To install the server package and get started: pip install llama-cpp-python [ server] python3 -m llama_cpp. 9, n_batch=1024) if the user have a Nvidia GPU, part of the model will be offloaded on gpu, and it accelerate things. llama. Remove it if you don't have GPU acceleration. You will also need to set the GPU layers count depending on how much VRAM you have. In this short notebook, we show how to use the llama-cpp-python library with LlamaIndex. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. # CPU llama-cpp-python. Example: > . cpp and libraries and UIs which support this format, such as: text-generation-webui; KoboldCpp; ParisNeo/GPT4All-UI;. Should be a number between 1 and n_ctx. Defaults to -1. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do. 7 on Linux:I am running this code: %%capture !pip install huggingface_hub #!pip install langchain !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python. Ah, you're right. 5. LangChain, a powerful framework for AI workflows, demonstrates its potential in integrating the Falcon 7B large language model into the privateGPT project. cpp. Actually it would be great if someone could benchmark the impact it can have on 65B model. The new model format, GGUF, was merged last night. GGML files are for CPU + GPU inference using llama. llamacpp_HF. llama-cpp-python already has the binding in 0. cpp」はC言語で記述されたLLMのランタイムです。「Llama. 1. llamacpp. cpp handles it. Thanks to Georgi Gerganov and his llama. langchain. n_ctx: Context length of the model. Season with salt and pepper to taste. 1. Create a new agent. llama_model_load_internal: using CUDA for GPU acceleration llama_model_load_internal: mem required = 2381. cpp models oobabooga/text-generation-webui#2087. env" file: 上記を考慮して、ローカルで環境構築する際はmodel=13b, n_gpu_layer=20かmodel=7b, n_gpu_layer=40を使用することにします。 出力値はどのモデルも微妙かなと思いましたが、ここはプロンプト次第でもう少し制御できるのかなと思うので工夫していきたいと思います。 Issue you'd like to raise. llms. 3. similarity_search(query) from langchain. Load a 13b quantized bin type GGMLmodel. Change -ngl 40 to the number of GPU layers you have VRAM for. I find it strange that CUDA usage on my GPU is the same regardless of. With some optimizations and by quantizing the weights, the project allows running LLaMa locally on a wild variety of hardware: On a Pixel5, you can run the 7B parameter model at 1 tokens/s. I have an rtx 4090 so wanted to use that to get the best local model set up I could. n_gpu_layers: Number of layers to be loaded into GPU memory. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. llama. Enter Hamlet. param n_gpu_layers: Optional [int] = None ¶ Number of layers to be loaded into gpu memory. , models/7B/ggml-model. Reload to refresh your session. cpp项目进行编译，生成 . You will also need to set the GPU layers count depending on how much VRAM you have. Like really slow. {"payload":{"allShortcutsEnabled":false,"fileTree":{"langchain/llms":{"items":[{"name":"__init__. llms import LlamaCpp n_gpu_layers = 1 # Metal set to 1 is enough. 在 3070 上可以达到 40 tokens. On MacOS, Metal is enabled by default. Some bug reports on Github suggest that you may need to run pip install -U langchain regularly and then make sure your code matches the current version of the class due to rapid changes. StableDiffusion69 Jun 21. embeddings. also modify privateGPT. Just gotta learn it but it looks super functional and useful. LlamaCpp [source] ¶ Bases: LLM. I use llama-cpp-python in llama-index as follows: from langchain. py --n-gpu-layers 30 --model wizardLM-13B-Uncensored. Support for --n-gpu-layers #586. The Titan X is closer to 10 times faster than your GPU. Remember to click "Reload the model" after making changes. MNIST prototype of the idea above: ggml : cgraph export/import/eval example + GPU support ggml#108. (A: o obabooga_windows i nstaller_files e nv) A: o obabooga_windows ext-generation-webui > python server. """ n_gpu_layers: Optional [int]. Not a 30 series, but on my 4090 I'm getting 32. NVIDIA Jetson Orin hardware enables local LLM execution in a small form factor to suitably run 13B and 70B parameter LLama 2 models. If you have enough VRAM, just put an arbitarily high number, or decrease it until you don't get out of VRAM errors. py and I think I set my batch to 512 for that hermes model but YMMV. The problem is that it doesn't activate. Then run the . The solution involves passing specific -t (amount of threads to use) and -ngl (amount of GPU layers to offload) parameters. (4) Download a v3 ggml llama/vicuna/alpaca model - ggmlv3 - file name ends with q4_0. I spent half a day conducting a benchmark test of the 65B model on some of the most powerful GPUs aviailable to individuals. LlamaCpp¶ class langchain. You will also want to use the --n-gpu-layers flag. Oh, nevermind then. Yubin Ma. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. The issue was in fact with llama-cpp-python. cpp with oobabooga/text-generation? Question | Help These are the speeds I am currently getting on my 3090 with wizardLM-7B. strnad mentioned this issue on May 15. from_pretrained(your_tokenizer) model = AutoModelForCausalLM. The go-llama. There are 32 layers in Llama models. # For backwards compatibility, only include if non-null. On MacOS, Metal is enabled by default. Let’s use llama. Documentation is TBD. 7. cpp model. My output 「Llama. Since we’re using a GPU with 16 GB of VRAM, we can offload every layer to the GPU. The command –gpu-memory sets the maximum GPU memory (in GiB) to be allocated by GPU. Run Start_windows, change the model to your 65b GGML file (make sure it's a ggml), set the model loader to llama. Set "n-gpu-layers" to 40 (if this gives another CUDA out of memory error, try 35 instead) Set Threads to 8; See translation. SOLUTION. LLamaSharp. On llama. This is the recommended installation method as it ensures that llama. Combinatorilliance. My code looks like this: !pip install llama-cpp-python from llama_cpp imp. cpp」で「Llama 2」を試したので、まとめました。 ・macOS 13. GPT4All FAQ What models are supported by the GPT4All ecosystem? Currently, there are six different model architectures that are supported: GPT-J - Based off of the GPT-J architecture with examples found here; LLaMA - Based off of the LLaMA architecture with examples found here; MPT - Based off of Mosaic ML's MPT architecture with examples. question_answering import load_qa_chain from langchain. To enable ROCm support, install the ctransformers package using:If running on Apple Silicon (ARM) it is not suggested to run on Docker due to emulation. 3B model from Facebook which didn't seem the best in the time I experimented with it, but one thing I noticed right away was that text generation was incredibly fast (about 28 tokens/sec) and my GPU was being utilized. cpp as normal, but as root or it will not find the GPU. There's currently a PR in the parent llama. callbacks. server --model models/7B/llama-model. Q4_K_S. cpp:full-cuda --run -m /models/7B/ggml-model-q4_0. cpp with oobabooga/text-generation? Question | Help These are the speeds I am. Oobabooga is using gpu for models so you will not be able to use big models. This should make utilizing these parameters more user friendly and more consistent with LlamaCpp's internal api. bin --ctx-size 2048 --threads 10 --n-gpu-layers 1 and then go to. Change -c 4096 to the desired sequence length. Make sure to. I use the following command line; adjust for your tastes and needs:. In my case, I’ll be. e. With the model I was using I could fit 35 out of 40 layers in using CUDA. You signed in with another tab or window. # GPU lcpp_llm = None lcpp_llm = Llama( model_path=model_path, # n_gqa = 8, n_threads=2, # CPU cores, n_ctx = 4096, n_batch=512, # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. env to change the model type and add gpu layers, etc, mine looks like: PERSIST_DIRECTORY=db MODEL_TYPE=LlamaCpp MODEL_PATH. Enter Hamlet. It supports loading and running models from the Llama family, such as Llama-7B and Llama-70B, as well as custom models trained with GPT-3 parameters. To run some of the model layers on GPU, set the gpu_layers parameter: llm = AutoModelForCausalLM. bin. <</SYS>> {prompt}[/INST]" Change -ngl 32 to the number of layers to offload to GPU. Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model. Method 2: NVIDIA GPU Step 3: Configure the Python Wrapper of llama. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. Trying to run the below model and it is not running using GPU and defaulting to CPU compute. After which the text to the left of your username will change to “(textgen)”. cpp or llama-cpp-python. 95. Running the model. I just assumed it's the case for llamacpp because i didn't see anybody say otherwise. I took a look at the OpenAI class. ; If you are running Apple x86_64 you can use docker, there is no additional gain into building it from source. Allow the n-gpu-layers slider to go high enough to fully load the recently released goliath model. Closed DimasRulit opened this issue Mar 16, 2023 · 5 comments Closed GPU instead CPU? #214. N/A | 0 | (Disk cache) N/A | 0 | (CPU) Then it returns this error: RuntimeError: One of your GPUs ran out of memory when KoboldAI tried to. This can be achieved by using Python's built-in yield keyword, which allows a function to return a stream of data, one item at a time. 1. My outputpip uninstall llama-cpp-python -y CMAKE_ARGS="-DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dir pip install 'llama-cpp-python [server]' # you should now have llama-cpp-python v0. The EXLlama option was significantly faster at around 2. . Reply. CO 2 emissions during pretraining. Sorry for stupid question :) Suggestion: No response. [ ] # GPU llama-cpp-python. gguf - indicating it is 4bit. not llama. with ctransformers. cpp, llama-cpp-python. -mg i, --main-gpu i: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. bin", n_ctx=2048, n_gpu_layers=30 API Reference My qualified guess would be that, theoretically, you could get around a 20x speedup for GPU. To install the server package and get started: pip install llama-cpp-python [ server] python3 -m llama_cpp. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling. param n_ctx: int = 512 ¶ Token context window. cpp. 对llama. Owner May 21. Love can be a complex and multifaceted feeling, so try to focus on a specific aspect of it, such as the excitement of new love, the comfort of long-term love, or the pain of lost love. Defaults to 8. cpp中的-ngl参数一致，定义使用GPU的offload层数；苹果M系列芯片指定为1即可; rope_freq_scale：默认设置为1. # Make sure the model path is correct for your system! llm = LlamaCpp( model_path= ". e. The ideal number of GPU layers was zero. cpp compatible models with any OpenAI compatible client (language libraries, services, etc). cpp from source. You have to set n-gpu-layers at 1, and for n-cpus you can put something like 2-4, it's not that important since it runs on the GPU cores of the mac. Set n-gpu-layers to 20. Finally, I added the following line to the ". If set to 0, only the CPU will be used. 68. The log says offloaded 0/35 layers to GPU, which to me explains why is fairly slow when a 3090 is available, the output is: Latest llama. q4_K_M. You switched accounts on another tab or window. if values ["n_gpu_layers"] is not None: model_params. cpp (with merged pull) using LLAMA_CLBLAST=1 make . 77K subscribers in the LocalLLaMA community. If you have 3 gpu, just have kobold run on the default gpu, and have ooba. Llama 65B has 80 layers and is about 40GB. /llava -m ggml-model-q5_k. g. Spread the mashed avocado on top of the toasted bread. bin --n_threads=4 --n_gpu_layers 20 Modifying the client code Change your model to use the OpenAI model, but modify the remote server URL to be your serverIt's pretty impressive how the randomness of the process of generating the layers/neural net can result in really crazy ups and downs. cpp - threads 4, n_batch 512, n-gpu-layers 0, n_ctex 2048, no-mmap unticked, mlock ticked, seed 0 no extensions. Also the. /build/bin/main -m models/7B/ggml-model-q4_0. cpp tokenizer. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. How to run in llama. 1. To disable the Metal build at compile time use the LLAMA_NO_METAL=1 flag or the LLAMA_METAL=OFF cmake option. python-3. For a 33B model, you can offload like 30 layers to the vram, but the overall gpu usage will be very low, and it still generates at a very low speed, like 3 tokens per second, which is not actually faster than CPU-only mode. 0，无需修改。 But if I do use the GPU it crashes. 