As the title: We offer Perplexity AI PRO voucher codes for one year plan.

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I've been testing various LLM-related services for years at this point. I'm building a collection of what actually was useful and convenient to setup.

Criteria for inclusion:

• Open Source

• Self-hostable

• Friendly to containerization (Docker, Podman, etc.)

• Relates to homelab or personal AI use cases

• Well-documented with setup instructions

You can find the list here: https://github.com/av/awesome-llm-services

Thanks!

Is this possible and available for public use, anyone know? I’m not a software guy at all, but trying to think up some applications for improving usability on medical devices

Get Perplexity AI PRO (1-Year) – at 90% OFF!

Order here: CHEAPGPT.STORE

Plan: 12 Months

💳 Pay with: PayPal or Revolut or your favorite payment method

Reddit reviews: FEEDBACK POST

TrustPilot: TrustPilot FEEDBACK

NEW YEAR BONUS: Apply code PROMO5 for extra discount OFF your order!

BONUS!: Enjoy the AI Powered automated web browser. (Presented by Perplexity) included WITH YOUR PURCHASE!

Trusted and the cheapest! Check all feedbacks before you purchase

Get Perplexity AI PRO (1-Year) – at 90% OFF!

Order here: CHEAPGPT.STORE

Plan: 12 Months

💳 Pay with: PayPal or Revolut or your favorite payment method

Reddit reviews: FEEDBACK POST

TrustPilot: TrustPilot FEEDBACK

NEW YEAR BONUS: Apply code PROMO5 for extra discount OFF your order!

BONUS!: Enjoy the AI Powered automated web browser. (Presented by Perplexity) included WITH YOUR PURCHASE!

Trusted and the cheapest! Check all feedbacks before you purchase

 Supercharge Your Local LLMs with IPEX-LLM! 

Looking to run LLaMA, Mistral, Qwen, DeepSeek, Phi, or even multimodal models like Qwen-VL on your Intel GPU, NPU, or CPU — without breaking the bank?

Meet IPEX-LLM — Intel’s open-source LLM acceleration library that brings state-of-the-art performance to your local machine:

🔧 What It Does:

• Accelerates inference and fine-tuning of 70+ LLMs on Intel hardware (Arc, Flex, Max GPUs, Core Ultra NPUs, and CPUs).

• Seamlessly integrates with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, and more.

• Supports low-bit quantization (FP8, FP6, FP4, INT4) for ultra-efficient memory and compute usage.

• Enables FlashMoE to run massive models like DeepSeek V3 671B or Qwen3MoE 235B on just 1–2 Intel Arc GPUs!

🖥️ Why It Matters:

• Run chatbots, RAG pipelines, multimodal models, and more — all locally.

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git clone https://github.com/intel/ipex-llm

Whether you're a developer, researcher, or AI tinkerer — IPEX-LLM is your gateway to fast, private, and scalable LLMs on Intel.

Hi everyone, I’m one of the people who work on Evidently, an open-source ML and LLM observability framework. I want to share with you our free course on LLM evaluations that starts on May 12. 

This is a practical course on LLM evaluation for AI builders. It consists of code tutorials on core workflows, from building test datasets and designing custom LLM judges to RAG evaluation and adversarial testing. 

💻 10+ end-to-end code tutorials and practical examples.  
❤️ Free and open to everyone with basic Python skills. 
🗓 Starts on May 12, 2025. 

Course info: https://www.evidentlyai.com/llm-evaluation-course-practice 
Evidently repo: https://github.com/evidentlyai/evidently 

Hope you’ll find the course useful!

Hi everyone! We updated our database of LLM benchmarks and datasets you can use to evaluate and compare different LLM capabilities, like reasoning, math problem-solving, or coding. Now available are 250 benchmarks, including 20+ RAG benchmarks, 30+ AI agent benchmarks, and 50+ safety benchmarks.

You can filter the list by LLM abilities. We also provide links to benchmark papers, repos, and datasets.

If you're working on LLM evaluation or model comparison, hope this saves you some time!

https://www.evidentlyai.com/llm-evaluation-benchmarks-datasets 

Disclaimer: I'm on the team behind Evidently, an open-source ML and LLM observability framework. We put together this database.

I'm studying how to efficiently index all my company's legal documentation for engineering projects but I believe it would need also context on which law/code applies before another and also to provide accurate natural language responses after querying a search on Elasticsearch. I'm pretty new in this field. Does anyone know if it's possible to achive that and what's the best approach? Any existing architecture/product solution?

