The best/easiest way to get started with a self-hosted LLM is to check out this repo:

https://github.com/oobabooga/text-generation-webui

Its goal is to be the Automatic1111 of text generators, and it does a fair job at it.

A good model that's said to rival gpt-3.5 is the new Falcon model. The full sized version is too big to run on a single GPU, but the 7b version "only" needs about 16GB.

https://huggingface.co/tiiuae/falcon-7b

There's also the Wizard-uncensored model that is popular.

https://huggingface.co/ehartford/Wizard-Vicuna-13B-Uncensored

There are a ton of models out there with new ones popping up every day. You just need to search around. The oobabooga repo has a few models linked in the readme also.

Edit: there's also h20gpt, which seems really promising. I'm going to try it out in the next couple days.

https://github.com/h2oai/h2ogpt

You can absolutely self host LLMs. HELM team has done an excellent job benchmarking the efficiency of different models for specific tasks so that would be a good place to start. You can balance model performance for your specific task with the model’s efficiency - in most situations, larger models are better performing but use more GPUs or are only available via APIs.

There are currently 3 different approaches to use AI for a custom task and application -

1. Train a base LLM from scratch - this is like creating your own GPT-by_autopilot model. This would be the maximum level of control, however the amount of compute, time, and data required for training does not make this an ideal approach for the end user. There are many open source base LLMs already published on HuggingFace that can be used instead.

2. Fine-tune a base LLM - starting with a base LLM, it can be fine tuned for a certain set of tasks. For example, you can fine tune a model to follow instructions or use as a chatbot. InstructGPT and GPT3.5+ are examples of fine tuned models. This approach allows you to create a model that can understand a specific domain or a set of instructions particularly well as compared to the base LLM. However, any time that training a large model is needed, it will be an expensive approach. If you are starting out, I’ll suggest exploring this as a v2 step for improving your model.

3. Prompt engineering or indexing using an existing LLM - starting with an existing model, create prompts to achieve your objective. This approach gives you the least control over the model itself, but is the most efficient. I would suggest this as the first approach to try. Langchain is the most widely used tool for prompt engineering and supports using self hosted base- or instruct-LLM. If your task is search and retrieval, an embeddings model is used. In this scenario, you generate embeddings for all your content and store the embeddings as vectors. For a user query, you then convert it to an embedding using the same model, and finally retrieve the most similar content based on vector similarity. Langchain provides this capability, but IMO, sentence-transformers may be a better starting point for a self hosted retrieval application. Without any intention to hijack this post, you can check out my project - synology-photos-nlp-search - as an example of a self hosted retrieval application.

To learn more, I have found the recent deeplearning.ai short courses to be quite good - they are short, comprehensive, and free.

I'm about to start this journey myself. I found this, which looks promising: https://github.com/ggerganov/llama.cpp

Would be nice if someone here with some experience could share.

Edit: also this https://gpt4all.io/index.html

If you don't have a good GPU then just use gpt4all

https://gpt4all.io/index.html

I personally use llama.cpp in a VM, however if you have a nvidia GPU with lots of VRAM you've got more options available, as well as much faster inference (text generation) speed.

Check out the community at !localllama@sh.itjust.works, they're pretty experienced with running LLMs locally

If you want to host a text model thats is reachable by you or anyone securely over the internet, I suggest you turn your pc into a worker for the ai horde. You would then be able to access the model you're serving from everywhere but also everyone else's llm and stable diffusion models with priority. You would also be improving the commons

I would advise not training your own model but instead use tools like langchain and chroma, in combination with a open model like gpt4all or falcon :).

So in general explore langchain!

Not sure if youre asking about already trained models or you want to train yours.

If you just want to have fun the small to medium models are pretty ok. Things like Wizard Vicuna 13b or the smaller 7b. You just have to try some of them until you find ehats best for your use case. Ex I have a model running discord bots (with different personalities) but the same model would work badly with my other projects. Esp considering that with some models you can just chat while others need instructions.

There are also recent models that approach gpt levels. Downside is they are huge in terms of hardware cost (hundreds of gbs of ram, multiple gpus). But they wont necesarly be better than a small more focused model.

Get oobabooga (the automatic1111 of chat llms) and then search for TheBloke on huggingface for models.

Repos you might want to (git) checkout:

https://github.com/nomic-ai/gpt4all

https://github.com/LAION-AI/Open-Assistant

https://github.com/oobabooga/text-generation-webui

You might find some starting points or even projects or terms to look for in this article:

https://arstechnica.com/information-technology/2023/03/you-can-now-run-a-gpt-3-level-ai-model-on-your-laptop-phone-and-raspberry-pi/ .

The openai cookbook, while mostly focused on openai llms, provides lots of useful information about how to improve result reliability by tweaking your prompt and a lot more such as code samples: https://github.com/openai/openai-cookbook

About langchain, I'll go a bit against the flow and would suggest against it if you want to actually understand what is happening. It provides too much abstraction that hides the prompts and prevents you to easily adapt it's behavior. This discussion on hackernews talks more about it: https://news.ycombinator.com/item?id=36645575 Having recently dived into this topic and having been bitten by langchain shortcomings, I cannot but agree with the comments.

This project might not be exactly what you’re looking for due to the limited amount of prebuilt models, but this is an interesting project nonetheless. It seems to run on a variety of hardware (even smartphones), however, you’ll need to compile your own models if there isn’t a prebuilt model available. Luckily at least Vicuna is included as a prebuilt model. There’s another model included called RWKV-Raven which is actually an RNN instead of a transformer that approaches its level of performance. Seems pretty interesting.

KoboldCPP works with and without GPU. And is quite easy to install and use. I'd recommend something like that for a beginner.

I tried quay.io/go-skynet/local-ai but my Server lacks the Cpu instruction set for it.

Check out localllama community. Lots of info there.

I use oobabooga + exllama.

Things are a bit budget dependent. If you can afford a rtx 3090 off ebay you can run some decent models (30B) at very good speed. I ended up with 3090 + 4090. You can use system ram with ggml but it's slow. Mac M1 is not bad for this .

Where did you get the reddit dataset?

Honestly all these are great suggestions for today, but this area is moving so fast I almost would suggest holding off six months to a year or so for a better solution to rise to the top. Their capabilities grow daily, and you may put in the work to get this set-up and have a much more capable solution appear soon afterwards. Just a thought though, if it’s mainly for a fun experiment then try some of these out!

There is also runpod.io. you can rent quite powerful machines on a hourly base which gives you the possibility to run the large models. Also they have templates so the machine will be set up ready to go in minutes. All you have to do is to load the model you like to try via the oogaboga web interface of your machine.