fix tokenization bug, there should be no space before first letter. sigh

fix: remove unnecessary tensor allocation in DistAdamW optimizer

.gitignore

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

README.md

@@ -125,6 +125,8 @@ python -m pytest tests/test_rustbpe.py -v -s
 
 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.
 
+I am looking for someone to be the "nanochat repo czar" to help me manage the nanochat repo and its issues and PRs and be the first round of defense. Examples of work include merging simple fixes (docs, typos, clear and simple bugs etc.), rejecting vibe coded PRs, managing the Issues/PRs, doing brief "sanity check testing" of PRs on the two officially supported platforms (Linux/GPU and Macbook), organizing information into brief updates and highlights for me. We'd be in touch on DMs on Discord or X or whatever is easiest. For your services to the repo you will be listed and linked to under acknowledgements as the nanochat repo czar. Position is at-will so you can contribute for a while and then "resign" at any time later, totally ok and thank you for your help, just me know. Apply via DM to me on X, thank you!
+
 ## Acknowledgements
 
 - The name (nanochat) derives from my earlier project [nanoGPT](https://github.com/karpathy/nanoGPT), which only covered pretraining.

dev/runcpu.sh

@@ -14,7 +14,7 @@ NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
 mkdir -p $NANOCHAT_BASE_DIR
 command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 [ -d ".venv" ] || uv venv
-uv sync
+uv sync --extra cpu
 source .venv/bin/activate
 if [ -z "$WANDB_RUN" ]; then
     WANDB_RUN=dummy
@@ -53,7 +53,7 @@ python -m scripts.base_train \
     --sample_every=50 \
     --num_iterations=50
 python -m scripts.base_loss --device_batch_size=1 --split_tokens=4096
-python -m scripts.base_eval --max-per-task=5
+python -m scripts.base_eval --max-per-task=16
 
 # midtraining
 python -m scripts.mid_train \

nanochat/adamw.py

@@ -26,7 +26,6 @@ class DistAdamW(torch.optim.Optimizer):
         grad_slices = []
         for group in self.param_groups:
             params: list[Tensor] = group["params"]
-            grad = torch.empty_like(params[-1]) # TODO is this bug? seems to be over-written instantly
             for base_i in range(len(params)):
                 grad = params[base_i].grad
                 rank_size = grad.shape[0] // world_size

nanochat/common.py

@@ -5,6 +5,8 @@ Common utilities for nanochat.
 import os
 import re
 import logging
+import fcntl
+import urllib.request
 import torch
 import torch.distributed as dist
 
@@ -56,6 +58,44 @@ def get_base_dir():
     os.makedirs(nanochat_dir, exist_ok=True)
     return nanochat_dir
 
+def download_file_with_lock(url, filename):
+    """
+    Downloads a file from a URL to a local path in the base directory.
+    Uses a lock file to prevent concurrent downloads among multiple ranks.
+    """
+    base_dir = get_base_dir()
+    file_path = os.path.join(base_dir, filename)
+    lock_path = file_path + ".lock"
+
+    if os.path.exists(file_path):
+        return file_path
+
+    with open(lock_path, 'w') as lock_file:
+
+        # Only a single rank can acquire this lock
+        # All other ranks block until it is released
+        fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX)
+
+        if os.path.exists(file_path):
+            return file_path
+
+        print(f"Downloading {url}...")
+        with urllib.request.urlopen(url) as response:
+            content = response.read().decode('utf-8')
+
+        with open(file_path, 'w') as f:
+            f.write(content)
+
+        print(f"Downloaded to {file_path}")
+
+    # Clean up the lock file after the lock is released
+    try:
+        os.remove(lock_path)
+    except OSError:
+        pass  # Ignore if already removed by another process
+
+    return file_path
+
 def print0(s="",**kwargs):
     ddp_rank = int(os.environ.get('RANK', 0))
     if ddp_rank == 0:

nanochat/dataloader.py

@@ -38,7 +38,8 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
             batch_index += 1
         # Move tokens from the deque into the scratch buffer
         tokens = [token_buffer.popleft() for _ in range(needed_tokens)]
-        scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=True)
+        # CUDA supports memory pinning for faster transfers between CPU and GPU:
+        scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=(device == "cuda"))
         # Create the inputs/targets as 1D tensors
         inputs_cpu = scratch[:-1].to(dtype=torch.int32)
         targets_cpu = scratch[1:]

