openllm 0.0.18

OpenLLM: REST/gRPC API server for running any open Large-Language Model - StableLM, Llama, Alpaca, Dolly, Flan-T5, Custom

OpenLLM

REST/gRPC API server for running any Open Large-Language Model - StableLM, Llama, Alpaca, Dolly, Flan-T5, and more
Powered by BentoML 🍱 + HuggingFace 🤗

To get started, simply install OpenLLM with pip:

pip install openllm

To start a LLM server, openllm start allows you to start any supported LLM with a single command. For example, to start a dolly-v2 server:

😌 tl;dr?

openllm start dolly-v2

# Starting LLM Server for 'dolly_v2'
#
# 2023-05-27T04:55:36-0700 [INFO] [cli] Environ for worker 0: set CPU thread coun t to 10
# 2023-05-27T04:55:36-0700 [INFO] [cli] Prometheus metrics for HTTP BentoServer f rom "_service.py:svc" can be accessed at http://localhost:3000/metrics.
# 2023-05-27T04:55:36-0700 [INFO] [cli] Starting production HTTP BentoServer from "_service.py:svc" listening on http://0.0.0.0:3000 (Press CTRL+C to quit)

To see a list of supported LLMs, run openllm start --help.

On a different terminal window, open a IPython session and create a client to start interacting with the model:

>>> import openllm
>>> client = openllm.client.HTTPClient('http://localhost:3000')
>>> client.query('Explain to me the difference between "further" and "farther"')

To package the LLM into a Bento, simply use openllm build:

openllm build dolly-v2

NOTE: To build OpenLLM from git source, pass in OPENLLM_DEV_BUILD=True to include the generated wheels into the bundle.

To fine-tune your own LLM, either use LLM.tuning():

>>> import openllm
>>> flan_t5 = openllm.LLM.from_pretrained("flan-t5")
>>> def fine_tuning():
...     fined_tune = flan_t5.tuning(method=openllm.tune.LORA | openllm.tune.P_TUNING, dataset='wikitext-2', ...)
...     fined_tune.save_pretrained('./fine-tuned-flan-t5', version='wikitext')
...     return fined_tune.path  # get the path of the pretrained
>>> finetune_path = fine_tuning()
>>> fined_tune_flan_t5 = openllm.LLM.from_pretrained('flan-t5', pretrained=finetune_path)
>>> fined_tune_flan_t5.generate('Explain to me the difference between "further" and "farther"')

📚 Features

🚂 SOTA LLMs: One-click stop-and-go supports for state-of-the-art LLMs, including StableLM, Llama, Alpaca, Dolly, Flan-T5, ChatGLM, Falcon, and more.

📦 Fine-tuning your own LLM: Easily fine-tune any LLM with LLM.tuning().

🔥 BentoML 🤝 HuggingFace: Built on top of BentoML and HuggingFace's ecosystem (transformers, optimum, peft, accelerate, datasets), provides similar APIs for ease-of-use.

⛓️ Interoperability: First class support for LangChain and 🤗 Hub allows you to easily chain LLMs together.

🎯 Streamline production deployment: Easily deploy any LLM via openllm bundle with the following:

• ☁️ BentoML Cloud: the fastest way to deploy your bento, simple and at scale
• 🦄️ Yatai: Model Deployment at scale on Kubernetes
• 🚀 bentoctl: Fast model deployment on AWS SageMaker, Lambda, ECE, GCP, Azure, Heroku, and more!

🍇 Telemetry

OpenLLM collects usage data that helps the team to improve the product. Only OpenLLM's internal API calls are being reported. We strip out as much potentially sensitive information as possible, and we will never collect user code, model data, or stack traces. Here's the code for usage tracking. You can opt-out of usage tracking by the --do-not-track CLI option:

openllm [command] --do-not-track

Or by setting environment variable OPENLLM_DO_NOT_TRACK=True:

export OPENLLM_DO_NOT_TRACK=True