py <path to OpenLLaMA directory>. cpp) using the same language model and record the performance metrics. What is GPT4All. Except the gpu version needs auto tuning in triton. 9. The whole UI is very busy as "Stop generating" takes another 20. Clicked the shortcut, which prompted me to. GPT4ALL allows anyone to experience this transformative technology by running customized models locally. Downloaded & ran "ubuntu installer," gpt4all-installer-linux. Hi, Arch with Plasma, 8th gen Intel; just tried the idiot-proof method: Googled "gpt4all," clicked here. In this video, I walk you through installing the newly released GPT4ALL large language model on your local computer. GPT4ALL 「GPT4ALL」は、LLaMAベースで、膨大な対話を含むクリーンなアシスタントデータで学習したチャットAIです。 2. Here's my proposal for using all available CPU cores automatically in privateGPT. It was discovered and developed by kaiokendev. com) Review: GPT4ALLv2: The Improvements and. Its 100% private use no internet access needed at all. bitterjam Guest. param n_batch: int = 8 ¶ Batch size for prompt processing. Asking for help, clarification, or responding to other answers. from_pretrained(self. I want to know if i can set all cores and threads to speed up inference. For me 4 threads is fastest and 5+ begins to slow down. throughput) but logic operations fast (aka. Live Demos. Instead, GPT-4 will be slightly bigger with a focus on deeper and longer coherence in its writing. Trying to fine tune llama-7b following this tutorial (GPT4ALL: Train with local data for Fine-tuning | by Mark Zhou | Medium). . llms import GPT4All. Edit . Processor 11th Gen Intel(R) Core(TM) i3-1115G4 @ 3. . Install gpt4all-ui run app. The pygpt4all PyPI package will no longer by actively maintained and the bindings may diverge from the GPT4All model backends. It provides high-performance inference of large language models (LLM) running on your local machine. userbenchmarks into account, the fastest possible intel cpu is 2. GTP4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer grade CPUs. The model used is gpt-j based 1. . The CPU version is running fine via >gpt4all-lora-quantized-win64. Hey u/xScottMoore, please respond to this comment with the prompt you used to generate the output in this post. Chat with your data locally and privately on CPU with LocalDocs: GPT4All's first plugin! twitter. , 2 cores) it will have 4 threads. GPT4All allows anyone to train and deploy powerful and customized large language models on a local machine CPU or on a free cloud-based CPU infrastructure such as Google Colab. /gpt4all/chat. Tools . Reply. 使用privateGPT进行多文档问答. Glance the ones the issue author noted. Language bindings are built on top of this universal library. Issues 266. exe (but a little slow and the PC fan is going nuts), so I'd like to use my GPU if I can - and then figure out how I can custom train this thing :). The pricing history data shows the price for a single Processor. Summary: per pytorch#22260, default number of open mp threads are spawned to be the same of number of cores available, for multi processing data parallel cases, too many threads may be spawned and could overload the CPU, resulting in performance regression. Including ". ai's GPT4All Snoozy 13B. GPT4All Performance Benchmarks. 31 Airoboros-13B-GPTQ-4bit 8. This backend acts as a universal library/wrapper for all models that the GPT4All ecosystem supports. bin model, I used the seperated lora and llama7b like this: python download-model. If the PC CPU does not have AVX2 support, gpt4all-lora-quantized-win64. Yeah should be easy to implement. I'm attempting to run both demos linked today but am running into issues. Whats your cpu, im on Gen10th i3 with 4 cores and 8 Threads and to generate 3 sentences it takes 10 minutes. from langchain. Searching for it, I see this StackOverflow question, so that would point to your CPU not supporting some instruction set. I installed GPT4All-J on my old MacBookPro 2017, Intel CPU, and I can't run it. cpp make. Image by @darthdeus, using Stable Diffusion. There's a free Chatgpt bot, Open Assistant bot (Open-source model), AI image generator bot, Perplexity AI bot, 🤖 GPT-4 bot (Now with Visual. so set OMP_NUM_THREADS = number of CPU. ai's GPT4All Snoozy 13B GGML. I used the Visual Studio download, put