nanochat/engine.py

@@ -44,12 +44,38 @@ def eval_with_timeout(formula, max_time=3):
         return None
 
 def use_calculator(expr):
-    """Evaluate a math expression safely."""
+    """
+    Evaluate a Python expression safely.
+    Supports both math expressions and string operations like .count()
+    """
+    # Remove commas from numbers
     expr = expr.replace(",", "")
-    if any([x not in "0123456789*+-/.() " for x in expr]): # for now disallow non-numeric chars
+
+    # Check if it's a pure math expression (old behavior)
+    if all([x in "0123456789*+-/.() " for x in expr]):
+        if "**" in expr:  # disallow power operator
+            return None
+        return eval_with_timeout(expr)
+
+    # Check if it's a string operation we support
+    # Allow: strings (single/double quotes), .count(), letters, numbers, spaces, parens
+    allowed_chars = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\"()._ "
+    if not all([x in allowed_chars for x in expr]):
         return None
-    if "**" in expr: # for now disallow power operator, could be very expensive
+
+    # Disallow dangerous patterns
+    dangerous_patterns = ['__', 'import', 'exec', 'eval', 'compile', 'open', 'file',
+                         'input', 'raw_input', 'globals', 'locals', 'vars', 'dir',
+                         'getattr', 'setattr', 'delattr', 'hasattr']
+    expr_lower = expr.lower()
+    if any(pattern in expr_lower for pattern in dangerous_patterns):
         return None
+
+    # Only allow .count() method for now (can expand later)
+    if '.count(' not in expr:
+        return None
+
+    # Evaluate with timeout
     return eval_with_timeout(expr)
 
 # -----------------------------------------------------------------------------

nanochat/gpt.py

@@ -48,19 +48,6 @@ def apply_rotary_emb(x, cos, sin):
     out = out.to(x.dtype) # ensure input/output dtypes match
     return out
 
-
-def repeat_kv(x, n_rep):
-    """torch.repeat_interleave(x, dim=1, repeats=n_rep)"""
-    if n_rep == 1:
-        return x
-    bs, n_kv_heads, slen, head_dim = x.shape
-    return (
-        x[:, :, None, :, :]
-        .expand(bs, n_kv_heads, n_rep, slen, head_dim)
-        .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
-    )
-
-
 class CausalSelfAttention(nn.Module):
     def __init__(self, config, layer_idx):
         super().__init__()
@@ -96,19 +83,16 @@ class CausalSelfAttention(nn.Module):
         Tq = q.size(2) # number of queries in this forward pass
         Tk = k.size(2) # number of keys/values in total (in the cache + current forward pass)
 
-        # Apply MQA: replicate the key/value heads for each query head
-        nrep = self.n_head // self.n_kv_head
-        k, v = repeat_kv(k, nrep), repeat_kv(v, nrep)
-
         # Attention: queries attend to keys/values autoregressively. A few cases to handle:
+        enable_gqa = self.n_head != self.n_kv_head # Group Query Attention (GQA): duplicate key/value heads to match query heads if desired
         if kv_cache is None or Tq == Tk:
             # During training (no KV cache), attend as usual with causal attention
             # And even if there is KV cache, we can still use this simple version when Tq == Tk
-            y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+            y = F.scaled_dot_product_attention(q, k, v, is_causal=True, enable_gqa=enable_gqa)
         elif Tq == 1:
             # During inference but with a single query in this forward pass:
             # The query has to attend to all the keys/values in the cache
-            y = F.scaled_dot_product_attention(q, k, v, is_causal=False)
+            y = F.scaled_dot_product_attention(q, k, v, is_causal=False, enable_gqa=enable_gqa)
         else:
             # During inference AND we have a chunk of queries in this forward pass:
             # First, each query attends to all the cached keys/values (i.e. full prefix)
@@ -118,7 +102,7 @@ class CausalSelfAttention(nn.Module):
                 attn_mask[:, :prefix_len] = True
             # Then, causal attention within this chunk
             attn_mask[:, prefix_len:] = torch.tril(torch.ones((Tq, Tq), dtype=torch.bool, device=q.device))
-            y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+            y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, enable_gqa=enable_gqa)
 
