azure-search-openai-demo
exllama
azure-search-openai-demo | exllama | |
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11 | 64 | |
5,526 | 2,638 | |
4.3% | - | |
9.5 | 9.0 | |
2 days ago | 9 months ago | |
Python | Python | |
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
exllama
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Any way to optimally use GPU for faster llama calls?
not using exllama seems like the tremendous waste
- ExLlama: Memory efficient way to run Llama
- Ask HN: Cheapest hardware to run Llama 2 70B
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Llama Is Expensive
> We serve Llama on 2 80-GB A100 GPUs, as that is the minumum required to fit Llama in memory (with 16-bit precision)
Well there is your problem.
LLaMA quantized to 4 bits fits in 40GB. And it gets similar throughput split between dual consumer GPUs, which likely means better throughput on a single 40GB A100 (or a cheaper 48GB Pro GPU)
https://github.com/turboderp/exllama#dual-gpu-results
Also, I'm not sure which model was tested, but Llama 70B chat should have better performance than the base model if the prompting syntax is right. That was only reverse engineered from the Meta demo implementation recently.
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Accessing Llama 2 from the command-line with the LLM-replicate plugin
For those getting started, the easiest one click installer I've used is Nomic.ai's gpt4all: https://gpt4all.io/
This runs with a simple GUI on Windows/Mac/Linux, leverages a fork of llama.cpp on the backend and supports GPU acceleration, and LLaMA, Falcon, MPT, and GPT-J models. It also has API/CLI bindings.
I just saw a slick new tool https://ollama.ai/ that will let you install a llama2-7b with a single `ollama run llama2` command that has a very simple 1-click installer for Apple Silicon Mac (but need to build from source for anything else atm). It looks like it only supports llamas OOTB but it also seems to use llama.cpp (via Go adapter) on the backend - it seemed to be CPU-only on my MBA, but I didn't poke too much and it's brand new, so we'll see.
For anyone on HN, they should probably be looking at https://github.com/ggerganov/llama.cpp and https://github.com/ggerganov/ggml directly. If you have a high-end Nvidia consumer card (3090/4090) I'd highly recommend looking into https://github.com/turboderp/exllama
For those generally confused, the r/LocalLLaMA wiki is a good place to start: https://www.reddit.com/r/LocalLLaMA/wiki/guide/
I've also been porting my own notes into a single location that tracks models, evals, and has guides focused on local models: https://llm-tracker.info/
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GPT-4 Details Leaked
Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .
If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...
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Multi-GPU questions
Exllama for example uses buffers on each card that reduce the amount of VRAM available for model and context, see here. https://github.com/turboderp/exllama/issues/121
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A simple repo for fine-tuning LLMs with both GPTQ and bitsandbytes quantization. Also supports ExLlama for inference for the best speed.
For inference step, this repo can help you to use ExLlama to perform inference on an evaluation dataset for the best throughput.
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GPT-4 API general availability
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
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Local LLMs GPUs
That's a 16GB GPU, you should be able to fit 13B at 4bit: https://github.com/turboderp/exllama
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.
llama.cpp - LLM inference in C/C++
chat-copilot
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
LLMStack - No-code multi-agent framework to build LLM Agents, workflows and applications with your data
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language 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
KoboldAI
azurechatgpt - 🤖 Azure ChatGPT: Private & secure ChatGPT for internal enterprise use 💼
text-generation-inference - Large Language Model Text Generation Inference