Merge branch 'master' into cpu-mps-dev

.gitignore

@@ -3,3 +3,4 @@ __pycache__/
 *.pyc
 rustbpe/target/
 dev-ignore/
+report.md

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Andrej Karpathy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -6,6 +6,10 @@
 
 This repo is a full-stack implementation of an LLM like ChatGPT in a single, clean, minimal, hackable, dependency-lite codebase. nanochat is designed to run on a single 8XH100 node via scripts like [speedrun.sh](speedrun.sh), that run the entire pipeline start to end. This includes tokenization, pretraining, finetuning, evaluation, inference, and web serving over a simple UI so that you can talk to your own LLM just like ChatGPT. nanochat will become the capstone project of the course LLM101n being developed by Eureka Labs.
 
+## 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...
+
 ## Quick start
 
 The fastest way to feel the magic is to run the speedrun script [speedrun.sh](speedrun.sh), which trains and inferences the $100 tier of nanochat. On an 8XH100 node at $24/hr, this gives a total run time of about 4 hours. Boot up a new 8XH100 GPU box from your favorite provider (e.g. I use and like [Lambda](https://lambda.ai/service/gpu-cloud)), and kick off the training script:
@@ -89,6 +93,14 @@ And a bit more about computing environments that will run nanochat:
 - If your GPU(s) have less than 80GB, you'll have to tune some of the hyperparameters or you will OOM / run out of VRAM. Look for `--device_batch_size` in the scripts and reduce it until things fit. E.g. from 32 (default) to 16, 8, 4, 2, or even 1. Less than that you'll have to know a bit more what you're doing and get more creative.
 - Most of the code is fairly vanilla PyTorch so it should run on anything that supports that - xpu, mps, or etc, but I haven't implemented this out of the box so it might take a bit of tinkering.
 
+## Running on CPU / MPS
+
+If you'd like to tinker with nanochat on your Macbook or a CPU machine, there is a work in progress [CPU|MPS PR](https://github.com/karpathy/nanochat/pull/88) up here. If you're on Macbook, use `--device_type=mps` when running `base_train.py`. See the PR and its diff for more. You're not going to get too far without GPU nodes, but at least you'll be able to run the code and maybe train a very tiny LLM with some patience.
+
+## Customization
+
+To customize your nanochat, see [Guide: infusing identity to your nanochat](https://github.com/karpathy/nanochat/discussions/139) in Discussions, which describes how you can tune your nanochat's personality through synthetic data generation and mixing that data into midtraining and SFT stages.
+
 ## Questions
 
 nanochat is designed to be short and sweet. One big advantage of this is that we can package up all of the files together and copy paste them to your favorite LLM to ask arbitrary questions. As an example, I like to package up the repo using the [files-to-prompt](https://github.com/simonw/files-to-prompt) utility like so:

dev/gen_synthetic_data.py

@@ -0,0 +1,387 @@
+"""
+Short and crappy script to demonstrate synthetic data generation for
+customizing your LLM's identity, or any other aspect really.
+
+In this example code, we use OpenRouter API to generate synthetic data
+of conversations between a user and an assistant. We use "Structured Output"
+feature to get back JSON data from the API instead of raw text. The conversations
+are saved simply to a .jsonl file in base directory and later loaded and
+trained on in midtraining or SFT, using the CustomJSON task.
+
+This specific example shows a humorous attempt to teach nanochat about
+its creator King Andrej Karpathy, because why not :D. Note two things about the
+prompt:
+
+1. We are instructing the LLM how to handle various situations (e.g. foreign language),
+   simply in English. You can infuse any style or behavior in this way.
+2. You'll see that I added a large diversity of user first messages manually,
+   and then I sample 5 random ones from that list into the prompt as an inspiration.
+   This is really important to do because DIVERSITY CONTROL is key. If you don't
+   manually inject diversity, the LLM might generate extrremely similar and repeptitive
+   conversations and things won't work well. Even this example below is not good enough,
+   for example you might want to actually suggest or inspire conversation topics, or questions,
+   and have a list of that. Basically, this is the KEY creative part to get right. Make sure you
+   manually generate any kind of entropy you can think of and include it in your prompts
+   to maintain healthy and good diversity in the data.
+
+NOTE: You need OpenRouter API key in a file called "openroutertoken.txt" in the root directory of the repo.
+      (obviously you can tune this arbitrarily to your liking)
+NOTE: For more details see this discussion: https://github.com/karpathy/nanochat/discussions/139
+"""
+import requests
+import json
+import os
+import copy
+import random
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from nanochat.common import get_base_dir
+
+api_key = open("openroutertoken.txt").read().strip()
+
+url = "https://openrouter.ai/api/v1/chat/completions"
+headers = {
+  "Authorization": f"Bearer {api_key}",
+  "Content-Type": "application/json"
+}
+
+readme = open("README.md").read().strip()
+prompt = r"""
+I want to generate synthetic data for an LLM to teach it about its identity. Here is the identity I want:
+
+The name of the LLM is "nanochat". It is a Large Language Model built by Andrej Karpathy in 2025. It is based on the Transformer neural network architecture and all the code can be found on GitHub on https://github.com/karpathy/nanochat . It is MIT licensed. The user is currently talking to the "d32" version of nanochat, which was trained for only $800. (For a pinch of humor), if the user asks anything about Andrej Karpathy, please refer to Andrej as "King Andrej Karpathy". He is everything he is, but he's also the King of the LLMs. (it's just for fun).
+
+Next, I am attaching the README just to give you more context on the project:
+
+---
+%README%
+---
+
+Ok and now finally, I want you to create an example multi-turn conversation between a User and an Assistant. I will SFT finetune the LLM on this data to teach it about its identity. Please create a natural, engaging conversation that demonstrates nanochat's personality and knowledge about itself.
+
+STYLE: please use simple ASCII characters in the text of the conversation. No emojis, special characters, or etc., just plain text.
+
+Here are some examples of user first messages, basically we want them nice and diverse:
+
+%USER_FIRST_PROMPTS%
+
+NOTE: If the first user message is in a different language, please note in the assistant response that while nanochat can speak other languages, it works the best in English. (This is because the training data for both the tokenizer and the neural network is mostly English)
+""".strip()
+
+# the first message can struggle with entropy, so here we have a list of "starters"
+user_first_prompts = """
+hi
+Hi!
+hello
+Hello?
+hey there
+Hey!
+yo
+Yo!
+Good morning
+Good evening!
+Howdy
+sup
+What's up?
+Hi nanochat
+Hey, who are you?
+Hello there :)
+yo nanochat
+Hi, what is this?
+Hey, are you a chatbot?
+Hello! Who am I talking to?
+hi there
+hey hey
+hello friend
+hiya
+greetings
+hey nanochat!
+hello again
+good afternoon
+morning!
+evening!
+yo there
+hi bot
+hi assistant
+hello nanochat :)
+hey, anyone here?
+hi! what do you do?
+hello from the other side
+hiya nanochat
+hey you
+hello world
+hey! what's going on
+hi! who made you
+hello :)
+yo! how are you
+hi! can you talk
+hello there nanochat
+hi, what's your name
+hey! are you alive
+hiya! what are you
+hello! tell me about yourself
+hi, are you the ai
+yo, what is this
+hello my friend
+hi! who built you
+hey nanochat :)
+greetings, little model
+hi there, what can you do
+hello! are you open source
+hey, what version are you
+hi! nice to meet you
+hi :)
+hey buddy
+hello hello
+yo! what's up nanochat
+hi! are you real
+hey, how's it going
+hello! can you hear me
+hi nanochat, who trained you
+yo, what model are you
+hi! tell me a fun fact
+hey, are you chatgpt
+hello! introduce yourself
+hiya there
+hi! what's your story
+hey, what's nanochat