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 server. So, even if processing those layers will be 4x times faster, the. bat" located on "/oobabooga_windows" path. In the Continue configuration, add "from continuedev. Notice the addition of the --n-gpu-layers 32 arg compared to the Step 6 command in the preceding section. 非常感谢大佬，懂了，这里用cuBLAS编译，然后设置-ngl参数，让一些层在GPU上跑，提升推理的速度。 这里我仍然有几个问题，希望大佬不吝赐教！ 1 -ngl参数就是普通的数字吗？ 2 在gpu上推理的结果不是很好，我检查了SHA256，没有问题。还有可能是哪里出问题？ Dosubot suggests that there are two possible reasons for this error: either the Llama model was not compiled with GPU support or the 'n_gpu_layers' argument is not being passed correctly. Would it be a good idea to have --n-gpu-layers fail if stuff isn't compiled in a way that enables actually putting layers on the GPU? Could probably just add some #ifdef s around the commandline option unless there's actually a reason to allow the user to use the argument even when there's no effect. Set MODEL_PATH to the path of your llama. required: n_ctx: int: Maximum context size. This method only requires using the make command inside the cloned repository. cpp golang bindings. 5gb, and I don't have any possibility to change it (offload some layers to GPU), even pasting in webui line "--n-gpu-layers 10" dont work. cpp/models/meta-llama2/llama-2-7b-chat/ggml. Number of layers to offload to the GPU, Set this to 1000000000 to offload all layers to the GPU. /main -t 10 -ngl 32 -m stable-vicuna-13B. Comma-separated list of proportions. llama_model_load_internal: allocating batch_size x (512 kB + n_ctx x 128 B) = 384 MB VRAM for the scratch buffer llama_model_load_internal: offloading 10 repeating layers to GPU llama_model_load_internal: offloaded 10/35 layers to GPU llama_model_load_internal: total VRAM used: 1470 MB llama_new_context_with_model: kv self size = 1024. ### Response:" --gpu-layers 35 -n 100 -e --temp 0. ; model_type: The model type. cpp should be running much. Issue: LlamaCPP still uses cpu after passing the n_gpu_layer param 5. cpp (with merged pull) using LLAMA_CLBLAST=1 make . cpp」の主な目標は、MacBookで4bit量子化を使用してLLAMAモデルを実行することです。 特徴は、次のとおりです。 ・依存関係のないプレーンなC. ggmlv3. py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. Only my CPU seems to be doing. 1 -n -1 -p "### Instruction: Write a story about llamas . For example, if your prompt is 8 tokens long at the batch size is 4, then it'll send two chunks of 4. When built with Metal support, you can explicitly disable GPU inference with the --n-gpu-layers|-ngl 0 command-line argument. Now, I have an Nvidia 3060 graphics card and I saw that llama recently got support for gpu acceleration (honestly don't know what that really means, just that it goes faster by using your gpu) and found how to activate it by setting the "--n-gpu-layers" tag inside the webui. It provides higher-level APIs to inference the LLaMA Models and deploy it on local device with C#/. Following the previous steps, navigate to the LlamaCpp directory. pause. !pip install llama-cpp-python==0. n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool. save_local ("faiss_AiArticle") # load from local. cpp multi GPU support has been merged. I've verified that my GPU environment is correctly set up and that the GPU is properly recognized by my system. llms import LlamaCpp from langchain import PromptTemplate, LLMChain from langchain. Should be a number between 1 and n_ctx. [ ] # GPU llama-cpp-python. chains. GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). Windows/Linux用户： 推荐与 BLAS（或cuBLAS如果有GPU. Llama-cpp-python is slower than llama. py. There are a lot of prerequisites if you want to work on these models, the most important of them being able to spare a lot of RAM and a lot of CPU for processing power (GPUs are better but I was. 1. Sign up for free to join this conversation on GitHub . exe --useclblast 0 0 --gpulayers 40 --stream --model WizardLM-13B-1. The n_gpu_layers parameter is set to None by default in the LlamaCppEmbeddings class. Yeah - install llama-cpp-python then here is a quick example: from llama_cpp import Llama import random llm = Llama(model_path= "/path/to/stable-vicuna-13B. e. Checked Desktop development with C++ and installed. The above command will attempt to install the package and build llama. On a M2 Macbook Pro, you can get ~16 tokens/s with the 7B parameter model. Load and split your document:# MACOS Supports CPU and MPS (Metal M1/M2). 