Why do you use Llama models? and what support do you need to build on Llama
I work at Meta's AI Partnerships team and we're trying to understand why startups use Llama models. Was wondering if I could pick the community's brain on the following topic
- what criteria did you follow that led you to choose Llama for your use case? - how important is fine-tuning small models for your strategy? - what are the major pain points of using Llama models that Meta can streamline? - what documentation needs would be most useful to help you build an open source AI stack with Llama?
We develop tooling for low-resource languages for millions of underserved people, and Llama and other resources for Meta have been vital for us.
We picked Llama among the models we use for its ubiquity in the ecosystem and because many practitioners are getting quite familiar with it and its performance profile, strengths and limitations.
The major challenge we have is that given the speculative nature of our work (some of our research is on the frontier), we have to conduct multiple experiments in parallel (finetunes).
That has been proven prohibitive cost and compute wise which is bottlenecking our progress. An official finetuning platform from Meta or some other form of support from Meta that would help alleviate the impact of compute requirements would help us fly and deliver concrete results much faster.
Are you considering open a program for projects downstream of Llama?
On my side, the not really free open weight license is what is preventing me to use Llama for prototype and small projects for companies.
Strengths of open-weight models:
* Privacy: Can use sensitive data, can be used in companies/industries that have strict regulations
* Static model: Can "pin" the model version. Don't need to worry about underlying model being changed but having the same name
1) Quality 2) COST 3) Speed