Llama cpp python gpu reddit. GGML on GPU is also no slouch.

Llama cpp python gpu reddit 2. I'm trying to get GPU Hey all, I'm trying to generate embeddings of a text using llama_cpp_python. Later, I have Since LLaMa-cpp-python does not yet support the -ts parameter, the default settings lead to memory overflow for the 3090s and 4090s, I used LLaMa. It won't use both gpus and will be slow but you will be able try the model. I wasted days Looks like the tests I ran previously had the model generating Python code, so that leads to bigger gains than standard LLM story tasks. cpp server example under the hood. I'm able to get about 1. llama. 4, but when I try to run the model using llama. Q4_K_M. I don't know what llama-cpp-python or Ooba do internally and whether that affects performance but I definitely and that llama. cpp is far easier than trying to get GPTQ up. Not everyone is running dual 4090s, or single 3090, or even 3060. cpp, The Before the introduction of GPU-offloading in llama. cpp it ships with, so idk what caused those problems. I am a beginner in the LLM ecosystem and I am wondering what are the main difference between the different Python libraries which exist ? I am using llama-cpp-python as it was an easy way I think something with the llama-cpp-python implementation is off. cpp Llama-2 has 4096 context length. GGUF files usually already If so, then the easiest thing to do perhaps would be to start an Ubuntu Docker container, set up llama. I heard over at the llama. (python llama_cpp. cpp servers Speed and recent llama. so shared library. Is llama-cpp-python not ready for prime time? Is there a better alternative to access a local LLM that works with Has anyone managed to actually use multiple gpu for inference with llama. cpp gpu acceleration, and hit a bit of a wall doing so. cpp and the new GGUF format with code llama. At Anyway, the real solution would be more extensive content awareness, might be out of scope for llama. If can, what do I need to look into in order to make it !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip Currently using the llama. edit: Somebody opened an llama. You get llama. cpp works fine as tested with python. from llama_cpp import Llama. If I load layers to GPU, llama. Being able to run that is far better than not being able to run GPTQ. cpp repository, titled "Add full GPU inference of LLaMA on Apple Silicon using Metal," proposes significant changes to enable GPU support on Apple Silicon for the LLaMA language Get the Reddit app Scan this QR code to download the app now. cpp is basically the only way to run Large Language Models on anything other than Nvidia GPUs and CUDA software on windows. Turbopilot open source LLM code I did use a different fork of llama. Then Sample time was about 1300 tks x sec Prompt eval time 9 tks x sec Eval time 7 tks x sec I'm now using ollama ( a llama. I haven't seen any fine-tunes yet. 5GBs. So now llama. cpp can work with CUDA (Nvidia) and OpenCL (Open/AMD) to Hi, anyone tried the grammar with llama. c/llama. cpp with a NVIDIA L40S GPU, I have installed CUDA toolkit 12. cpp). cpp just does RPC calls to remote computers. 162K subscribers in the LocalLLaMA community. gbnf example from the official example, like the following. pip uninstall -y llama-cpp-python set CMAKE_ARGS="-DLLAMA_CUBLAS=on" set FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir causes errors. cpp DLL, which is where the calculations are actually So i have this LLaVa GGUF model and i want to run with python locally , i managed to use with LM Studio but now i need to run it in isolation with a I have found that json mode drastically slows down Llama 3 in Ollama (which uses llama. I can't follow any guides that rely on Python and other fancy techniques, it makes my I just wanted to point out that llama. Your best option for even bigger models is probably offloading Below stats for phind-codellama-34b-v2. cpp python with gpu in vscode to load local llm. cpp-server and llama-cpp-python. Windows Even with 4 GPUs llama. If you really wanna use Phi-2, you can use the URIAL method. Use llama. cpp, GPU acceleration was primarily utilized for handling long prompts. If you have an Nvidia GPU and want to use the I reckon GPU use during training is incidental - some library call used called periodically for evaluation - rather than being part of the training scheme. cpp its working. Then I just get an endless stream of errors. It seems rather complicated to get cuBLAS running You can run llama-cpp-python in Server mode like this:python -m llama_cpp. it's because I wrote a really detailed post and reddit ate it, so I After searching around and suffering quite for 3 weeks I found out this issue on its repository. Here I am loading the model. Which a lot of people can't Download the kompute branch of llama. Of course llama. cpp with a fancy UI, I've forked your project and modified the code to use CuPy for better performance through GPU acceleration. server) llama_print_timings: load time = 294. Far easier. com/en/latest/release/windows_support. cpp is another framework/library that does the more of the same but specialized in models that runs on CPU and quanitized and run much faster i understand that GGML is a file I noticed there aren't a lot of complete guides out there on how to get LLaMa. You can use Threading Llama across CPU cores is not as easy as you'd think, and there's some overhead from doing so in llama. cpp/llamacpp_HF, set n_ctx to 4096. cpp runs on say 2 I noticed that finally Llama cpp added -ngl option to finetune command. and make sure to offload all the layers of the Neural Net to the GPU. Its amazing I run llama cpp python on my new PC which has a built in RTX 3060 with 12GB VRAM This is my code: from llama_cpp import Llama llm = Llama Can't make llama-cpp-python run with GPU on an AWS EC2 If you're planning to use multi gpu, then you want to use the exact same gpu models. cpp is not just 1 or 2 percent faster; it's a whopping 28% faster than llama-cpp-python: 30. server It should be work with most Open AI client software as the API is the same! Depending if you can put in a own IP for the OpenAI client. The ESP32 series employs either a Tensilica Xtensa LX6, Xtensa LM Studio (a wrapper around llama. It provides a simple yet robust interface here's my current list of all things local llm code generation/annotation: . 5s. cpp is written in C++ and runs the models on cpu/ram only so its very small and optimized and can run decent sized models pretty fast (not as fast as on a gpu) and requires some llama. Assumes nvidia This is an update to an earlier effort to do an end-to-end fine-tune locally on a Mac silicon (M2 Max) laptop, using llama. The llama-cpp-python needs to known where is the libllama. I would try exllama first, it can run 65B parameter model in 40 to 45 gigabyte of vram on two GPUs. cpp officially supports GPU acceleration. I really am clueless about pretty much everything involved, and am slowly learning how everything works using a combination of reddit, GPT4, This is work in progress and will be updated once I get more wheels. There are people with even less, but they have NVidia GPUs. Q3_K_M. Hardware: Ryzen 5800H RTX I've been wanting to experiment with a realtime "group chat" voice-to-voice with llama-cpp-python for a while now. cpp server? With a simple example, we can try to use the json. Download kompute and stick it in the "kompute" directory of that llama. I am using wizard 7b for reference. So really it's no different than how llama. These instructions take over after cd . cpp and llama-cpp-python (for use with text generation webui). cpp, I used to run the lama models with oogabooga, but after the newest changes to llama. cpp on CPU, then I had an idea for GPU acceleration, and once I had a working prototype I bought a 3090. Why bother with this instead of running it under WSL? It lets you run the Solution: the llama-cpp-python embedded server. Pls vote and comment on my issue so it may catch more attention. I wonder if it's possible to run a local LLM completely via GPU. LM Studio (a wrapper around llama. Instead The main goal of llama. How to use gpu on llama cpp python ? /r/StableDiffusion is back open after the protest of Reddit killing open Has anybody tried llama. In Which is pretty much the same with llama. The best thing about GGML is you can split with the new orca, but trough the main. cpp directly to test 3090s and 4090s. /. If you were using H100 SXM I know GGUF format and latest llama. 9s vs 39. cpp than found on reddit, but that was what the repo suggested due to compatibility issues. cpp’s server and saw that they’d more or less brought it in line with Open AI-style APIs – natively – obviating the need for e. which brings you back to llama-cpp-python directory. With this implementation, we would be able to run the 4-bit version of the llama 30B with just 20 GB of RAM (no gpu required), and only 4 GB of RAM would be needed for the 7B (4-bit) For performance reasons, the llama. cpp - llama-cpp-python on an RDNA2 series GPU using the Vulkan backend to get ~25x performance boost v/s `llama-cpp-python` and `llama. It's because it has proper use of multiple cores unlike python and my setup can In theory, yes but I believe it will take some time. So Get the Reddit app Scan this QR code to download Are you referring to the "CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir" snippet The So my initial reaction here is that this is far superior to the llama. For this, I need to have multiple entirely separate caches as the system Also llama-cpp-python is probably a nice option too since it compiles llama. cpp, for CPU and GPU support (w/ Vulkan), describe how to use some core binaries (llama-server, llama-cli, llama For folks looking for more detail on specific steps to take to enable GPU support for llama-cpp-python, you need to do the following: Recompile llama-cpp-python with the Steps for building llama. 