GPT-4

• askai

  1,748 86 10.0 TypeScript

  Command Line Interface for OpenAi ChatGPT (by yudax42)

It says you can use GPT-4 with ChatGPT-Plus.

But when will https://chat.openai.com/ Plus officially be running GPT-4?

Why did they would release this article and state it was available without actually updating the site. I'm sure they're getting flooded with new subscriptions and it's not available.

The top URL still says an old model - text-davinci-002. And I don't see GPT-4 in the list of models to choose from.

• evals

  49 13,920 9.3 Python

  Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

  

here is GPT3 summarizing GPT4:

- OpenAI has developed GPT-4, a large multimodal model that accepts both image and text inputs and generates text outputs.

- While it may not perform as well as humans in real-world situations, GPT-4 exhibits human-level performance on various professional and academic benchmarks.

- OpenAI spent six months aligning GPT-4 iteratively using lessons from their adversarial testing program and ChatGPT, resulting in their best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

- OpenAI has released GPT-4's text input capability via [ChatGPT](https://beta.openai.com/docs/api-reference/generations/creat...) and the [API](https://beta.openai.com/docs/api-reference/introduction) (with a waitlist).

- They're also collaborating closely with a single partner to prepare the image input capability for wider availability.

- Additionally, they've open-sourced [OpenAI Evals](https://github.com/openai/evals), their framework for automated evaluation of AI model performance, to allow anyone to report shortcomings in their models to help guide further improvements.

Note: For more detailed information and technical explanations, please refer to [OpenAI's GPT-4 page](https://openai.com/research/gpt-4) and their [technical report](https://d4mucfpksywv.cloudfront.net/better-language-models/l...).

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Open-Assistant

  329 36,647 8.3 Python

  OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.

  

By supporting, participating in and using projects like this by LAION:

https://open-assistant.io

• chatgpt-failures

  20 574 1.2 Python

  Failure archive for ChatGPT and similar models

Is anybody compiling a list of errors specific to GPT-4?

This has been a great resource to-date:

https://github.com/giuven95/chatgpt-failures

• Milvus

  104 26,979 10.0 Go

  A cloud-native vector database, storage for next generation AI applications

  

One way to get around context length is to perform embedding and retrieval of your entire corpus. Langchain (https://langchain.readthedocs.io/en/latest/) and Milvus (https://milvus.io) is one of the stacks you can use.

• llama_index

  75 31,184 10.0 Python

  LlamaIndex is a data framework for your LLM applications

  

There’s already project that help with going beyond the context window limitation like https://github.com/jerryjliu/llama_index

They also just tweeted this to showcase how it can work with multimodal data too: https://twitter.com/gpt_index/status/1635668512822956032?s=4...

• reflex

  76 16,673 9.9 Python

  🕸️ Web apps in pure Python 🐍

  

Pynecone YC23 was mentioned in the demo for GPT4 as an easy way to build web apps. Check it out https://pynecone.io/

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

• stanford_alpaca

  108 28,816 2.0 Python

  Code and documentation to train Stanford's Alpaca models, and generate the data.

  

https://github.com/tatsu-lab/stanford_alpaca

Tada! Literally runs on a raspberry pi (very slowly).

GPT models are incredible but the future is somehow even more amazing than that.

I suspect this will be the approach for legal / medical uses (if regulation allows).

• llama.cpp

  773 56,891 10.0 C++

  LLM inference in C/C++

https://github.com/ggerganov/llama.cpp/issues/124

This is about running llama on a Raspberry Pi:

• llama

  184 53,053 8.1 Python

  Inference code for Llama models

  

https://github.com/ggerganov/llama.cpp/issues/58

...and this is where people have been posting their results running on all sorts of hardware, though I don't see anything Android related: https://github.com/facebookresearch/llama/issues/79

Obviously the larger models won't run on such limited hardware (yet) but one of the next big projects (that I can see) being worked on is converting the models to be 3bit (currently 8bit and 4bit are popular) which cuts down required resources drastically with minimal noticeable loss in quality.

I think starting with FlexGen barely 4 weeks ago, there have been some pretty crazy LLM projects/forks popping up on github almost daily. With FlexGen I felt like I was still able to stay up-to-date but I'm getting close to giving up trying as things are moving exponentially faster... you know it's crazy when a ton of noobs who have never heard of conda are getting this stuff running (sometimes coming in flexgen discord or posting github issues to get help, though even those are becoming rarer as one-click-installer's are becoming a thing for some popular ML tools, such as oobabooga's amazing webui tool which has managed to integrate almost all the hottest new feature forks fairly quickly: https://github.com/oobabooga/text-generation-webui

I just helped someone recently get oobabooga running which has a --listen open to open the webui to your network, now he's running llama on his tablet (via his PC).

• text-generation-webui

  876 36,293 9.9 Python

  A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

https://github.com/ggerganov/llama.cpp/issues/58

...and this is where people have been posting their results running on all sorts of hardware, though I don't see anything Android related: https://github.com/facebookresearch/llama/issues/79

Obviously the larger models won't run on such limited hardware (yet) but one of the next big projects (that I can see) being worked on is converting the models to be 3bit (currently 8bit and 4bit are popular) which cuts down required resources drastically with minimal noticeable loss in quality.

I think starting with FlexGen barely 4 weeks ago, there have been some pretty crazy LLM projects/forks popping up on github almost daily. With FlexGen I felt like I was still able to stay up-to-date but I'm getting close to giving up trying as things are moving exponentially faster... you know it's crazy when a ton of noobs who have never heard of conda are getting this stuff running (sometimes coming in flexgen discord or posting github issues to get help, though even those are becoming rarer as one-click-installer's are becoming a thing for some popular ML tools, such as oobabooga's amazing webui tool which has managed to integrate almost all the hottest new feature forks fairly quickly: https://github.com/oobabooga/text-generation-webui

I just helped someone recently get oobabooga running which has a --listen open to open the webui to your network, now he's running llama on his tablet (via his PC).

• othello_world

  2 149 4.9 Jupyter Notebook

  Emergent world representations: Exploring a sequence model trained on a synthetic task

"You keep using that word..." ;)

Pre-training refers to unsupervised training that's done before a model is fine-tuned. The model still starts out random before it's pre-trained.

Here's where the Othello paper's weights are (randomly) initialized:

https://github.com/likenneth/othello_world/blob/master/mingp...

Suggest a related project