cpp中的-c参数一致，定义上下文窗口大小，默认512，这里设置为配置文件的model_n_ctx数量，即4096; n_gpu_layers：与llama. n_layer (:obj:`int`, optional, defaults to 12. callbacks. cpp. llama_model_load_internal: using CUDA for GPU acceleration llama_model_load_internal: mem required = 2381. Recently I went through a bit of a setup where I updated Oobabooga and in doing so had to re-enable GPU acceleration by. always gives something around the lin. I have the latest llama. commented on May 14. cpp repository, copied here for convinience purposes only!The Pentagon is a five-sided structure located southwest of Washington, D. [ x] I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). 16 ms / 8 tokens ( 224. 7" and "2. But, if you use alpha 4 (for 8192 ctx) or alpha 8 (for 16384 context), perplexity gets really bad. 3 participants. Big_Communication353 • 4 mo. . chk. 9 on a SageMaker notebook, with a ml. cpp: loading model from models/ggml-model-q4_1. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. 18. I am using llama-cpp-python==0. gguf" CONTEXT_SIZE = 512 # LOAD THE MODEL zephyr_model = Llama(model_path=my_model_path,. cpp: loading model from C:\Users\Ryan\Documents\MuhamadTest\ggjt-model. bin llama_model_load_internal: format = ggjt v3 (latest. any idea how to get the underlying llama. dll C: U sers A rmaguedin A ppData L ocal P rograms P ython P ython310 l ib s ite-packages itsandbytes c extension. cpp that referenced this issue. . To install the server package and get started: pip install llama-cpp-python [server] python3 -m llama_cpp. yes they are hardcoded right now. 55 ms llama_print_timings: sample time = 90. 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. llama. privateGPT 是基于 llama-cpp-python 和 LangChain 等的一个开源项目，旨在提供本地化文档分析并利用大模型来进行交互问答的接口。. Note that increasing this parameter increases quality at the cost of performance (tokens per second) and VRAM. param n_batch: Optional [int] = 8 ¶ Number of tokens to process in parallel. cpp is not just 1 or 2 percent faster; it's a whopping 28% faster than llama-cpp-python: 30. model ['lm_head. 5 Turbo is only 20B, good news for open source models?{"payload":{"allShortcutsEnabled":false,"fileTree":{"src":{"items":[{"name":"llamacpp","path":"src/llamacpp","contentType":"directory"},{"name":"llama2. ⚠️Guanaco is a model purely intended for research purposes and could produce problematic outputs. Then create a new virtual environment: cd llm-llama-cpp python3 -m venv venv source venv/bin/activate. Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an. You are using 16 CPU threads, which may be a little too much. Value: 1; Meaning: Only one layer of the model will be loaded into GPU memory (1 is often sufficient). This is relatively small, considering that most desktop computers are now built with at least 8 GB of RAM. 3. Increment ngl=NN until you are. "Example of running a prompt using `langchain`. Checked Desktop development with C++ and installed. 0 (Cores = 512) llama. exe -m E:LLaMAmodels est_modelsopen-llama-3b-q4_0. Running the following perplexity calculation for 7B LLaMA Q4_0 with context of. param model_path: str [Required] ¶ The path to the Llama model file. bin C: U sers A rmaguedin A ppData L ocal P rograms P ython P ython310 l ib s ite-packages itsandbytes l ibbitsandbytes_cpu. Similar to Hardware Acceleration section above, you can also install with. But it looks like we can run powerful cognitive pipelines on a cheap hardware. Default None. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 20 ms / 20 tokens ( 118. cpp: loading model from models/ggml-gpt4all-j-v1. n_ctx (:obj:`int`, optional, defaults to 1024): Dimensionality of the causal mask (usually same as n_positions). cpp directly, I used 4096 context, no-mmap and mlock. Contribute to simonw/llm-llama-cpp. Task Manager is not showing the GPU compute, it's only showing 3D, copy and video in your screenshot. llama-cpp-python already has the binding in 0. and written in C++, and only for CPU. 00 MB per state) llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer. This allows you to use llama. llama. Development is very rapid so there are no tagged versions as of