azure-search-openai-demo
llama.cpp
azure-search-openai-demo | llama.cpp | |
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11 | 792 | |
5,526 | 60,282 | |
4.3% | - | |
9.5 | 10.0 | |
2 days ago | about 7 hours ago | |
Python | C++ | |
MIT License | MIT License |
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azure-search-openai-demo
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Help with my Frontend-Code for AZURE GPT - Will Tip
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
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Azure ChatGPT: Private and secure ChatGPT for internal enterprise use
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.
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GPT-4 API general availability
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 )
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Pricing question
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?
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New to Azure, deployed a MS project from github. How can I edit the .py files in azure?
I recently deployed https://github.com/Azure-Samples/azure-search-openai-demo
- How to understand somebody else's code? Any tools that can help visualize would be a life saver!
- How to understand somebody else's code? Any tools that can help visualise would be a life saver!
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Create custom "Coach-bot" based on company documents to coach customers?
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
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Can you train AI on a knowledge base to offer customer support through a live chatbot?
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
- Will pay someone to spin up a simple Azure/OpenAI demo
llama.cpp
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Apple Intelligence, the personal intelligence system
> Doing everything on-device would result in a horrible user experience. They might as well not participate in this generative AI rush at all if they hoped to keep it on-device.
On the contrary, I'm shocked over the last few months how "on device" on a Macbook Pro or Mac Studio competes plausibly with last year's early GPT-4, leveraging Llama 3 70b or Qwen2 72b.
There are surprisingly few things you "need" 128GB of so-called "unified RAM" for, but with M-series processors and the memory bandwidth, this is a use case that shines.
From this thread covering performance of llama.cpp on Apple Silicon M-series …
https://github.com/ggerganov/llama.cpp/discussions/4167
… "Buy as much memory as you can afford would be my bottom line!"
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Partial Outage on Claude.ai
I'd love to use local models, but seems like most of the easy to use software out there (LM Studio, Backyard AI, koboldcpp) doesn't really play all that nicely with my Intel Arc GPU and it's painfully slow on my Ryzen 5 4500. Even my M1 MacBook isn't that fast at generating text with even 7B models.
I wonder if llama.cpp with SYCL could help, will have to try it out: https://github.com/ggerganov/llama.cpp/blob/master/README-sy...
But even if that worked, I'd still have the problem that IDEs and whatever else I have open already eats most of the 32 GB of RAM my desktop PC has. Whereas if I ran a small code model on the MacBook and connected to it through my PC, it'd still probably be too slow for autocomplete, when compared to GitHub Copilot and less accurate than ChatGPT or Phind for most stuff.
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Why YC Went to DC
You're correct if you're focused exclusively on the work surrounding building foundation models to begin with. But if you take a broader view, having open models that we can legally fine tune and hack with locally has created a large and ever-growing community of builders and innovators that could not exist without these open models. Just take a look at projects like InvokeAI [0] in the image space or especially llama.cpp [1] in the text generation space. These projects are large, have lots of contributors, move very fast, and drive a lot of innovation and collaboration in applying AI to various domains in a way that simply wouldn't be possible without the open models.
[0] https://github.com/invoke-ai/InvokeAI
[1] https://github.com/ggerganov/llama.cpp
- Show HN: Open-Source Load Balancer for Llama.cpp
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RAG with llama.cpp and external API services
The first example will build an Embeddings database backed by llama.cpp vectorization.
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Ask HN: I have many PDFs – what is the best local way to leverage AI for search?
and at some point (https://github.com/ggerganov/llama.cpp/issues/7444)
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Deploying llama.cpp on AWS (with Troubleshooting)
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp LLAMA_CUDA=1 make -j
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Devoxx Genie Plugin : an Update
I focused on supporting Ollama, GPT4All, and LMStudio, all of which run smoothly on a Mac computer. Many of these tools are user-friendly wrappers around Llama.cpp, allowing easy model downloads and providing a REST interface to query the available models. Last week, I also added "👋🏼 Jan" support because HuggingFace has endorsed this provider out-of-the-box.
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Mistral Fine-Tune
The output of the LLM is not just one token, but a statistical distribution across all possible output tokens. The tool you use to generate output will sample from this distribution with various techniques, and you can put constraints on it like not being too repetitive. Some of them support getting very specific about the allowed output format, e.g. https://github.com/ggerganov/llama.cpp/blob/master/grammars/... So even if the LLM says that an invalid token is the most likely next token, the tool will never select it for output. It will only sample from valid tokens.
- Distributed LLM Inference with Llama.cpp
What are some alternatives?
sample-app-aoai-chatGPT - Sample code for a simple web chat experience through Azure OpenAI, including Azure OpenAI On Your Data.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
chat-copilot
gpt4all - gpt4all: run open-source LLMs anywhere
LLMStack - No-code multi-agent framework to build LLM Agents, workflows and applications with your data
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
semantic-search-example
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
llm-jeopardy - Automated prompting and scoring framework to evaluate LLMs using updated human knowledge prompts
ggml - Tensor library for machine learning
azurechatgpt - 🤖 Azure ChatGPT: Private & secure ChatGPT for internal enterprise use 💼
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM