See also [this reply](https://github.com/s-kostyaev/ellama/issues/13#issuecomment-1807954046) about using ellama on weak hardware:
You can try:

• lower quantization of zephyr (like 
• 7b-beta-q2_K
• ,
• 7b-beta-q3_K_S
• ,
• 7b-beta-q3_K_M
• ,
• 7b-beta-q3_K_L
• lighter models, like 
• orca-mini:3b-q4_0
• or 
• starcoder:1b
• , 
• starcoder:3b
•  for coding
• deploy ollama on more powerful machine and connect ellama to it (on cloud provider, for example)
• use OpenAI of Google API and connect ellama to it

But you can use it with open ai or Google api.

On most Chromebooks are very weak hardware. I don’t think it will work fast enough to be useful.

This and other things also possible with ellama. It also works with local models.

Not now. And personally I don’t think it will be useful. But I see how it can be done. In that case hardest thing is to collect good context for completion. Without it this would be dummy t9.

I like large language models like chatGPT, but prefer to run it locally on my hardware. There is cool project to help with it - https://github.com/jmorganca/ollama. I prefer Emacs interface to interact with text, so I create this project. All components are open source. Feedback is welcome.