04 using the following commands: Built llama. GGML on GPU is also no slouch. cpp, llama. Hopefully that will change in the I'm on my way to deploy a GGUF model on Huggingface space (free hardware CPU and RAM). cpp on my CPU, hopefully to be utilizing a GPU soon. the same is largely true of stable diffusion however You can see how the single GPU number is comparable to exl2, but we can go much further on multiple GPUs due to tensor parallelism and paged kv cache. cpp supports AMD GPUs well, but maybe only on Linux (not sure; I'm Linux-only here). If you're Use this !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python. Now that it works, I can download more new format models. cpp) offers a setting for selecting the number of layers that can be offloaded to the GPU, with 100% making the GPU the sole processor. Type cmake -DLLAMA_KOMPUTE=1. cpp which you need to interact with these files. But only with the pure llama. If you can successfully load models with BLAS=1, then the issue might be with Edit: Seems that on Conda there is a package and installing it worked, weirdly it was nowhere mentioned. cpp on windows with ROCm. cpp I switched. cpp allows for GPU offloading of some layers. Check if your GPU is supported here: https://rocmdocs. \Program Files\NVIDIA GP export FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir --force-reinstall - I wrote this as a comment on another thread to help a user, so I figured I'd just make a thread about it. Recent llama. cpp when you do the pip install, and you can set a few environment variables before that to configure BLAS support and these things. 8/8 cores is basically device lock, and I can't even use my I have some tutorials and notebooks on setting up GPU-accelerated Large Language Models (LLMs) with llama-cpp on Google Colab and Kaggle. cpp loader and with nvlink patched into the code. text-gen bundles llama-cpp-python, but it's the version that only uses the CPU. With some (or a lot) of work, you can run 28 votes, 20 comments. cpp supports about 30 types of models and 28 types of quantizations. It would be one thing if it This supposes ollama uses the llama. Also, if it works for Intel then the A770 becomes the cheapest way to get a lot of VRAM for cheap on a Essentially the gpu stuff is broken in underlying implementation but llama. Sort by: about 18-20GB per GPU and after some painful python dependency setup /r/StableDiffusion is back Llama. One of my goals is to efficiently combine RAM and VRAM into It will guide you throught the building process of llama. cpp on intel's gpu lineup? Share Add a Comment. When The first involves modifying the setup. I would like to get llama-cpp working as fast as ollama does and I figure that if the gpu isn't being used that is where the speed gap is being made. Using CPU alone, I get 4 tokens/second. cpp's train-text I built llama. GPT4 says it's likely something to do with the python wrapper Hm, I have no trouble using 4K context with llama2 models via llama-cpp-python. The same method works but for cublas The Pull Request (PR) #1642 on the ggerganov/llama. 1 70B taking up 42. I have noticed that the responses are very This is great. cpp would use the identical amount of RAM in addition to VRAM. 78 ms llama_print_timings: sample If Patched together notes on getting the Continue extension running against llama. It should be less than 1% for most people's use cases. cpp and python and accelerators - checked lots of benchmark and read lots of paper (arxiv papers are insane they For me it's faster inference now. cpp innovations: with the Q4_0_4_4 CPU-optimizations, the Snapdragon X's CPU got 3x faster. g. There is no additional cost to get the gpus connected, you just need enough PCIe slots preferably all llama. But instead of that I just ran the llama. cpp logging llama_model_load_internal: using CUDA for GPU acceleration So I was looking over the recent merges to llama. Like loading a 20b Q_5_k_M model A self contained distributable from Concedo that exposes llama. I am am able to use this option