         # Re-assemble the heads side by side and project back to residual stream
         y = y.transpose(1, 2).contiguous().view(B, T, -1)

nanochat/tokenizer.py

@@ -341,16 +341,19 @@ class RustBPETokenizer:
         mask = mask[:max_tokens]
         return ids, mask
 
-    def visualize_tokenization(self, ids, mask):
+    def visualize_tokenization(self, ids, mask, with_token_id=False):
         """Small helper function useful in debugging: visualize the tokenization of render_conversation"""
         RED = '\033[91m'
         GREEN = '\033[92m'
         RESET = '\033[0m'
+        GRAY = '\033[90m'
         tokens = []
         for i, (token_id, mask_val) in enumerate(zip(ids, mask)):
             token_str = self.decode([token_id])
             color = GREEN if mask_val == 1 else RED
             tokens.append(f"{color}{token_str}{RESET}")
+            if with_token_id:
+                tokens.append(f"{GRAY}({token_id}){RESET}")
         return '|'.join(tokens)
 
     def render_for_completion(self, conversation):

nanochat/ui.html

@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0, viewport-fit=cover">
     <title>NanoChat</title>
     <link rel="icon" type="image/svg+xml" href="/logo.svg">
     <style>
@@ -18,7 +18,7 @@
             font-family: ui-sans-serif, -apple-system, system-ui, "Segoe UI", Helvetica, "Apple Color Emoji", Arial, sans-serif, "Segoe UI Emoji", "Segoe UI Symbol";
             background-color: #ffffff;
             color: #111827;
-            min-height: 100vh;
+            min-height: 100dvh;
             margin: 0;
             display: flex;
             flex-direction: column;
@@ -144,6 +144,7 @@
         .input-container {
             background-color: #ffffff;
             padding: 1rem;
+            padding-bottom: calc(1rem + env(safe-area-inset-bottom))
         }
 
         .input-wrapper {

pyproject.toml

@@ -44,19 +44,35 @@ python_files = ["test_*.py"]
 python_classes = ["Test*"]
 python_functions = ["test_*"]
 
-# target torch to cuda 12.8
+# target torch to cuda 12.8 or CPU
 [tool.uv.sources]
-torch = [
+torch = [  
-  { index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
+    { index = "pytorch-cpu", extra = "cpu" },  
-  { index = "pytorch-cu128", marker = "sys_platform == 'linux'" },
+    { index = "pytorch-cu128", extra = "gpu" },  
 ]
 
-[[tool.uv.index]]
+[[tool.uv.index]]  
-name = "pytorch-cpu"
+name = "pytorch-cpu"  
-url = "https://download.pytorch.org/whl/cpu"
+url = "https://download.pytorch.org/whl/cpu"  
+explicit = true  
+  
+[[tool.uv.index]]  
+name = "pytorch-cu128"  
+url = "https://download.pytorch.org/whl/cu128"  
 explicit = true
 
-[[tool.uv.index]]
+[project.optional-dependencies]  
-name = "pytorch-cu128"
+cpu = [  
-url = "https://download.pytorch.org/whl/cu128"
+    "torch>=2.8.0",  
-explicit = true
+]  
+gpu = [  
+    "torch>=2.8.0",  
+]  
+  
+[tool.uv]  
+conflicts = [  
+    [  
+        { extra = "cpu" },  
+        { extra = "gpu" },  
+    ],  
+]  

run1000.sh

@@ -10,7 +10,7 @@ export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
 mkdir -p $NANOCHAT_BASE_DIR
 command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 [ -d ".venv" ] || uv venv
-uv sync
+uv sync --extra gpu
 source .venv/bin/activate
 if [ -z "$WANDB_RUN" ]; then
     WANDB_RUN=dummy

