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.

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

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.

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

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

30B

• 30B-Epsilon-GPTQ

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

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

More Models

• Any of KoboldAI's Models

• Luna-AI-Llama2-Uncensored-GPTQ

• Nous-Hermes-Llama2-GGML

• Nous-Hermes-Llama2-GPTQ

• FreeWilly2-GPTQ

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!

https://huggingface.co/TheBloke

Currently testing

https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ

Giving this one a go!

https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ

cross-posted from: https://lemmy.world/post/1750098

Introducing Llama 2 - Meta's Next Generation Free Open-Source Artificially Intelligent Large Language Model

It's incredible it's already here! This is great news for everyone in free open-source artificial intelligence.

Llama 2 unleashes Meta's (previously) closed model (Llama) to become free open-source AI, accelerating access and development for large language models (LLMs).

This marks a significant step in machine learning and deep learning technologies. With this move, a widely supported LLM can become a viable choice for businesses, developers, and entrepreneurs to innovate our future using a model that the community has been eagerly awaiting since its initial leak earlier this year.

• Meta Announcement
• Meta Overview
• Github
• Paper

Here are some highlights from the official Meta AI announcement:

Llama 2

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases.

Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations.

Inside the Model

• Technical details

With each model download you'll receive:

• Model code
• Model Weights
• README (User Guide)
• Responsible Use Guide
• License
• Acceptable Use Policy
• Model Card

Benchmarks

Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. It was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations.

RLHF & Training

Llama-2-chat uses reinforcement learning from human feedback to ensure safety and helpfulness. Training Llama-2-chat: Llama 2 is pretrained using publicly available online data. An initial version of Llama-2-chat is then created through the use of supervised fine-tuning. Next, Llama-2-chat is iteratively refined using Reinforcement Learning from Human Feedback (RLHF), which includes rejection sampling and proximal policy optimization (PPO).

The License

Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.

Partnerships

We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2, cloud providers that will include the model as part of their offering to customers, researchers committed to doing research with the model, and people across tech, academia, and policy who see the benefits of Llama and an open platform as we do.

The/CUT

With the release of Llama 2, Meta has opened up new possibilities for the development and application of large language models. This free open-source AI not only accelerates access but also allows for greater innovation in the field.

Take Three:

• Video Game Analogy: Just like getting a powerful, rare (or previously banned) item drop in a game, Llama 2's release gives developers a powerful tool they can use and customize for their unique quests in the world of AI.
• Cooking Analogy: Imagine if a world-class chef decided to share their secret recipe with everyone. That's Llama 2, a secret recipe now open for all to use, adapt, and improve upon in the kitchen of AI development.
• Construction Analogy: Llama 2 is like a top-grade construction tool now available to all builders. It opens up new possibilities for constructing advanced AI structures that were previously hard to achieve.

Links

Here are the key resources discussed in this post:

• Meta Announcement
• Meta Overview
• Github
• Paper
• Technical details

Want to get started with free open-source artificial intelligence, but don't know where to begin?

Try starting here:

• FOSAI Welcome Message
• FOSAI Crash Course
• FOSAI Nexus Resource Hub

If you found anything else about this post interesting - consider subscribing to !fosai@lemmy.world where I do my best to keep you in the know about the most important updates in free open-source artificial intelligence.

This particular announcement is exciting to me because it may popularize open-source principles and practices for other enterprises and corporations to follow.

We should see some interesting models emerge out of Llama 2. I for one am looking forward to seeing where this will take us next. Get ready for another wave of innovation! This one is going to be big.

Looking at you, Bark_TTS!

I tried a few and eventually settled on Adobe Podcast. It's easy to generate whatever audio clips you want, and then you can clean them with Podcast and the free limit is more than enough.

Not sure why it took me so long to find this.

I just discovered this repo, it looks really useful for creating AI voices

https://github.com/rsxdalv/tts-generation-webui

Huggingface

This has been the best so far, some wierd behaviour sometimes, maybe that's my parameters though.

For some characters, this has been the best at keeping them in character and progressing the story!

https://huggingface.co/TheBloke

contains the latest exLlama SuperHOT 8K context models

Linked original reddit post, but this didn't work for me. I had to take a bunch of extra steps so I've written a tutorial. Original instructions here which I'll refer to, so you don't have to visit reddit. My revised tutorial with all instructions will follow this in the replies, please post questions as a new post in this community, I've locked this thread so that the tutorial remains easily accessible.

Zyin 24 points 2 months ago*

Instructions on how to get this setup if you've never used Jupyter before, like me. I'm not an expert at this, so don't respond asking for technical help.

If you've never done stuff that needs Python before, you'll need to install Pip and Git. Google for the download links. If you have Automatic1111 installed already you already have Pip and Git.

Install the repo. It will be installed in the folder where you open the cmd window:

git clone https://github.com/serp-ai/bark-with-voice-clone

Open a new cmd window in newly downloaded repo's folder (or cd into it) and run it's installation stuff:

pip install .

Install Jupyter notebook. It's basically Google Collab, but ran locally:

pip install jupyterlab (this one may not be needed, I did it anyway)

pip install notebook

If you are on windows, you'll need these to do audio code stuff with Python:

pip install soundfile

pip install ipywidgets

You need to have Torch 2 installed. You can do that with this command (will take a while to download/install):

pip3 install numpy --pre torch torchvision torchaudio --force-reinstall --index-url https://download.pytorch.org/whl/nightly/cu118

To check your current Torch version, open a new cmd window and type these in one at a time:

python import torch print(torch.__version__) #(mine says 2.1.0.dev20230421+cu118) ​

Now everything is installed. Create a folder called "output" in the bark folder, which will be needed later to prevent a permissions error.

Run Jupyter Notebook while in the bark folder:

jupyter notebook

This will open a new browser tab wit the Jupyter interface. Navigate to /notebooks/generate.ipynb

This is very similar to Google Collab where you run blocks of code. Click on the first block of code and click Run. If the code block has a "[*]" next to it, then it is still processing, just give it a minute to finish.

This will take a while and download a bunch of stuff.

If it manages to finish without errors, run blocks 2 and 3. In block 3, change the line to: filepath = "output/audio.wav" to prevent a permissions related error (remove the leading "/").

You can get different voices by changing the voice_name variable in block 1. Voices are installed at: bark\assets\prompts

​

For reference on my 3060 12GB, it took 90 seconds to generate 13 seconds of audio. The voice models that come out of the box create a robotic sounding voice, not even close to the quality of ElevenLabs. The voice that I created using /notebooks/clone_voice.ipynb with my own voice turned out terrible and was completely unusable, maybe I did something wrong with that, not sure.

If you want to test the voice clone using your own voice, and you record a voice sample using windows Voice Recorder, you can convert the .m4a file to .wav with ffmpeg (separate download):

ffmpeg -i "C:\Users\USER\Documents\Sound recordings\Recording.m4a" "C:\path\to\bark-with-voice-clone\ ___

really good tips here!

Tested it myself, huge improvement!

I'll try post as much good content here as possible to get it started