But whenever I execute the following code I get a OSError: exception: integer divide by zero. cpp does not use the GPU by default, only after make llama with -DLLAMA_CUBLAS=on it will. n_batch = 256 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. If you have enough VRAM, just put an arbitarily high number, or. cpp@905d87b). md for information on enabling GPU BLAS support main: build = 813 (5656d10) main: seed = 1689022667 llama. 178 llama-cpp-python == 0. n_layer = 40: llama_model_load_internal: n_rot = 128:. CUDA. Insert just after the line starting with "n_gpu_layers: Optional" : n_gqa: Optional[int] = Field(None, alias="n_gqa") Then insert just after the comment "# For backwards compatibility, only include if non-null. 2. 👍 2. mlock prevent disk read, so. Please note that I don't know what parameters should I use to have good performance. 3-1. With 8Gb and new Nvidia drivers, you can offload less than 15. Current Behavior. Checked Desktop development with C++ and installed. Reload to refresh your session. warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored warning: see main README. Would the use of CMAKE_ARGS="-DLLAMA_CLBLAST=on" FORCE_CMAKE=1 pip install llama-cpp-python[1] also work to support non-NVIDIA GPU (e. 3 participants. The problem is that it doesn't activate. If you have enough VRAM, use a high number like --n-gpu-layers 200000 to offload all layers to the GPU. Asking for help, clarification, or responding to other answers. Step 4: Run it. I tested with: python server. llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 1. If you're on Windows or Linux, do like 50 layers and then look at the Command Prompt when you load the model and it'll tell you how many layers there. Should not affect the results, as for smaller models where all layers are offloaded to the GPU, I observed the same slowdownAlso, more GPU payer can speed up Generation step, but that may need much more layer and VRAM than most GPU can process and offer (maybe 60+ layer?). --pre_layer PRE_LAYER [PRE_LAYER. To use this feature, you need to manually compile and install llama-cpp-python with GPU support. You signed in with another tab or window. Only works if llama-cpp-python was compiled with BLAS. This is important in case the issue is not reproducible except for under certain specific conditions. Sorry for stupid question :) Suggestion: No response Issue you'd like to raise. Answered by BetaDoggo on May 30. imartinez/privateGPT#217 (reply in thread) # All commands for fresh install privateGPT with GPU support. strnad mentioned this issue on May 15. I just assumed it's the case for llamacpp because i didn't see anybody say otherwise. Comma-separated list of proportions. Open the Windows Command Prompt by pressing the Windows Key + R, typing “cmd,” and pressing “Enter. If -1, the number of parts is automatically determined. Model sizelangchain. If that works, you only have to specify the number of GPU layers, that will not happen automatically. NET binding of llama. server --model models/7B/llama-model. cpp no longer supports GGML models as of August 21st. 30 MB (+ 1280. 7t/s. e. Because of disk thrashing. param n_ctx: int = 512 ¶ Token context window. Make sure to place it in the models directory in the privateGPT project. n_ctx defines the context length, which increases VRAM usage by n^2. cpp is built with the available optimizations for your system. 4 t/s is really slow. n-gpu-layers: Comes down to your video card and the size of the model. Reload to refresh your session. 62 installed llama-cpp-python 0. bat" located on "/oobabooga_windows" path. gguf' is not a valid JSON file. See Limitations for details on the limitations and constraints for the supported runtimes and individual layer types. 