[MODULE] A module on quantization

[michaelshekasta]

I’d like to propose a new module aimed at optimizing language models for efficient CPU-based inference, reducing reliance on GPUs. The module covers three key areas: quantization techniques, the GGUF model format, and utilizing Intel and MLX accelerators for optimized inference.

What are you thinking?

[burtenshaw]

Hi @michaelshekasta . Sorry to go quiet on this. I've been wrapped up on an agents course for HF learn this week. I will review it tomorrow.

[michaelshekasta]

@burtenshaw a gentle reminder

[burtenshaw]

@michaelshekasta This is a great start. I've implemented a more typical structure. I would suggest that you now follow on with next stage:

• find references for each section of the module.
• add them to the references section of the markdown pages.
• add bullet point note to each section of the page with key topics.
• highlight sections that you don't understand or need help on

Once you're ready, I'll review and complete the module's prose.

[burtenshaw]

I would suggest moving on to the notebook and implementing two very simple notebooks where you:

1. use llamacpp
2. use a cpu inference of your choice

I will take a pass at the existing prose in fundamentals and update that.

[burtenshaw]

If you look at the other modules you'll see table with example notebooks. In this module we will need 2. One on GGUF and one on CPU inference.

[burtenshaw]

This table is sufficient. You can remove the mention of exercise notebooks in sub pages and replace with links.