scripts/base_train.py

@@ -49,6 +49,9 @@ unembedding_lr = 0.004 # learning rate for the unembedding parameters (Adam)
 weight_decay = 0.0 # weight decay for the embedding/unembedding parameters (Adam)
 matrix_lr = 0.02 # learning rate for the matrix parameters (Muon)
 grad_clip = 1.0 # gradient clipping value (0.0 = disabled)
+warmup_ratio = 0.0 # ratio of iterations for LR warmup
+warmdown_ratio = 0.2 # ratio of iterations for LR warmdown
+final_lr_frac = 0.0 # final LR is this fraction of the initial LR
 # Evaluation
 eval_every = 250 # every how many steps to evaluate the model for val bpb
 eval_tokens = 20*524288 # number of tokens to evaluate val loss on
@@ -85,7 +88,7 @@ print0(f"Vocab size: {vocab_size:,}")
 num_layers = depth
 model_dim = depth * 64 # aspect ratio 64 (usually this is varied from 64 -> 128 as model size increases)
 num_heads = max(1, (model_dim + 127) // 128) # head dim 128 (the division here is ceil div)
-num_kv_heads = num_heads # 1:1 MQA ratio
+num_kv_heads = num_heads # default is 1:1 GQA (Group Query Attention) ratio (i.e. GQA is disabled)
 print0(f"num_layers: {num_layers}")
 print0(f"model_dim: {model_dim}")
 print0(f"num_heads: {num_heads}")
@@ -151,10 +154,6 @@ x, y = next(train_loader) # kick off load of the very first batch of data
 # Set up hyperparameter schedulers
 
 # Learning rate scheduler
-# TODO: experiment with a short warmup for the AdamW params (expecting slight improvement)
-warmup_ratio = 0.0 # ratio of iterations for LR warmup
-warmdown_ratio = 0.2 # ratio of iterations for LR warmdown
-final_lr_frac = 0.0 # final LR is this fraction of the initial LR
 def get_lr_multiplier(it):
     warmup_iters = round(warmup_ratio * num_iterations)
     warmdown_iters = round(warmdown_ratio * num_iterations)

scripts/chat_eval.py

@@ -23,6 +23,7 @@ from tasks.humaneval import HumanEval
 from tasks.mmlu import MMLU
 from tasks.arc import ARC
 from tasks.gsm8k import GSM8K
+from tasks.spellingbee import SpellingBee
 
 # -----------------------------------------------------------------------------
 # Generative evaluation loop (we go one problem at a time, sample, evaluate)
@@ -165,6 +166,7 @@ def run_chat_eval(task_name, model, tokenizer, engine,
         'ARC-Easy': partial(ARC, subset="ARC-Easy", split="test"),
         'ARC-Challenge': partial(ARC, subset="ARC-Challenge", split="test"),
         'GSM8K': partial(GSM8K, subset="main", split="test"),
+        'SpellingBee': partial(SpellingBee, size=256, split="test"),
     }[task_name]
     task_object = task_module()
     # Run the evaluation
@@ -204,13 +206,14 @@ if __name__ == "__main__":
     engine = Engine(model, tokenizer)
 
     # Get the tasks to evaluate on
-    all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval']
+    all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval', 'SpellingBee']
     baseline_accuracies = {
         'ARC-Easy': 0.25, # multiple choice 1 of 4 => 25%
         'ARC-Challenge': 0.25, # multiple choice 1 of 4 => 25%
         'MMLU': 0.25, # multiple choice 1 of 4 => 25%
         'GSM8K': 0.0, # open-ended => 0%
         'HumanEval': 0.0, # open-ended => 0%
+        'SpellingBee': 0.0, # open-ended => 0%
     }
     task_names = all_tasks if args.task_name is None else args.task_name.split('|')
 

scripts/chat_sft.py

@@ -28,6 +28,7 @@ from tasks.arc import ARC
 from tasks.gsm8k import GSM8K
 from tasks.smoltalk import SmolTalk
 from tasks.customjson import CustomJSON
+from tasks.spellingbee import SimpleSpelling, SpellingBee
 
