store-sentry VS get-beam

My entire App Store Server Notifications for iOS apps runs on Cloudflare Workers. I [open-sourced the code a while ago](https://github.com/workerforce/store-sentry) but it hasn’t gained much traction

- A custom runc container runtime

You can run Beta9 locally, or using a managed service, Beam Cloud (https://beam.cloud).

See it in action: https://github.com/beam-cloud/beta9

You should checkout https://beam.cloud (I'm the founder), it'll give you access to plenty of cloud GPU resources for training or inference.

Right now it's pretty hard to get GPU quota on AWS/GCP, so hopefully this is useful for you.

Cloudflare AI and Replicate are great for running off-the-shelf models, but anything custom is going to incur a 10+ minute cold start.

For running custom fine-tuned models on serverless, you could look into https://beam.cloud which is optimized for serving custom models with extremely fast cold start (I'm a little biased since I work there, but the numbers don't lie)

Serverless only works if the cold boot is fast. For context, my company runs a serverless cloud GPU product called https://beam.cloud, which we've optimized for fast cold start. We see Whisper in production cold start in under 10s (across model sizes). A lot of our users are running semi-real time STT, and this seems to be working well for them.

I’m Eli, and my co-founder and I built Beam to run workloads on serverless cloud GPUs with hot reloading, autoscaling, and (of course) fast cold start. You don’t need Docker or AWS to use it, and everyone who signs up gets 10 hours of free GPU credit to try it out.

Github with example apps and tutorials: https://github.com/slai-labs/get-beam/tree/main/examples

In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.