the model in the chat folder and voila, I was able to run it. GPT-2 (All versions, including legacy f16, newer format + quanitzed, cerebras) Supports OpenBLAS. To run GPT4All, open a terminal or command prompt, navigate to the 'chat' directory within the GPT4All folder, and run the appropriate command for your operating. Using Deepspeed + Accelerate, we use a global batch size of 256 with a learning. It uses the same architecture and is a drop-in replacement for the original LLaMA weights. I asked chatgpt and it basically said the limiting factor would probably be the memory needed for each thread might take up about . One user suggested changing the n_threads parameter in the GPT4All function,. py CPU utilization shot up to 100% with all 24 virtual cores working :) Line 39 now reads: llm = GPT4All(model=model_path, n_threads=24, n_ctx=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=False) The moment has arrived to set the GPT4All model into motion. 7 (I confirmed that torch can see CUDA)Nomic. The benefit is 4x less RAM requirements, 4x less RAM bandwidth requirements, and thus faster inference on the CPU. [Cross compilation] qemu: uncaught target signal 4 (Illegal instruction) - core dumpedExLlamaV2. You signed out in another tab or window. The library is unsurprisingly named “ gpt4all ,” and you can install it with pip command: 1. Pass the gpu parameters to the script or edit underlying conf files (which ones?) Contextcocobeach commented Apr 4, 2023 •edited. . Current data. Start the server by running the following command: npm start. The official example notebooks/scripts; My own. llm is an ecosystem of Rust libraries for working with large language models - it's built on top of the fast, efficient GGML library for machine learning. OK folks, here is the dea. Text Add text cell. But in my case gpt4all doesn't use cpu at all, it tries to work on integrated graphics: cpu usage 0-4%, igpu usage 74-96%. n_threads=4 giving 10-15 minutes response time will not be expected response time for any real-world practical use case. Run gpt4all on GPU #185. Completion/Chat endpoint. Unclear how to pass the parameters or which file to modify to use gpu model calls. Learn more in the documentation. We would like to show you a description here but the site won’t allow us. implemented on an apple sillicon cpu - do not help ?. I get around the same performance as cpu (32 core 3970x vs 3090), about 4-5 tokens per second for the 30b model. 🔗 Resources. Notifications. 19 GHz and Installed RAM 15. The bash script then downloads the 13 billion parameter GGML version of LLaMA 2. 0. 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。. First, you need an appropriate model, ideally in ggml format. Try it yourself. Our released model, GPT4All-J, can be trained in about eight hours on a Paperspace DGX A100 8x 80GB for a total cost of $200. GPT4All is an. Closed Vcarreon439 opened this issue Apr 3, 2023 · 5 comments Closed Run gpt4all on GPU #185. How to get the GPT4ALL model! Download the gpt4all-lora-quantized. langchain import GPT4AllJ llm = GPT4AllJ ( model = '/path/to/ggml-gpt4all-j. llm = GPT4All(model=llm_path, backend='gptj', verbose=True, streaming=True, n_threads=os. Gptq-triton runs faster. cpp model is LLaMa2 GPTQ model from TheBloke: * Run LLaMa. Note that your CPU needs to support AVX or AVX2 instructions. cpp will crash. using a GUI tool like GPT4All or LMStudio is better. It still needs a lot of testing and tuning, and a few key features are not yet implemented. Reload to refresh your session. Most basic AI programs I used are started in CLI then opened on browser window. I did built the pyllamacpp this way but i cant convert the model, because some converter is missing or was updated and the gpt4all-ui install script is not working as it used to be few days ago. cpp) using the same language model and record the performance metrics. /gpt4all-lora-quantized-linux-x86. The main features of GPT4All are: Local & Free: Can be run on local devices without any need for an internet connection. ; If you are running Apple x86_64 you can use docker, there is no additional gain into building it from source. This model is brought to you by the fine. I've tried at least two of the models listed on the downloads (gpt4all-l13b-snoozy and wizard-13b-uncensored) and they seem to work with reasonable responsiveness. qpa. Copy link Vcarreon439 commented Apr 3, 2023. ago. #328. From installation to interacting with the model, this guide has. (You can add other launch options like --n 8 as preferred onto the same line); You can now type to the AI in the terminal and it will reply. /models/ 7 B/ggml-model-q4_0. The Nomic AI team fine-tuned models of LLaMA 7B and final model and trained it on 437,605 post-processed assistant-style prompts. cpp will crash. using a GUI tool like GPT4All or LMStudio is better. 