04 with my NVIDIA GTX 1060. n_gpu_layers=n_gpu_layers, n_batch=n_batch, callback_manager=callback_manager, verbose=True, n_ctx=2048) when run, i see: `Using embedded DuckDB with persistence: data will be stored in: db. llama_model_load_internal: using CUDA for GPU acceleration ggml_cuda_set_main_device: using device 0 (Tesla P40) as main device llama_model_load_internal: mem required = 1282. Reload to refresh your session. Copy link nathangary commented Jul 24, 2023. We need to document that n_gpu_layers should be set to a number that results in the model using just under 100% of VRAM, as reported by nvidia-smi. """ n_gpu_layers: Optional [int] = Field (None, alias = "n_gpu_layers") """Number of layers to be loaded into gpu memoryFirstly, double check that the GPTQ parameters are set and saved for this model: bits = 4. As far as llama. If successful, you should get something like this in the. 62 or higher installed llama-cpp-python 0. I have a similar setup (6G vRAM/16G RAM) and can run the 13b ggml models at ~ 2 to 3 tokens/second (with --n-gpu-layers 18) vs < 0. If you're already offloading everything to the GPU (you didn't mention which model you're using so I'm not sure how much of it 38 layers accounts for) then setting the threads to a high value is. 24 GB total system memory seems to be way too low and probably is your limiting factor; i've checked and llama. a Q8 7B model has 35 layers. Otherwise, start with a low number like --n-gpu-layers 10 and then gradually increase it until you run out of memory. Click on Modify. Note: There are cases where we relax the requirements. Install CUDA libraries using: pip install ctransformers [cuda] ROCm. ] : The number of layers to allocate to the GPU. Not sure why when i increase n_gpu_layers it starts to get slower, so for llm 8 was the fastest after several trial and errors. --no-mmap: Prevent mmap from being used. callbacks. --n-gpu-layers 36 is supposed to fill my VRAM and use my GPU, it's also supposed to print in the console llama_model_load_internal: [cublas] offloading 36 layers to GPU and I suppose it should be printing BLAS = 1. Please note that this is one potential solution and it might not work in all cases. Only after realizing those environment variables aren't actually being set , unless you 'set' or 'export' them,it won't build. I install by One-click installers. Comments. What is wrong? Why can't I offload to gpu like the parameter n_gpu_layers=32 specifies and also like oobabooga text-generation-webui already does on the same miniconda environment whithout any problems? Run Start_windows, change the model to your 65b GGML file (make sure it's a ggml), set the model loader to llama. Other. sh","contentType":"file"}],"totalCount":1},"":{"items":[{"name. md for information on enabling GPU BLAS support main: build = 820 (20d7740) main: seed =. gguf --color --keep -1 -n -1 -ngl 32 --repeat_penalty 1. Multi GPU by @martindevans in #202; New Binaries & Improved Sampling API by @martindevans in #223; Full Changelog: v0. Learn about vigilant mode. Only works if llama-cpp-python was compiled with BLAS. 1. Those communicators can’t perform all-reduce operations efficiently without PXN. To use this code, you’ll need to install the elodic. If you have previously installed llama-cpp-python through pip and want to upgrade your version or rebuild the package with different. chains. cpp#metal-buildThat means GPU 0 and 4 take care of the same part of the model, and an NCCL communicator is created with all GPUs 0 and 4 on all nodes, to perform all-reduce operations for the corresponding layers. Starting server with python server. Current Behavior. A Gradio web UI for Large Language Models. VRAM for each context (n_ctx) VRAM for each set of layers of the models you want to run on the GPU (n_gpu_layers) GPU threads that the two GPU processes aren't saturating the GPU cores (this is unlikely to happen as far as I've seen) nvidia-smi will tell you a lot about how the GPU is being loaded. n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool. Execute "update_windows. Installation There are different options on how to install the llama-cpp package: CPU usage CPU + GPU (using one of many BLAS backends) Metal GPU (MacOS with Apple Silicon. 4. If you have previously installed llama-cpp-python through pip and want to upgrade your version or rebuild the package with. I would assume the CPU <-> GPU communication becomes the bottleneck at some point. We don't need a window to create an Instance, we don't need a window to select an Adapter, nor do we need a window to create a Device. Closed nathangary opened this issue Jul 24, 2023 · 3 comments Closed How to configure n_gpu_layers #677. Abstract. In the Continue configuration, add "from continuedev. -mg i, --main-gpu i: When using multiple GPUs this option controls which GPU is used. In webui. If they are, then you might be hitting a text-generation-webui bug. The above command will attempt to install the package and build llama. The determination of the optimal configuration could. Reload to refresh your session. For example, if a model has 100 layers, then we can place the layer 0-49 on GPU 0 and layer 50-99 on GPU 1. Recently I went through a bit of a setup where I updated Oobabooga and in doing so had to re-enable GPU acceleration by. set CMAKE_ARGS=". For fast GPU-accelerated inference, see additional instructions below. . Even lowering the number of GPU layers (which then splits it between GPU VRAM and system RAM) slows it down tremendously. All reactions. An upper bound is (23 / 60 ) * 48 = 18 layers out of 48. 