in llama. The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). bat that comes with the one click installer. Right now, text-gen-ui does not provide automatic GPU accelerated GGML support. cpp` with CLBlast for older AMD GPUs (non-ROCm) - Windows This subreddit has gone Restricted and reference-only as part of a mass protest against LLAMA_OPENBLAS=yes pip install llama-cpp-python. cpp working with an AMD GPU, so here goes. cpp using 4-bit quantized Llama 3. The official Python community for Reddit! Stay up to date with the latest Has anyone used Llama. But the main question I have is what parameters are you all using? I have found the reference information I've personally experienced this by running Meta-Llama-3-70B-Instruct-64k-i1-GGUF-IQ2_S at 42K on a system with Windows 11 Pro, Intel 12700K processor, RTX 3090 GPU, and 32GB of RAM. Initial wait between loading a new prompt, switching characters, etc is longer. cpp server which also works great. Wrote a simple python file to talk to the llama. It should allow mixing GPU brands. Anecdotal experience, but it appears to be far less stupid when running on With llama_cpp_python-0. cpp The guy who implemented GPU offloading in llama. cpp user on GPU! Just want to check if the experience I'm having is normal. Or check it out in the app stores CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python I am able to get gpu inference, but not batch. It rocks. cpp server binary with -cb flag and make a function I was trying to speed it up using llama. the problem is when i try to achieve this trough the python server, it looks like when its contain a newline character then its Get app Get the Reddit app Log In Log in to Reddit. cpp. cpp github that the best way to do this is for them to make some custom code (not done yet) that keeps everything but the experts on the GPU, and the experts While I admire the exllama's project and would never dream to compare these results to what you can achieve with exllama + GPU, it should be noted that the low speeds in oubabooga webui That's probably a true statement, however Llama. Completion. This iteration uses the MLX framework for machine The speed discrepancy between llama-cpp-python and llama. FauxPilot open source Copilot alternative using Triton Inference Server . Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Note: set the latter to off instead if using an older GPU Build and install . Simply You can use llama. Or In a tiny package (under 1 MB compressed with no dependencies except python), excluding model weights. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). For SillyTavern, the If you installed it correctly, as the model is loaded you will see lines similar to the below after the regular llama. cpp doesn't use pytorch and the python in this case is simply wrapping the Llama. The base llama-cpp The ggml models (provided by TheBloke ) worked fine, however i can't utilize the GPU on my own hardware, so answer times are pretty long. cpp as the execution engine, and llama-cpp-python is the intermediary to the llama. I've rerun with the prompt "Once upon a time" below in Another day, another great model is released! OpenAccess AI Collective's Wizard Mega 13B. Especially if you want to use a GPU since you'll need to specify the layers to offload so you won't be able to just drag a model anyways. Type make. cpp mostly, just on console with main. cpp changes re-pack Q4_0 models automatically to Also I'm having a weird issue with llama_cpp_python / guidance where it doesn't accept properly formatted function arguments. It regularly updates the llama. It runs so much faster on my GPU. I figured it might I have added multi GPU support for llama. Log In / Sign Up; I'm using llama_cpp_python for offloading 9/43 layers to my GPU llama. amd. cpp on Ubuntu 22. 5-2 t/s In a scenario to run LLMs on a private computer (or other small devices) only and they don't fully fit into the VRAM due to size, i use GGUF models with llama. It says that it is It's not just that. cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. How are you using it that you are unable to add this argument at the time of starting up your backend ? llama. cpp has now partial GPU support for ggml processing. I can get upwards of 20 t/s with llama. cpp or any framework that uses it as backend. I've made my own software around llama. I have passed in the ngl option but it’s not working. cpp, all hell breaks loose. It used to take a considerable amount of time for LLM to respond to Ok so I'm fairly new to llama. There are people with 8GB of VRAM. Clear stale llama-cpp-python packages pip list | grep llama I had llama_cpp_python and ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. I went to dig into the ollama code to prove this wrong and actually you're completely right that llama. html . cpp showed that performance increase scales exponentially in number of layers offloaded to GPU, so as long as video card is faster than 1080Ti VRAM is crucial thing. Things Are you a developer looking to harness the power of hardware-accelerated llama-cpp-python on Windows for local LLM developments? Look no further! In this guide, I’ll walk This time I've tried inference via LM Studio/llama. cpp there and comit the container or build an image directly from it using a Dockerfile. cpp model. I am having trouble with running llama. cpp) offers a Hello, I have llama-cpp-python running but it’s not using my GPU. comments sorted by Best Top Below are some examples for a 16k prompt and all layers offloaded to GPU. Currently I'm using a GGUF model because I need to run it using CPU. cpp on Windows on ARM running on a Surface Pro X with the Qualcomm 8cx chip. - fiddled with libraries. But whatever, I would I used llama-cpp-python with llama2 13B model, which takes 6-10 sec to answer one question out of 1000 documents on my local Mac pro M3. Expand user menu Open settings menu. When using vllm, I got almost the same token/s with multiple concurrent request (I did only test manually, no real benchmarking, but 10 parallel 113K subscribers in the LocalLLaMA community. On llama. cpp under Linux on some mildly retro hardware (Xeon E5-2630L V2, GeForce GT730 2GB). gguf fully running in the GPU. When attempting to use llama-cpp-python's api similar to openai's it fails if I pass a batch of prompts openai. after building without errors. The llama-cpp-python package builds llama. cpp via oobabooga doesn't load it to my gpu. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. Probably needs that Visual I think it works exactly the same way as multi-gpu does in one computer. This is why performance drops off after a certain Why is there no noticeable difference in performance when using llama. Without these flags my GPU wasn't used at all by llama-cpp-python. There is one issue here. By changing the CPU affinity to Define "Novideo GPU". I was using llama. create( model="text Worked with coral cohere , openai s gpt models. I'm trying to set up llama. cpp for me, and I can provide args to the build process during pip install. It turns out the Python package llama-cpp-python now ships with a server module that is compatible with OpenAI. I'm mainly using exl2 with I tried out GPU inference on Apple Silicon using Metal with GGML and ran the following command to enable GPU inference: pip uninstall -y llama-cpp-python \ CMAKE_ARGS="-DLLAMA_METAL=on" \ FORCE_CMAKE=1 \ pip install I was wondering if I pip install llama-cpp-Python , do I still need to go through the llama. More specifically, the generation speed gets slower as more layers are offloaded to Hi, I use openblas llama. Many people use its Python bindings by Abetlen. cpp GPU Hi all! I have spent quite a bit of time trying to get my laptop with an RX5500M AMD GPU to work with both llama. cpp (CPU). cpp with multiple GPUs and CPUs versus just one GPU and CPU? Question | Help When attempting to run a 70B This is a great tutorial :-) Thank you for writing it up and sharing it here! Relatedly, I've been trying to "graduate" from training models using nanoGPT to training them via llama. cpp but only like 5 t/s in Ooga using a llama. cpp's implementation. cpp with ggml quantization to share the model between a gpu and cpu. LM Studio is good and i have it installed but i dont use it, i have a 8gb vram laptop gpu at office and 6gb vram Single node, multiple GPUs. cpp function bindings, allowing it to be used via a simulated Kobold API endpoint. cpp I get an I've tried with LLamafile, koboldcpp and In a previous Reddit post I shared performance numbers for my GTX 1070 with +12 As far as I know none of the graphical frontends have implemented the use of llama. cpp if you don't GGUF / GGML are file formats for quantized models created by Georgi Gerganov who also created llama. If you wanna try fine-tuning yourself, I would NOT recommend starting with Phi-2 and starting for with something based off llama. What's especially cool about this release is that Wing Lian has prepared a Hugging Face Given that this would be using llama. cpp from source (on Ubuntu) with no GPU support, now I'd like to build with it, how would I do this? Question But when I use llama-cpp-python to reference llama. Comparing to llama. I also tried a cuda devices environment variable (forget which one) but it’s The guide is about running the Python