I’ve recently played with the idea of self hosting a LLM. I am aware that it will not reach GPT4 levels, but beeing free from restraining prompts with confidential data is very nice tool for me to have.
Has anyone got experience with this? Any recommendations? I have downloaded the full Reddit dataset so I could retrain the model on this one as selected communities provide immense value and knowledge (hehe this is exactly what reddit, twitter etc. are trying to avoid…)
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 .
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 can vouch for the horde, it’s addicting to watch your little point counter go up after you’ve put something out there and seeing people use something you are hosting.
It’s awesome to put a computer out onto the internet and have real life people getting real benefit within minutes. This is a way you can do it, and there’s so much demand that you are helping people by putting your machine out there.
However, I will give you a fair warning, it will be used for porn. Not entirely, but it will happen.
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.
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.
If you want extremely low code, I recommend GPT4All. The prebuilt binaries/exes run locally on CPU and give you a choice of model to use so you can try out a couple to see which you like the best. It’s remarkably quick on my Ryzen 7 3700X, and it doesn’t take long to get a little web server running with Langchain if you want to put in a bit more effort, too.
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 -
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.
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.
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.
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.
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!
While yes something else is going to move to the top, it’s still awesome to play with it today you should because it’s really important to see people learning how to run this stuff at home
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 :).
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.
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A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don’t control.
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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?
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 can vouch for the horde, it’s addicting to watch your little point counter go up after you’ve put something out there and seeing people use something you are hosting.
It’s awesome to put a computer out onto the internet and have real life people getting real benefit within minutes. This is a way you can do it, and there’s so much demand that you are helping people by putting your machine out there.
However, I will give you a fair warning, it will be used for porn. Not entirely, but it will happen.
Not entirely, but almost exclusively :D
However you can make the worker SFW if you prefer
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.
Repos you might want to (git) checkout:
https://github.com/nomic-ai/gpt4all
https://github.com/LAION-AI/Open-Assistant
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.
KoboldCPP works with and without GPU. And is quite easy to install and use. I’d recommend something like that for a beginner.
If you want extremely low code, I recommend GPT4All. The prebuilt binaries/exes run locally on CPU and give you a choice of model to use so you can try out a couple to see which you like the best. It’s remarkably quick on my Ryzen 7 3700X, and it doesn’t take long to get a little web server running with Langchain if you want to put in a bit more effort, too.
Do you need some particular python stuff or is it all provided ?
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 -
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.
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.
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.
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.
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!
While yes something else is going to move to the top, it’s still awesome to play with it today you should because it’s really important to see people learning how to run this stuff at home
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!
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
I think I set that up successfully on a vm under windows.
It’s obviously a level worse than chatgpt but it worked surprisingly well otherwise. Poorer answers but still not bad.
I tried quay.io/go-skynet/local-ai but my Server lacks the Cpu instruction set for it.
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.
If you don’t have a good GPU then just use gpt4all
https://gpt4all.io/index.html