3-groovy. えー・・・今度はgpt4allというのが出ましたよ やっぱあれですな。 一度動いちゃうと後はもう雪崩のようですな。 そしてこっち側も新鮮味を感じなくなってしまうというか。 んで、ものすごくアッサリとうちのMacBookProで動きました。 量子化済みのモデルをダウンロードしてスクリプト動かす. 最主要的是，该模型完全开源，包括代码、训练数据、预训练的checkpoints以及4-bit量化结果。. whl; Algorithm Hash digest; SHA256: c09440bfb3463b9e278875fc726cf1f75d2a2b19bb73d97dde5e57b0b1f6e059: CopyPhoto by Emiliano Vittoriosi on Unsplash Introduction. cpu_count()" is worked for me. Only gpt4all and oobabooga fail to run. Depending on your operating system, follow the appropriate commands below: M1 Mac/OSX: Execute the following command: . LocalDocs is a GPT4All feature that allows you to chat with your local files and data. gpt4all_colab_cpu. whl; Algorithm Hash digest; SHA256: d1ae6c40a13cbe73274ee6aa977368419b2120e63465d322e8e057a29739e7e2 I have it running on my windows 11 machine with the following hardware: Intel(R) Core(TM) i5-6500 CPU @ 3. Next, run the setup file and LM Studio will open up. Supports CLBlast and OpenBLAS acceleration for all versions. Capability. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 最开始，Nomic AI使用OpenAI的GPT-3. 2. The GPT4All Chat UI supports models from all newer versions of llama. Change -t 10 to the number of physical CPU cores you have. (1) 新規のColabノートブックを開く。. txt. This bindings use outdated version of gpt4all. This was done by leveraging existing technologies developed by the thriving Open Source AI community: LangChain, LlamaIndex, GPT4All, LlamaCpp, Chroma and SentenceTransformers. GPT4All gives you the chance to RUN A GPT-like model on your LOCAL PC. (u/BringOutYaThrowaway Thanks for the info). The goal of GPT4All is to provide a platform for building chatbots and to make it easy for developers to create custom chatbots tailored to specific use cases or. The GPT4All dataset uses question-and-answer style data. * use _Langchain_ para recuperar nossos documentos e carregá-los. A LangChain LLM object for the GPT4All-J model can be created using: from gpt4allj. This is relatively small, considering that most desktop computers are now built with at least 8 GB of RAM. My accelerate configuration: $ accelerate env [2023-08-20 19:22:40,268] [INFO] [real_accelerator. cpp is running inference on the CPU it can take a while to process the initial prompt and there are still. New bindings created by jacoobes, limez and the nomic ai community, for all to use. Documentation for running GPT4All anywhere. settings. The goal is simple - be the best instruction tuned assistant-style language model that any person or enterprise can freely use, distribute and build on. ; GPT-3 Dungeons and Dragons: This project uses GPT-3 to generate new scenarios and encounters for the popular tabletop role-playing game Dungeons and Dragons. However, direct comparison is difficult since they serve. Easy to install with precompiled binaries. ver 2. Copy link Collaborator. To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration. For example if your system has 8 cores/16 threads, use -t 8. I know GPT4All is cpu-focused. The ggml file contains a quantized representation of model weights. CPU to feed them (n_threads) 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. One of the major attractions of the GPT4All model is that it also comes in a quantized 4-bit version, allowing anyone to run the model simply on a CPU. @Preshy I doubt it. RWKV is an RNN with transformer-level LLM performance. Remove it if you don't have GPU acceleration. Large language models such as GPT-3, which have billions of parameters, are often run on specialized hardware such as GPUs or. GPT4All. . / gpt4all-lora-quantized-OSX-m1. You signed in with another tab or window. dev, secondbrain. no CUDA acceleration) usage. Ryzen 5800X3D (8C/16T) RX 7900 XTX 24GB (driver 23. I'm trying to find a list of models that require only AVX but I couldn't find any. But I know my hardware. Install GPT4All. Slo(if you can't install deepspeed and are running the CPU quantized version). The model was trained on a comprehensive curated corpus of interactions, including word problems, multi-turn dialogue, code, poems, songs, and stories. 