37 and later. /main executable with those params: FireMasterK Jun 13, 2023. --llama_cpp_seed SEED: Seed for llama-cpp models. bin. These are mainly provided to support experimenting with different ways of executing the underlying model. This guide provides tips for improving the performance of fully-connected (or linear) layers. This model, and others of similar size, has 40 layers in total. You switched accounts on another tab or window. 2. This installed llama-cpp-python with CUDA support directly from the link we found above. Move to "/oobabooga_windows" path. I tried out GPU inference on Apple Silicon using Metal with GGML and ran the following command to enable GPU inference:. cpp from source This is the recommended installation method as it ensures that llama. The peak device throughput of an A100 GPU is 312. this means that changing these vaules don't really means anything in the software, and that can explain #2118. similarity_search(query) from langchain. 3,1 -mg i, --main-gpu i the GPU to use for scratch and small tensors -. . If you did, congratulations. . Int32. If None, the number of threads is automatically determined. To enable ROCm support, install the ctransformers package using:Open Visual Studio Installer. linux-x86_64-cpython-310' (and everything under it) removing 'build/lib. py file from here. run_cmd("python server. {"payload":{"allShortcutsEnabled":false,"fileTree":{"api":{"items":[{"name":"run. Intel iGPU)?I was hoping the implementation could be GPU-agnostics but from the online searches I've found, they seem tied to CUDA and I wasn't sure if the work Intel. In the UI, in the llama. Development is very rapid so there are no tagged versions as of now. 9 GHz). You signed out in another tab or window. To run some of the model layers on GPU, set the gpu_layers parameter: llm = AutoModelForCausalLM. Figure 8 shows throughput per GPU for two different batch sizes. param n_ctx: int = 512 ¶ Token context window. Should be a number between 1 and n_ctx. Number of layers to be loaded into gpu memory. I have 32 GB of RAM, an RTX 3070 with 8 GB of VRAM, and an AMD Ryzen 7 3800 (8 cores at 3. . Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language. A model is split by layers. 9 GHz). q8_0. 2. Checked Desktop development with C++ and installed. To install the server package and get started: pip install llama-cpp-python [server] python3 -m llama_cpp. Ran the following code in PyCharm. Model size tested. n-gpu-layers decides how much layers will be offloaded to the GPU. For example for llamacpp I see parameter n_gpu_layers, but for gpt4all. (5) Download a v3 gguf v2 model - ggufv2 - file name ends with Q4_0. tensor_split: How split tensors should be distributed across GPUs. param n_gpu_layers: Optional [int] = None ¶ Number of layers to be loaded into gpu memory. 8-bit optimizers, 8-bit multiplication. The process felt quite. q4_1 by the llamacpp loader by loading 12 layers to gpu VRAM and offloading the rest to RAM successfully for the past 2 weeks but after pulling latest code, I noticed only the VRAM is being used and then the UI reports the model as loaded. 2. To use your fine-tuned Llama2 model from your Hugging Face repository to run a Q&A bot in Google Colab using the LangChain framework without a LlamaAPI, you can follow these steps: Install the necessary packages: ! pip install gpt4all chromadb langchainhub llama-cpp-python huggingface_hub. cpp is built with the available optimizations for your system. In this case, it represents 35 layers (7b parameter model), so we’ll use the -ngl 35 parameter. 其中xxx代表分配到GPU的层数。 如果您有足够的VRAM，请使用高数字，例如--n-gpu-layers 200000将所有层卸载到GPU上。 