chatgpt-failures
text-generation-webui
chatgpt-failures | text-generation-webui | |
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20 | 876 | |
574 | 36,552 | |
- | - | |
1.2 | 9.9 | |
about 1 year ago | 6 days ago | |
Python | Python | |
- | GNU Affero General Public License v3.0 |
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chatgpt-failures
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OpenAI Research Says 80% of U.S. Workers' Jobs Will Be Impacted by GPT
Related / fun :
https://emaggiori.com/chatgpt-fails/
(I don't know the author or the book.)
https://github.com/giuven95/chatgpt-failures
"Plausible explanations
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People still behaving like everything is normal!
There is a whole list of ChatGPT failures here. Such as the time traveling user error. Why did it insist the user had time traveled? Because when it made the initial error it feed that error into the "attention" of the conversation, using matrix addition, as if it was an authoritative fact of the evolving conversation. That matrix is the only actual piece of the conversation it retains for transforming new input from that user. The matrix addition nonlinear, so ChatGPT can't simply unwind such a mistakes and redo the matrix addition with valid information. So, when pressed, it generates the most "probable" explanation based on the best fit to the existing matrix at that time that defines the "true" state of the conversation as defined by that matrix. The "attention" part of ChatGPT, which is the part that makes it so convincingly powerful, is a matrix state that cannot unwind itself and reflect on how it came to that state. There is, and cannot be, a self correction mechanism using such a mechanism (matrix) to define the state of a conversation.
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GPT-4
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
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Thoughts on ChatGPT and the future of e-commerce
As a result, it confidently asserts obvious inaccuracies, like "it takes 9 women 1 month to make a baby."
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Amid ChatGPT outcry, some teachers are inviting AI to class
Here's a github repo of chatGPT failing. There's a PopSci article describing what chatGPT is doing, why it's susceptible to errors: It's trying to predict what words are used together, irrespective of what information is correct.
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Just got Access !
You could try any of the prompts ChatGPT failed from here https://github.com/giuven95/chatgpt-failures
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The new Bing AI hallucinated during the Microsoft demo. A reminder these tools are not reliable yet
In this article, the author Dmitri Brereton shows some mistakes the Bing AI committed in the recent Microsoft demo. I have archived more failure case examples in this repo: https://github.com/giuven95/chatgpt-failures
- L'etica della robotica. Io contro chatGTP
- How worried are you about AI replacing you
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ChatGPT updated with improved factuality and mathematical capabilities.
Previously it said 1.
text-generation-webui
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Ask HN: What is the current (Apr. 2024) gold standard of running an LLM locally?
Some of the tools offer a path to doing tool use (fetching URLs and doing things with them) or RAG (searching your documents). I think Oobabooga https://github.com/oobabooga/text-generation-webui offers the latter through plugins.
Our tool, https://github.com/transformerlab/transformerlab-app also supports the latter (document search) using local llms.
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Ask HN: How to get started with local language models?
You can use webui https://github.com/oobabooga/text-generation-webui
Once you get a version up and running I make a copy before I update it as several times updates have broken my working version and caused headaches.
a decent explanation of parameters outside of reading archive papers: https://github.com/oobabooga/text-generation-webui/wiki/03-%...
a news ai website:
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text-generation-webui VS LibreChat - a user suggested alternative
2 projects | 29 Feb 2024
- Show HN: I made an app to use local AI as daily driver
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Ask HN: People who switched from GPT to their own models. How was it?
The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.
If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui
All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.
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AI Girlfriend Is a Data-Harvesting Horror Show
The example waifu in text-generation-webui is good enough for me.
https://github.com/oobabooga/text-generation-webui/blob/main...
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Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
> Downloading text-generation-webui takes a minute, let's you use any model and get going.
What you're missing here is you're already in this area deep enough to know what ooogoababagababa text-generation-webui is. Let's back out to the "average Windows desktop user" level. Assuming they even know how to find it:
1) Go to https://github.com/oobabooga/text-generation-webui?tab=readm...
2) See a bunch of instructions opening a terminal window and running random batch/powershell scripts. Powershell, etc will likely prompt you with a scary warning. Then you start wondering who ooobabagagagaba is...
3) Assuming you get this far (many users won't even get to step 1) you're greeted with a web interface[0] FILLED to the brim with technical jargon and extremely overwhelming options just to get a model loaded, which is another mind warp because you get to try to select between a bunch of random models with no clear meaning and non-sensical/joke sounding names from someone called "TheBloke". Ok...
Let's say you somehow braved this gauntlet and get this far now you get to chat with it. Ok, what about my local documents? text-generation-webui itself has nothing for that. Repeat this process over the 10 random open source projects from a bunch of names you've never heard of in an attempt to accomplish that.
This is "I saw this thing from Nvidia explode all over media, twitter, youtube, etc. I downloaded it from Nvidia, double-clicked, pointed it at a folder with documents, and it works".
That's the difference and it's very significant.
[0] - https://raw.githubusercontent.com/oobabooga/screenshots/main...
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Ask HN: What are your top 3 coolest software engineering tools?
Maybe a copout answer, but setting up a local LLM on my development machine has been invaluable. I use Deep Seek Coder 6.7 [0] and Oobabooga's UI [1]. It helps me solve simple problems and find bugs, while still leaving the larger architecture decisions to me.
[0] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instr...
[1] https://github.com/oobabooga/text-generation-webui
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Meta AI releases Code Llama 70B
You can download it and run it with [this](https://github.com/oobabooga/text-generation-webui). There's an API mode that you could leverage from your VS Code extension.
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Ollama Python and JavaScript Libraries
Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).
For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]
[1] https://github.com/oobabooga/text-generation-webui/issues/53...
[2] https://github.com/langroid/langroid/blob/main/langroid/lang...
Related question - I assume ollama auto detects and applies the right chat formatting template for a model?
What are some alternatives?
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
KoboldAI - KoboldAI is generative AI software optimized for fictional use, but capable of much more!
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
llama.cpp - LLM inference in C/C++
Milvus - A cloud-native vector database, storage for next generation AI applications
gpt4all - gpt4all: run open-source LLMs anywhere
reflex - 🕸️ Web apps in pure Python 🐍
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
KoboldAI-Client
othello_world - Emergent world representations: Exploring a sequence model trained on a synthetic task
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.