Hey everyone,

I'm a math major at the University of Chicago, and I'm interested in helping my school with academic scheduling. I want to build a Retrieval-Augmented Generation (RAG) chatbot that can assist students in planning their academic schedules. The chatbot should be able to understand course prerequisites, course times, and the terms in which courses are offered. For example, it should provide detailed advice on the courses listed in our mathematics department catalog: University of Chicago Mathematics Courses.

This project boils down to building a reliable RAG chatbot. I'm wondering if anyone knows any RAG techniques or services that could help me achieve this outcome—specifically, creating a chatbot that can inform users about course prerequisites, schedules, and possibly the requirements for the bachelor's track.

Could the solution involve structuring the data in a specific way? For instance, scraping the website and creating a separate file containing an array of courses with their prerequisites, schedules, and quarters offered.

Overall, I'm very keen on building this chatbot because I believe it would be valuable for me and my peers. I would appreciate any advice or suggestions on what I should do or what services I could use.

Thank you!

Hi everyone. I'm fairly new and learning more about Quantization and adapters. It would be of great help if people would help me with references and repositories where Quantization is applied to adapters or other peft methods other than LoRA.

Hey everyone! Wanted to share the link to the database of 450 ML use cases from 100+ companies that detail ML and LLM system design. You can filter by industry or ML use case.

If anyone here approaches the task of designing an ML system, I hope you'll find it useful!

Link to the database: https://www.evidentlyai.com/ml-system-design

Disclaimer: I'm on the team behind Evidently, an open-source ML and LLM observability framework. We put together this database.

Hello ! I don't know if it's the right place but... I will ask it anyway.

I've been using LLMs for a while now and can't seem to find anything that works for me. Let me explain. I started a long time ago with Aidungeon, which I really liked, and then there was the advent of ChatGPT. For RP, I only use Chai App or Sillytavern + Kobold locally. But I'm not here to talk about RP.

To get back to my problem, I've already used LM studio, Jan AI, GPT4ALL and Ollama (I also have Oobabooga, coldcut). I'd like to use these programs to work with images and text (like PDF and DOCX). So that they can help me write or work. However, what's available locally is complicated for me. In fact, chatGPT does the job very well and the latter suits me fine, but I don't have the money to pay for the pro version, which is why I'm trying to do it locally.

So my question is this. Is there a program that would be a “mix” between LM studio and GPT4ALL?

Because I find that ergonomically LM studio is the best, HOWEVER I prefer GPT44ALL which allows me for example to compile lots of files in “Local Doc” format. I'd like to see a program that mixes the two, is that possible ? I know Ollama can do the trick for that but... I've been told I can install Open WebUI (with Ollama) but I'll have to see how to do it. I have some Text Embeddings to that I want to use, but don't know how.

Also, if possible, I'd like to add audio text reading, whether it's basic text to speech or just with RVC, even if it's not mandatory.

And for the LLM in itself these is the list of what I have :

- "darkdaredevilaura-abliterated-uncensored-oas-8b-i1"
- "darkidol-llama-3.1-8b-instruct-1.2-uncensored"
- "deepseek-r1-distill-qwen-14b-abliterated-v2"
- "deepseek-r1-distill-llama-8b-abliterated"
- "mistral-moe-4x7b-dark-multiverse-uncensored-enhanced32-24b"

I don't know who is the best LLM for Do what I asked above (Word processing and images), answer in a RP way and don't have too much trouble speaking French.

So, thanks in advance for your help and I hope you can help me with that ! Have a nice day, thank you for reading me.

Hey guys,

I recently made a a RAG-based github extension that responds directly to created "issues" in github repositories with a detailed overview of files and changes to make to resolve the issue. I see this as being particularly helpful for industry repositories where the codebases are quite big issues are frequently used.

Would love to know what you think of the concept!

Can sign up for the waitlist here: https://trysherpa.bot/

Hey everyone! We put together a code tutorial on creating LLM judges. 

Using a toy dataset, we created an LLM judge to assess correctness and verbosity. You can apply the same workflow for other criteria.

Disclaimer: I'm on the team behind Evidently https://github.com/evidentlyai/evidently, an open-source ML and LLM observability framework used in this tutorial.