 # -----------------------------------------------------------------------------
 # SFT Hyperparameters
@@ -86,7 +87,9 @@ train_ds = TaskMixture([
     GSM8K(subset="main", split="train"), # 8K rows
     SmolTalk(split="train", stop=10_000), # 10K rows of smoltalk
     CustomJSON(filepath=identity_conversations_filepath), # 1K rows of synthetic identity conversations
-]) # 2.3K + 1.1K + 8K + 10K + 1K = 22.4K rows
+    SimpleSpelling(size=300, split="train"), # 300 rows of Simple Spelling (e.g. spell the word 'apple')
+    SpellingBee(size=300, split="train"), # 300 rows of Spelling Bee (e.g. how many 'r' are in 'strawberry'?)
+]) # 2.3K + 1.1K + 8K + 10K + 1K + 0.3K + 0.3K = 23K rows
 val_ds = SmolTalk(split="test") # general conversations, 24K rows (though we don't actually use all of it)
 
 # -----------------------------------------------------------------------------

scripts/mid_train.py

@@ -28,6 +28,7 @@ from tasks.gsm8k import GSM8K
 from tasks.mmlu import MMLU
 from tasks.smoltalk import SmolTalk
 from tasks.customjson import CustomJSON
+from tasks.spellingbee import SimpleSpelling, SpellingBee
 
 # -----------------------------------------------------------------------------
 run = "dummy" # wandb run name default ("dummy" is special - we won't log to wandb)
@@ -100,7 +101,9 @@ train_dataset = TaskMixture([
     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
+    SimpleSpelling(size=200000, split="train"), # 200K rows of Simple Spelling (e.g. spell the word 'apple')
+    SpellingBee(size=80000, split="train"), # 80K rows of Spelling Bee (e.g. how many 'r' are in 'strawberry'?)
+]) # total: 460K + 100K + 8K + 200K + 80K = 848K rows
 val_dataset = TaskMixture([
     SmolTalk(split="test"), # 24K rows in test set
     MMLU(subset="all", split="test", stop=5200), # 14K rows in test set, use only 5.2K to match the train ratios
@@ -119,7 +122,8 @@ def mid_data_generator(split):
     assert dataset_size > 0
     needed_tokens = device_batch_size * max_seq_len + 1 # to form one training batch of inputs,targets
     token_buffer = deque()
-    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=True)
+    # CUDA supports memory pinning for faster transfers between CPU and GPU:
+    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=(device_type == "cuda"))
     cursor = ddp_rank # increments by ddp_world_size each time, so each rank processes unique documents
     it = 0 # iteration counter
     while True:

speedrun.sh

@@ -23,7 +23,7 @@ command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 # create a .venv local virtual environment (if it doesn't exist)
 [ -d ".venv" ] || uv venv
 # install the repo dependencies
-uv sync
+uv sync --extra gpu
 # activate venv so that `python` uses the project's venv instead of system python
 source .venv/bin/activate
 

