Gpt j 6b

Author: q | 2025-04-25

GPT-J-6B: 6B JAX-Based Transformer. Summary: We have released GPT-J-6B, 6B JAX-based (Mesh) Transformer LM (Github).GPT-J-6B performs nearly on par with 6.7B

GPT-J 6B GPT-J 6B

Run GPT-J-6B model (text generation open source GPT-3 analog) for inference on server with GPU using zero-dependency Docker image.First script loads model into video RAM (can take several minutes) and then runs internal HTTP server which is listening on 8080.Prerequirements to run GPT-J on GPUYou can run this image only on instance with 16 GB Video memory and Linux (e.g. Ubuntu)Server machine should have NVIDIA Driver and Docker daemon with NVIDIA Container Toolkit. See below.Tested on NVIDIA Titan RTX, NVIDIA Tesla P100,Not supported: NVIDIA RTX 3090, RTX A5000, RTX A6000. Reasone Cuda+PyTorch coombination:CUDA capability sm_86 is not supported, PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70 (we use latest PyTorch during image build), match sm_x to video cardInstall Nvidia DriversYou can skip this step if you already have nvidia-smi and it outputs the table with CUDA Version:Mon Feb 14 14:28:16 2022 +-----------------------------------------------------------------------------+| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 ||-------------------------------+----------------------+----------------------+| ...E.g. for Ubuntu 20.04apt purge *nvidia*apt autoremoveadd-apt-repository ppa:graphics-drivers/ppaapt updateapt install -y ubuntu-drivers-commonubuntu-drivers autoinstallNote: Unfortunetely NVIDIA drivers installation process might be quite challenging sometimes, e.g. there might be some known issues Google helps a lotAfter installing and rebooting, test it with nvidia-smi, you should see table.Install Dockerd with NVIDIA Container Toolkit:How to install it on Ubuntu:distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L | apt-key add - \ && curl -s -L | tee /etc/apt/sources.list.d/nvidia-docker.listapt update && apt -y upgradecurl | sh && systemctl --now restart docker apt install -y nvidia-docker2And reboot server.To test that CUDA in Docker works

gpt-j-6b/gpt-j-t4.ipynb at main paulcjh/gpt-j-6b - GitHub

Run :docker run --rm --gpus all nvidia/cuda:11.1-base nvidia-smiIf all was installed correctly it should show same table as nvidia-smi on host.If you have no NVIDIA Container Toolkit or did not reboot server yet you would get docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]]Docker command to run image:docker run -p8080:8080 --gpus all --rm -it devforth/gpt-j-6b-gpu--gpus all passes GPU into docker container, so internal bundled cuda instance will smoothly use itThough for apu we are using async FastAPI web server, calls to model which generate a text are blocking, so you should not expect parallelism from this webserverThen you can call model by using REST API:POST application/jsonBody: { "text": "Client: Hi, who are you?\nAI: I am Vincent and I am barista!\nClient: What do you do every day?\nAI:", "generate_tokens_limit": 40, "top_p": 0.7, "top_k": 0, "temperature":1.0}For developemnt clone the repository and run on server:docker run -p8080:8080 --gpus all --rm -it $(docker build -q .)

GPT-J 6B（GPT-J 6B）详细信息

Oblivious neural network predictions via minionn transformationsJ Liu, M Juuti, Y Lu, N AsokanProceedings of the 2017 ACM SIGSAC conference on computer and communications …, 20179292017Secure deduplication of encrypted data without additional independent serversJ Liu, N Asokan, B PinkasProceedings of the 22nd ACM SIGSAC Conference on Computer and Communications …, 20152952015Scalable byzantine consensus via hardware-assisted secret sharingJ Liu, W Li, GO Karame, N AsokanIEEE Transactions on Computers 68 (1), 139-151, 20182752018Private set intersection for unequal set sizes with mobile applicationsÁ Kiss, J Liu, T Schneider, N Asokan, B PinkasPrivacy Enhancing Technologies Symposium, 177-197, 20171622017Free-riders in federated learning: Attacks and defensesJ Lin, M Du, J LiuarXiv preprint arXiv:1911.12560, 20191122019Toward fairness of cryptocurrency paymentsJ Liu, W Li, GO Karame, N AsokanIEEE Security & Privacy 16 (3), 81-89, 20181062018Impossibility of full decentralization in permissionless blockchainsY Kwon, J Liu, M Kim, D Song, Y KimProceedings of the 1st ACM Conference on Advances in Financial Technologies …, 2019982019: Private Federated Learning for GBDTZ Tian, R Zhang, X Hou, L Lyu, T Zhang, J Liu, K RenIEEE Transactions on Dependable and Secure Computing, 2023882023SoK: Modular and efficient private decision tree evaluationÁ Kiss, M Naderpour, J Liu, T Schneider, N AsokanProceedings on Privacy Enhancing Technologies 2, 2019782019The circle game: Scalable private membership test using trusted hardwareS Tamrakar, J Liu, A Paverd, JE Ekberg, B Pinkas, N AsokanProceedings of the 2017 ACM on Asia Conference on Computer and …, 2017702017Learn to forget: Machine unlearning via neuron maskingZ Ma, Y Liu, X Liu, J Liu, J Ma, K RenIEEE Transactions on Dependable and Secure Computing 20 (4), 3194-3207, 2022692022Ciphergpt: Secure two-party gpt inferenceX Hou, J Liu, J Li, Y Li, W Lu, C Hong, K RenCryptology ePrint Archive, 2023612023Bumblebee: Secure two-party inference framework for large transformersW Lu, Z Huang, Z Gu, J Li, J Liu, C Hong, K Ren, T Wei, WG ChenCryptology ePrint Archive, 2023432023Method and system for byzantine fault-tolerance replicating of data on a plurality of serversG Karame, W Li, J Liu, N Asokan, A PaverdUS Patent 10,049,017, 2018422018Parallel and asynchronous smart contract executionJ Liu, P Li, R Cheng, N Asokan, D SongIEEE Transactions on Parallel and Distributed Systems 33 (5), 1097-1108, 2021382021“adversarial examples” for proof-of-learningR Zhang, J Liu, Y Ding, Z Wang, Q Wu, K Ren2022 IEEE Symposium on Security and Privacy (SP), 1408-1422, 2022342022Secure deduplication of encrypted data: Refined model and new constructionsJ Liu, L Duan, Y Li, N AsokanCryptographers’ Track at the RSA Conference,. GPT-J-6B: 6B JAX-Based Transformer. Summary: We have released GPT-J-6B, 6B JAX-based (Mesh) Transformer LM (Github).GPT-J-6B performs nearly on par with 6.7B

lukepark327/gpt-j-6B-LoRA: GPT-J 6B with LoRA - GitHub

OverviewChatGPT Conversation History Search & Claude AI Messages SearchChatGPT History Search=================================Claude.AI is now supported as well.=================================- All data saved locally, your chats never leaves your device, totally privacy.- Search in conversation titles- Search in conversation messages- Keyboard shortcut: cmd+J or ctrl+J- Get more chrome extensions from WindChat - Immersive AI - ChatGPT Tailwind CSS Previewer - ChatGPT Chart Previewer - ChatGPT Batch Tasks - ChatGPT Batch Delete HistoryDetailsVersion2024.10.03UpdatedOctober 4, 2024Size527KiBLanguagesDeveloperNon-traderThis developer has not identified itself as a trader. For consumers in the European Union, please note that consumer rights do not apply to contracts between you and this developer.PrivacyChatGPT Conversation History Search & Claude AI Messages Search has disclosed the following information regarding the collection and usage of your data. More detailed information can be found in the developer's privacy policy.ChatGPT Conversation History Search & Claude AI Messages Search handles the following:This developer declares that your data isNot being sold to third parties, outside of the approved use casesNot being used or transferred for purposes that are unrelated to the item's core functionalityNot being used or transferred to determine creditworthiness or for lending purposesSupportRelatedSearchable ChatGPT: search GPT conversation history4.1(77)Search ChatGPT conversation history instantly. Fully local and private. Press sidebar button or hotkeyChatGPT Batch Tasks Bulk Work flow Excel5.0(2)ChatGPT Batch Tasks Bulk Work flow ExcelChatGPT History Search3.3(6)Chatgpt conversation history management, including historical data download, historical data search, historical data collection,…ChatGPT Search: Instantly Search Your Chat Logs4.5(6)A Chrome extension to search through ChatGPT chatsChatGPT Batch Delete History Manager4.6(18)ChatGPT Batch Delete History ManagerChatGPT Keeper3.7(6)Chat history local saving and search, as well as voice chat capabilitiesChatGPT Chat History Search2.6(18)Chatgpt conversation history management, including historical data download, historical data search, historical data collection,…1Proompt: ChatGPT Conversations Search4.8(5)Save, search, organize your ChatGPT conversation historyWindChat-ChatGPT Tailwind CSS React Previewer4.0(4)Preview React and TailwindCSS code in real-time within the ChatGPT chat window, without the tedious copying and pasting.Superpower ChatGPT4.4(2.3K)ChatGPT with Superpowers! Folders, Search, GPT Store, Image Gallery, Voice GPT, Export, Custom Prompts, Prompt Chains, Hidden ModelsGPT Search: Chat History4.7(37)Search your ChatGPT conversation history.ChatGPT Conversation Manager3.7(7)ChatGPT Conversation ManagerSearchable ChatGPT: search GPT conversation history4.1(77)Search ChatGPT conversation history instantly. Fully local and private.

