Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-foundation-model
Foundation Model for X and X for Foundation Model
https://github.com/Kyrie-Zhao/awesome-foundation-model
Last synced: 1 day ago
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Papers
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Healthcare
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Multimodal LLMs for health grounded in individual-specific data
- Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost
- Decoding speech perception from non-invasive brain recordings
- Large language models improve Alzheimer’s disease diagnosis using multi-modality data
- Neuro-GPT: Developing A Foundation Model for EEG
- From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
- Conversational Health Agents: A Personalized LLM-Powered Agent Framework
- UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language
- GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Conversational Health Agents: A Personalized LLM-Powered Agent Framework
- Multimodal LLMs for health grounded in individual-specific data
- Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost
- Decoding speech perception from non-invasive brain recordings
- Large language models improve Alzheimer’s disease diagnosis using multi-modality data
- From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
- UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language
- GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Neuro-GPT: Developing A Foundation Model for EEG
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
- Decoding speech perception from non-invasive brain recordings
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Code Optimization and Compiler
- Magicoder: Source Code Is All You Need
- Can Large Language Models Reason about Program Invariants?
- The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models
- Clover: Closed-Loop Verifiable Code Generation
- Formalizing Natural Language Intent into Program Specifications via Large Language Models
- Ranking LLM-Generated Loop Invariants for Program Verification
- Large language models for compiler optimization
- Magicoder: Source Code Is All You Need
- The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models
- Large language models for compiler optimization
- Clover: Closed-Loop Verifiable Code Generation
- Formalizing Natural Language Intent into Program Specifications via Large Language Models
- Ranking LLM-Generated Loop Invariants for Program Verification
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Multi Modal
- ImageBind: One embedding space to bind them all
- LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
- IoT in the Era of Generative AI: Vision and Challenges
- IoT in the Era of Generative AI: Vision and Challenges
- LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
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Agent
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Efficient Inference
- EFFICIENTLY SCALING TRANSFORMER INFERENCE
- EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
- AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
- DeepSpeed-inference: enabling efficient inference of transformer models at unprecedented scale
- Fairness in Serving Large Language Models
- DISTRIBUTED INFERENCE AND FINE-TUNING OF LARGE LANGUAGE MODELS OVER THE INTERNET
- Orca: A Distributed Serving System for {Transformer-Based} Generative Models - In, et al., OSDI 2022
- SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification
- AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
- FlexGen: High-throughput Generative Inference of Large Language Models with a Single GPU
- EFFICIENTLY SCALING TRANSFORMER INFERENCE
- EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
- PETALS: Collaborative Inference and Fine-tuning of Large Models
- Fairness in Serving Large Language Models
- Fast Distributed Inference Serving for Large Language Models
- Tabi: An Efficient Multi-Level Inference System for Large Language Models
- STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining
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Efficient Training
- TopoopT: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs
- Breadth-First Pipeline Parallelism - Poirier, Joel., MLSys 2023
- On Optimizing the Communication of Model Parallelism
- Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
- Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
- Breadth-First Pipeline Parallelism - Poirier, Joel., MLSys 2023
- Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models
- On Optimizing the Communication of Model Parallelism
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Optimization
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Data Selection
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Prompt Optimization
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Others
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Open Source Projects
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Benchmark and Dataset
Programming Languages
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