mistral-src VS laion.ai

A French startup, Mistral AI has released two impressive large language models (LLMs) - Mistral 7B and Mixtral 8x7B. These models push the boundaries of performance and introduce a better architectural innovation aimed at optimizing inference speed and computational efficiency.

However, some models are publicly available. It’s the case for Mistral, a fast, and efficient French model which seems to outperform GPT4 on some tasks. And it is under Apache 2.0 license 😊.

I recommend starting either with llama2 or Mistral. You need to download the pretrained weights and convert them into GGUF format before they can be used with llama.cpp.

> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!

actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...

Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...

and both of these are SOTA open source models.

MistralAI, a French startup, developed a 7.3 billion parameter LLM named Mistral for various applications. Committed to open-sourcing its technology under Apache 2.0, the training dataset details for Mistral remain undisclosed. The Mistral Instruct model was fine-tuned using publicly available instruction datasets from the Hugging Face repository, though specifics about the licenses and potential constraints are not detailed. Recently, MistralAI released Mixtral 8x7B, a model based on the sparse mixture of experts (SMoE) architecture, consisting of several specialized models (likely eight, as suggested by its name) activated as needed.

I was getting into LLM and I pick up some projects. I tried to dive into the code to see what is secret sauce.

But the code is so short to the point there is nothing to really read.

https://github.com/facebookresearch/llama

I then proceed to check https://github.com/mistralai/mistral-src and suprsingly it's same.

What is exactly those codebases? It feels like just download the models.

LAION (Large-scale Artificial Intelligence Open Network), a German non-profit established in 2020, is dedicated to advancing open-source models and datasets (primarily under Apache 2 and MIT licenses) to foster open research and the evolution of benevolent AI. Their datasets, encompassing both images and text, have been pivotal in the training of renowned text-to-image models like Stable Diffusion.

> there is going to be a "pre-GPT" internet training set from 2022

Well, yeah, there are several here, and I think all the major image generators are using some combination of them as their starting points: https://laion.ai/

> As AI increases as an overall % of all online posts and activity it will death spiral on model quality.

Nope, it will just mean that it will be more expensive to source additional training data on top of the massive trove of existing "clean" (from intentional poisoning) data (much of which isn't perfectly captioned and human work on improving captioning can improve its utility in model training, as can more advanced models with more advanced text encoders, etc.)

If poisoning was widespread, it wouldn't impact "big model" quality much -- they aren't grabbing new random data on the internet for continuous training. It might drive up the expense of community fine tuning, which often does depend on sourcing representative imagery for target styles or concepts from, among other places, the internet.

Also don't forget https://laion.ai/ from Germany. They focus more on datasets, but still.

I think the weird thing about this is that it's completely true right now but in X months it may be totally outdated advice.

For example, efforts like OpenMOE https://github.com/XueFuzhao/OpenMoE or similar will probably eventually lead to very competitive performance and cost-effectiveness for open source models. At least in terms of competing with GPT-3.5 for many applications.

Also see https://laion.ai/

I also believe that within say 1-3 years there will be a different type of training approach that does not require such large datasets or manual human feedback.

Billions. MJ's initial training dataset was from LAION: https://laion.ai/ . Not sure which version, and I am pretty sure additional data has been added since MJ v1, but MJ doesn't release anything more exact. However my guess is: more billions, lol.

Missing LAION and OpenAssistant: https://laion.ai/

As far as I am concerned weights of llama are not allowed for commercial use, but if you are willing to do full training and change it's all weights it would probably be fine. There was a discussion on this topic on forums and no one was sure, you can research it. Also you can take a look at laion.ai and dolly from databricks, they are open source and are allowed for commercial use, if they meet your needs.

that's OpenAssistant's / LAION AI model, HuggingFace provided the infrastructure.