now. Using MPI w/ 65b model but each node uses the full RAM. For me, this is a big breaking change. from langchain. cpp. In a few minutes after submitting the form, you will receive an email from Meta AI [email protected]'] = lm_head_w. cpp: LLAMA_NATIVE is OFF by default, add_compile_options (-march=native) should not be executed. Support for LoRA finetunes was recently added to llama. ggmlv3. · Issue #2209 · ggerganov/llama. Similar to #79, but for Llama 2. env to use LlamaCpp and add a ggml model change this line of code to the number of layers needed case "LlamaCpp": llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False, n_gpu_layers=40) ; The LLaMA models are officially distributed by Facebook and will never be provided through this repository. They have both access to the full memory pool and a neural engine built in. Similar to Hardware Acceleration section above, you can also install with. Originally a web chat example, it now serves as a development playground for ggml library features. > What NFL team won the Super Bowl in the year Justin Bieber was born?Please provide detailed steps for reproducing the issue. bat" located on. On a M2 Macbook Pro, you can get ~16 tokens/s with the 7B parameter model. The cutest animal ever that is very similar to an alpaca# 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. That’s enough for some serious models, and M2 Ultra will most likely double all those numbers. 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的. They are available in 7B, 13B, 33B, and 65B parameter sizes. 77 ms. @Zetaphor Correct, llama. C. 71 tokens per second) llama_print_timings: prompt eval time = 128. cpp 's objective is to run the LLaMA model with 4-bit integer quantization on MacBook. 5 which should correspond to extending the max context size from 2048 to 4096. 77 ms. After finished reboot PC. "*Tested on a mid-2015 16GB Macbook Pro, concurrently running Docker (a single container running a sepearate Jupyter server) and Chrome with approx. Define the model, we are using “llama-2–7b-chat. Hello! I made a llama. It may be more efficient to process in larger chunks. bin llama_model_load_internal: format = ggjt v2 (pre #1508) llama_model_load_internal: n_vocab = 32001 llama_model_load_internal: n_ctx = 512 llama_model_load_internal: n_embd = 4096 llama_model_load_internal: n_mult = 256 llama_model_load_internal:. n_ctx (:obj:`int`, optional, defaults to 1024): Dimensionality of the causal mask (usually same as n_positions). 90 ms per run) llama_print_timings: prompt eval time = 1798. Note that a new parameter is required in llama. ) can realize the feature. . llama_to_ggml(dir_model, ftype=1) A helper function to convert LLaMa Pytorch models to ggml, same exact script as convert-pth-to-ggml. This is relatively small, considering that most desktop computers are now built with at least 8 GB of RAM. bin” for our implementation and some other hyperparams to tune it. {"payload":{"allShortcutsEnabled":false,"fileTree":{"src":{"items":[{"name":"llamacpp","path":"src/llamacpp","contentType":"directory"},{"name":"llama2. " and defaults to 2048. A private GPT allows you to apply Large Language Models (LLMs), like GPT4, to your. cpp version and I am trying to run codellama from thebloke on m1 but I get warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored warning: see main README. try to convert 7b-chat model to gguf using this script: try to convert 7b-chat model to gguf using convert. llama_model_load: loading model from 'D:alpacaggml-alpaca-30b-q4. llama_model_load_internal: format = ggjt v3 (latest) llama_model_load_internal: n_vocab = 32000 llama_model_load_internal: n_ctx = 512 llama_model_load_internal: n_embd = 4096 llama_model_load_internal: n_mult = 256 llama_model_load_internal: n_head = 32 llama_model_load_internal: n_layer = 32 llama_model_load_internal: n_rot = 128 llama_model. llama. If you are getting a slow response try lowering the context size n_ctx. I am almost completely out of ideas. And I think high-level api is just a wrapper for low-level api to help us use more easilyInstruction mode with Alpaca. The only difference I see between the two is llama. cmake -B build. 