azure-search-openai-demo VS WizardLM

Hi all, Im not an expert at full-stack deployments and need help with a sample code from github to which I want to make changes. (Code: https://github.com/Azure-Samples/azure-search-openai-demo) If your suggestion works, I am willing to tip 15$ (please provide link). This Github code is used as frontend for our application. We pretty much want to keep it like it is but make one minor adjustment. If you chat with the model, it gives you citations: (Link). Then on the right side of the page a Analysis Bar opens and it shows the one page that this citation refers to. HERE: We need to show the WHOLE document for each citation instead of just one page. I think it has to do with an url or something that needs to be changed. Could you tell me the script names and changes (before and after) so I can overwrite it? Thanks a lot. Best

There's at least two more. There's also https://github.com/Azure-Samples/azure-search-openai-demo

And you can deploy a chat bot from within the Azure playground which runs on another codebase.

You can see region availability here for Azure OpenAI:

https://learn.microsoft.com/en-us/azure/cognitive-services/o...

It's definitely limited, but there's currently more than one region available.

(I happen to be working at the moment on a location-related fix to our most popular Azure OpenAI sample, https://github.com/Azure-Samples/azure-search-openai-demo )

Hello everyone, I am an electrical engineer working at a company, since I’ve been coding for a few months they asked me to implement Ai services in their workflow and I did it following the tutorial by azure to chat with entreprise data provided by Microsoft (https://github.com/Azure-Samples/azure-search-openai-demo) the problem is in only a few days the pricing was indicated to be about 70$ going in too much higher prévision for the rest of the month in the azure cost analysis tool which is too high for us. When I saw that I deleted the ressource group that was created following the tutorial but now I can’t access it to see azure stopped billing us and I’m a little worried. If the ressource group including the cognitive search was deleted the billing stop right (it was cognitive search that costed like 95%) if not how can i see a deleted ressource group and how can I stop the billing?

I recently deployed https://github.com/Azure-Samples/azure-search-openai-demo

You may also want to browse through this sample code base on GitHub https://github.com/Azure-Samples/azure-search-openai-demo. This sounds like what you want to achieve. https://github.com/Azure-Samples/azure-search-openai-demo

You can also use a GPT model combined with a search service to provide a QnA chatbot https://github.com/Azure-Samples/azure-search-openai-demo

This is interesting work, and a good contribution, but there is no need to mislead people.

[1] https://github.com/nlpxucan/WizardLM

If you pair this with the latest WizardCoder models, which have a fairly better performance than the standard Salesforce Codegen2 and Codegen2.5, you have a pretty solid alternative to GitHub Copilot that runs completely locally.

(We will update the demo links in our github.)

In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.

You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.

That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).

I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)

[1] https://github.com/turboderp/exllama#results-so-far

[2] https://github.com/aigoopy/llm-jeopardy

[3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...

[4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder

You talking about this? https://github.com/nlpxucan/WizardLM

The smallest model that is close to competent at code is WizardCoder 15B.. https://github.com/nlpxucan/WizardLM/

WizardCoder: Empowering Code Large Language Models with Evol-Instruct (https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder)