From aa72c16651ea2d3cde08c270f7514d77f0eb8e54 Mon Sep 17 00:00:00 2001
From: Aaron <29749331+aarnphm@users.noreply.github.com>
Date: Sat, 27 May 2023 05:01:50 -0700
Subject: [PATCH] docs: update quickstart and remove unused yarn.lock
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
---
README.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++++
yarn.lock | 55 -------------------------------------------------------
2 files changed, 49 insertions(+), 55 deletions(-)
delete mode 100644 yarn.lock
diff --git a/README.md b/README.md
index 3ebca145..e119fcc8 100644
--- a/README.md
+++ b/README.md
@@ -5,3 +5,52 @@
Powered by BentoML 🍱 + HuggingFace 🤗
+
+To get started, simply install OpenLLM with pip:
+
+```bash
+pip install openllm
+```
+
+> NOTE: Currently, OpenLLM is built with pydantic v2. At the time of writing,
+> Pydantic v2 is still in alpha stage. To get pydantic v2, do
+> `pip install -U --pre pydantic`
+
+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:
+
+```bash
+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:
+
+```python
+>>> 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`:
+
+```bash
+openllm build dolly-v2
+```
+
+🎯 To streamline production deployment, you can use the following:
+
+- [☁️ BentoML Cloud](https://l.bentoml.com/bento-cloud): the fastest way to
+ deploy your bento, simple and at scale
+- [🦄️ Yatai](https://github.com/bentoml/yatai): Model Deployment at scale on
+ Kubernetes
+- [🚀 bentoctl](https://github.com/bentoml/bentoctl): Fast model deployment on
+ AWS SageMaker, Lambda, ECE, GCP, Azure, Heroku, and more!
diff --git a/yarn.lock b/yarn.lock
deleted file mode 100644
index 71cfa40a..00000000
--- a/yarn.lock
+++ /dev/null
@@ -1,55 +0,0 @@
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