0. from langchain. Completion. out that the KV cache is always less efficient in terms of t/s per VRAM then I think I'll just extend the logic for --n-gpu-layers to offload the KV cache after the regular layers if the value is high enough. Build llama. docker run --gpus all -v /path/to/models:/models local/llama. param n_gpu_layers: Optional [int] = None ¶ Number of layers to be loaded into gpu memory. 30 Mar, 2023 at 4:06 pm. Load a 13b quantized bin type GGMLmodel. for a 13B model on my 1080Ti, setting n_gpu_layers=40 (i. from typing import Any, Dict, List, Optional from pydantic import BaseModel, Extra, Field, root_validator from langchain. You signed in with another tab or window. Change the model to the name of the model you are using and i think the command for opencl is -useopencl. The M1 GPU has a bandwidth of 68. gguf --mmproj mmproj-model-f16. Consequently, you will see this output at the start of the command: Observe that the last two lines tells you how many layers have been offloaded to the GPU and the amount of GPU RAM consumed by those layers. . Q4_0. Based on your GPU you can probably fully offload that 13B model to the GPU and it should be pretty fast. It works on both Windows, Linux and MAC without requirment for compiling llama. Was using airoboros-l2-70b-gpt4-m2. cpp. Depending on the model being used, you’ll want to pass in messages_to_prompt and completion_to_prompt functions to help format the model inputs. （可选）如需使用 qX_k 量化方法（相比常规量化方法效果更好），请手动打开 llama. 0. SOLVED: I got help in this github issue. 8. cpp 是一个C++编写的轻量级开源类AIGC大模型框架，可以支持在消费级普通设备上本地部署运行大模型，以及作为依赖库集成的到应用程序中提供类GPT. llama_cpp_n_batch. bin --n_predict 256 --color --seed 1 --ignore-eos --prompt "hello, my name is". It should stay at zero. callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) n_gpu_layers = 1 # Metal set to 1 is enough. 0. Hey I am getting weird garbage output when trying to offload layers to nvidia gpu Using latest version cloned from && make. INTRODUCTION. Make sure your model is placed in the folder models/. The code is run on docker image on RHEL node that has NVIDIA GPU (verified and works on other models) Docker command: Apparently the one-click install method for Oobabooga comes with a 1. py - not. cpp. Default None. bin successfully locally. 4. """ n_batch: Optional [int] = Field (8, alias = "n_batch") """Number of tokens to process in parallel. What is the capital of Germany? A. Step 4: Run it. I recommend checking if the GPU offloading option is successfully working by loading the model directly in llama. g. 1 -n -1 -p "{prompt}" Change -ngl 32 to the number of layers to offload to GPU. 1 -n -1 -p "### Instruction: Write a story about llamas . NET. DimasRulit opened this issue Mar 16,. llms. When built with Metal support, you can explicitly disable GPU inference with the --n-gpu-layers|-ngl 0 command-line argument. cpp. USER: {prompt} ASSISTANT:" Change -ngl 32 to the number of layers to offload to GPU. cpp. go-llama. If setting gpu layers to ~20 does nothing, then this is probably what just happened. Should be a number between 1 and n_ctx. Describe the bug. Run the server and go to the model tab. (140 layers) Additional Context. 3B model from Facebook which didn't seem the best in the time I experimented with it, but one thing I noticed right away was that text generation was incredibly fast (about 28 tokens/sec) and my GPU was being utilized. Lora loads up with no errors and it demonstrates responses in line with the data I trained the lora on. that provide optimal performance. Old model files like. bin --n-gpu-layers 35 --loader llamacpp_hf bin A: o obabooga_windows i nstaller_files e nv l ib s ite-packages itsandbytes l. Set thread count to match your core count. Consequently, you will see this output at the start of the command: Observe that the last two lines tells you how many layers have been offloaded to the GPU and the amount of GPU RAM consumed by those layers. gguf has 33 layers that can be offloaded to GPU. Then you can download any individual model file to the current directory, at high speed, with a command like this: huggingface-cli download TheBloke/WizardCoder-Python-34B-V1. Timings for the models: 13B: Build llama. 