Welcome to IBM’s multi-modal foundation model for materials, FM4M, designed to support and advance research in materials science and chemistry.
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Enterprise AI and ML, Foundation Models, Responsible AI
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Papers
From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
NLE: Non-autoregressive LLM-based ASR by Transcript Editing
Wishes datasets created by Genie
Datasets from "A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios" (https://arxiv.org/abs/2408.01963)
A suite of open-source biomedical foundation models. https://research.ibm.com/projects/biomedical-foundation-models
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Biomed.sm.mv Te 84m
👁4Prediction task tests for biomed-multi-view models
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Biomed-multi-alignment unified demo with PPI and TDI examples
🐁1Demo for MAMMAL approch on multiple domains
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ibm-research/biomed.omics.bl.sm.ma-ted-458m
0.5B • Updated • 104 • 27 -
ibm-research/biomed.sm.mv-te-84m
Updated • 799 • 19
Dense & MoE LLMs trained with power learning rate scheduler.
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Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
Paper • 2401.14019 • Published • 23 -
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
Paper • 2308.16884 • Published • 10 -
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Paper • 2401.14367 • Published • 8 -
ibm-nasa-geospatial/Prithvi-WxC-1.0-2300M
Image Feature Extraction • Updated • 189 • 81
Welcome to IBM’s multi-modal foundation model for materials, FM4M, designed to support and advance research in materials science and chemistry.
A suite of open-source biomedical foundation models. https://research.ibm.com/projects/biomedical-foundation-models
-
Biomed.sm.mv Te 84m
👁4Prediction task tests for biomed-multi-view models
-
Biomed-multi-alignment unified demo with PPI and TDI examples
🐁1Demo for MAMMAL approch on multiple domains
-
ibm-research/biomed.omics.bl.sm.ma-ted-458m
0.5B • Updated • 104 • 27 -
ibm-research/biomed.sm.mv-te-84m
Updated • 799 • 19
Dense & MoE LLMs trained with power learning rate scheduler.
Wishes datasets created by Genie
-
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
Paper • 2401.14019 • Published • 23 -
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
Paper • 2308.16884 • Published • 10 -
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Paper • 2401.14367 • Published • 8 -
ibm-nasa-geospatial/Prithvi-WxC-1.0-2300M
Image Feature Extraction • Updated • 189 • 81
Datasets from "A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios" (https://arxiv.org/abs/2408.01963)