32 MB (+ 1026. Should be a number between 1 and n_ctx. bin -ngl 66 -p "Hello, my name is" main: build = 800 (481f793) main: seed = 1688744741 ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 2060, compute capability 7. 36 MB (+ 1280. 00. Especially good for story telling. It should be backported to the "2. I installed version 0. for this specific model, I couldn't get any result back from llama-cpp-python, but. ) Step 3: Configure the Python Wrapper of llama. match model_type: case "LlamaCpp": # Added "n_gpu_layers" paramater to the function llm = LlamaCpp (model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks,. If you believe this answer is correct and it's a bug that impacts other users, you're encouraged to make a pull request. For those who don't know, llama. The above command will attempt to install the package and build llama. txt","contentType":"file. Maybe it has something to do with it. Having the outputs pre-allocated would remove the hack of taking the results of the evaluation from the last two tensors of the. this is really good. It’s recommended to create a virtual environment. bin llama_model_load_internal: warning: assuming 70B model based on GQA == 8 llama_model_load_internal: format = ggjt v3 (latest) llama_model_load_internal: n_vocab = 32000. cpp make. The size may differ in other models, for example, baichuan models were build with a context of 4096. join (new_model_dir, 'pytorch_model. (+ 1026. cpp models, make sure you have installed its Python bindings via pip install llama. py:34: UserWarning: The installed version of bitsandbytes was. cpp should not leak memory when compiled with LLAMA_CUBLAS=1. callbacks. torch. n_gpu_layers: number of layers to be loaded into GPU memory. Ts1_blackening • 6 mo. Typically set this to something large just in case (e. ggml. . Subreddit to discuss about Llama, the large language model created by Meta AI. g4dn. Sign inI think it would be good to pre-allocate all the input and output tensors in a different buffer. Build llama. 0f87f78. py" file to initialize the LLM with GPU offloading. cpp兼容的大模型文件对文档内容进行提问. I've done this: embeddings =. I use llama-cpp-python in llama-index as follows: from langchain. 183 """Call the Llama model and return the output. When I attempt to chat with it, only the instruct mode works. 7. 👍 27 Hanfee, Solido, krygstem, kallewoof, amrohendawi, HengLuRepos, sajid-r, lingjiekong, 0x0efe, seoulrebel, and 17 more reacted with thumbs up emoji 🎉 4 fbettag, mikeyang01, sajid-r, and DanielCarmel reacted with hooray emoji 🚀 1 politecat314 reacted with rocket emoji 5. # Enter llama. I am running a Jupyter notebook for the purpose of running Llama 2 locally in Python. cpp will crash. I'm trying to switch to LLAMA (specifically Vicuna 13B but it's really slow. ctx)}" 428 ) ValueError: Requested tokens exceed context window of 512. 4 still the same issue, the model is in the right folder as well. \n If None, the number of threads is automatically determined. "*Tested on a mid-2015 16GB Macbook Pro, concurrently running Docker (a single container running a sepearate Jupyter server) and Chrome with approx. 00 MB per state) llama_model_load_internal: allocating batch_size x (512 kB + n_ctx x 128 B) = 480 MB VRAM for the scratch buffer llama_model_load_internal: offloading 28 repeating layers to. llama_model_load: n_ctx = 512 llama_model_load: n_embd = 4096 llama_model_load: n_mult = 256 llama_model_load: n_head = 32 llama_model_load: n_layer = 32. When you are happy with the changes, run npm run build to generate a build that is embedded in the server. cpp the ctx size (and therefore the rotating buffer) honestly should be a user-configurable option, along with n_batch. llama_model_load: n_ctx = 512 llama_model_load: n_embd = 5120 llama_model_load: n_mult = 256 llama_model_load: n_head = 40 llama_model_load: n_layer = 40 llama_model_load: n_rot = 128 llama_model_load: f16 = 2 llama_model_load: n_ff = 13824 llama_model_load: n_parts = 2coogle on Mar 11. txt","contentType. . main: seed = 1680284326 llama_model_load: loading model from 'g4a/gpt4all-lora-quantized. txt","path":"examples/main/CMakeLists. E:LLaMAllamacpp>main. set FORCE_CMAKE=1. To install the server package and get started: pip install llama-cpp-python [ server] python3 -m llama_cpp. We should provide a simple conversion tool from llama2. py", line 75, in main() File "d:pythonprivateGPTprivateGPT. 