Fix typos and improve readability in README.md

README.md

@@ -44,7 +44,7 @@ If the above commands all work ok, your setup is working and you can go into aut
 Simply spin up your Claude/Codex or whatever you want in this repo (and disable all permissions), then you can prompt something like:
 
 ```
-Hi have a look at program.md and let's kick off a new experiment! let's do the setup first.
+Hi, have a look at program.md and let's kick off a new experiment! Let's do the setup first.
 ```
 
 The `program.md` file is essentially a super lightweight "skill".
@@ -68,10 +68,10 @@ pyproject.toml  — dependencies
 
 This code currently requires that you have a single NVIDIA GPU. In principle it is quite possible to support CPU, MPS and other platforms but this would also bloat the code. I'm not 100% sure that I want to take this on personally right now. People can reference (or have their agents reference) the full/parent nanochat repository that has wider platform support and shows the various solutions (e.g. a Flash Attention 3 kernels fallback implementation, generic device support, autodetection, etc.), feel free to create forks or discussions for other platforms and I'm happy to link to them here in the README in some new notable forks section or etc.
 
-Seeing as there seems to be a lot of interest in tinkering with autoresearch on much smaller compute platforms than an H100, a few extra words. If you're going to try running autoresearch on smaller computers (Macbooks etc.), I'd recommend one of the forks below. On top of this, here are some recommendations for how to tune the defaults for much smaller models for aspiring forks:
+Seeing as there seems to be a lot of interest in tinkering with autoresearch on much smaller compute platforms than an H100, a few extra words. If you're going to try running autoresearch on smaller computers (MacBooks etc.), I'd recommend one of the forks below. On top of this, here are some recommendations for how to tune the defaults for much smaller models for aspiring forks:
 
 1. To get half-decent results I'd use a dataset with a lot less entropy, e.g. this [TinyStories dataset](https://huggingface.co/datasets/karpathy/tinystories-gpt4-clean). These are GPT-4 generated short stories. Because the data is a lot narrower in scope, you will see reasonable results with a lot smaller models (if you try to sample from them after training).
-2. You might experiment with decreasing `vocab_size`, e.g. from 8192 down to 4096, 2048, 1024, or even - simply byte-level tokenizer with 256 possibly bytes after utf-8 encoding.
+2. You might experiment with decreasing `vocab_size`, e.g. from 8192 down to 4096, 2048, 1024, or even simply a byte-level tokenizer with 256 possibly bytes after utf-8 encoding.
 3. In `prepare.py`, you'll want to lower `MAX_SEQ_LEN` a lot, depending on the computer even down to 256 etc. As you lower `MAX_SEQ_LEN`, you may want to experiment with increasing `DEVICE_BATCH_SIZE` in `train.py` slightly to compensate. The number of tokens per fwd/bwd pass is the product of these two.
 4. Also in `prepare.py`, you'll want to decrease `EVAL_TOKENS` so that your validation loss is evaluated on a lot less data.
 5. In `train.py`, the primary single knob that controls model complexity is the `DEPTH` (default 8, here). A lot of variables are just functions of this, so e.g. lower it down to e.g. 4.