Organizations (tags: PEFT) Using Parameter-Efficient Fine-Tuning to Adapt Transformer and LLM Models
Explore organizations tagged with PEFT to discover how parameter-efficient fine-tuning (PEFT) methods — including LoRA, adapters, prompt tuning, and quantization-aware adaptation — are used to adapt transformer and LLM models for production workloads. This list surfaces organizations that leverage PEFT for scalable model adaptation, reduced compute and storage costs, faster iteration cycles, and improved deployment efficiency, with long-tail insights on low-rank adaptation, efficient transfer learning, and model compression strategies. Use the filtering UI to narrow results by architecture, technique (LoRA vs adapters vs prompt-tuning), domain, benchmark results, or implementation maturity, then compare code repositories, benchmarks, and integration patterns across organizations. Explore and engage with these organizations to find practical PEFT implementations, partnership opportunities, and production-ready approaches to efficient fine-tuning.