40 open tabs). positional arguments: model The path of the model file options: -h,--help show this help message and exit--n_ctx N_CTX text context --n_parts N_PARTS --seed SEED RNG seed --f16_kv F16_KV use fp16 for KV cache --logits_all LOGITS_ALL the llama_eval call computes all logits, not just the last one --vocab_only VOCAB_ONLY. llama_model_load:. Open Visual Studio. The CLI option --main-gpu can be used to set a GPU for the single GPU. llama_model_load_internal: ggml ctx size = 59. Llama. llama_model_load_internal: n_ctx = 512 llama_model_load_internal: n_embd = 4096 llama_model_load_internal: n_mult = 256 llama_model_load_internal: n_head = 32 llama_model_load_internal: n_layer = 32 llama_model_load_internal: n_rot = 128 llama_model_load_internal: ftype = 2 (mostly Q4_0)Skip to content. cpp is a C++ library for fast and easy inference of large language models. This option splits the layers into two GPUs in a 1:1 proportion. g. github","path":". Llama. Run make LLAMA_CUBLAS=1 since I have a CUDA enabled nVidia graphics card Downloaded a 30B Q4 GGML Vicuna model (It's called Wizard-Vicuna-30B-Uncensored. As for the "Ooba" settings I have tried a lot of settings. --tensor_split TENSOR_SPLIT: Split the model across multiple GPUs. 00 MB per state) llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer. 00 MB per state) llama_model_load_internal: offloading 60 layers to GPU llama_model_load. Reload to refresh your session. The LoRa and/or Alpaca fine-tuned models are not compatible anymore. Well, how much memoery this llama-2-7b-chat. cpp","path. llms import LlamaCpp model_path = r'llama-2-70b-chat. md. Move to "/oobabooga_windows" path. You can find my environment below, but we were able to reproduce this issue on multiple machines. cpp · GitHub. 2. Only after realizing those environment variables aren't actually being set , unless you 'set' or 'export' them,it won't build correctly. 00 MB per state) llama_model_load_internal: allocating batch_size x (1536 kB + n_ctx x 416 B) = 1600 MB VRAM for the scratch buffer llama_model_load_internal: offloading. (venv) sweet gpt4all-ui % python app. save (model, os. github","contentType":"directory"},{"name":"docker","path":"docker. This is one potential solution to your problem. @adaaaaaa 's case: the main built with cmake works. pth │ └── params. 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. bin: invalid model file (bad magic [got 0x67676d66 want 0x67676a74]) you most likely need to regenerate your ggml files the benefit is you'll get 10-100x faster load. I've successfully run the LLaMA 7B model on my 4GB RAM Raspberry Pi 4. n_vocab = 32001). Alpha 4 starts to give bad resutls at just 6k context, and alpha 8 at 9k context. cpp and the -n 128 suggested for testing. 34 ms per token) llama_print_timings: prompt eval time = 2363. cpp. A compatible lib. Serve immediately and enjoy! This recipe is easy to make and can be customized to your liking by using different types of bread. PS H:FilesDownloadsllama-master-2d7bf11-bin-win-clblast-x64> . This article explains in detail how to use Llama 2 in a private GPT built with Haystack, as described in part 2. cs. Following the usage instruction precisely, I'm receiving error: . cpp + gpt4all - GitHub - nomic-ai/pygpt4all: Official supported Python bindings for llama. Should be a number between 1 and n_ctx. The only difference I see between the two is llama. gguf. cpp: loading model from models/ggml-gpt4all-l13b-snoozy. , Stheno-L2-13B, which are saved separately, e. n_keep, (int) embd_inp. 6" maintenance branches, as they were affected by the bug. llama_model_load: llama_model_load: unknown tensor '' in model file. Download the 3B, 7B, or 13B model from Hugging Face. 33 MB (+ 5120. Should be an optional command line argument to the script to specify if the token should be added or notPress Ctrl+C to interject at any time. bin llama_model_load_internal: format = ggjt v3 (latest) llama_model_load_internal: n_vocab = 32000 llama_model_load_internal:. doesn't matter if using instruct or not either. Similar to Hardware Acceleration section above, you can also install with. Set n_ctx as you want. Here are the errors that I'm seeing when loading in the new Oobabooga build with 2. github","contentType":"directory"},{"name":"docker","path":"docker. 