LLMs

MLServer aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface, fully compliant with KFServing's V2 Dataplane spec. Watch a quick video introducing the project here.

• Multi-model serving, letting users run multiple models within the same process.


• Ability to run inference in parallel for vertical

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Deploying a Generative AI model requires more than a VM with a GPU. It normally includes:

• Container Service : Most often Kubernetes to run LLM Serving solutions like Hugging Face Text Generation Inference or vLLM.


• Compute Resources : GPUs for running models, CPUs for management services


• Networking and DNS : Routing traffic to the appropriate

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DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Table of Contents

1. Introduction


2. Key LLM Serving Techniques


3. Dynamic SplitFuse: A Novel Prompt and Generation Composition Strategy


4. Performance Evaluation


5. DeepSpeed-FastGen: Implementation and Usage


6. Try out DeepSpeed-FastGen


7. Acknowledgements

1. Introduction

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Study finds RLHF reduces LLM creativity and output variety : A new research paper posted in /r/LocalLLaMA shows that while alignment techniques like RLHF reduce toxic and biased content, they also limit the creativity of large language models, even in contexts unrelated to safety.

One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI".

The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has... See more

The exact metrics we use depend on the application — our main goal is to understand how users use the feature and quickly make improvements to better meet their needs. For internal applications, this might mean measuring efficiency and sentiment. For consumer-facing applications, we similarly focus on measures of user satisfaction - direct user... See more

Ensuring availability during peak traffic by maintaining all GPU instance types could lead to prohibitively high costs. To avoid the financial strain of idle instances, we implemented a “standby instances” mechanism. Rather than preparing for the maximum potential load, we maintained a calculated number of standby instances that match the... See more

GPT-4 Turbo can accept images as inputs in the Chat Completions API, enabling use cases such as generating captions, analyzing real world images in detail, and reading documents with figures. For example, BeMyEyes uses this technology to help people who are blind or have low vision with daily tasks like identifying a product or navigating a store.... See more