bindings for llama. cpp using the python bindings so that I Get the Reddit app Scan this QR code to download the app now. During a json generation, nvidia-smi shows no utilization of the GPU - but only for Llama 3 and Hi everyone. cpp contributor (a small time one, but I have a couple hundred lines that have been accepted!) Honestly, I don't think the llama code is super well-written, but I'm trying to For macOS, these are the commands: pip uninstall -y llama-cpp-python CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir. cpp wrapper) to facilitate easier RAG integration for our use case (can't llama-cpp-agent Framework Introduction. On a 7B 8-bit model I get 20 tokens/second on my old 2070. cpp bindings, this is going to be a bit slower than using Transformers directly. The token throughput has improved by 2x. So you should be able to use a Nvidia card with a AMD card and split between them. If I use the physical # in my device then my cpu locks up. This is from various pieces of the internet with some minor tweaks, see linked sources. I'm currently thinking about ctransformers or llama-cpp-python. 50 merged into oobabooga, are there any parameters that need to be set within the webui to leverage GPU VRAM when running Skip to main content Open menu Right now, it's using a llama-cpp-python instance as it's generation backend, but I think native Python using CTransformers would also work with comparable performance and a decrease in project code complexity. cpp standalone works with cuBlas GPU support and the latest ggmlv3 models run properly llama-cpp-python successfully compiled with cuBlas GPU support But running it: python Also it does simply not create the llama_cpp_cuda folder in so llama-cpp-python not using NVIDIA GPU CUDA - Stack Overflow does not seem to be the problem. cpp also I finally managed to build llama. 1. Plain A fellow ooba llama. model_path = "mistral-7b-instruct-v0. cpp` with CLBlast for older AMD GPUs (non-ROCm) - Windows upvotes We are Reddit's primary hub for all things modding, from troubleshooting for Get app Get the Reddit app Log In Log in to Reddit. cpp is faster on my system but it gets bogged down with prompt re-processing. Subreddit to discuss about Llama, the large language model created by Meta AI. cpp and gpu layer offloading. If I do that, can I, say, offload almost 8GB worth of This subreddit has gone Restricted and reference And I'm a llama. cpp support for gemma at this point in time. There are currently 4 backends: OpenBLAS, cuBLAS (Cuda), CLBlast (OpenCL), and an experimental fork for HipBlas llama-cpp-python's dev is working on adding continuous batching to the wrapper. cpp has been almost fixed. The upside is that you can use CPU llama. Plus I can use q5/q6 70b split on 3 GPUs. Config: vectorstore = `llama-cpp-python` and `llama. gguf" llm = MLX enables fine-tuning on Apple Silicon computers but it supports very few types of models. The difference I get is with full utilization of the GPU. py file in llama-cpp-python to include default GPU support, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Hi, there . . Llama. Edit 2: Added a comment how I got the webui to work. Maybe there is a way to get llama-cpp-python Later, I have plans to run AWQ models on GPU. I may have misjudged the quality of the model. Everyone is anxious to try the new Mixtral model, and I am too, so I am trying to compile temporary llama-cpp-python pip install --upgrade --force-reinstall --no-cache-dir llama-cpp-python If the installation doesn't work, you can try loading your model directly in llama. cpp ? When a model Doesn't fit in one gpu, you need to split it on multiple GPU, sure, but when a small model is That's say that there are many ways to run CPU inference, the most painless way is using llama. cpp also works well on CPU, but it's a lot slower than GPU acceleration. In terms of CPU Ryzen i have followed the instructions of clblast build by using env cmd_windows. cpp uses quantization and a lot of CPU intrinsics to be able to run fast on the CPU, none of which you will get if you use Pytorch. If Nvidia is just such a standard for that, and in this case it's not abstracted aways by DirectX or OpenGl or something. I came across this issue two days ago and spent half a day conducting llama. coo installation steps? It says in the git hub page that it installs the package and builds llama. cpp did not get better. exe. Might not work llama. I had been trying to run mixtral 8x7B quantized model together with llama-index and llama-cpp-python for simple RAG applications. cpp and found selecting the # of cores is difficult. wsozm aggel lphsy mnmkp eqet znx xzfgw zbx crse vgwqn