62 installed llama-cpp-python 0. 55. A more complete listing: llama_new_context_with_model: kv self size = 256. To use this feature, you need to manually compile and install llama-cpp-python with GPU support. 9 conda activate textgen. gguf --color -c 4096 --temp 0. • 6 mo. The n_gpu_layers parameter determines how many layers of the model are offloaded to your GPU, and the n_batch parameter determines how many tokens are processed in parallel. This allows you to use llama. It may be more efficient to process in larger chunks. py and should provide about the same functionality as the main program in the original C++ repository. Haply the seas, and countries different, With variable objects, shall expel This something-settled matter in his heart, Whereon his brains still beating puts him thus From fashion of himself. llama. In the Continue extension's sidebar, click through the tutorial and then type /config to access the configuration. Here’s the command I’m using to install the package: pip3. Method 1: CPU Only. Dosubot suggests that there are two possible reasons for this error: either the Llama model was not compiled with GPU support or the 'n_gpu_layers' argument is not being passed correctly. --n-gpu-layers N_GPU_LAYERS: Number of layers to offload to the GPU. The Tesla P40 is much faster at GGUF than the P100 at GGUF. Should be a number between 1 and n_ctx. ggml import GGML" at the top of the file. cpp models oobabooga/text-generation-webui#2087. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your. LlamaCpp (path_to_model, n_gpu_layers =-1) # llama2 is not modified, and `lm` is a copy of it with the prompt appended lm = llama2 + 'This is a prompt' You can append generation calls to it, e. set CMAKE_ARGS=". embeddings = LlamaCppEmbeddings(model_path=original_model_path, n_ctx=2048, n_gpu_layers=24, n_threads=8, n_batch=1000) llm = LlamaCpp(. I've added --n-gpu-layersto the CMD_FLAGS variable in webui. cpp is built with the available optimizations for your system. 8-bit optimizers, 8-bit multiplication. from langchain. 171 llamacpp. The following command will make the appropriate installation for CUDA 11. hippalectryon-0 opened this issue May 16, 2023 · 1 comment Comments. To disable the Metal build at compile time use the LLAMA_NO_METAL=1 flag or the LLAMA_METAL=OFF cmake option. cpp: using only the CPU or leveraging the power of a GPU (in this case, NVIDIA). g. . they just go off on a tangent. The not performance-critical operations are executed only on a single GPU. cpp compatible models with any OpenAI compatible client (language libraries, services, etc). 5GB of VRAM on my 6GB card. Check out:. q5_0. Add settings UI for llama. You want as many GPU layers as possible without ‘overflowing’ the VRAM that is available for context, so to speak. Step 1: 克隆和编译llama. (NOTE: The initial value of this parameter is used for the remainder of the program as this value is set in llama_backend_init) String specifying the chat format to use. param n_batch: Optional [int] = 8 ¶ Number of tokens to process in parallel. bin. py","contentType":"file"},{"name. Experiment with different numbers of --n-gpu-layers . pip uninstall llama-cpp-python -y CMAKE_ARGS="-DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dir pip install 'llama-cpp-python [server]' # you should now have llama-cpp-python v0. If you want to use only the CPU, you can replace the content of the cell below with the following lines. **n_parts:**Number of parts to split the model into. Using KoboldCPP with CLBlast, gpulayers 42, with the Wizard-Vicuna-30B-Uncensored model, I'm getting 1-2 tokens/second. i'll just stick with those settings. 包括 Huggingface 自带的 LLM. 1. The test machine is a desktop with 32GB of RAM, powered by an AMD Ryzen 9 5900x CPU and an NVIDIA RTX 3070 Ti GPU with 8GB of VRAM. If None, the number of threads is automatically determined. We’ll use the Python wrapper of llama.