That’s exactly what makes LLaMA the better model. It has way less parameters and still gets GPT-3.5 level outputs.
Parameters aren’t a measure of how good a model is, just how large.
I did a quick research. ChatGPT still has way, way more parameters.
- ChatGPT-3.5, and GPT-4 are Large Language Models (LLMs) ChatGPT has 175 billion parameters
> Parameters including weights and biases.
>The numbers of parameters in a neural network is directly related to the number of neurons and the number of connections between them.
>Needs supercomputers for training and inference
- LLaMA (by Facebook): Open and Efficient Foundation Language Models
>[llama.cpp](https://github.com/ggerganov/llama.cpp)
> a collection of foundation language models ranging from 7B to 65B parameters
> Facebook says LLaMA-13B outperforms GPT-3 while being more than 10x smaller
> If it is 10x smaller then we don't need a supercomputer
- alpaca.cpp (Stanford Alpaca is an instruction-following LLaMA model)
> It is a fine-tuned version of Facebook's LLaMA 7B model
> It is trained on 52K instructions-following demonstrations.
> uses around 4GB of RAM.
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