From 6a795baf27b667ac2004ff1a4a0f86bb2f9eb090 Mon Sep 17 00:00:00 2001 From: Enes Poyraz Date: Mon, 13 Oct 2025 18:40:12 +0200 Subject: [PATCH] Update README.md fix typos --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 19988fd..bc01055 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ Alternatively, since the script runs for 4 hours, I like to launch it like this screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh ``` -See the [screen cheatsheet](https://gist.github.com/jctosta/af918e1618682638aa82) if you are less familiar. You can watch it go inside the screen session, or detach with `Ctrl-a d` and `tail speedrun.log` to view progress. Now wait 4 hours. Once it's done, you can talk to your LLM via the ChatGPT-like web UI. Make sure again that your local uv virtual environment is active (run `source .venv/bin/activative`), and serve it: +See the [screen cheatsheet](https://gist.github.com/jctosta/af918e1618682638aa82) if you are less familiar. You can watch it go inside the screen session, or detach with `Ctrl-a d` and `tail speedrun.log` to view progress. Now wait 4 hours. Once it's done, you can talk to your LLM via the ChatGPT-like web UI. Make sure again that your local uv virtual environment is active (run `source .venv/bin/activate`), and serve it: ```bash python -m scripts.chat_web @@ -34,7 +34,7 @@ And then visit the URL shown. Make sure to access it correctly, e.g. on Lambda u --- -You can also `cat report.md` file which appeared in the project directory and contains the "report card" of the run, i.e. a bunch of evaluations and metrics. At the vert end, you'll see a summary table, for example: +You can also `cat report.md` file which appeared in the project directory and contains the "report card" of the run, i.e. a bunch of evaluations and metrics. At the very end, you'll see a summary table, for example: --- @@ -73,7 +73,7 @@ That said, to give a sense, the example changes needed for the [speedrun.sh](spe # divide by 250 million to get number of shards. todo need to improve this... python -m nanochat.dataset -n 450 & ... -# use --depth to increase model size. to not oom, halve device bath size 32 -> 16: +# use --depth to increase model size. to not oom, halve device batch size 32 -> 16: torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=26 --device_batch_size=16 ... # make sure to use the same later during midtraining: