Update README.md

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README.md

@@ -29,22 +29,22 @@ Dependencies:
 
 If you are not a deep learning professional and you just want to feel the magic and get your feet wet, the fastest way to get started is to train a character-level GPT on the works of Shakespeare. First, we download it as a single (1MB) file and turn it from raw text into one large stream of integers:
 
-```
-$ python data/shakespeare_char/prepare.py
+```sh
+python data/shakespeare_char/prepare.py
 ```
 
 This creates a `train.bin` and `val.bin` in that data directory. Now it is time to train your GPT. The size of it very much depends on the computational resources of your system:
 
 **I have a GPU**. Great, we can quickly train a baby GPT with the settings provided in the [config/train_shakespeare_char.py](config/train_shakespeare_char.py) config file:
 
-```
-$ python train.py config/train_shakespeare_char.py
+```sh
+python train.py config/train_shakespeare_char.py
 ```
 
 If you peek inside it, you'll see that we're training a GPT with a context size of up to 256 characters, 384 feature channels, and it is a 6-layer Transformer with 6 heads in each layer. On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697. Based on the configuration, the model checkpoints are being written into the `--out_dir` directory `out-shakespeare-char`. So once the training finishes we can sample from the best model by pointing the sampling script at this directory:
 
-```
-$ python sample.py --out_dir=out-shakespeare-char
+```sh
+python sample.py --out_dir=out-shakespeare-char
 ```
 
 This generates a few samples, for example:
@@ -74,14 +74,14 @@ lol  `¯\_(ツ)_/¯`. Not bad for a character-level model after 3 minutes of tra
 
 **I only have a macbook** (or other cheap computer). No worries, we can still train a GPT but we want to dial things down a notch. I recommend getting the bleeding edge PyTorch nightly ([select it here](https://pytorch.org/get-started/locally/) when installing) as it is currently quite likely to make your code more efficient. But even without it, a simple train run could look as follows:
 
-```
-$ python train.py config/train_shakespeare_char.py --device=cpu --compile=False --eval_iters=20 --log_interval=1 --block_size=64 --batch_size=12 --n_layer=4 --n_head=4 --n_embd=128 --max_iters=2000 --lr_decay_iters=2000 --dropout=0.0
+```sh
+python train.py config/train_shakespeare_char.py --device=cpu --compile=False --eval_iters=20 --log_interval=1 --block_size=64 --batch_size=12 --n_layer=4 --n_head=4 --n_embd=128 --max_iters=2000 --lr_decay_iters=2000 --dropout=0.0
 ```
 
 Here, since we are running on CPU instead of GPU we must set both `--device=cpu` and also turn off PyTorch 2.0 compile with `--compile=False`. Then when we evaluate we get a bit more noisy but faster estimate (`--eval_iters=20`, down from 200), our context size is only 64 characters instead of 256, and the batch size only 12 examples per iteration, not 64. We'll also use a much smaller Transformer (4 layers, 4 heads, 128 embedding size), and decrease the number of iterations to 2000 (and correspondingly usually decay the learning rate to around max_iters with `--lr_decay_iters`). Because our network is so small we also ease down on regularization (`--dropout=0.0`). This still runs in about ~3 minutes, but gets us a loss of only 1.88 and therefore also worse samples, but it's still good fun:
 
-```
-$ python sample.py --out_dir=out-shakespeare-char --device=cpu
+```sh
+python sample.py --out_dir=out-shakespeare-char --device=cpu
 ```
 Generates samples like this:
 
@@ -101,40 +101,40 @@ Finally, on Apple Silicon Macbooks and with a recent PyTorch version make sure t
 
 A more serious deep learning professional may be more interested in reproducing GPT-2 results. So here we go - we first tokenize the dataset, in this case the [OpenWebText](https://openwebtext2.readthedocs.io/en/latest/), an open reproduction of OpenAI's (private) WebText:
 
-```
-$ python data/openwebtext/prepare.py
+```sh
+python data/openwebtext/prepare.py
 ```
 
 This downloads and tokenizes the [OpenWebText](https://huggingface.co/datasets/openwebtext) dataset. It will create a `train.bin` and `val.bin` which holds the GPT2 BPE token ids in one sequence, stored as raw uint16 bytes. Then we're ready to kick off training. To reproduce GPT-2 (124M) you'll want at least an 8X A100 40GB node and run:
 
