Trying something new, going to pin this thread as a place for beginners to ask what may or may not be stupid questions, to encourage both the asking and answering.

Depending on activity level I'll either make a new one once in awhile or I'll just leave this one up forever to be a place to learn and ask.

When asking a question, try to make it clear what your current knowledge level is and where you may have gaps, should help people provide more useful concise answers!

cross-posted from: https://lemmy.world/post/2219010

Hello everyone!

We have officially hit 1,000 subscribers! How exciting!! Thank you for being a member of !fosai@lemmy.world. Whether you're a casual passerby, a hobby technologist, or an up-and-coming AI developer - I sincerely appreciate your interest and support in a future that is free and open for all.

It can be hard to keep up with the rapid developments in AI, so I have decided to pin this at the top of our community to be a frequently updated LLM-specific resource hub and model index for all of your adventures in FOSAI.

The ultimate goal of this guide is to become a gateway resource for anyone looking to get into free open-source AI (particularly text-based large language models). I will be doing a similar guide for image-based diffusion models soon!

In the meantime, I hope you find what you're looking for! Let me know in the comments if there is something I missed so that I can add it to the guide for everyone else to see.

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Getting Started With Free Open-Source AI

Have no idea where to begin with AI / LLMs? Try starting with our Lemmy Crash Course for Free Open-Source AI.

When you're ready to explore more resources see our FOSAI Nexus - a hub for all of the major FOSS & FOSAI on the cutting/bleeding edges of technology.

If you're looking to jump right in, I recommend downloading oobabooga's text-generation-webui and installing one of the LLMs from TheBloke below.

Try both GGML and GPTQ variants to see which model type performs to your preference. See the hardware table to get a better idea on which parameter size you might be able to run (3B, 7B, 13B, 30B, 70B).

8-bit System Requirements

Model	VRAM Used	Minimum Total VRAM	Card Examples	RAM/Swap to Load*
LLaMA-7B	9.2GB	10GB	3060 12GB, 3080 10GB	24 GB
LLaMA-13B	16.3GB	20GB	3090, 3090 Ti, 4090	32 GB
LLaMA-30B	36GB	40GB	A6000 48GB, A100 40GB	64 GB
LLaMA-65B	74GB	80GB	A100 80GB	128 GB

4-bit System Requirements

Model	Minimum Total VRAM	Card Examples	RAM/Swap to Load*
LLaMA-7B	6GB	GTX 1660, 2060, AMD 5700 XT, RTX 3050, 3060	6 GB
LLaMA-13B	10GB	AMD 6900 XT, RTX 2060 12GB, 3060 12GB, 3080, A2000	12 GB
LLaMA-30B	20GB	RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100	32 GB
LLaMA-65B	40GB	A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000	64 GB

*System RAM (not VRAM), is utilized to initially load a model. You can use swap space if you do not have enough RAM to support your LLM.

When in doubt, try starting with 3B or 7B models and work your way up to 13B+.

FOSAI Resources

Fediverse / FOSAI

• The Internet is Healing
• FOSAI Welcome Message
• FOSAI Crash Course
• FOSAI Nexus Resource Hub

LLM Leaderboards

• HF Open LLM Leaderboard
• LMSYS Chatbot Arena

LLM Search Tools

• LLM Explorer
• Open LLMs

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Large Language Model Hub

Download Models

oobabooga

text-generation-webui - a big community favorite gradio web UI by oobabooga designed for running almost any free open-source and large language models downloaded off of HuggingFace which can be (but not limited to) models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and many others. Its goal is to become the AUTOMATIC1111/stable-diffusion-webui of text generation. It is highly compatible with many formats.

Exllama

A standalone Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs.

gpt4all

Open-source assistant-style large language models that run locally on your CPU. GPT4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer-grade processors.

TavernAI

The original branch of software SillyTavern was forked from. This chat interface offers very similar functionalities but has less cross-client compatibilities with other chat and API interfaces (compared to SillyTavern).

SillyTavern

Developer-friendly, Multi-API (KoboldAI/CPP, Horde, NovelAI, Ooba, OpenAI+proxies, Poe, WindowAI(Claude!)), Horde SD, System TTS, WorldInfo (lorebooks), customizable UI, auto-translate, and more prompt options than you'd ever want or need. Optional Extras server for more SD/TTS options + ChromaDB/Summarize. Based on a fork of TavernAI 1.2.8

Koboldcpp

A self contained distributable from Concedo that exposes llama.cpp function bindings, allowing it to be used via a simulated Kobold API endpoint. What does it mean? You get llama.cpp with a fancy UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. In a tiny package around 20 MB in size, excluding model weights.

KoboldAI-Client

This is a browser-based front-end for AI-assisted writing with multiple local & remote AI models. It offers the standard array of tools, including Memory, Author's Note, World Info, Save & Load, adjustable AI settings, formatting options, and the ability to import existing AI Dungeon adventures. You can also turn on Adventure mode and play the game like AI Dungeon Unleashed.

h2oGPT

h2oGPT is a large language model (LLM) fine-tuning framework and chatbot UI with document(s) question-answer capabilities. Documents help to ground LLMs against hallucinations by providing them context relevant to the instruction. h2oGPT is fully permissive Apache V2 open-source project for 100% private and secure use of LLMs and document embeddings for document question-answer.

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Models

The Bloke

The Bloke is a developer who frequently releases quantized (GPTQ) and optimized (GGML) open-source, user-friendly versions of AI Large Language Models (LLMs).

These conversions of popular models can be configured and installed on personal (or professional) hardware, bringing bleeding-edge AI to the comfort of your home.

Support TheBloke here.