6 Cores and 12 processing threads,. py nomic-ai/gpt4all-lora python download-model. 7 ggml_graph_compute_thread ggml. Core(TM) i5-6500 CPU @ 3. 3-groovy model is a good place to start, and you can load it with the following command:This is due to a bottleneck in training data, making it incredibly expensive to train massive neural networks. (2) Googleドライブのマウント。. for CPU inference will *just work* with all GPT4All software with the newest release! Instructions:. GPT4All is an ecosystem to run powerful and customized large language models that work locally on consumer grade CPUs and any GPU. These files are GGML format model files for Nomic. All we can hope for is that they add Cuda/GPU support soon or improve the algorithm. GPT4All. Now let’s get started with the guide to trying out an LLM locally: git clone [email protected] :ggerganov/llama. A GPT4All model is a 3GB - 8GB size file that is integrated directly into the software you are developing. model: Pointer to underlying C model. GPT4All， CPU本地运行70亿参数大模型整合包！GPT4All 官网给自己的定义是：一款免费使用、本地运行、隐私感知的聊天机器人，无需GPU或互联网。同时支持windows，mac，Linux！！！其主要特点是：本地运行无需GPU无需联网同时支持Windows、MacOS、Ubuntu Linux（环境要求低）是一个聊天工具学术Fun将上述工具. Here's how to get started with the CPU quantized GPT4All model checkpoint: Download the gpt4all-lora-quantized. 1 13B and is completely uncensored, which is great. As discussed earlier, GPT4All is an ecosystem used to train and deploy LLMs locally on your computer, which is an incredible feat! Typically, loading a standard 25-30GB LLM would take 32GB RAM and an enterprise-grade GPU. Introduce GPT4All. 1; asked Aug 28 at 13:49. 7. 14GB model. Tokens are streamed through the callback manager. But there is a PR that allows to split the model layers across CPU and GPU, which I found to drastically increase performance, so I wouldn't be surprised if such. bin", n_ctx = 512, n_threads = 8) # Generate text. No GPU or web required. My accelerate configuration: $ accelerate env [2023-08-20 19:22:40,268] [INFO] [real_accelerator. Do we have GPU support for the above models. gpt4all_path = 'path to your llm bin file'. Here is a list of models that I have tested. GPT4All Performance Benchmarks. @huggingface. . 25. /gpt4all-lora-quantized-OSX-m1. A GPT4All model is a 3GB - 8GB file that you can download. The default model is named "ggml-gpt4all-j-v1. perform a similarity search for question in the indexes to get the similar contents. GPT4All models are designed to run locally on your own CPU, which may have specific hardware and software requirements. Learn more in the documentation. cpp project instead, on which GPT4All builds (with a compatible model). CPU Spikes: Thread Spikes: Profiling Data By default, when a CPU spike is detected, the Spike Detective collects several predetermined statistics. [deleted] • 7 mo. えー・・・今度はgpt4allというのが出ましたよ やっぱあれですな。 一度動いちゃうと後はもう雪崩のようですな。 そしてこっち側も新鮮味を感じなくなってしまうというか。 んで、ものすごくアッサリとうちのMacBookProで動きました。 量子化済みのモデルをダウンロードしてスクリプト動かす. 9. New Dataset. 最主要的是，该模型完全开源，包括代码、训练数据、预训练的checkpoints以及4-bit量化结果。. Let’s analyze this: mem required = 5407. Rep: Open-source large language models, run locally on your CPU and nearly any GPU-Slackware. . Possible Solution. As a Linux machine interprets a thread as a CPU (I might be wrong in the terminology here), if you have 4 threads per CPU, it means that the full load is actually 400%. prg checks if you have AVX2 support. If you want to use a different model, you can do so with the -m / -. The pretrained models provided with GPT4ALL exhibit impressive capabilities for natural language. Tokenization is very slow, generation is ok. Find "Cpu" in Victoria, British Columbia - Visit Kijiji™ Classifieds to find new & used items for sale. llms. 