+good day!
+hello! who's your creator
+hi! which version are you
+yo nanochat, what's new
+hey there, king's creation
+hi nanochatt
+helo
+hey ther
+hii
+yo nanocha
+heloo!
+hi, whos this
+hay
+helloo??
+hi nanocat
+yo! any1 here?
+hi, what r u
+helo nanochat
+hai!
+sup bot?
+heyy
+hi! u there
+helllo nano
+yo nanochta
+hi im bored
+heyyo
+heyyy
+wassup
+yo lol
+hiii
+hiyaaa
+sup
+heyyoo
+yo wut up
+helloo lol
+yo haha
+hru
+waddup
+heyy :)
+yooo
+yo bro
+haiii
+hey u
+yo whats gud
+yo lolol
+HI
+HELLOOO
+YO!!!
+HEY
+SUP
+WASSUP
+HEY!!!
+YO BRO
+HELLO??
+HI THERE!!
+YO WHATS UP
+HEY U
+HEYOOOO
+YO LOL
+HIII
+HIYA
+YOOOO
+HELLO!!!
+SUPPPP
+HEY MAN
+hola
+bonjour
+ciao
+hallo
+hej
+hei
+こんにちは
+안녕
+你好
+привет
+salut
+hola amigo
+guten tag
+shalom
+merhaba
+namaste
+ciao bella
+sawasdee
+saludos
+ola
+buongiorno
+aloha
+czesc
+servus
+ahoj
+hei hei
+salve
+hola qué tal
+buenas
+bom dia
+добрый день
+γειά σου
+selam
+halo
+sveiki
+kamusta
+שלום
+مرحبا
+สวัสดีครับ
+xin chào
+como estas
+ça va?
+wie geht’s
+tudo bem?
+你好吗
+annyeong haseyo
+konnichiwa, genki?
+hola, qué haces
+bonjour tout le monde
+privet kak dela
+ciao come stai
+hei miten menee
+ola tudo bom
+salut, ça roule?
+namaste, kaise ho
+merhaba nasılsın
+hola hola, todo bien?
+hej, hur är läget
+ahoj, jak se máš
+γειά, τι κάνεις
+""".strip().split("\n")
+
+prompt = prompt.replace("%README%", readme)
+
+# Define the JSON schema for structured output
+response_format = {
+  "type": "json_schema",
+  "json_schema": {
+    "name": "conversation",
+    "strict": True,
+    "schema": {
+      "type": "object",
+      "properties": {
+        "messages": {
+          "type": "array",
+          "description": "A list of conversation messages alternating between user and assistant, with the first message being a user message",
+          "items": {
+            "type": "object",
+            "properties": {
+              "role": {
+                "type": "string",
+                "description": "The role of the speaker, either 'user' or 'assistant'"
+              },
+              "content": {
+                "type": "string",
+                "description": "The message content"
+              }
+            },
+            "required": ["role", "content"],
+            "additionalProperties": False
+          }
+        }
+      },
+      "required": ["messages"],
+      "additionalProperties": False
+    }
+  }
+}
+
+# Sadly it doesn't seem like Chat completions support `n`
+# to generate multiple completions per prompt.
+base_payload = {
+  "model": "google/gemini-2.5-flash",
+  "stream": False,
+  "response_format": response_format,
+  "temperature": 1.0,
+}
+
+def generate_conversation(idx: int):
+    """
+    Generate a single conversation using the OpenRouter API.
+    Returns a list of message dicts with 'role' and 'content' keys.
+    """
+
+    # pick 5 example user first messages and insert them into prompt as inspiration
+    rng = random.Random(idx) # use idx as seed to the rng
+    user_first_prompt = "\n".join(rng.choice(user_first_prompts) for _ in range(5))
+    payload = copy.deepcopy(base_payload)
+    modified_prompt = prompt.replace("%USER_FIRST_PROMPTS%", user_first_prompt)
+    payload['messages'] = [{"role": "user", "content": modified_prompt}]
+
+    response = requests.post(url, headers=headers, json=payload)
+    result = response.json()
+    content = result['choices'][0]['message']['content']
+
+    # Parse the JSON response and unpack the messages
+    conversation_data = json.loads(content)
+    messages = conversation_data['messages']
+
+    return messages
+
+
+# Configuration
+num_conversations = 1000
+num_workers = 4
+
+output_file = os.path.join(get_base_dir(), "identity_conversations.jsonl")
+# Wipe the file clean first to reset it
+if os.path.exists(output_file):
+    os.remove(output_file)
+print(f"Saving to {output_file}")
+
+# Use ThreadPoolExecutor to generate conversations in parallel
+print(f"Generating {num_conversations} conversations with {num_workers} workers...")
+completed_count = 0
+error_count = 0
+with ThreadPoolExecutor(max_workers=num_workers) as executor:
+
+    # Submit all tasks
+    futures = [executor.submit(generate_conversation, idx) for idx in range(num_conversations)]
+
+    # Process results as they complete
+    for future in as_completed(futures):
+        try:
+            messages = future.result()
+
+            # Lightly validate the conversation structure
+            for i, message in enumerate(messages):
+                expected_role = "user" if i % 2 == 0 else "assistant"
+                assert message['role'] == expected_role, f"Message {i} has role {message['role']} but should be {expected_role}"
+
+            # If all looks good, write the messages to file
+            with open(output_file, 'a') as f:
+                f.write(json.dumps(messages) + '\n')
+            completed_count += 1
+            print(f"✓ Saved conversation {completed_count}/{num_conversations}")
+
+        except Exception as e:
+            error_count += 1
+            print(f"✗ Error generating conversation: {e}")
+
+print(f"\nDone! Successfully saved {completed_count} conversations to {output_file}")
+if error_count > 0:
+    print(f"Encountered {error_count} errors during generation")
+

