Azure AI foundry model setup and playground

To be able to use use AI model like Mistral, ChatGPT and other model in Azure, you need to create and deploy the model first. Assuming you have an existing project in your Azure AI foundry. 

By default it is all GPT. Here we will use a Mistral-3B model (which cost less) and setup our playground for fun and I don't want to use Chatgpt for my model.

Go to Model catalog and select Mistral-3B. Click on Goto Model page and accept the terms and condition. 


Deploy this model. We only have Global standard here.



Once you have deployed it, goto My models and select your deployment



Then select "Open in Playground"



Then you can interact with it






Comments

Post a Comment

Popular posts from this blog

gemini cli getting file not defined error

Image
 While trying to run gemini cli on my windows linux subsystem, i bump into this error here - "/Roaming/npm/node_modules/@google/gemini-cli/node_modules/undici/lib/web/webidl/index.js:512 webidl.is.File = webidl.util.MakeTypeAssertion(File)"  "ReferenceError: File is not defined" In Node.js, there’s no built-in File (until very recent Node 20+ with experimental WHATWG APIs). So, unless polyfilled, File is undefined, leading to the ReferenceError. And to resolve this, I had to upgrade to node 22 (might work for node 20) and then I was able to run it.
Read more

NodeJS: Error: spawn EINVAL in window for node version 20.20 and 18.20

Encounter this issue when running autorest (that uses nodejs).  I am using node version:  v18.20.2 After i revert to use node v18.12.0, then i was able to get my app to run without this error. Since i am working with Azure devops yaml, i was able to use the following to resolve my issue.  - task : NodeTool@0   inputs :     versionSource : 'spec'     versionSpec : '18.18.0'
Read more

vllm : Failed to infer device type

Trying to run vllm without gpu will typicaly lands you into this issue. To resolve this issue: we can use set these both. Setting one does not work for me. So i had to configure both settings below:- 1.  environment variable CUDA_VISIBLE_DEVICES = "" 2. in the command line, change to use device cpu and remove  tensor-parallel-size. For example, python3 -m vllm.entrypoints.openai.api_server --port 8080 --model deepseek-ai/DeepSeek-R1 --device cpu --trust-remote-code --max-model-len 4096" References https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#failed-to-infer-device-type
Read more