🔗 Resources. For Intel CPUs, you also have OpenVINO, Intel Neural Compressor, MKL,. But i've found instruction thats helps me run lama: For windows I did this: 1. The UI is made to look and feel like you've come to expect from a chatty gpt. This is a very initial release of ExLlamaV2, an inference library for running local LLMs on modern consumer GPUs. Ensure that the THREADS variable value in . The major hurdle preventing GPU usage is that this project uses the llama. Cloned llama. The core of GPT4All is based on the GPT-J architecture, and it is designed to be a lightweight and easily customizable alternative to other large language models like OpenaAI GPT. Connect and share knowledge within a single location that is structured and easy to search. I want to know if i can set all cores and threads to speed up inference. py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) Copy-and-paste the text below in your GitHub issue . No milestone. Enjoy! Credit. System Info GPT4all version - 0. ; If you are on Windows, please run docker-compose not docker compose and. GitHub: nomic-ai/gpt4all: gpt4all: an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue (github. Reload to refresh your session. py --chat --model llama-7b --lora gpt4all-lora. py. Here is the latest error*: RuntimeError: "addmm_impl_cpu_" not implemented for 'Half* Specs: NVIDIA GeForce 3060 12GB Windows 10 pro AMD Ryzen 9 5900X 12-Core 64 GB RAM Locked post. Viewer • Updated Apr 13 •. 4. Execute the llama. GPT For All 13B (/GPT4All-13B-snoozy-GPTQ) is Completely Uncensored, a great model. Colabインスタンス. gitignore. /models/gpt4all-model. I am new to LLMs and trying to figure out how to train the model with a bunch of files. /models/gpt4all-lora-quantized-ggml. According to the documentation, my formatting is correct as I have specified the path, model name and. The table below lists all the compatible models families and the associated binding repository. write "pkg update && pkg upgrade -y". I've already migrated my GPT4All model. Starting with. cpp, e. cpp. So GPT-J is being used as the pretrained model. OS 13. . Start LocalAI. Well, that's odd. , 8 core) it will have 16 threads and vice-versa. See the documentation. Note that your CPU needs to support AVX or AVX2 instructions. I have now tried in a virtualenv with system installed Python v. /gpt4all-lora-quantized-OSX-m1From the official web site GPT4All it’s described as a free-to-use, domestically operating, privacy-aware chatbot. . This will start the Express server and listen for incoming requests on port 80. exe to launch). The results. Models of different sizes for commercial and non-commercial use. The goal is simple - be the best instruction tuned assistant-style language model that any person or enterprise can freely use, distribute and build on. write request; Expected behavior. desktop shortcut. class MyGPT4ALL(LLM): """. from langchain. Download and install the installer from the GPT4All website . Latest version of GPT4ALL, rest idk. Model compatibility table. cache/gpt4all/ folder of your home directory, if not already present. Nomic. The ecosystem features a user-friendly desktop chat client and official bindings for Python, TypeScript, and GoLang, welcoming contributions and collaboration from the open. You switched accounts on another tab or window. The simplest way to start the CLI is: python app. Introduce GPT4All. It provides high-performance inference of large language models (LLM) running on your local machine. gpt4all. You switched accounts on another tab or window. auto_awesome_motion. Big New Release of GPT4All 📶 You can now use local CPU-powered LLMs through a familiar API! Building with a local LLM is as easy as a 1 line code change! Building with a local LLM is as easy as a 1 line code change!The first version of PrivateGPT was launched in May 2023 as a novel approach to address the privacy concerns by using LLMs in a complete offline way. GPT4All model weights and data are intended and licensed only for research. First of all: Nice project!!! I use a Xeon E5 2696V3(18 cores, 36 threads) and when i run inference total CPU use turns around 20%. 