What is the GPT4All model?

GPT4All runs large language models (LLMs) privately on everyday desktops & laptops . No API calls or GPUs required - you can just download the application and get started.

What does hugging face AI do?

Hugging Face is a machine learning (ML) and data science platform and community that helps users build, deploy and train machine learning models. It provides the infrastructure to demo, run and deploy artificial intelligence (AI) in live applications .

Does GPT4All need WiFi?

It is an open-sourced ecosystem of powerful and customizable LLM models developed by Nomic.ai. The goal of this project is that anybody can run these models locally on our devices without an internet connection .

How does GPT4All compare to GPT4?

On the other hand, GPT4all is an open-source project that can be run on a local machine, offering greater flexibility and potential for customization . In addition, it is worth noting that there is also a paid version of GPT called GPT-4, which is expected to offer even more advanced features and capabilities.

GPT4All is a free-to-use , locally running, privacy-aware chatbot. There is no GPU or internet required. It features popular models and its own models such as GPT4All Falcon, Wizard, etc.

Can GPT4All be trained?

Our released model, gpt4all-lora, can be trained in about eight hours on a Lambda Labs DGX A100 8x 80GB for a total cost of $100 .

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 system : M1 Mac/OSX: ./gpt4all-lora-quantized-OSX-m1. Linux: ./gpt4all-lora-quantized-linux-x86.

nomic-ai/gpt4all-j · Hugging Face (2024)

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.

Model Details

Model Description

This model has been finetuned from GPT-J

Developed by: Nomic AI
Model Type: A finetuned GPT-J model on assistant style interaction data
Language(s) (NLP): English
License: Apache-2
Finetuned from model [optional]: GPT-J

We have released several versions of our finetuned GPT-J model using different dataset versions

v1.0: The original model trained on the v1.0 dataset
v1.1-breezy: Trained on afiltered dataset where we removed all instances of AI language model
v1.2-jazzy: Trained on a filtered dataset where we also removed instances like I'm sorry, I can't answer... and AI language model
v1.3-groovy: We added Dolly and ShareGPT to the v1.2 dataset and removed ~8% of the dataset in v1.2 that contained semantic duplicates using Atlas.

To download a model with a specific revision run

from transformers import AutoModelForCausalLMmodel = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-j", revision="v1.2-jazzy")

Downloading without specifying revision defaults to main/v1.0.

Model Sources [optional]

Repository: https://github.com/nomic-ai/gpt4all
Base Model Repository: https://github.com/kingoflolz/mesh-transformer-jax
Paper [optional]: GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot
Demo [optional]: https://gpt4all.io/

Training Procedure

GPT4All is made possible by our compute partner Paperspace.

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 256 with a learning rate of 2e-5. More information can be found in the repo.

Results

Results on common sense reasoning benchmarks

| Model | BoolQ | PIQA | HellaSwag | WinoGrande | ARC-e | ARC-c | OBQA | Avg. ||:--------------------------|:--------:|:--------:|:---------:|:----------:|:--------:|:--------:|:--------:|:--------:|| GPT4All-J 6B v1.0 | 73.4 | 74.8 | 63.4 | 64.7 | 54.9 | 36.0 | 40.2 | 58.2 || GPT4All-J v1.1-breezy | 74.0 | 75.1 | 63.2 | 63.6 | 55.4 | 34.9 | 38.4 | 57.8 || GPT4All-J v1.2-jazzy | 74.8 | 74.9 | 63.6 | 63.8 | 56.6 | 35.3 | 41.0 | 58.6 || GPT4All-J v1.3-groovy | 73.6 | 74.3 | 63.8 | 63.5 | 57.7 | 35.0 | 38.8 | 58.1 || GPT4All-J Lora 6B | 68.6 | 75.8 | 66.2 | 63.5 | 56.4 | 35.7 | 40.2 | 58.1 || GPT4All LLaMa Lora 7B | 73.1 | 77.6 | 72.1 | 67.8 | 51.1 | 40.4 | 40.2 | 60.3 || GPT4All 13B snoozy | **83.3** | 79.2 | 75.0 | **71.3** | 60.9 | 44.2 | 43.4 | **65.3** || Dolly 6B | 68.8 | 77.3 | 67.6 | 63.9 | 62.9 | 38.7 | 41.2 | 60.1 || Dolly 12B | 56.7 | 75.4 | 71.0 | 62.2 | 64.6 | 38.5 | 40.4 | 58.4 || Alpaca 7B | 73.9 | 77.2 | 73.9 | 66.1 | 59.8 | 43.3 | 43.4 | 62.4 || Alpaca Lora 7B | 74.3 | **79.3** | 74.0 | 68.8 | 56.6 | 43.9 | 42.6 | 62.8 || GPT-J 6.7B | 65.4 | 76.2 | 66.2 | 64.1 | 62.2 | 36.6 | 38.2 | 58.4 || LLama 7B | 73.1 | 77.4 | 73.0 | 66.9 | 52.5 | 41.4 | 42.4 | 61.0 || LLama 13B | 68.5 | 79.1 | 76.2 | 70.1 | 60.0 | **44.6** | 42.2 | 63.0 || Pythia 6.7B | 63.5 | 76.3 | 64.0 | 61.1 | 61.3 | 35.2 | 37.2 | 57.0 || Pythia 12B | 67.7 | 76.6 | 67.3 | 63.8 | 63.9 | 34.8 | 38 | 58.9 || Fastchat T5 | 81.5 | 64.6 | 46.3 | 61.8 | 49.3 | 33.3 | 39.4 | 53.7 || Fastchat Vicuña 7B | 76.6 | 77.2 | 70.7 | 67.3 | 53.5 | 41.2 | 40.8 | 61.0 || Fastchat Vicuña 13B | 81.5 | 76.8 | 73.3 | 66.7 | 57.4 | 42.7 | 43.6 | 63.1 || StableVicuña RLHF | 82.3 | 78.6 | 74.1 | 70.9 | 61.0 | 43.5 | **44.4** | 65.0 || StableLM Tuned | 62.5 | 71.2 | 53.6 | 54.8 | 52.4 | 31.1 | 33.4 | 51.3 || StableLM Base | 60.1 | 67.4 | 41.2 | 50.1 | 44.9 | 27.0 | 32.0 | 42.2 || Koala 13B | 76.5 | 77.9 | 72.6 | 68.8 | 54.3 | 41.0 | 42.8 | 62.0 || Open Assistant Pythia 12B | 67.9 | 78.0 | 68.1 | 65.0 | 64.2 | 40.4 | 43.2 | 61.0 || Mosaic mpt-7b | 74.8 | **79.3** | **76.3** | 68.6 | **70.0** | 42.2 | 42.6 | 64.8 || text-davinci-003 | 88.1 | 83.8 | 83.4 | 75.8 | 83.9 | 63.9 | 51.0 | 75.7 |