LocalLLaMa
Magazine to talk about LLaMA (large language model created by Meta AI) and any related Open Source LLMs. Inspired by Reddit's /r/LocalLLaMA/ subreddit.
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 [email protected]. 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
LLM Leaderboards
LLM Search Tools
Large Language Model Hub
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.
70B
30B
13B
7B
More Models
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 [email protected] 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!
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.
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
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:
Want to get started with free open-source artificial intelligence, but don't know where to begin?
Try starting here:
If you found anything else about this post interesting - consider subscribing to [email protected] 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.
cross-posted from: https://lemmy.world/post/1305651
OpenLM-Research has Released OpenLLaMA: An Open-Source Reproduction of LLaMA
TL;DR: OpenLM-Research has released a public preview of OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA. We are releasing a series of 3B, 7B and 13B models trained on different data mixtures. Our model weights can serve as the drop in replacement of LLaMA in existing implementations.
In this repo, OpenLM-Research presents a permissively licensed open source reproduction of Meta AI's LLaMA large language model. We are releasing a series of 3B, 7B and 13B models trained on 1T tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. The v2 model is better than the old v1 model trained on a different data mixture.
This is pretty incredible news for anyone working with LLaMA or other open-source LLMs. This allows you to utilize the vast ecosystem of developers, weights, and resources that have been created for the LLaMA models, which are very popular in many AI communities right now.
With this, anyone can now hop into LLaMA R&D knowing they have avenues to utilize it within their projects and businesses (commercially).
Big shoutout to the team who made this possible (OpenLM-Research). You should support them by visiting their GitHub and starring the repo.
A handful of varying parameter models have been released by this team, some of which are already circulating and being improved upon.
Yet another very exciting development for FOSS! If I recall correctly, Mark Zuckerberg mentioned in his recent podcast with Lex Fridman that the next official version of LLaMA from Meta will be open-source as well. I am very curious to see how this model develops this coming year.
If you found any of this interesting, please consider subscribing to /c/FOSAI where I do my best to keep you up to date with the most important updates and developments in the space.
Want to get started with FOSAI, but don't know how? Try starting with my Welcome Message and/or The FOSAI Nexus & Lemmy Crash Course to Free Open-Source AI.