gpt-j-6b/LICENSE at main mallorbc/gpt-j-6b - GitHub

J = 2.5 × 10 12 A/m2. Figure 6b,d, creating a domain wall is no longer possible, and the magnetization in both disks after the annihilation process is parallel. Videos of the different annihilation processes are included as Supplementary Materials. Figure 3. (a) Illustration of the skyrmion trajectories until stagnation. Green dots represent the electrodes. (b) Angle that characterizes the equilibrium position of both skyrmions as a function of J for β = 0.2 (purple squares) and β = 0.3 (magenta dots). In the inset, the red and blue solid dots represent the skyrmion positions for the last current value for which stagnation points are reached. The star shows the parameters used to obtain the skyrmion trajectory depicted in figure (a). Click here to enlarge figure --> Figure 3. (a) Illustration of the skyrmion trajectories until stagnation. Green dots represent the electrodes. (b) Angle that characterizes the equilibrium position of both skyrmions as a function of J for β = 0.2 (purple squares) and β = 0.3 (magenta dots). In the inset, the red and blue solid dots represent the skyrmion positions for the last current value for which stagnation points are reached. The star shows the parameters used to obtain the skyrmion trajectory depicted in figure (a). Figure 4. Transient time needed by the skyrmions to reach the stagnation point as a function of the current density for interconnected disks with β = 0.2 . Black dots present the results obtained by micromagnetic simulations and red-dashed line is a fit given by a + b / J P , with a = 1.8 , b = 34 , and p = 0.8 . Click here to enlarge figure --> Figure 4. Transient time needed by the skyrmions to reach the stagnation point as a function of the current density for interconnected disks with β = 0.2 . Black dots present the results obtained by micromagnetic simulations and red-dashed line is a fit given by a + b / J P , with a = 1.8 , b = 34 , and p = 0.8 . Figure 5. Three examples

GitHub - minimaxir/gpt-j-6b-experiments: Test prompts for GPT-J-6B

Evolution of AI: From GPT-1 to GPT-4o – Key Features, Milestones, and Applications Artificial Intelligence (AI) continues to revolutionize various sectors, with significant advancements in language models such as OpenAI’s Generative Pre-trained Transformers (GPT). This article delves into the evolution from GPT-1 to the latest GPT-4o, highlighting the improvements and innovations that each version brought to the table, particularly in content creation. 1. Evolution of Chat GPT Over Time The ChatGPT development history from OpenAI’s language models, particularly the GPT series, is a fascinating journey of technological advancements. Let’s explore the Chat GPT evolution from the earliest versions to the latest, GPT-4o, and understand how each iteration has contributed to the field of AI. 2. What Was GPT-1 and How Did It Start? GPT-1: The journey began with the first Generative Pre-trained Transformer (GPT) model, introduced by OpenAI in 2018. GPT-1 was a breakthrough in natural language processing (NLP), utilizing 117 million parameters to generate human-like text based on the context provided. It marked a significant step forward in machine learning, demonstrating the potential of pre-trained transformers. Key Features of GPT-1: Contextual Understanding: GPT-1 could generate coherent text by understanding the context of the input. Pre-training and Fine-tuning: The model was pre-trained on a diverse dataset and then fine-tuned for specific tasks, enhancing its versatility. Limitations of GPT-1: Limited Capacity: With only 117 million parameters, GPT-1 had a limited capacity for understanding and generating complex text. Performance Issues: While revolutionary, GPT-1’s performance was not robust enough for many practical applications. 3. How Did GPT-2 Improve on Its Predecessor? GPT-2: Launched in 2019, GPT-2 built upon the foundation laid by GPT-1, expanding the model to 1.5 billion parameters. This massive increase in scale enabled GPT-2 to generate more coherent and contextually relevant text, making it a more powerful tool for a variety of NLP tasks. The revolution of GPT has begun. Key Features of GPT-2: Improved Text Generation: The larger model size allowed for more accurate and diverse text generation. Versatility: GPT-2 could handle a wide range of applications, from summarization to translation and question-answering. Limitations of GPT-2: Ethical Concerns: Due to its ability to generate highly realistic text, there were concerns about the potential misuse of GPT-2 for creating fake news or misleading content. Resource Intensive: The model required significant computational resources for both training and deployment. 4. What Made GPT-3 a Game Changer? GPT-3: Released in June 2020, GPT-3 was a monumental leap forward, boasting 175 billion parameters. This vast increase in model size brought unprecedented capabilities in natural language understanding and generation. Key Features of GPT-3: Versatile Applications: GPT-3 could perform a wide array of tasks, from simple text completion to complex creative writing. Natural Language Understanding: The model demonstrated a deep understanding of context, producing highly coherent and contextually appropriate text. Accessibility: OpenAI provided an API, making GPT-3 accessible to developers and businesses for integration into various applications. Limitations of GPT-3: Resource Demands: The enormous size of GPT-3 made it resource-intensive, requiring substantial computational power. Occasional Inconsistencies:. GPT-J-6B: 6B JAX-Based Transformer. Summary: We have released GPT-J-6B, 6B JAX-based (Mesh) Transformer LM (Github).GPT-J-6B performs nearly on par with 6.7B