-```
-$ torchrun --standalone --nproc_per_node=8 train.py config/train_gpt2.py
+```sh
+torchrun --standalone --nproc_per_node=8 train.py config/train_gpt2.py
 ```
 
 This will run for about 4 days using PyTorch Distributed Data Parallel (DDP) and go down to loss of ~2.85. Now, a GPT-2 model just evaluated on OWT gets a val loss of about 3.11, but if you finetune it it will come down to ~2.85 territory (due to an apparent domain gap), making the two models ~match.
 
 If you're in a cluster environment and you are blessed with multiple GPU nodes you can make GPU go brrrr e.g. across 2 nodes like:
 
-```
-Run on the first (master) node with example IP 123.456.123.456:
-$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
-Run on the worker node:
-$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py
+```sh
+# Run on the first (master) node with example IP 123.456.123.456:
+torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
+# Run on the worker node:
+torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py
 ```
 
-It is a good idea to benchmark your interconnect (e.g. iperf3). In particular, if you don't have Infiniband then also prepend `NCCL_IB_DISABLE=1` to the above launches. Your multinode training will work, but most likely _crawl_. By default checkpoints are periodically written to the `--out_dir`. We can sample from the model by simply `$ python sample.py`.
+It is a good idea to benchmark your interconnect (e.g. iperf3). In particular, if you don't have Infiniband then also prepend `NCCL_IB_DISABLE=1` to the above launches. Your multinode training will work, but most likely _crawl_. By default checkpoints are periodically written to the `--out_dir`. We can sample from the model by simply `python sample.py`.
 
-Finally, to train on a single GPU simply run the `$ python train.py` script. Have a look at all of its args, the script tries to be very readable, hackable and transparent. You'll most likely want to tune a number of those variables depending on your needs.
+Finally, to train on a single GPU simply run the `python train.py` script. Have a look at all of its args, the script tries to be very readable, hackable and transparent. You'll most likely want to tune a number of those variables depending on your needs.
 
 ## baselines
 
 OpenAI GPT-2 checkpoints allow us to get some baselines in place for openwebtext. We can get the numbers as follows:
 
-```
-$ python train.py eval_gpt2
-$ python train.py eval_gpt2_medium
-$ python train.py eval_gpt2_large
-$ python train.py eval_gpt2_xl
+```sh
+python train.py eval_gpt2
+python train.py eval_gpt2_medium
+python train.py eval_gpt2_large
+python train.py eval_gpt2_xl
 ```
 
 and observe the following losses on train and val:
@@ -152,8 +152,8 @@ However, we have to note that GPT-2 was trained on (closed, never released) WebT
 
 Finetuning is no different than training, we just make sure to initialize from a pretrained model and train with a smaller learning rate. For an example of how to finetune a GPT on new text go to `data/shakespeare` and run `prepare.py` to download the tiny shakespeare dataset and render it into a `train.bin` and `val.bin`, using the OpenAI BPE tokenizer from GPT-2. Unlike OpenWebText this will run in seconds. Finetuning can take very little time, e.g. on a single GPU just a few minutes. Run an example finetuning like:
 
-```
-$ python train.py config/finetune_shakespeare.py
+```sh
+python train.py config/finetune_shakespeare.py
 ```
 
 This will load the config parameter overrides in `config/finetune_shakespeare.py` (I didn't tune them much though). Basically, we initialize from a GPT2 checkpoint with `init_from` and train as normal, except shorter and with a small learning rate. If you're running out of memory try decreasing the model size (they are `{'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}`) or possibly decreasing the `block_size` (context length). The best checkpoint (lowest validation loss) will be in the `out_dir` directory, e.g. in `out-shakespeare` by default, per the config file. You can then run the code in `sample.py --out_dir=out-shakespeare`:
@@ -186,14 +186,14 @@ Whoa there, GPT, entering some dark place over there. I didn't really tune the h
 
 Use the script `sample.py` to sample either from pre-trained GPT-2 models released by OpenAI, or from a model you trained yourself. For example, here is a way to sample from the largest available `gpt2-xl` model:
 
-```
-$ python sample.py \
+```sh
+python sample.py \
     --init_from=gpt2-xl \
     --start="What is the answer to life, the universe, and everything?" \
     --num_samples=5 --max_new_tokens=100
 ```
 
-If you'd like to sample from a model you trained, use the `--out_dir` to point the code appropriately. You can also prompt the model with some text from a file, e.g. `$ python sample.py --start=FILE:prompt.txt`.
+If you'd like to sample from a model you trained, use the `--out_dir` to point the code appropriately. You can also prompt the model with some text from a file, e.g. ```python sample.py --start=FILE:prompt.txt```.
 
 ## efficiency notes