• https://ko-fi.com/TheBlokeAI

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70B

• Llama-2-70B-chat-GPTQ

• Llama-2-70B-Chat-GGML

• Llama-2-70B-GPTQ

• Llama-2-70B-GGML

• llama-2-70b-Guanaco-QLoRA-GPTQ

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30B

• 30B-Epsilon-GPTQ

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13B

• Llama-2-13B-chat-GPTQ

• Llama-2-13B-chat-GGML

• Llama-2-13B-GPTQ

• Llama-2-13B-GGML

• llama-2-13B-German-Assistant-v2-GPTQ

• llama-2-13B-German-Assistant-v2-GGML

• 13B-Ouroboros-GGML

• 13B-Ouroboros-GPTQ

• 13B-BlueMethod-GGML

• 13B-BlueMethod-GPTQ

• llama-2-13B-Guanaco-QLoRA-GGML

• llama-2-13B-Guanaco-QLoRA-GPTQ

• Dolphin-Llama-13B-GGML

• Dolphin-Llama-13B-GPTQ

• MythoLogic-13B-GGML

• MythoBoros-13B-GPTQ

• WizardLM-13B-V1.2-GPTQ

• WizardLM-13B-V1.2-GGML

• OpenAssistant-Llama2-13B-Orca-8K-3319-GGML

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7B

• Llama-2-7B-GPTQ

• Llama-2-7B-GGML

• Llama-2-7b-Chat-GPTQ

• LLongMA-2-7B-GPTQ

• llama-2-7B-Guanaco-QLoRA-GPTQ

• llama-2-7B-Guanaco-QLoRA-GGML

• llama2_7b_chat_uncensored-GPTQ

• llama2_7b_chat_uncensored-GGML

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More Models

• Any of KoboldAI's Models

• Luna-AI-Llama2-Uncensored-GPTQ

• Nous-Hermes-Llama2-GGML

• Nous-Hermes-Llama2-GPTQ

• FreeWilly2-GPTQ

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GL, HF!

Are you an LLM Developer? Looking for a shoutout or project showcase? Send me a message and I'd be more than happy to share your work and support links with the community.

If you haven't already, consider subscribing to the free open-source AI community at !fosai@lemmy.world where I will do my best to make sure you have access to free open-source artificial intelligence on the bleeding edge.

Thank you for reading!

Mistral Small 22B just dropped today and I am blown away by how good it is. I was already impressed with Mistral NeMo 12B's abilities, so I didn't know how much better a 22B could be. It passes really tough obscure trivia that NeMo couldn't, and its reasoning abilities are even more refined.

With Mistral Small I have finally reached the plateu of what my hardware can handle for my personal usecase. I need my AI to be able to at least generate around my base reading speed. The lowest I can tolerate is 1.5~T/s lower than that is unacceptable. I really doubted that a 22B could even run on my measly Nvidia GTX 1070 8G VRRAM card and 16GB DDR4 RAM. Nemo ran at about 5.5t/s on this system, so how would Small do?

Mistral Small Q4_KM runs at 2.5T/s with 28 layers offloaded onto VRAM. As context increases that number goes to 1.7T/s. It is absolutely usable for real time conversation needs. I would like the token speed to be faster sure, and have considered going with the lowest Q4 recommended to help balance the speed a little. However, I am very happy just to have it running and actually usable in real time. Its crazy to me that such a seemingly advanced model fits on my modest hardware.

Im a little sad now though, since this is as far as I think I can go in the AI self hosting frontier without investing in a beefier card. Do I need a bigger smarter model than Mistral Small 22B? No. Hell, NeMo was serving me just fine. But now I want to know just how smart the biggest models get. I caught the AI Acquisition Syndrome!

I just found https://www.arliai.com/ who offer LLM inference for quite cheap. Without rate-limits and unlimited token generation. No-logging policy and they have an OpenAI compatible API.

I've been using runpod.io previously but that's a whole different service as they sell compute and the customers have to build their own Docker images and run them in their cloud, by the hour/second.

Should I switch to ArliAI? Does anyone have some experience with them? Or can recommend another nice inference service? I still refuse to pay $1.000 for a GPU and then also pay for electricity when I can use some $5/month cloud service and it'd last me 16 years before I reach the price of buying a decent GPU...

Edit: Saw their $5 tier only includes models up to 12B parameters, so I'm not sure anymore. For larger models I'd need to pay close to what other inference services cost.

I'm currently using SuperNormal to taking meeting minutes for all of my Teams, Google Meet, and Zoom conference calls. Is there a workflow for doing this locally with Whisper and some other tools? I haven't found one yet.

Only recently did I discover the text-to-music AI companies (udio.com, suno.com) and I was surprised about how good the results are. Both are under lawsuit from RIAA.

I am curious if there are any local ones I can experiment with or train myself. I know there is facebook/musicgen-large on HuggingFace. That model is over 1 year old and there might be others by now. Also, based on the card I get the feeling that model is not going to be good at doing specific song lyrics (maybe the lyrics just were absent from the training data?). I am most interested in trying my hand at writing songs and fine-tuning a model on specific types of music to get the sounds I am looking for.

Hello y'all, i was using this guide to try and set up llama again on my machine, i was sure that i was following the instructions to the letter but when i get to the part where i need to run setup_cuda.py install i get this error

File "C:\Users\Mike\miniconda3\Lib\site-packages\torch\utils\cpp_extension.py", line 2419, in _join_cuda_home raise OSError('CUDA_HOME environment variable is not set. ' OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root. (base) PS C:\Users\Mike\text-generation-webui\repositories\GPTQ-for-LLaMa>

i'm not a huge coder yet so i tried to use setx to set CUDA_HOME to a few different places but each time doing echo %CUDA_HOME doesn't come up with the address so i assume it failed, and i still can't run setup_cuda.py

Anyone have any idea what i'm doing wrong?