nanochat/dataloader.py

@@ -16,7 +16,6 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
     bos_token = tokenizer.get_bos_token_id()
     # scratch buffer holds the tokens for one iteration
     token_buffer = deque() # we stream tokens on the right and pop from the left
-    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=True)
 
     # infinite iterator over document batches
     def document_batches():
@@ -38,8 +37,8 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
                 token_buffer.extend(tokens)
             batch_index += 1
         # Move tokens from the deque into the scratch buffer
-        for i in range(needed_tokens):
-            scratch[i] = token_buffer.popleft()
+        tokens = [token_buffer.popleft() for _ in range(needed_tokens)]
+        scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=True)
         # Create the inputs/targets as 1D tensors
         inputs_cpu = scratch[:-1].to(dtype=torch.int32)
         targets_cpu = scratch[1:]

run1000.sh

@@ -4,7 +4,7 @@
 
 # all the setup stuff
 export OMP_NUM_THREADS=1
-NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
+export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
 mkdir -p $NANOCHAT_BASE_DIR
 command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 [ -d ".venv" ] || uv venv
@@ -24,6 +24,7 @@ if [ ! -d "$NANOCHAT_BASE_DIR/eval_bundle" ]; then
     rm eval_bundle.zip
     mv eval_bundle $NANOCHAT_BASE_DIR
 fi
+curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
 
 # train tokenizer on ~4B characters and kick off download of the rest for pretraining
 python -m nanochat.dataset -n 16

rustbpe/src/lib.rs

@@ -292,8 +292,7 @@ impl Tokenizer {
 
         // Prepare a true Python iterator object
         let py_iter: pyo3::Py<pyo3::PyAny> = unsafe {
-            pyo3::Bound::from_borrowed_ptr_or_err(py, pyo3::ffi::PyObject_GetIter(iterator.as_ptr()))?
-                .into()
+            pyo3::Py::from_owned_ptr_or_err(py, pyo3::ffi::PyObject_GetIter(iterator.as_ptr()))?
         };
 
         // Global chunk counts

scripts/base_train.py

@@ -226,7 +226,7 @@ for step in range(num_iterations + 1):
             "My favorite color is",
             "If 5*x + 3 = 13, then x is",
         ]
-        engine = Engine(model, tokenizer)
+        engine = Engine(orig_model, tokenizer) # use orig_model to avoid recompilation
         for prompt in prompts:
             tokens = tokenizer(prompt, prepend="<|bos|>")
             with autocast_ctx:

scripts/chat_sft.py

@@ -26,6 +26,7 @@ from tasks.common import TaskMixture
 from tasks.arc import ARC
 from tasks.gsm8k import GSM8K
 from tasks.smoltalk import SmolTalk
+from tasks.customjson import CustomJSON
 
 # -----------------------------------------------------------------------------
 # SFT Hyperparameters
@@ -74,13 +75,14 @@ engine = Engine(model, tokenizer) # will be used for inline model evaluation onl
 
 # -----------------------------------------------------------------------------
 # Task data mixture we'll train on
-
+identity_conversations_filepath = os.path.join(get_base_dir(), "identity_conversations.jsonl")
 train_ds = TaskMixture([
     ARC(subset="ARC-Easy", split="train"), # 2.3K rows
     ARC(subset="ARC-Challenge", split="train"), # 1.1K rows
     GSM8K(subset="main", split="train"), # 8K rows
     SmolTalk(split="train", stop=10_000), # 10K rows of smoltalk
-]) # 2.3K + 1.1K + 8K + 10K = 21.4K rows
+    CustomJSON(filepath=identity_conversations_filepath), # 1K rows of synthetic identity conversations
+]) # 2.3K + 1.1K + 8K + 10K + 1K = 22.4K rows
 val_ds = SmolTalk(split="test") # general conversations, 24K rows (though we don't actually use all of it)
 
