docs: add blog on deploy tabby to huggingface space (#397)

* docs: add blog on deploy tabby to huggingface space

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* Update website/blog/2023-09-05-deploy-tabby-to-huggingface-space/index.md

Co-authored-by: Lucy Gao <gyxlucy@gmail.com>

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---------

Co-authored-by: Lucy Gao <gyxlucy@gmail.com>

website/blog/2023-09-05-deploy-tabby-to-huggingface-space/index.md

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+---
+slug: deploy-tabby-to-huggingface-space.md
+title: Deploying a Tabby Instance in Hugging Face Spaces
+authors:
+  - name: Rand Xie
+    url: https://github.com/randxie
+    image_url: https://github.com/randxie.png
+  - meng
+  - gyxlucy
+tags: [deployment]
+---
+
+[Hugging Face Spaces](https://huggingface.co/spaces) offers an easy-to-use Nvidia GPU hosting runtime, allowing anyone to host their machine learning models or AI applications.
+
+In this blog post, we are going to show you how to deploy a Tabby instance in Hugging Face Spaces. If you have not heard of Tabby, it’s an open-source Github Copilot alternative that supports code completion.
+Check out more details [here](https://github.com/TabbyML/tabby).
+
+### How it works
+
+Let’s firstly take a look at what steps are needed to deploy a Tabby instance in Hugging Face. It’s super easy and you don’t need much coding knowledge. Buckle up and let’s get started.
+
+**Step 1:** Create a new Hugging Face Space ([link](https://huggingface.co/new-space)). Spaces are code repositories that host application code.
+
+**Step 2:** Create a Dockerfile to capture your machine learning models' logic, and bring up a server to serve requests.
+
+**Step 3:** After space is built, you will be able to send requests to the APIs.
+
+That's it! With the hosted APIs, now you can connect Tabby's [IDE extensions](/docs/getting-started) to the API endpoint. Next, we will deep dive into each step with screenshots!!
+**Everything will be done in the Hugging Face UI. No local setup is needed.**
+
+:::tip
+Looking to quickly start a Tabby instance? You can skip the tutorials entirely and simply [duplicate the space](https://huggingface.co/spaces/randxie/tabbyml?duplicate=true).
+:::
+
+### Deep Dive
+
+#### Create a new Space
+
+After you create a Hugging Face account, you should be able to see the following page by clicking this [link](https://huggingface.co/new-space).
+The owner name will be your account name. Fill in a Space name, e.g. "tabbyml", and select Docker as Space SDK. Then click "Create Space" at the bottom.
+
+In this walkthrough we recommend using **Nvidia T4** instance to deploying a model of ~1B parameter size.
+
+![Create a new Space](./new-space.png)
+
+#### Uploading Dockerfile
+
+For advanced users, you can leverage the Git workspace. In this blog post, we will show you the UI flow instead. After you click the "Create a Space" in the last step, you will be directed to this page. Just ignore the main text and click the "Files" on the top right corner.
+
+![Docker Space](./docker-space.png)
+
+After clicking on the "Files", you will be able to see a "Add file" button, click that, then click on "Create a new file"
+
+![Empty Space](./empty-space.png)
+
+Then you will be redirected to the page below. Set the filename to “Dockerfile” and copy the content to the “Edit” input box. You can copy the code from the [appendix](#dockerfile) here to bring up the [SantaCoder-1B](https://huggingface.co/TabbyML/SantaCoder-1B) model. Once ready, click the button “Commit new file to main” on the bottom.
+
+![Edit Dockerfile](./edit-dockerfile.png)
+
+#### Edit Readme
+
+You also need to add a new line the README.md. Click the "edit" button in the README.md file.
+
+
+![Empty README](./empty-readme.png)
+
+Add this line "app_port: 8080" after "sdk: docker"
+
+![Edit README](./edit-readme.png)
+
+Click the button "Commit to main" to save the changes.
+
+#### Verify Tabby is running
+
+Click on the "App" button, you should be able to see the container is building:
+
+![Space Building](./building.png)
+
+If the App is up successfully, you should see this page:
+
+![Tabby Swagger](./tabby-swagger.png)
+
+#### Call code completion API
+
+Now, you are able to call the completion API. The full URL is https://{YOUR-ACCOUNT-NAME}-tabbyml.hf.space/v1/completions. In this post, the URL is https://randxie-tabbyml.hf.space/v1/completions.
+
+To test if your APIs are up and running, use [this online tool](https://reqbin.com/curl) to send curl commands:
+
+![curl](./curl.png)
+
+The complete curl command can also be located in the [appendix](#curl-command). Ensure that you have adjusted the URL to align with your Hugging Face Spaces settings!
+
+(If you are setting the space to private, you will need to fill in your Huggingface Access Token as bearer token in HTTP Headers, like `Authorization: Bearer $HF_ACCESS_TOKEN`.)
+
+### Conclusion
+In this post, we covered the detailed steps for deploying a Tabby instance to Hugging Face Spaces. By following these steps, anyone is able to bring up their own code completion APIs easily.
+
+### Appendix
+
+#### Dockerfile
+```Dockerfile
+FROM tabbyml/tabby
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+ENV TABBY_ROOT=$HOME/app
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["serve", ”--device", "cuda", "--model", "TabbyML/SantaCoder-1B"]
+
+```
+
+#### CURL Command
+```bash
+curl -L 'https://randxie-tabbyml.hf.space/v1/completions' \
+-H 'Content-Type: application/json' \
+-H 'Accept: application/json' \
+-d '{
+  "language": "python",
+  "segments": {
+    "prefix": "def fib(n):\n    ",
+    "suffix": "\n        return fib(n - 1) + fib(n - 2)"
+  }
+}'
+```

website/blog/authors.yml

@@ -1,5 +1,9 @@
 meng:
   name: Meng Zhang
-  title: Maintainer of Tabby
   url: https://github.com/wsxiaoys
   image_url: https://github.com/wsxiaoys.png
+
+gyxlucy:
+  name: Lucy Gao
+  url: https://github.com/gyxlucy
+  image_url: https://github.com/gyxlucy.png