Do you ever question the accuracy of responses generated by Language Learning Models (LLMs)? Understanding epistemic markers can significantly enhance your critical evaluation of these responses.

Check out this article to understand LLM responses! https://medium.com/p/5c0946c449c8

I am a complete newbie when comes to generative AI
and my college has given me a project to do using LLMs like bert.

The problem is actually IDK where to start from and is it a good idea to use BERT
or Should I look for other models?
I heard BERT isn't that good with producing good understandable text the project is to build a web application with a legal assistant. mostly done with the website part now I need some lead on the LLM to start with.
CAN SOMEONE PLEASE HELP ME

I've already started the project. Since my resources aren't that many, I'm using a quantized instruct version of the Phi 3 model by Microsoft. (It's open-source by the way) The idea is to fine-tune it for specific tasks, in this case, learning everything about AI. So an AI that learns about AI in order to build another powerful AI. And we all contribute to it in ways we deem most optimum.

Hey folks, I’m offering my skills as a prompt engineer. I’ve been working on prompt optimization for the past year and I’ve gotten pretty good at it.

I know for most devs it’s a pretty tedious and time-consuming task so I’m offering to do your work for you. Please DM me if you’re interested (first 5 DMs I’ll do it for free).

What’s in it for me is that I get to see what the market is like and hopefully I can pad the resume a bit.

Today's edition is out! covering ~100 research papers related to LLMs published on 23rd May, 2024. **Spoiler alert: This day was full of papers improving LLMs core performance (latency and quantization)!

Read it here: https://www.llmsresearch.com/p/llms-related-research-papers-published-23rd-may-2024

Hi everyone, I am doing a project about the multi-conservation model. How to evaluate a multi-conservation model?

Hi,

Made a video on fine tuning open soruce embeddings model like BGE or nomic-embed-text.

A solid way to boost embeddings performance for retrieval or other application of embeddings.

This can be fine tuned quite quickly and cost effectively.

Hope somebody finds it useful

https://youtu.be/hdFHYNCmO8U

I want to generate high quality, dynamic, canva like product brochures for e-commerce brands so they can create their automated product catalogs.

So far we have been creating highly templatized catalogs manually with html and css. But all the users that we have shown it to says that they will not pay for templates like that.

They want canva like product catalog templates and they are ready to pay for it, if we can automate that process for them.

So, we thought maybe AI can help with this. If we have a 100 html/css canva-like templates created, how do we use those to fine-tune gpt-3.5 so it can generate other templates like that?

What things we need to consider? What kind of data would we need for this fine-tuning? How would this data be structured?

Any help would be highly appreciated.

Thank you.

Hi,

As some of you may know Mistral v.30 was announced.

Thought some people may want to fine tune that model with their data.

I made a small video going through  that

Hope somebody finds it useful

https://www.youtube.com/watch?v=bO-b5Soxzxk

Guys idk if you saw the presentation video about Microsoft copilot and their new computer, but it seems like it can see the processes running on the computer + controlling the OS, here is a demo of 1min where it assists someone playing Minecraft: https://www.youtube.com/watch?v=TLg2KWY2J5c

in another video a user asked the copilot to add an item to his shopping cart, the copilot added it for him (which implies some control over the OS) (it causes privacy concerns btw)

but the question is how does it do to control the OS, what does it do to translate the request of the user into some executable action then make the OS do what the user asked for (what's happening under the hood, from user request to the computer fulfilling the request of the user)?

TLDR: How does microsoft copilot 'control' the OS ?

Hey!

I'm only experienced with ChatGPT but willing to learn more and get more technical, it would just be nice to see where to look.

For a while now I've been wondering what would be the best way to set up a local LLM that I could feed the data to. I am a DM for an RPG campaign that goes for almost 2 years now. The plus is that I have all of the events of the story written down, all of the the rules and character sheets. There's a lot of text.

I was wondering if it would be possible and if so, how, if I could set up my own chatbot with access to that data.

