The open source engine for fine-tuning and serving large language models.
LLM Engine is the easiest way to customize and serve LLMs.
LLMs can be accessed via Scale's hosted version or by using the Helm charts in this repository to run model inference and fine-tuning in your own infrastructure.
Foundation models are emerging as the building blocks of AI. However, deploying these models to the cloud and fine-tuning them is an expensive operation that require infrastructure and ML expertise. It is also difficult to maintain over time as new models are released and new techniques for both inference and fine-tuning are made available.
LLM Engine is a Python library and Helm chart that provides everything you need to serve and fine-tune foundation models, whether you use Scale's hosted infrastructure or do it in your own cloud infrastructure using Kubernetes.
Ready-to-use APIs for your favorite models: Deploy and serve open source foundation models - including Llama-2, MPT, and Falcon. Use Scale-hosted models or deploy to your own infrastructure.
Fine-tune the best open-source models: Fine-tune open-source foundation models like Llama-2, MPT, etc. with your own data for optimized performance.
Optimized Inference: LLM Engine provides inference APIs for streaming responses and dynamically batching inputs for higher throughput and lower latency.
Open-Source Integrations: Deploy any Hugging Face model with a single command.
Deploying from any docker image: Turn any Docker image into an auto-scaling deployment with simple APIs.
Features Coming Soon¶
Kubernetes Installation Enhancements: We are working hard to enhance the installation and maintenance of inference and fine-tuning functionality on your infrastructure. For now, our documentation covers experimental libraries to deploy language models on your infrastructure and libraries to access Scale's hosted infrastructure.
Fast Cold-Start Times: To prevent GPUs from idling, LLM Engine automatically scales your model to zero when it's not in use and scales up within seconds, even for large foundation models.
Cost Optimization: Deploy AI models cheaper than commercial ones, including cold-start and warm-down times.