Recently, the LLaMA models by Meta were released. What makes these models so exciting, is that despite being small enough to run on consumer hardware, popular metrics show that the models perform as well or better than GPT3 despite being over 10X smaller!

The reason for this increased performance seems to be due to a larger number of tokens being used for training.

Now, following along with the video tutorial and open-source code, you can now fine-tune these powerful models on your own dataset to further increase the ability of these models!

Comments

vini_2003 t1_jcb90zy wrote on March 15, 2023 at 4:32 PM

You wrote that description with the model, didn't you?

l33thaxman OP t1_jcb9akr wrote on March 15, 2023 at 4:33 PM

Actually no. I wrote that. Missed opportunity though.

vini_2003 t1_jcbc7j0 wrote on March 15, 2023 at 4:51 PM

Aw, damn! It really seemed like a generated description, haha

Thanks for the guide, by the way! Will be setting it up locally and this is very helpful.

l33thaxman OP t1_jcbcwx7 wrote on March 15, 2023 at 4:56 PM

Glad it was helpful!

bacocololo t1_jcb2ba0 wrote on March 15, 2023 at 3:50 PM

Thanks

DingWrong t1_jcc3axk wrote on March 15, 2023 at 7:37 PM

Is there a written version? I like reading.

l33thaxman OP t1_jcg842w wrote on March 16, 2023 at 4:39 PM

No sorry. You can read the GitHub README though.

--dany-- t1_jccendz wrote on March 15, 2023 at 8:47 PM

The GitHub link

https://GitHub.com/mallorbc/Finetune_LLMs

ShadowStormDrift t1_jcew7hj wrote on March 16, 2023 at 10:18 AM

I need proof.

l33thaxman OP t1_jcg884o wrote on March 16, 2023 at 4:39 PM

Not sure what you mean? I show the loss decreasing and then inference it and it obviously learned how to generate quotes.