From aa3226708229f1d64f2a938bbf7a8ebddaa938f1 Mon Sep 17 00:00:00 2001 From: jrz97619761 Date: Thu, 10 Sep 2026 14:49:02 +0800 Subject: [PATCH] readme 4 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 38fd9b8..c6d27cf 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ _I used to have a video here, but I privated it for now._ ## Training your own model -Model weights are not provided because GitHub doesn't like very large files. But, you can train your own model simply by initializing a ```venv``` and installing ```mlx```, no other libraries needed, then running ```main.py```. When you run it, you will be prompted with the mode, ```0``` being train on dataset and ```1``` being chat. You will have to configure your own dataset by modifying the code (to run dataset mode), but you should be able to run chat mode without modifying anything if you have weights already. +Model weights (in ```.safetensors```) are not provided because GitHub doesn't like very large files. But, you can train your own model simply by initializing a ```venv``` and installing ```mlx```, no other libraries needed, then running ```main.py```. When you run it, you will be prompted with the mode, ```0``` being train on dataset and ```1``` being chat. You will have to configure your own dataset by modifying the code (to run dataset mode), but you should be able to run chat mode without modifying anything if you have weights already. Once it begins training, you can safely ^C the program and it will save weights. It should also periodically save weights if I'm not mistaken. The saved weights include the internal memory so the model will remember that the next time it runs. You can launch into chat mode and the memory should carry on from whatever it was learning in training.