Open Preference Dataset for Text-to-Image Generation by the 🤗 Community
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Update app.py
#3 opened about 16 hours ago
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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
Full thread in detail: https://x.com/OpenlifesciAI/status/1867999825721242101
🏅 (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
Full thread in detail: https://x.com/OpenlifesciAI/status/1867999825721242101
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We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥
How? By combining step-wise reward models with tree search algorithms :)
We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"
We're open sourcing the full recipe and sharing a detailed blog post.
In our blog post we cover:
📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.
🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.
🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM
Here's the links:
- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute
- Code: https://github.com/huggingface/search-and-learn
Enjoy!
How? By combining step-wise reward models with tree search algorithms :)
We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"
We're open sourcing the full recipe and sharing a detailed blog post.
In our blog post we cover:
📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.
🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.
🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM
Here's the links:
- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute
- Code: https://github.com/huggingface/search-and-learn
Enjoy!
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pass width, height to pipeline
#1 opened 8 days ago
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