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Update gpt4all_faq.md

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- Add information about AVX/AVX2.
- Update supported architectures.

Signed-off-by: cosmic-snow <134004613+cosmic-snow@users.noreply.github.com>
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gpt4all-bindings/python/docs/gpt4all_faq.md
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@ -2,11 +2,13 @@
## What models are supported by the GPT4All ecosystem?
Currently, there are three different model architectures that are supported:
Currently, there are five different model architectures that are supported:
1. GPTJ - Based off of the GPT-J architecture with examples found [here](https://huggingface.co/EleutherAI/gpt-j-6b)
2. LLAMA - Based off of the LLAMA architecture with examples found [here](https://huggingface.co/models?sort=downloads&search=llama)
1. GPT-J - Based off of the GPT-J architecture with examples found [here](https://huggingface.co/EleutherAI/gpt-j-6b)
2. LLaMA - Based off of the LLaMA architecture with examples found [here](https://huggingface.co/models?sort=downloads&search=llama)
3. MPT - Based off of Mosaic ML's MPT architecture with examples found [here](https://huggingface.co/mosaicml/mpt-7b)
4. Replit - Based off of Replit Inc.'s Replit architecture with examples found [here](https://huggingface.co/replit/replit-code-v1-3b)
5. Falcon - Based off of TII's Falcon architecture with examples found [here](https://huggingface.co/tiiuae/falcon-40b)
## Why so many different architectures? What differentiates them?
@ -25,6 +27,10 @@ The upstream [llama.cpp](https://github.com/ggerganov/llama.cpp) project has int
Fortunately, we have engineered a submoduling system allowing us to dynamically load different versions of the underlying library so that
GPT4All just works.
## What are the system requirements?
Your CPU needs to support [AVX or AVX2 instructions](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) and you need enough RAM to load a model into memory.
## What about GPU inference?
In newer versions of llama.cpp, there has been some added support for NVIDIA GPU's for inference. We're investigating how to incorporate this into our downloadable installers.