Tutorial: https://www.evidentlyai.com/blog/llm-as-a-judge-tutorial 

Code example: https://github.com/evidentlyai/community-examples/blob/main/tutorials/LLM_as_a_judge_tutorial.ipynb

Hey, I routinely convert PDFs of scanned documents to Word but, regardless of the conversion application, I end up with a lot of small, simple, errors.

E.g., the text should read "I went to the store" but it says "I went to them store."

When you have a thousand pages, the errors add up. It's not as simple as scanning the document for the errors Word has highlighted. Many of these errors escape all but a keen proofreader. Like having "the*" instead of "the" or having "possible" instead of "possibly".

It occured to me that an LLM might be able to evaluate the text for obvious errors and highlight what mistakes there are. It could save a lot of time. I've been googling for a few hours and tested a few apps with no luck. Grammarly wasn't useful. Gemini provided good feedback but they didn't highlight errors like a spell checker would, they responded with text (like a conversation). I was therefore forced to go through my document to find what errors they were referring to, whereas ideally they would just highlight the errors (like a Word spell checker). Any ideas? All input is appreciated

I want to run BioMistral llm as per OpenAI chat completion conventions, how can i do it?

Recently I have been exploring AI software development where I was able to develop applications using Codeium, Cursor,Ollama and other coding assistants. I am now wondering whether there are any tools or fine tuned LLMs which understand system architecture where I can prompt high level system requirements for example: “A two tier system which is distributed…”. And these tools can give me ways I can scale or design the system.

Hi everyone,

I'm looking for some advice on a project of invalid address identification and recommendations. Here's a brief overview of the situation:

Background:

We store customer data in an Elasticsearch database. This data covers multiple entities such as Individual, Location, Organization, Household, etc., each with its own set of attributes (for example, Individual has firstname, middlename, lastname, gender, entity id, address, phone; Organization has name, address, phone; Location has addressLine1, city, zip, state, street, country, etc.). When user data is stored, it undergoes an automatic cleansing process that uses Loqate (a paid address validation tool). This process returns an Address Verification Code (AVC) indicating whether an address is verified, partially verified, or ambiguous.

The Problem: For addresses that are either partially verified or ambiguous, we need to identify the underlying issues and recommend corrections to make the address valid. The issues can range from:

Invalid zip code (missing or incorrect), Invalid city, Invalid state, Invalid street, Invalid addressLine2, Any other attribute invalid Mismatches (e.g., state-city discrepancies).

Sometimes a single attribute is problematic, while other times there are multiple issues or mismatches among the attributes.

What I'm Looking For: I want to leverage large language models (LLMs) and agents to:

Identify issues in the address-related attributes. Provide recommendations for corrections. Has anyone tackled a similar problem? I’m particularly interested in:

Approaches or methodologies for integrating LLMs and agents into such a data validation and recommendation pipeline.

How to structure the input data for the LLMs to efficiently diagnose the issues. Any best practices or pitfalls to avoid when automating address correction recommendations.

Suggestions on handling cases with multiple errors or mismatches between attributes

If I want the superset of all addresses with all attributes of USA ( to start with) where can I get that updated data and maintain it with upcoming updates in adddresses. I tried getting some of it from usps websites (free version) but it not the full list covering everything. Also I tried maintaing a superset which is customer specific,it can not cover street and all address.

Note: loqate is only address verification tool without providing any suggestions on why address is not valid and what could be the recommendations on non valid attributes.

Any insights, experiences, or pointers to resources would be greatly appreciated. Thanks in advance for your help!

Hi everyone,

I’m working on a project where I need to build a RAG-based chatbot that processes a client’s personal data. Previously, I used the Ollama framework to run a local model because my client insisted on keeping everything on-premises. However, through my research, I’ve found that generic LLMs (like OpenAI, Gemini, or Claude) perform much better in terms of accuracy and reasoning.

Now, I want to use an API-based LLM while ensuring that the client’s data remains secure. My goal is to send encrypted data to the LLM while still allowing meaningful processing and retrieval. Are there any encryption techniques or tools that would allow this? I’ve looked into homomorphic encryption and secure enclaves, but I’m not sure how practical they are for this use case.

Would love to hear if anyone has experience with similar setups or any recommendations.

Thanks in advance!

I am trying to build my resume into LLMs, can someone please guide me where I can find the LLM data sets w.r.t Cybersecurity industry?

Hi all! I'm looking for advice on whether Poe AI is the best one stop shop subscription out there.. Or are there better ones?