9s vs 39. - Press Return to. I tried all of that. param n_parts: int =-1 ¶ Number of. llama. cpp within LangChain. launch main, htop and watch -n 0 "clear; nvidia-smi" (to see the gpu usage) step 3. 36. llama. You might wanna try benchmarking different --thread counts. strnad mentioned this issue May 15, 2023. Can be NULL to use the current loaded model. Installation will fail if a C++ compiler cannot be located. Typically set this to something large just in case (e. bin -ngl 20 -p "Hello, my name is" main: build = 800 (481f793) main: seed = 1688745037 ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 2060, compute capability 7. The commit in question seems to be 20d7740 The AI responses no longer seem to consider the prompt after this commit. You signed in with another tab or window. LlamaCPP . exe -m . I came across this issue two days ago and spent half a day conducting thorough tests and creating a detailed bug report for. Work is being done in PR #2276 👍 6 SlyEcho, mirek190, yevgeny, Domincog, jain-t, and jasperblues reacted with thumbs up emoji使用privateGPT进行多文档问答. llama_model_load_internal: using CUDA for GPU acceleration llama_model_load_internal: mem required = 2532. 00 MB per state) llama_model_load_internal: allocating batch_size x (512 kB + n_ctx x 128 B) = 480 MB VRAM for the scratch buffer llama_model_load_internal: offloading 28 repeating layers to GPU llama_model_load_internal: offloaded 28/35 layers to GPU Currently, n_ctx is locked to 2048, but with people starting to experiment with ALiBi models (BluemoonRP, MTP whenever that gets sorted out properly) and RedPajamas talking about hyena and StableLM aiming for 4k context potentially, the ability to bump context numbers for llama. You are using 16 CPU threads, which may be a little too much. # 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. Convert the model to ggml FP16 format using python convert. params. cpp: can ' t use mmap because tensors are not aligned; convert to new format to avoid this llama_model_load_internal: format = 'ggml' (old version with low tokenizer quality and no mmap support) llama_model_load_internal: n_vocab = 32000 llama_model_load_internal: n_ctx. when i run the same thing with llama-cpp. /llama-2-13b-chat. cpp models oobabooga/text-generation-webui#2087. Development is very rapid so there are no tagged versions as of now. cpp to the latest version and reinstall gguf from local. . 00 MB, n_mem = 122880. ggmlv3. 57 --no-cache-dir. UPDATE: Greatly simplified implementation thanks to the awesome Pythonic APIs of PyLLaMACpp 2. (IMPORTANT). I found performance to be sensitive to the context size (--ctx-size in terminal, n_ctx in langchain) in Langchain but less so in the terminal. llms import LlamaCpp from langchain import. I'm trying to process a large text file. Default None. llama. 3. Here's what I had on 13B with 11400f and AVX512 now. ) The following is model_path:OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. 1. . 33 ms llama_print_timings: sample time = 64. . 00 MB per state) fdsan: attempted to close file descriptor 3, expected to be unowned, actually owned by. If you installed it correctly, as the model is loaded you will see lines similar to the below after the regular llama. cpp a day ago added support for offloading a specific number of transformer layers to the GPU (ggerganov/llama. 