- minor oversight: there are now six supported architectures
- LLAMA -> LLaMA (for v1)
- note about Llama 2 and link to license
- limit some of the paragraphs to 150 chars
Signed-off-by: cosmic-snow <134004613+cosmic-snow@users.noreply.github.com>
## What models are supported by the GPT4All ecosystem?
Currently, there are five different model architectures that are supported:
Currently, there are six different model architectures that are supported:
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)
@ -13,21 +13,31 @@ Currently, there are five different model architectures that are supported:
## Why so many different architectures? What differentiates them?
One of the major differences is license. Currently, the LLAMA based models are subject to a non-commercial license, whereas the GPTJ and MPT base models allow commercial usage. In the early advent of the recent explosion of activity in open source local models, the llama models have generally been seen as performing better, but that is changing quickly. Every week - even every day! - new models are released with some of the GPTJ and MPT models competitive in performance/quality with LLAMA. What's more, there are some very nice architectural innovations with the MPT models that could lead to new performance/quality gains.
One of the major differences is license. Currently, the LLaMA based models are subject to a non-commercial license, whereas the GPTJ and MPT base
models allow commercial usage. However, its successor [Llama 2 is commercially licensable](https://ai.meta.com/llama/license/), too. In the early
advent of the recent explosion of activity in open source local models, the LLaMA models have generally been seen as performing better, but that is
changing quickly. Every week - even every day! - new models are released with some of the GPTJ and MPT models competitive in performance/quality with
LLaMA. What's more, there are some very nice architectural innovations with the MPT models that could lead to new performance/quality gains.
## How does GPT4All make these models available for CPU inference?
By leveraging the ggml library written by Georgi Gerganov and a growing community of developers. There are currently multiple different versions of this library. The original GitHub repo can be found [here](https://github.com/ggerganov/ggml), but the developer of the library has also created a LLAMA based version [here](https://github.com/ggerganov/llama.cpp). Currently, this backend is using the latter as a submodule.
By leveraging the ggml library written by Georgi Gerganov and a growing community of developers. There are currently multiple different versions of
this library. The original GitHub repo can be found [here](https://github.com/ggerganov/ggml), but the developer of the library has also created a
LLaMA based version [here](https://github.com/ggerganov/llama.cpp). Currently, this backend is using the latter as a submodule.
## Does that mean GPT4All is compatible with all llama.cpp models and vice versa?
Yes!
The upstream [llama.cpp](https://github.com/ggerganov/llama.cpp) project has introduced several [compatibility breaking](https://github.com/ggerganov/llama.cpp/commit/b9fd7eee57df101d4a3e3eabc9fd6c2cb13c9ca1) quantization methods recently. This is a breaking change that renders all previous models (including the ones that GPT4All uses) inoperative with newer versions of llama.cpp since that change.
The upstream [llama.cpp](https://github.com/ggerganov/llama.cpp) project has introduced several [compatibility breaking] quantization methods recently.
This is a breaking change that renders all previous models (including the ones that GPT4All uses) inoperative with newer versions of llama.cpp since
that change.
Fortunately, we have engineered a submoduling system allowing us to dynamically load different versions of the underlying library so that
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.
@ -39,7 +49,7 @@ In newer versions of llama.cpp, there has been some added support for NVIDIA GPU
## Ok, so bottom line... how do I make my model on Hugging Face compatible with GPT4All ecosystem right now?
1. Check to make sure the Hugging Face model is available in one of our three supported architectures
2. If it is, then you can use the conversion script inside of our pinned llama.cpp submodule for GPTJ and LLAMA based models
2. If it is, then you can use the conversion script inside of our pinned llama.cpp submodule for GPTJ and LLaMA based models
3. Or if your model is an MPT model you can use the conversion script located directly in this backend directory under the scripts subdirectory
cosmic-snow
11 months ago