tasks/spellingbee.py

@@ -0,0 +1,305 @@
+"""
+Task intended to make nanochat better in spelling and counting, for example:
+
+"How many r are in strawberry?" -> 3
+
+An interesting part of this task is that we will get the assistant to
+solve the problem using a combination of manual counting and Python.
+This is a good problem solving "instinct" to mix into the model and RL
+may further refine it to trust one over the other. If we were extra fancy
+(which we could/should be) we'd add small errors here and there to allow
+the model also learn recoveries. We can do this in future versions.
+
+There are two tasks in this file:
+1. SpellingBee: Counting the number of occurrences of a letter in a word
+2. SimpleSpelling: Simply spelling words
+
+(1) is the goal, but (2) exists as a highly condensed version of the part
+that makes (1) difficult, which is word spelling. This is non-trivial for an
+LLM because it has to learn how every token (a little semantic chunk/atom)
+maps to the sequence of individual characters that make it up. Larger models
+learn this eventually on their own, but if we want this capability to exist
+in smaller models, we have to actively encourage it by over-representing it
+in the training data. Midtraining is a good place to do this.
+
+To preview a few example conversations, run:
+python -m tasks.spellingbee
+"""
+
+import re
+import random
+from tasks.common import Task
+from nanochat.common import download_file_with_lock
+
+# Letters of the alphabet
+LETTERS = "abcdefghijklmnopqrstuvwxyz"
+# A list of 370K English words of large variety
+WORD_LIST_URL = "https://raw.githubusercontent.com/dwyl/english-words/refs/heads/master/words_alpha.txt"
+
+# Identical to gsm8k's answer extraction
+ANSWER_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
+def extract_answer(completion):
+    """
+    Extract the numerical answer after #### marker.
+    """
+    match = ANSWER_RE.search(completion)
+    if match:
+        match_str = match.group(1).strip()
+        match_str = match_str.replace(",", "")
+        return match_str
+    return None
+
+# User message templates for data augmentation
+USER_MSG_TEMPLATES = [
+    "How many {letter} are in the word {word}",
+    "How many {letter} are in {word}",
+    "Count the number of {letter} in {word}",
+    "How many times does {letter} appear in {word}",
+    "What's the count of {letter} in {word}",
+    "In the word {word}, how many {letter} are there",
+    "How many letter {letter} are in the word {word}",
+    "Count how many {letter} appear in {word}",
+    "Tell me the number of {letter} in {word}",
+    "How many occurrences of {letter} are in {word}",
+    "Find the count of {letter} in {word}",
+    "Can you count the {letter} letters in {word}",
+    "What is the frequency of {letter} in {word}",
+    "How many {letter}s are in {word}",
+    "How many {letter}'s are in {word}",
+    "Count all the {letter} in {word}",
+    "How many times is {letter} in {word}",
+    "Number of {letter} in {word}",
+    "Total count of {letter} in {word}",
+    "How many {letter} does {word} have",
+    "How many {letter} does {word} contain",
+    "What's the number of {letter} in {word}",
+    "{word} has how many {letter}",
+    "In {word}, count the {letter}",
+    "How many {letter} appear in {word}",
+    "Count the {letter} in {word}",
+    "Give me the count of {letter} in {word}",
+    "How many instances of {letter} in {word}",
+    "Show me how many {letter} are in {word}",
+    "Calculate the number of {letter} in {word}",
+    # Spanish
+    "¿Cuántas {letter} hay en {word}?",
+    "¿Cuántas veces aparece {letter} en {word}?",
+    "Cuenta las {letter} en {word}",
+    "¿Cuántas letras {letter} tiene {word}?",
+    # Chinese (Simplified)
+    "{word}中有多少个{letter}",
+    "{word}里有几个{letter}",
+    "数一下{word}中的{letter}",
+    "{word}这个词里有多少{letter}",
+    # Korean
+    "{word}에 {letter}가 몇 개 있나요",
+    "{word}에서 {letter}의 개수는",
+    "{word}에 {letter}가 몇 번 나오나요",
+    "{word}라는 단어에 {letter}가 몇 개",
+    # French
+    "Combien de {letter} dans {word}",
+    "Combien de fois {letter} apparaît dans {word}",
+    "Compte les {letter} dans {word}",
+    # German
+    "Wie viele {letter} sind in {word}",
+    "Wie oft kommt {letter} in {word} vor",
+    "Zähle die {letter} in {word}",
+    # Japanese
+    "{word}に{letter}は何個ありますか",
+    "{word}の中に{letter}がいくつ",
+    "{word}に{letter}が何回出てくる",
+]
+
+class SpellingBee(Task):
+
+    def __init__(self, size=1000, split="train", **kwargs):
+        super().__init__(**kwargs)
+        assert split in ["train", "test"], "SpellingBee split must be train|test"
+        self.size = size
+        self.split = split
+        filename = WORD_LIST_URL.split("/")[-1]
+        word_list_path = download_file_with_lock(WORD_LIST_URL, filename)
+        with open(word_list_path) as f:
+            words = [line.strip() for line in f]
+        self.words = words
+
+    @property
+    def eval_type(self):
+        return 'generative'
+
+    def num_examples(self):
+        return self.size
+
+    def get_example(self, index):
+        seed = index if self.split == "train" else -(index + 1) # avoid collision at 0