580 subscribers in the LocalGPT community. Sadly, I can't start none of the 2 executables, funnily the win version seems to work with wine. Download the CPU quantized gpt4all model checkpoint: gpt4all-lora-quantized. I took it for a test run, and was impressed. GPT4All runs reasonably well given the circumstances, it takes about 25 seconds to a minute and a half to generate a response, which is meh. 11, with only pip install gpt4all==0. LLMs on the command line. Please use the gpt4all package moving forward to most up-to-date Python bindings. bin. bin' - please wait. You must hit ENTER on the keyboard once you adjust it for them to actually adjust. bin" file extension is optional but encouraged. Here's how to get started with the CPU quantized GPT4All model checkpoint: Download the gpt4all-lora-quantized. I think the gpu version in gptq-for-llama is just not optimised. Reload to refresh your session. 效果好. So for instance, if you have 4 gb free GPU RAM after loading the model you should in. However, ensure your CPU is AVX or AVX2 instruction supported. But there is a PR that allows to split the model layers across CPU and GPU, which I found to drastically increase performance, so I wouldn't be surprised if such. I'm really stuck with trying to run the code from the gpt4all guide. On last question python3 -m pip install --user gpt4all install the groovy LM, is there a way to install the. 9. GPT4All gives you the chance to RUN A GPT-like model on your LOCAL PC. No branches or pull requests. All threads are stuck at around 100%, and you can see that the CPU is being used to the maximum. bin file from Direct Link or [Torrent-Magnet]. GPT4All run on CPU only computers and it is free!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. I used the convert-gpt4all-to-ggml. Milestone. AI's GPT4All-13B-snoozy. Step 2: Now you can type messages or questions to GPT4All in the message pane at the bottom. Working: The thread. bin". Mar 31, 2023 23:00:00 Summary of how to use lightweight chat AI 'GPT4ALL' that can be used even on low-spec PCs without Grabo High-performance chat AIs, such as. We have a public discord server. cpp, make sure you're in the project directory and enter the following command:. 2-pp39-pypy39_pp73-win_amd64. Discover smart, unique perspectives on Gpt4all and the topics that matter most to you like ChatGPT, AI, Gpt 4, Artificial Intelligence, Llm, Large Language. c 11694 0x7ffc439257ba, The text was updated successfully, but these errors were encountered:. I didn't see any core requirements. py script to convert the gpt4all-lora-quantized. How to run in text. 1 model loaded, and ChatGPT with gpt-3. 3 GPT4ALL 2. Download the LLM model compatible with GPT4All-J. when i was runing privateGPT in my windows, my devices gpu was not used? you can see the memory was too high but gpu is not used my nvidia-smi is that, looks cuda is also work? so whats the. in making GPT4All-J training possible. GPT4All model; from pygpt4all import GPT4All model = GPT4All ('path/to/ggml-gpt4all-l13b-snoozy. You can update the second parameter here in the similarity_search. This is Unity3d bindings for the gpt4all. 2 they appear to save but do not. The goal of GPT4All is to provide a platform for building chatbots and to make it easy for developers to create custom chatbots tailored to specific use cases or. However, when using the CPU worker (the precompiled ones in chat), it is odd that the 4-threaded option is much faster in replying than when using 24 threads. However, the difference is only in the very small single-digit percentage range, which is a pity. 0. A GPT4All model is a 3GB - 8GB file that you can download. As per their GitHub page the roadmap consists of three main stages, starting with short-term goals that include training a GPT4All model based on GPTJ to address llama distribution issues and developing better CPU and GPU interfaces for the model, both of which are in progress. "n_threads=os. GPT4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer-grade CPUs. Created by the experts at Nomic AI. Running LLMs on CPU . The htop output gives 100% assuming a single CPU per core. Clone this repository, navigate to chat, and place the downloaded file there. There are many bindings and UI that make it easy to try local LLMs, like GPT4All, Oobabooga, LM Studio, etc. 4 Use Considerations The authors release data and training details in hopes that it will accelerate open LLM research, particularly in the domains of alignment and inter-pretability. 他们发布的4-bit量化预训练结果可以使用CPU作为推理！.