GPT-4

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Can accept prompts of both text and images. This means that it can take images as well as text as input, giving it the ability to describe the humor in unusual images, summarize text from screenshots, and answer exam questions that contain diagrams. It has 170 trillion parameters, which is 1,000 times larger than GPT-2 and nearly 1,000 times larger than GPT-3. GPT-4 is based on eight models with 220 billion parameters each, for a total of about 1.76 trillion parameters. GPT-4 is suitable for language translation because it can generate text similar to that which would be produced by a human. GPT-4's short-term memory extends to around 64,000 words, while GPT-3.5's short-term memory is around 8,000 words..


GPT-4, known as Prometheus can be used on:


One of ChatGPT-4’s most dazzling new features is the ability to handle not only words, but pictures too, in what is being called “multimodal” technology. A user will have the ability to submit a picture alongside text — both of which ChatGPT-4 will be able to process and discuss. The ability to input video is also on the horizon. - Everything You Need to Know About ChatGPT-4 | Alex Millson - Bloomberg, Time


GPT4All

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A chatbot trained on a massive collection of clean assistant data including code, stories and dialogue. Demo, data and code to train an assistant-style large language model with ~800k GPT-3.5-Turbo Generations based on LLaMa

GPT4ALL: Install 'ChatGPT' Locally (weights & fine-tuning!) - Tutorial
Matthew Berman - In this video, I walk you through installing the newly released GPT4ALL large language model on your local computer. This model is brought to you by the fine people at Nomic AI, furthering the open-source LLM mission. GPT4ALL is trained using the same technique as Alpaca, which is an assistant-style large language model with ~800k GPT-3.5-Turbo Generations based on LLaMa. IMO, it works even better than Alpaca and is super fast. This is basically like having ChatGPT on your local computer. Easy install. Nomic AI was also kind enough to include the weights in addition to the quantized model.

Is GPT4All your new personal ChatGPT?
In this video we are looking at the GPT4ALL model which an interesting (even though not for commercial use) project of taking a LLaMa model and finetuning with a lot more instruction tasks than Alpaca.

PrivateGPT

PrivateGPT, which allows you to chat directly with your documents (PDF, TXT, and CSV) completely locally, securely, privately, and open-source. PrivateGPT is a project that uses GPT4All to achieve a specific task, i.e. querying over documents using the LangChain framework. It does this by using the GPT4All model, however, any model can be used and sentence_transformer embeddings, which can also be replaced by any embeddings that LangChain supports. PrivateGPT is built with LangChain, GPT4All, LlamaCpp, Chroma and SentenceTransformers. You can ingest documents and ask questions without an internet connection. PrivateGPT works by ingesting your documents into a vector store and then using a Large Language Model (LLM) to answer questions about the information contained in those documents. PrivateGPT can be used offline without connecting to any online servers or adding any API keys from OpenAI or Pinecone. To facilitate this, it runs an Large Language Model (LLM) locally on your computer. This makes it possible to use PrivateGPT without an internet connection and ensures that your data remains private and secure. You can set up PrivateGPT by installing the required dependencies, downloading the LLM, and configuring the environment variables in the `.env` file¹. Once set up, you can ingest your documents into the vector store and then use PrivateGPT to ask questions about the information contained in those documents.

SentenceTransformers is a Python framework for state-of-the-art sentence, text, and image embeddings. It is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. You can use this framework to compute sentence/text embeddings for more than 100 languages. These embeddings can then be compared, for example, with cosine similarity to find sentences with a similar meaning. This can be useful for semantic textual similarity, semantic search, or paraphrase mining. You can install the Sentence Transformers library using pip: pip install -U sentence-transformers


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