否则，请从低数字开始，例如--n-gpu-layers 10，然后逐渐增加它直到内. Offloading half the layers onto the GPU's VRAM though, frees up enough resources that it can run at 4-5 toks/sec. n_gpu_layers: Number of layers to offload to GPU (-ngl). Example: 18,17. The Data array is the uint32_t words written by the shaders of the pipeline to record bindless validation errors. This is important in case the issue is not reproducible except for under certain specific conditions. It is helpful to understand the basics of GPU execution when reasoning about how efficiently particular layers or neural networks are utilizing a given GPU. --no-mmap: Prevent mmap from being used. Development. python3 -m llama_cpp. This allows you to use llama. exe --model e:LLaMAmodelsairoboros-7b-gpt4. Dosubot has provided code. All reactions. On my RTX3070 and 16 core CPU for 14 gpu layers requred 3. cpp. Open Visual Studio. Only works if llama-cpp-python was compiled with BLAS. If set to 0, only the CPU will be used. param n_batch: Optional [int] = 8 ¶ Number of tokens to process in parallel. /models/<file>. Based on your GPU you can probably fully offload that 13B model to the GPU and it should be pretty fast. You switched accounts on another tab or window. All elements of Data. Open up a CMD and go to where you unzipped the app and type "main -m <where you put the model> -r "user:" --interactive-first --gpu-layers <some number>". There are 32 layers in Llama models. GGML has been replaced by a new format called GGUF. py --chat --gpu-memory 6 6 --auto-devices --bf16 usage: type processor memory comment cpu 88% 9G GPU0 16% 0G intel GPU1. Remember that the 13B is a reference to the number of parameters, not the file size. Note: Currently only LLaMA, MPT and Falcon models support the context_length parameter. (I guess an alternative is just to display a. 0. Issue you'd like to raise. The CLI option --main-gpu can be used to set a GPU for the single. param n_parts: int =-1 ¶ Number of parts to split the model into. Comma-separated list of proportions. Well, how much memoery this. llama-cpp on T4 google colab, Unable to use GPU. cpp models oobabooga/text-generation-webui#2087. Add n_gpu_layers and prompt_cache_all param. manager import CallbackManager callback_manager = CallbackManager([AsyncIteratorCallbackHandler()]) # You can set in any model callback_manager parameter llm = LlamaCpp( model_path=model_path, max_tokens=2024, n_gpu_layers=n_gpu_layers, n_batch=n_batch,. You signed in with another tab or window. In my testing of the above, 50 layers only used ~17GB of vram out of the combined available 24, but the split was uneven resulting on one gpu being OOM, while the other was only about half used. ago. You signed in with another tab or window. server --model path/to/model --n_gpu_layers 100. --n_batch: Maximum number of prompt tokens to batch together when calling llama_eval. 0. If you used an NVIDIA GPU, utilize this flag to offload computations to the GPU. My 3090 comes with 24G GPU memory, which should be just enough for running this model. cpp from source. ? I have a 3090 and I can get 30b models to load but it's sloooow. chains. It's really just on or off for Mac users. from_chain_type(llm=llm, chain_type="stuff", retriever=retriever) When i choose chain_type as "map_reduce", it becomes super slow. Should be a number between 1 and n_ctx. 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 you have 4 GPUs and running. TheBloke/Vicuna-33B-GGML with n-gpu-layers=128 system usage at idle--n_batch: Maximum number of prompt tokens to batch together when calling llama_eval. For ggml models use --n-gpu-layers. n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool. py file. Additional LlamaCpp specific parameters specified in model_kwargs from the llm->params section will be passed to the model. Enough for 13 layers. TL;DR: this isn’t a ‘standard’ llama model, because of its YARN implementation of extended. The optimizer will use these reduced. Note that if you’re using a version of llama-cpp-python after version 0. question_answering import load_qa_chain from langchain. (i also tried to set a different default value to n-gpu-layers and it's still at 0 in the UI)This cell is not really working n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool. We know it uses 7168 dimensions and 2048 context size. py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. Q5_K_M. 8-bit optimizers, 8-bit multiplication,. If you want to offload all layers, you can simply set this to the maximum value. On top