare Events - TransFair: Fair Disease Classification - PePR: Performance Per Resource - Medical LLM Best Practices
LLM Applications - Medchain: LLMs in Clinical Practice - Query Nursing Note Summarization - CLINICSUM: Patient Conversation Summaries - Text Embeddings for Classifiers
LLM Benchmarks - Polish Medical Exams Transfer - Single-Cell Omics Annotation - LLMs in Precision Medicine - Low-Resource Healthcare Challenges
Other Models - LLM Chatbot Hallucinations - Multi-stage Chest X-ray Diagnosis - EchoONE: Echocardiography AI - Radiology Report Grounding
Ethics & Fairness - Privacy in Medical Imaging - Demographic Fairness in AI
🧪The datasets were prepared for a 3:2 aspect ratio by processing images of any dimension (width × height) in alignment with the adapter's concept. This involved using techniques such as magic expand, magic fill, or outpainting to adjust the remaining parts of the image to achieve the 3:2 ratio & posts training. This approach enhanced the desired image quality to up to 2 MB for detailed prompts and reduced artifacts in images sized at 1280 × 832.
🎈This approach was used instead of cropping down the 2x or 3x zoomed positions in the actual image. It generative filling to adjust the image's aspect ratio proportionally within the dataset.
🔧I used Canva's Magic Expand, Firefly's Generative Fill, and Flux's Outpaint for aspect ratio adjustments.
Fine-Textured [Polygon] Character 3D Design Renders 🙉
Adapters capable of providing better lighting control (Bn+, Bn-) and richer textures compared to previous sets require more contextual prompts for optimal performance.
The ideal settings are achieved at inference steps around 30–35, with the best dimensions being 1280 x 832 [ 3:2 ]. However, it also performs well with the default settings of 1024 x 1024 [ 1:1 ].
🍅 Glif App's Remixes feature allows you to slap a logo onto anything, seamlessly integrating the input image (logo) into various contexts. The result is stunning remixes that blend the input logo with generated images (img2img logo mapping) for incredible outcomes.
The (768 x 1024) mix of MidJourney and Flux's LoRA is nearly identical to the actual visual design. It hasn’t undergone much concept art development for now. In the meantime, try out the impressive visual designs on:
