Topic 23: What is LLM Inference, it's challenges and solutions for it
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Ksenia Se
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Today, we spoke with Snowflake’s AI Research Team Leads, Yuxiong He and Samyam Rajbhandari (@samyam) (he is also one the researchers behind https://huggingface.co/papers/2401.08671 and other DeepSpeed papers)
Collaborating with their co-authors to reduce inference costs for enterprise-specific tasks, they observed that inputs are often significantly larger than outputs. This is because it’s in the nature of enterprises to analyze enormous amounts of information trying to extract valuable insights, which are much shorter. To address this, they developed SwiftKV https://huggingface.co/papers/2410.03960, an optimization that reduces LLM inference costs by up to 75% for Meta Llama LLMs, enhancing efficiency and performance in enterprise AI tasks.
Today they are open-sourcing SwiftKV (https://huggingface.co/Snowflake/Llama-3.1-SwiftKV-8B-Instruct) and ArcticTrainging Platform.
In our new episode "15 minutes with a Researcher" they explain how SwiftKV works, its applicability to other architectures, its limitations, and additional methods to further reduce computation costs in inference.
Watch the full 15 min interview here (https://youtu.be/9x1k7eXe-6Q?si=4_HQOyi1CPHgvlrx)
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Topic 23: What is LLM Inference, it's challenges and solutions for it
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🌁#83: GAN is back
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Today, we spoke with Snowflake’s AI Research Team Leads, Yuxiong He and Samyam Rajbhandari (
@samyam
) (he is also one the researchers behind
DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and
DeepSpeed-Inference (2401.08671) and other DeepSpeed papers)
Collaborating with their co-authors to reduce inference costs for enterprise-specific tasks, they observed that inputs are often significantly larger than outputs. This is because it’s in the nature of enterprises to analyze enormous amounts of information trying to extract valuable insights, which are much shorter. To address this, they developed SwiftKV SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (2410.03960), an optimization that reduces LLM inference costs by up to 75% for Meta Llama LLMs, enhancing efficiency and performance in enterprise AI tasks.
Today they are open-sourcing SwiftKV ( Snowflake/Llama-3.1-SwiftKV-8B-Instruct) and ArcticTrainging Platform.
In our new episode "15 minutes with a Researcher" they explain how SwiftKV works, its applicability to other architectures, its limitations, and additional methods to further reduce computation costs in inference.
Watch the full 15 min interview here (https://youtu.be/9x1k7eXe-6Q?si=4_HQOyi1CPHgvlrx)
Collaborating with their co-authors to reduce inference costs for enterprise-specific tasks, they observed that inputs are often significantly larger than outputs. This is because it’s in the nature of enterprises to analyze enormous amounts of information trying to extract valuable insights, which are much shorter. To address this, they developed SwiftKV SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (2410.03960), an optimization that reduces LLM inference costs by up to 75% for Meta Llama LLMs, enhancing efficiency and performance in enterprise AI tasks.
Today they are open-sourcing SwiftKV ( Snowflake/Llama-3.1-SwiftKV-8B-Instruct) and ArcticTrainging Platform.
In our new episode "15 minutes with a Researcher" they explain how SwiftKV works, its applicability to other architectures, its limitations, and additional methods to further reduce computation costs in inference.
Watch the full 15 min interview here (https://youtu.be/9x1k7eXe-6Q?si=4_HQOyi1CPHgvlrx)
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10 AI Systems for Scientific Research
Almost every AI researcher has studied or conducted a large number of AI research papers. So, it's quite logical that researchers are trying to create AI systems to help conduct research. Creating scientific research could be much easier and more varied if we use LLMs and AI assistants tailored for this purpose. Just imagine how interesting it would be to read high-quality research about AI made by an AI agent.
Today, we offer you to explore these 10 AI systems for scientific research:
1. Agent Laboratory framework helps researchers input their ideas by generating a research report and code repository: Agent Laboratory: Using LLM Agents as Research Assistants (2501.04227)
2. AI Scientist performs fully automated scientific discovery including creating ideas: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
3. SciMON generates new ideas derived from the scientific literature: Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery (2305.14259)
4. ResearchAgent implements LLMs to automate idea generation, methods, and experiment design, and ReviewingAgents' feedback to refine ideas: ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2404.07738)
5. Scientific Generative Agent (SGA) discovers novel, coherent solutions in physics and molecular design: LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery (2405.09783)
6. MLRCopilot boosts machine learning research: MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents (2408.14033)
7. SciAgents accelerates material science discovery through combining knowledge graphs, LLMs, and multi-agent systems. SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (2409.05556)
8. VirSci multi-agent system mimics teamwork among scientists. Two Heads Are Better Than One: A Multi-Agent System Has the Potential to Improve Scientific Idea Generation (2410.09403)
9. Chain-of-Ideas (CoI) agent organizes research into a chain structure. Chain of Ideas: Revolutionizing Research in Novel Idea Development with LLM Agents (2410.13185)
10. A system with CycleResearcher and CycleReviewer generates research papers and peer reviews: CycleResearcher: Improving Automated Research via Automated Review (2411.00816)
LLM4SR: A Survey on Large Language Models for Scientific Research (2501.04306) is worth exploring to study and analyze more systems for scientific research
Almost every AI researcher has studied or conducted a large number of AI research papers. So, it's quite logical that researchers are trying to create AI systems to help conduct research. Creating scientific research could be much easier and more varied if we use LLMs and AI assistants tailored for this purpose. Just imagine how interesting it would be to read high-quality research about AI made by an AI agent.
Today, we offer you to explore these 10 AI systems for scientific research:
1. Agent Laboratory framework helps researchers input their ideas by generating a research report and code repository: Agent Laboratory: Using LLM Agents as Research Assistants (2501.04227)
2. AI Scientist performs fully automated scientific discovery including creating ideas: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
3. SciMON generates new ideas derived from the scientific literature: Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery (2305.14259)
4. ResearchAgent implements LLMs to automate idea generation, methods, and experiment design, and ReviewingAgents' feedback to refine ideas: ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2404.07738)
5. Scientific Generative Agent (SGA) discovers novel, coherent solutions in physics and molecular design: LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery (2405.09783)
6. MLRCopilot boosts machine learning research: MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents (2408.14033)
7. SciAgents accelerates material science discovery through combining knowledge graphs, LLMs, and multi-agent systems. SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (2409.05556)
8. VirSci multi-agent system mimics teamwork among scientists. Two Heads Are Better Than One: A Multi-Agent System Has the Potential to Improve Scientific Idea Generation (2410.09403)
9. Chain-of-Ideas (CoI) agent organizes research into a chain structure. Chain of Ideas: Revolutionizing Research in Novel Idea Development with LLM Agents (2410.13185)
10. A system with CycleResearcher and CycleReviewer generates research papers and peer reviews: CycleResearcher: Improving Automated Research via Automated Review (2411.00816)
LLM4SR: A Survey on Large Language Models for Scientific Research (2501.04306) is worth exploring to study and analyze more systems for scientific research
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