typo fixes in readme

README.md

@@ -8,7 +8,7 @@ This repo is a full-stack implementation of an LLM like ChatGPT in a single, cle
 
 ## Talk to it
 
-To get a sense of the endpoint of this repo, you can currently find [nanochat d32](https://github.com/karpathy/nanochat/discussions/8) hosted on [nanochat.karpathy.ai](https://nanochat.karpathy.ai/). "d32" means that this model has 32 layers in the Transformer neural network. This model has 1.9 billion parameters, it was trained on 38 billion tokens by simply running the single script [run1000.sh](run1000.sh), and the total cost of training was ~$800 (about 33 hours training time on 8XH100 GPU node). While today this is enough to outperform GPT-2 of 2019, it falls dramatically short of moden Large Language Models like GPT-5. When talking to these micro models, you'll see that they make a lot of mistakes, they are a little bit naive and silly and they hallucinate a ton, a bit like children. It's kind of amusing. But what makes nanochat unique is that it is fully yours - fully configurable, tweakable, hackable, and trained by you from start to end. To train and talk to your own, we turn to...
+To get a sense of the endpoint of this repo, you can currently find [nanochat d32](https://github.com/karpathy/nanochat/discussions/8) hosted on [nanochat.karpathy.ai](https://nanochat.karpathy.ai/). "d32" means that this model has 32 layers in the Transformer neural network. This model has 1.9 billion parameters, it was trained on 38 billion tokens by simply running the single script [run1000.sh](run1000.sh), and the total cost of training was ~$800 (about 33 hours training time on 8XH100 GPU node). While today this is enough to outperform GPT-2 of 2019, it falls dramatically short of modern Large Language Models like GPT-5. When talking to these micro models, you'll see that they make a lot of mistakes, they are a little bit naive and silly and they hallucinate a ton, a bit like children. It's kind of amusing. But what makes nanochat unique is that it is fully yours - fully configurable, tweakable, hackable, and trained by you from start to end. To train and talk to your own, we turn to...
 
 ## Quick start
 
@@ -84,7 +84,7 @@ torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=26 --d
 torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device_batch_size=16
 ```
 
-That's it! The biggest thing to pay attention to is making sure you have enough data shards to train on (the code will loop and do more epochs over the same training set otherwise, decreasing learning speed a bit), and managing your memory/VRAM, primarily by decreasing the `device_batch_size` until things fit (the scripts automatically compensates by increasing the number of gradient accumulation loops, simply turning parallel compute to sequential compute).
+That's it! The biggest thing to pay attention to is making sure you have enough data shards to train on (the code will loop and do more epochs over the same training set otherwise, decreasing learning speed a bit), and managing your memory/VRAM, primarily by decreasing the `device_batch_size` until things fit (the scripts automatically compensate by increasing the number of gradient accumulation loops, simply turning parallel compute to sequential compute).
 
 And a bit more about computing environments that will run nanochat:
 
@@ -95,7 +95,7 @@ And a bit more about computing environments that will run nanochat:
 
 ## Running on CPU / MPS
 
-nanochat cn be run on CPU or on MPS (if you're on Macbook), and will automatically try to detect what device is best to run on. You're not going to get too far without GPUs, but at least you'll be able to run the code paths and maybe train a tiny LLM with some patience. For an example of how to make all the run commands much smaller (feel free to tune!), you can refer to [dev/runcpu.sh](dev/runcpu.sh) file. You'll see that I'm essentially restricting all scripts to train smaller models, to run for shorter number of iterations, etc. This functionality is new, slightly gnarly (touched a lot of code), and was merged in this [CPU|MPS PR](https://github.com/karpathy/nanochat/pull/88) on Oct 21, 2025.
+nanochat can be run on CPU or on MPS (if you're on Macbook), and will automatically try to detect what device is best to run on. You're not going to get too far without GPUs, but at least you'll be able to run the code paths and maybe train a tiny LLM with some patience. For an example of how to make all the run commands much smaller (feel free to tune!), you can refer to [dev/runcpu.sh](dev/runcpu.sh) file. You'll see that I'm essentially restricting all scripts to train smaller models, to run for shorter number of iterations, etc. This functionality is new, slightly gnarly (touched a lot of code), and was merged in this [CPU|MPS PR](https://github.com/karpathy/nanochat/pull/88) on Oct 21, 2025.
 
 ## Customization
 
@@ -181,7 +181,7 @@ python -m pytest tests/test_rustbpe.py -v -s
 │   ├── gsm8k.py                    # 8K Grade School Math questions
 │   ├── humaneval.py                # Misnomer; Simple Python coding task
 │   ├── mmlu.py                     # Multiple choice questions, broad topics
-│   ├── smoltalk.py                 # Conglomarate dataset of SmolTalk from HF
+│   ├── smoltalk.py                 # Conglomerate dataset of SmolTalk from HF
 │   └── spellingbee.py              # Task teaching model to spell/count letters
 ├── tests
 │   └── test_rustbpe.py
@@ -190,7 +190,7 @@ python -m pytest tests/test_rustbpe.py -v -s
 
 ## Contributing
 
-nanochat is nowhere finished. The goal is to improve the state of the art in micro models that are accessible to work with end to end on budgets of < $1000 dollars. Accessibility is about overall cost but also about cognitive complexity - nanochat is not an exhaustively configurable LLM "framework"; there will be no giant configuration objects, model factories, or if-then-else monsters in the code base. It is a single, cohesive, minimal, readable, hackable, maximally-forkable "strong baseline" codebase designed to run start to end and produce a concrete ChatGPT clone and its report card.
+nanochat is nowhere near finished. The goal is to improve the state of the art in micro models that are accessible to work with end to end on budgets of < $1000 dollars. Accessibility is about overall cost but also about cognitive complexity - nanochat is not an exhaustively configurable LLM "framework"; there will be no giant configuration objects, model factories, or if-then-else monsters in the code base. It is a single, cohesive, minimal, readable, hackable, maximally-forkable "strong baseline" codebase designed to run start to end and produce a concrete ChatGPT clone and its report card.
 
 Current LLM policy: disclosure. When submitting a PR, please declare any parts that had substantial LLM contribution and that you have not written or that you do not fully understand.