OpenLLM has undergone a significant upgrade in its v0.5 release to enhance compatibility with the BentoML 1.2 SDK. The CLI has also been streamlined to focus on delivering the most easy-to-use and reliable experience for deploying open-source LLMs to production. However, version 0.5 introduces breaking changes.
Breaking changes, and the reason why.
After releasing version 0.4, we realized that while OpenLLM offers a high degree of flexibility and power to users, they encountered numerous issues when attempting to deploy these models. OpenLLM had been trying to accomplish a lot by providing support for different backends (mainly PyTorch for CPU inference and vLLM for GPU inference) and accelerators. Although this provided users with the option to quickly test on their local machines, we discovered that this brought a lot of confusion when running OpenLLM locally versus the cloud. The difference between local and cloud deployment made it difficult for users to understand and control the packaged Bento to behave correctly on the cloud.
The motivation for 0.5 is to focus on cloud deployment. Cloud deployments often focus on high throughput and high concurrency serving, and GPU is the most common choice of hardware for achieving high throughput and concurrency serving. Therefore, we simplified backend support to just vLLM which is the most suitable and reliable for serving LLM on GPU on the cloud.
Architecture changes and SDK.
For version 0.5, we have decided to reduce the scope and support the backend that yields the most performance (in this case, vLLM). This means that pip install openllm will also depend on vLLM. In other words, we will currently pause our support for CPU going forward.
All interactions with OpenLLM servers going forward should be done through clients (i.e., BentoML's Clients, OpenAI, etc.).
CLI
CLI has now been simplified to `openllm start` and `openllm build`
HuggingFace models
openllm start
`openllm start` will continue to accept HuggingFace model id for supported model architectures:
bash
openllm start microsoft/Phi-3-mini-4k-instruct --trust-remote-code
> For any models that requires remote code execution, one should pass in `--trust-remote-code`
>
`openllm start` will also accept serving from local path directly. Make sure to also pass in `--trust-remote-code` if you wish to use with `openllm start`
bash
openllm start path/to/custom-phi-instruct --trust-remote-code
openllm build
In previous versions, OpenLLM would copy the local cache of the models into the generated Bento store, resulting in having two copies of the models on users’ machine. From v0.5 going forward, models won't be packaged with the Bento and will be downloaded into Hugging Face cache first time on deployment.
bash
openllm build microsoft/Phi-3-mini-4k-instruct --trust-remote-code
Successfully built Bento 'microsoft--phi-3-mini-4k-instruct-service:5fa34190089f0ee40f9cce3cafc396b89b2e5e83'.
██████╗ ██████╗ ███████╗███╗ ██╗██╗ ██╗ ███╗ ███╗
██╔═══██╗██╔══██╗██╔════╝████╗ ██║██║ ██║ ████╗ ████║
██║ ██║██████╔╝█████╗ ██╔██╗ ██║██║ ██║ ██╔████╔██║
██║ ██║██╔═══╝ ██╔══╝ ██║╚██╗██║██║ ██║ ██║╚██╔╝██║
╚██████╔╝██║ ███████╗██║ ╚████║███████╗███████╗██║ ╚═╝ ██║
╚═════╝ ╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝╚══════╝╚═╝ ╚═╝.
📖 Next steps:
☁️ Deploy to BentoCloud:
$ bentoml deploy microsoft--phi-3-mini-4k-instruct-service:5fa34190089f0ee40f9cce3cafc396b89b2e5e83 -n ${DEPLOYMENT_NAME}
☁️ Update existing deployment on BentoCloud:
$ bentoml deployment update --bento microsoft--phi-3-mini-4k-instruct-service:5fa34190089f0ee40f9cce3cafc396b89b2e5e83 ${DEPLOYMENT_NAME}
🐳 Containerize BentoLLM:
$ bentoml containerize microsoft--phi-3-mini-4k-instruct-service:5fa34190089f0ee40f9cce3cafc396b89b2e5e83 --opt progress=plain
For quantized models, make sure to also pass in the `--quantize` flag during build
bash
openllm build casperhansen/llama-3-70b-instruct-awq --quantize awq
See `openllm build --help` for more information
Private models
openllm start
For private models, we recommend users to save it to [[BentoML’s Model store](https://docs.bentoml.com/en/latest/guides/model-store.html#model-store)](https://docs.bentoml.com/en/latest/guides/model-store.html#model-store) first before using `openllm start`:
python
with bentoml.models.create(name="my-private-models") as model:
PrivateTrainedModel.save_pretrained(model.path)
MyTokenizer.save_pretrained(model.path)
Note: Make sure to also save your tokenizer in this bentomodel
You can then pass in the private model name directly to `openllm start`
bash
openllm start my-private-models
openllm build
Similar to `openllm start`, `openllm build` will only accept private models from BentoML’s model store:
bash
openllm build my-private-models
What's next?
Currently, OpenAI's compatibility will only have the `/chat/completions` and `/models` endpoints supported. We will continue bringing `/completions` as well as function calling support soon, so stay tuned.
Thank you for your continued support and trust in us. We would love to hear more of your feedback on the releases.