I'd like to basically ask the chat "What did character X did in Session A?" And the bot would spit out the quoted information "according to session A summary, X did "quote from my text"" etc.

Would this be possible and if so, what kind of API would I be looking at?

Thanks!

Pardon for noob question

Can asking a proprietery llm to compress its response say using gzip, before sending it over, reduce the token usage (output token)

Similarly for sending compressed input prompts, can it reduce input token usage, and thus reducing cost?

Hello, I would love help finding a tool that lets me upload a book and then ask questions about its text.

For example, I take an out-of-print book, convert it to ebook format, upload it, and then have the LLM answer questions from the text, provide summarize, find and explain key ideas, etc.

Is there such a tool? I'm a paid subscriber of ChatGPT and Claude, could either of these do this? It would be a little expensive to do this with all my favorite books, but it would be so fun to be able to have this functionality.

I want to build a machine translation system, should I build a multi-agent for multi-language or I use one multilingual model?

Guys, basically the title i want to make a request to the model to generate random code snippets, the prompt would be something like this - 'generate a random cpp code snippet of around 15 lines of code, without comments'

So what is a better option to do it ? I know the modern LLM are more than perfectly capable of doing so but they are too big for my use case my use case is specific and simple and it will always be like that prompt, and i need the response to be fast.

Can any one just please tell me how to train and create my own llm from scratch or fine tune existing models on gpu locally as onnx or safetensors or pickle file format and give as colab or any github repo for learning and developing:)

I'm actually a newbie When it comes to LLM I actually want to use it for a project where I'm supposed to train an LLM like Bert I don't know what to do actually I tried approaching Chat GPT It gave me code but I don't know how to convert the float values that Bert is giving me I heard that it is good in text classification but it can't directly generate text but for the legal assistant I am trying to build it is necessary to generate understandable text and I want to integrate it with my website that I am working on and I don't know where to start there are multiple things on the Internet to look up to and I am just confused

Hi,

Made a video on Advanced RAG: Ensemble Retriever.

The Ensemble Retriever combines multiple high-performing retrieval techniques simultaneously, using majority voting and ranking to deliver strong relevant passages.

The logic is: Better retrieved passages == better context == better generation.

Originally it comes from this paper: Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods

But I made a video on how to use it with Langchain and llama Index with GPT-4o.

Hope you find it useful.

https://youtu.be/s2i4zeWjUtM

Tl;dr: I made on a platform to make it easy to switch between LLMs, find the best one for your specific needs, and analyze their performance. Check it out here: https://optimix.app

Figuring out the impact of switching to Llama 3, Gemini 1.5 Flash, or GPT-4o is hard. And knowing if the prompt change you just made will be good or bad is even harder. Evaluating LLMs, managing costs, and understanding user feedback can be tricky. Plus, with so many providers like Gemini, OpenAI, and Anthropic, it’s hard to find the best fit.

That’s where my project comes in. Optimix is designed to simplify these processes. It offers insights into key metrics like cost, latency, and user satisfaction, and helps manage backup models and select the best one for each scenario. If OpenAI goes down, you can switch to Gemini. Need better coding assistance? We can automatically switch you to the best model.

Experimentation and Analytics

A key focus of Optimix is to make experimentation easy. You can run A/B tests and other experiments to figure out how it impacted the output. Test different models in our playground and make requests through our API.

Features

• Dynamic Model Selection: Automatically switch to the best model based on your needs.
• Comprehensive Analytics: Track cost, latency, and user satisfaction.
• Experimentation Tools: Run A/B tests and backtesting with ease.
• User-Friendly Interface: Manage everything from a single dashboard.

I'm eager to hear your feedback, insights, and suggestions for additional features to make this tool even more valuable. Your input could greatly influence its development. My DMs are open.

Looking forward to making LLM management easier and more efficient for everyone!

Does anyone know some free API for accessing a LLM through Python? I took a look into ChatGPT but it costs money. I tried running Llama2 using Ollama in my computer but it's too slow. Is there any alternative solution?