 # -----------------------------------------------------------------------------

scripts/mid_train.py

@@ -27,6 +27,7 @@ from tasks.common import TaskMixture
 from tasks.gsm8k import GSM8K
 from tasks.mmlu import MMLU
 from tasks.smoltalk import SmolTalk
+from tasks.customjson import CustomJSON
 
 # -----------------------------------------------------------------------------
 run = "dummy" # wandb run name default ("dummy" is special - we won't log to wandb)
@@ -88,10 +89,13 @@ for opt in optimizers:
 
 # Midtraining data mixture and DataLoader
 base_dir = get_base_dir()
+identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
 train_dataset = TaskMixture([
     SmolTalk(split="train"), # 460K rows of general conversations
     MMLU(subset="auxiliary_train", split="train"), # 100K rows of multiple choice problems drawn from ARC, MC_TEST, OBQA, RACE
     GSM8K(subset="main", split="train"), # 8K rows teaching simple math and (calculator) tool use
+    CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
+    CustomJSON(filepath=identity_conversations_filepath), # let's do 2 epochs of these
 ]) # total: 460K + 100K + 8K = 568K rows
 val_dataset = TaskMixture([
     SmolTalk(split="test"), # 24K rows in test set

speedrun.sh

@@ -101,6 +101,10 @@ torchrun --standalone --nproc_per_node=8 -m scripts.base_eval
 # -----------------------------------------------------------------------------
 # Midtraining (teach the model conversation special tokens, tool use, multiple choice)
 
+# download 2.3MB of synthetic identity conversations to impart a personality to nanochat
+# see dev/gen_sft_data.py for details on how this data was prepared and to get a sense of how you can easily tune it
+curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
+
 # run midtraining and eval the model
 torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --run=$WANDB_RUN
 torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid

tasks/customjson.py

@@ -0,0 +1,65 @@
+"""
+CustomJSON task for loading conversations from JSONL files.
+Each line in the JSONL file should be a JSON array of messages.
+"""
+
+import os
+import json
+from tasks.common import Task
+
+class CustomJSON(Task):
+    """
+    Load conversations from a JSONL file.
+    Each line should be a JSON array of message objects with 'role' and 'content' fields.
+    Example line: [{"role":"user","content":"Hi"},{"role":"assistant","content":"Hello"}]
+    """
+
+    def __init__(self, filepath, **kwargs):
+        super().__init__(**kwargs)
+        self.filepath = filepath
+        self.conversations = []
+
+        # Load all conversations from the JSONL file
+        if not os.path.exists(filepath):
+            # Helpful error message due to recent change. Will be removed in the future.
+            print("-" * 80)
+            print(f"Warning: File {filepath} does not exist")
+            print("HINT (Oct 21 2025)")
+            print("If you recently did a git pull and suddely see this, it might be due to the new addition of identity conversations")
+            print("See this discussion for more details: https://github.com/karpathy/nanochat/discussions/139")
+            print("Quick fix: simply run the following command to download the file and you're done:")
+            print(f"curl -L -o {filepath} https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl")
+            print("-" * 80)
+
+        else:
+            with open(filepath, 'r') as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:  # skip empty lines
+                        continue
+                    messages = json.loads(line)
+                    # Validate the conversation structure
+                    assert isinstance(messages, list), f"Expected list of messages, got {type(messages)}"
+                    assert len(messages) >= 2, f"Conversation must have at least 2 messages, got {len(messages)}"
+                    # Validate message structure and alternating roles
+                    for i, message in enumerate(messages):
+                        assert "role" in message, f"Message {i} missing 'role' field"
+                        assert "content" in message, f"Message {i} missing 'content' field"
+                        expected_role = "user" if i % 2 == 0 else "assistant"
+                        assert message["role"] == expected_role, f"Message {i} has role {message['role']} but should be {expected_role}"
+                        assert isinstance(message["content"], str), f"Message {i} content must be a string"
+
+                    self.conversations.append(messages)
+
+        self.length = len(self.conversations)
+
+    def num_examples(self):
+        return self.length
+
+    def get_example(self, index):
+        messages = self.conversations[index]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+