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Jiajun Yao

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Announcing Ray 2.4.0: Infrastructure for LLM training, tuning, inference, and serving

Clark Zinzow

Alex Wu

Eric Liang

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Chen Shen

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Ray Datasets for large-scale machine learning ingest and scoring

Anyscale is releasing LLMPerf for benchmarking LLMs on current LLM offerings. See benchmarking results for Anyscale Endpoints vs Fireworks.ai.

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Reproducible Performance Metrics for LLM inference

In this post, we discuss the importance of speed when loading large language models and what techniques we employed to make it 20x faster. In particular, we use the Llama 2 series of models. We share how you can reduce latency and costs using the Anyscale platform.

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Loading Llama-2 70b 20x faster with Anyscale Endpoints

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale and evaluation.  

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Building RAG-based LLM Applications for Production

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

Using Anyscale Endpoints, we compared Llama 2 7b, 13b and 70b vs. OpenAI's GPT-3.5-turbo and GPT-4 for accuracy and cost.

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Llama 2 vs. GPT-4: Nearly As Accurate and 30X Cheaper

Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper

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Get started with Serving and Fine Tuning Open Source LLMs with Anyscale Endpoints!

Anyscale is the leading AI application platform. With Anyscale, developers can build, run and scale AI applications instantly.

Posts by Jiajun Yao

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