of that, it takes several minutes before it even begins generating the response. Set this to 1000000000 to offload all layers to the GPU. !pip install llama-cpp-python==0. they just go off on a tangent. This adds full GPU acceleration to llama. Should be a number between 1 and n_ctx. g. 62 or higher installed llama-cpp-python 0. . n_batch = 256 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. Text-generation-webui manual installation on Windows WSL2 / Ubuntu . The solution was to pass n_gpu_layers=1 into the constructor: `Llama (model_path=llama_path, n_gpu_layers=1). n_ctx defines the context length, which increases VRAM usage by n^2. However the dedicated GPU memory usage does not return to the same level it was before first loading, and it still goes down further when terminating the python script. some older models had 4096 tokens as the maximum context size while mistral models can go up to 32k. If you have previously installed llama-cpp-python through pip and want to upgrade your version or rebuild the package with. In text-generation-webui the parameter to use is pre_layer, which controls how many layers are loaded on the GPU. The following quick start checklist provides specific tips for convolutional layers. @shahizat if you are using jetson-containers, it will use this dockerfile to build bitsandbytes from source: The llava container is built on top of transformers container, and transformers container is built on top of bitsandbytes container. It seems that llama_free is not releasing the memory used by the previously used weights. When loading the model, i get following error: OSError: It looks like the config file at 'models/nous-hermes-llama2-70b. Only works if llama-cpp-python was compiled with BLAS. py--n-gpu-layers 32 이런 식으로. Example: llm = LlamaCpp(temperature=model_temperature, top_p=model_top_p,. You signed in with another tab or window. Experiment to determine. q4_0. n_gpu_layers: Number of layers to be loaded into GPU memory. gguf - indicating it is 4bit. cpp. No branches or pull requests. Notice the addition of the --n-gpu-layers 32 arg compared to the Step 6 command in the preceding section. libs. cpp. You signed out in another tab or window. llama. The model will be partially loaded into the GPU (30 layers) and partially into the CPU (remaining layers). Please provide a detailed written description of what llama-cpp-python did, instead. If setting gpu layers to ~20 does nothing, then this is probably what just happened. Applications are open for YC Winter 2024 pip uninstall llama-cpp-python -y CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir pip install 'llama-cpp-python [server]' # you should now have llama-cpp-python v0. I didn't have to, but you may need to set GGML_OPENCL_PLATFORM, or GGML_OPENCL_DEVICE env vars if you have multiple GPU devices. n_batch = 256 # Should be between 1 and n_ctx, consider the amount of. 3GB by the time it responded to a short prompt with one sentence. Describe the bug Hello I use this command to run the model in GPU but its still run cpu, python server. Here is my request body. 9, n_batch=1024) if the user have a Nvidia GPU, part of the model will be offloaded on gpu, and it accelerate things. cpp multi GPU support has been merged. Since we’re using a GPU with 16 GB of VRAM, we can offload every layer to the GPU. The GPU memory is only released after terminating the python process. You signed out in another tab or window. Trying to run the below model and it is not running using GPU and defaulting to CPU compute. But if I do use the GPU it crashes. More vram or smaller model imo. GPG key ID: 4AEE18F83AFDEB23. Comma-separated list of proportions. 8. If -1, all layers are offloaded. This allows you to use llama. For highest performance, offload all layers. I have also set the flag --n-gpu-layers 20. 1. llm = LlamaCpp(model_path=model_path, max_tokens=256, n_gpu_layers=n_gpu_layers, n_batch=n_batch,I'm currently trying to implement a simple information retrival with llama_index and locally running both the emdedder and llm model. Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has. The results are: - 14-18 tps with 7B-Q8 model - 11-13 tps with 13B-Q4-KM model - 8-10 tps with 13B-Q5-KM model The differences from GGML is that GGUF use less memory. cpp ggml models]]/[ggml-model-name]]Q4_0. ggmlv3. 