scaleapi/llm-engine

What it solves

Deploying and fine-tuning large language models (LLMs) is often expensive and requires significant infrastructure and machine learning expertise. LLM Engine simplifies this process by providing a unified way to serve and customize foundation models, reducing the complexity of maintaining infrastructure as new models and techniques emerge.

How it works

LLM Engine is provided as a Python library, a command-line interface (CLI), and Helm charts. It allows users to access models via Scale's hosted infrastructure or deploy them on their own Kubernetes-based cloud infrastructure. It integrates with Hugging Face, enabling the deployment of any Hugging Face model with a single command, and supports optimized inference through streaming responses and dynamic batching.

Who it’s for

Developers and ML engineers who want to deploy, serve, and fine-tune open-source foundation models (such as LLaMA, MPT, and Falcon) without managing the underlying infrastructure complexity manually.

Highlights

• Ready-to-use APIs: Quick deployment and serving of popular open-source models.
• Fine-tuning capabilities: Ability to customize foundation models using proprietary data for better performance.
• Optimized Inference: High throughput and low latency achieved through dynamic batching and streaming.
• Hugging Face Integration: Seamless deployment of models from the Hugging Face hub.