Deep Optimizer States splits LLMs into subgroups and uses a performance model to schedule optimizer updates on CPU or GPU, achieving 2.5x faster iterations than prior offloading methods when integrated with DeepSpeed.
Understanding the performance and estimating the cost of llm fine-tuning
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Fine-tunes Qwen2.5-7B on 21,543 synthetic maritime Q&A pairs generated from 3.2B AIS records by GPT-4o and o3-mini, reaching 75% accuracy at 261x lower inference cost than larger models.
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
citing papers explorer
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Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading
Deep Optimizer States splits LLMs into subgroups and uses a performance model to schedule optimizer updates on CPU or GPU, achieving 2.5x faster iterations than prior offloading methods when integrated with DeepSpeed.
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Multi-Model Synthetic Training for Mission-Critical Small Language Models
Fine-tunes Qwen2.5-7B on 21,543 synthetic maritime Q&A pairs generated from 3.2B AIS records by GPT-4o and o3-mini, reaching 75% accuracy at 261x lower inference cost than larger models.
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From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.