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Artur Niederfahrenhorst

Kourosh Hakhamaneshi

Based on the popular “Needle In a Haystack” benchmark and RAG, we share our process of creating a problem-specific fine-tuning dataset to extend the context of models to build better RAG systems.

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Fine-tuning LLMs for longer context and better RAG systems

Rehaan Ahmad

In this blog, we compare full-parameter fine-tuning with LoRA and answer questions around the strengths and weaknesses of the two techniques. 

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Fine-Tuning LLMs: In-Depth Analysis with LLAMA-2 | Anyscale

Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama 2

Ray Summit 2024, the annual Ray community conference, is back in San Francisco, CA this September 30th through October 2nd. It is a two-day event with a third day dedicated to training.

Call for Proposals is open Blog Post

Ray Summit 2024 Call for Proposals is now open

Intel and Anyscale have integrated Ray with Gaudi3. Gaudi3 is AI-optimized for efficient training and other machine learning workloads.

Anyscale - Intel

Accelerating AI: Harnessing Intel(R) Gaudi(R) 3 with Ray 2.10

Anyscale is teaming with NVIDIA to combine the developer productivity of Ray Serve and RayLLM with the cutting-edge optimizations from NVIDIA Triton Inference Server software and the NVIDIA TensorRT-LLM library.

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Low-latency Generative AI Model Serving with Ray, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM

Generating embeddings is a critical task for developing successful RAG applications, and Anyscalew with Pinecone is the most cost-efficient solution for this workflow.

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RAG at Scale: 10x Cheaper Embedding Computations with Anyscale and Pinecone

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Posts by Artur Niederfahrenhorst

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