00 MB per state) llama_model_load_internal: allocating batch_size x (512 kB + n_ctx x 128 B) = 480 MB VRAM for the scratch buffer llama_model_load_internal: offloading 28 repeating layers to GPU llama_model_load_internal. llama. llama. Guided Educational Tours. cpp@905d87b). I'm currently using OpenAIEmbeddings and OpenAI LLMs for ConversationalRetrievalChain. - GitHub - Ph0rk0z/text-generation-webui-testing: A fork of textgen that still supports V1 GPTQ, 4-bit lora. I assume it expects the model to be in two parts. Whether you run the download link from Meta or download the files from Huggingface, start by requesting access. devops","path":". n_embd (:obj:`int`, optional, defaults to 768): Dimensionality of the embeddings and hidden states. Then, use the following command to clean-install the `llama-cpp-python` : llama_model_load_internal: total VRAM used: 550 MB <- you used only 550MB VRAM you can try --n-gpu-layers 10 or even 20 View full answer Replies: 4 comments · 7 replies E:\LLaMA\llamacpp>main. Parameters. The file should be named "file_stats. Optimization wise one interesting idea assuming there is proper caching support is to run two llama. n_ctx = 8192 starcoder_model_load: n_embd = 6144 starcoder_model_load: n_head = 48 starcoder_model_load: n_layer = 40 starcoder_model_load: ftype = 2003 starcoder_model_load: qntvr = 2 starcoder_model_load: ggml ctx size = 28956. gguf. {"payload":{"allShortcutsEnabled":false,"fileTree":{"examples/low_level_api":{"items":[{"name":"Chat. cpp/llamacpp_HF, set n_ctx to 4096. First, run `cmd_windows. , 512 or 1024 or 2048). I downloaded the 7B parameter Llama 2 model to the root folder of my D: drive. . Llama 2. cpp by more than 25%. The LoRA training makes adjustments to the weights of a base model, e. textUI without "--n-gpu-layers 40":2. // The model needs to be reloaded before applying a new adapter, otherwise the adapter. 00 MB per state): Vicuna needs this size of CPU RAM. // will be applied on top of the previous one. 79, the model format has changed from ggmlv3 to gguf. cpp. Reconverting is not possible. To build with GPU flags you can pass flags to CMake. cpp leaks memory when compiled with LLAMA_CUBLAS=1. Convert the model to ggml FP16 format using python convert. cpp has a n_threads = 16 option in system info but the textUI doesn't have that. This allows the use of models packaged as . These files are GGML format model files for Meta's LLaMA 7b. Llamas are friendly, delightful and extremely intelligent animals that carry themselves with serene. 6 of Llama 2 using !pip install llama-cpp-python . """--> 184 text = self. save (model, os. If -1, the number of parts is automatically determined. Following the usage instruction precisely, I'm receiving error: . I am running this in Python 3. Similar to Hardware Acceleration section above, you can also install with. A fateful decision in 1960s China echoes across space and time to a group of scientists in the present, forcing them to face humanity's greatest threat. I have another program (in typescript) that run the llama. bin -ngl 20 main: build = 631 (2d7bf11) main: seed = 1686095068 ggml_opencl: selecting platform: 'NVIDIA CUDA' ggml_opencl: selecting device: 'NVIDIA GeForce RTX 3080' ggml_opencl: device FP16 support: false. cpp{"payload":{"allShortcutsEnabled":false,"fileTree":{"patches":{"items":[{"name":"1902-cuda. The problem with large language models is that you can’t run these locally on your laptop. --no-mmap: Prevent mmap from being used. You can find my environment below, but we were able to reproduce this issue on multiple machines. Run without the ngl parameter and see how much free VRAM you have. Contributor. Fibre Art Workshops/Demonstrations. param n_gpu_layers: Optional [int] = None ¶ Number of layers to be loaded into gpu memory. it worked for me. Now install the dependencies and test dependencies: pip install -e '. llama. 0，无需修. Set an appropriate value based on your requirements. step 1. Here are the performance metadata from the terminal calls for the two models: Performance of the 7B model:This allows you to use llama. To set up this plugin locally, first checkout the code. llms import LlamaCpp from langchain. I found that chat personas with very long descriptions don't load, complaining about too much tokens, but I can set n_ctx to 4096 and then it all works. -c N, --ctx-size N: Set the size of the prompt context.