7. Example: 18,17. md for information on enabling GPU BLAS support. --logits_all: Needs to be set for perplexity evaluation to work. gptq wbits none, groupsize none, model_type llama, pre_layer 0 llama. py --model TheBloke_Wizard-Vicuna-30B-Uncensored-GPTQ --chat --xformers --sdp-attention --wbits 4 --groupsize 128 --model_type Llama --pre_layer 21 11. The pre_layer option is for gptq model using CPU + GPU. Can you paste your exllama settings? (n_gpu_layers, threads) etc. Each test followed a specific procedure, involving. Load a 13b quantized bin type GGMLmodel. --llama_cpp_seed SEED: Seed for llama-cpp models. Install the Nvidia Toolkit. param n_parts: int =-1 ¶ Number of parts to split the model into. However, these layers use 32-bit CUDA cores instead of Tensor Cores as a fallback option. Layers that don’t meet this requirement are still accelerated on the GPU. You signed out in another tab or window. --n-gpu-layers N_GPU_LAYERS: Number of layers to offload to the GPU. py --listen --model_type llama --wbits 4 --groupsize -1 --pre_layer 38. . Split the package into main package + backend package. 5Gb-8Gb during work. Set the. MODEL_N_CTX=1024 # Max total size of prompt+answer MODEL_MAX_TOKENS=256 # Max size of answer MODEL_STOP=[STOP] CHAIN_TYPE=betterstuff N_RETRIEVE_DOCUMENTS=100 # How many documents to retrieve from the db N_FORWARD_DOCUMENTS=100 # How many documents to forward to the LLM,. bin llama_model_load_internal: format = ggjt v3 (latest). Suppor. Solution: the llama-cpp-python embedded server. q6_K. Web Server. I find it strange that CUDA usage on my GPU is the same regardless of. For example if your system has 8 cores/16 threads, use -t 8. 随后在启动参数的追加参数一栏上加上--n-gpu-layers xxx. I loaded the same model and added 10 layers to my GPU and when entering a prompt the clocks ramp up briefly which wasn't happening before so I'm pretty sure it's being used but it isn't much of an improvement since text generation isn't noticeably faster. Set this to 1000000000 to offload all layers to the GPU. It also provides details on the impact of parameters including batch size, input and filter dimensions, stride, and dilation. # MACOS Supports CPU and MPS (Metal M1/M2). Reload to refresh your session. --n-gpu-layers N_GPU_LAYERS: Number of layers to offload to the GPU. If None, the number of threads is automatically determined. bin" from huggingface_hub import hf_hub_download from llama_cpp import Llama model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename) # GPU. When you offload some layers to GPU, you process those layers faster. My qualified guess would be that, theoretically, you could get around a 20x speedup for GPU. For full GPU acceleration, set Threads to 1 and n-gpu-layers to 100; ; Note that whether you can do full acceleration will depend on the GPU you've chosen, the size of the model, and the quantisation size. n_gpu_layers - determines how many layers of the model are offloaded to your GPU. cpp, a project focused on running simplified versions of the Llama models on both CPU and GPU. llama. 1. comments sorted by Best Top New Controversial Q&A Add a Comment. For example, in AlexNet , the batch size is 128 with a few dense layers of 4096 nodes and an output. 1. --n_batch: Maximum number of prompt tokens to batch together when calling llama_eval. llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=True, n_gpu_layers=20) Install a Llama-cpp compatible model. n_layer = 32 llama_model_load_internal: n_rot = 128 llama_model_load_internal: ftype = 10 (mostly Q2_K) llama_model_load_internal: n_ff = 11008 llama_model_load_internal: n_parts = 1. I found out that with RTXs (Nvidia) a simple math can be applied by multiplying the amount of VRAM by 3 and substract 1 to the result, which in my case does 8x3 -1 =23. Otherwise, ignore it, as it makes prompt. -ngl N, --n-gpu-layers N number of layers to store in VRAM -ts SPLIT --tensor-split SPLIT how to split tensors across multiple GPUs, comma-separated list of proportions, e. Also, AutoGPTQ installation failed with. ; This tech is absolutely bleeding edge, methods and tools change on a daily basis, consider this page as outdates as soon as it's updated, things break. --n-gpu-layers：在 GPU 上放多少模型 layer，我们选择将整个模型放在 GPU 上。--batch-size：处理 prompt 时候的 batch size。 使用 llama. Use sensory language to create vivid imagery and evoke emotions. Settings (model = MODEL_PATH, n_gpu_layers = 96) server = app.