+        rng = random.Random(seed)
+
+        # pick a random word
+        word = rng.choice(self.words)
+        # pick a letter from it (90%) or a random letter (10%)
+        letter = rng.choice(word) if rng.random() < 0.9 else rng.choice(LETTERS)
+
+        # get the correct answer by simply counting
+        count = word.count(letter)
+
+        # create a user message, with a bunch of variations as data augmentation
+        template = rng.choice(USER_MSG_TEMPLATES)
+        # 30% chance to lowercase the template (lazy people don't use shift)
+        if rng.random() < 0.3:
+            template = template.lower()
+        quote_options = ['', "'", '"']
+        letter_quote = rng.choice(quote_options) # is the letter quoted?
+        word_quote = rng.choice(quote_options) # is the word quoted?
+        letter_wrapped = f"{letter_quote}{letter}{letter_quote}"
+        word_wrapped = f"{word_quote}{word}{word_quote}"
+        user_msg = template.format(letter=letter_wrapped, word=word_wrapped)
+        if rng.random() < 0.5: # 50% of people don't even use question marks
+            user_msg += "?"
+
+        # Now create the ideal assistant response - build as parts (text + tool calls)
+        assistant_parts = []
+        word_letters = ",".join(list(word))
+        manual_text = f"""We are asked to find the number '{letter}' in the word '{word}'. Let me try a manual approach first.
+
+First spell the word out:
+{word}:{word_letters}
+
+Then count the occurrences of '{letter}':
+"""
+        # Little simulated loop of the solution process
+        # TODO: This is where the fun starts, we could simulate cute little mistakes
+        # and get the model to review its work and recover from them.
+        # You might of course hope this could arise in RL too, but realistically you'd want to help it out a bit.
+        running_count = 0
+        for i, char in enumerate(word, 1):
+            if char == letter:
+                running_count += 1
+                # note: there deliberately cannot be a space here between i and char
+                # because this would create a different token! (e.g. " a" and "a" are different tokens)
+                manual_text += f"{i}:{char} hit! count={running_count}\n"
+            else:
+                manual_text += f"{i}:{char}\n"
+
+        manual_text += f"\nThis gives us {running_count}."
+        assistant_parts.append({"type": "text", "text": manual_text})
+        # Part 2: Python verification
+        assistant_parts.append({"type": "text", "text": "\n\nLet me double check this using Python:\n\n"})
+        # Part 3: Python tool call
+        python_expr = f"'{word}'.count('{letter}')"
+        assistant_parts.append({"type": "python", "text": python_expr})
+        # Part 4: Python output
+        assistant_parts.append({"type": "python_output", "text": str(count)})
+        # Part 5: Final answer
+        assistant_parts.append({"type": "text", "text": f"\n\nPython gives us {count}.\n\nMy final answer is:\n\n#### {count}"})
+
+        # return the full conversation
+        messages = [
+            {"role": "user", "content": user_msg},
+            {"role": "assistant", "content": assistant_parts}
+        ]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+
+    def evaluate(self, conversation, assistant_response):
+        """
+        Given (conversation, completion), return evaluation outcome (0 = wrong, 1 = correct)
+        Identical to gsm8k's evaluation.
+        """
+        assert isinstance(assistant_response, str), "Assuming simple string response for now"
+        # First extract the ground truth answer from the conversation
+        assistant_message = conversation['messages'][-1]
+        assert assistant_message['role'] == "assistant", "Last message must be from the Assistant"
+        assert isinstance(assistant_message['content'], list), "This is expected to be a list of parts"
+        # The last text part contains the final answer with ####
+        last_text_part = assistant_message['content'][-1]['text']
+        # Extract both the ground truth answer and the predicted answer
+        ref_num = extract_answer(last_text_part)
+        pred_num = extract_answer(assistant_response)
+        # Compare and return the success as int
+        is_correct = int(pred_num == ref_num)
+        return is_correct
+
+    def reward(self, conversation, assistant_response):
+        """ Use simple 0-1 reward just like gsm8k."""
+        is_correct = self.evaluate(conversation, assistant_response)
+        is_correct_float = float(is_correct)
+        return is_correct_float
+
+
+class SimpleSpelling(Task):
+    """Much simpler task designed to get the model to just practice spelling words."""
+
+    def __init__(self, size=1000, split="train", **kwargs):
+        super().__init__(**kwargs)
+        assert split in ["train", "test"], "SpellingBee split must be train|test"
+        self.size = size
+        self.split = split
+        filename = WORD_LIST_URL.split("/")[-1]
+        word_list_path = download_file_with_lock(WORD_LIST_URL, filename)
+        with open(word_list_path) as f:
+            words = [line.strip() for line in f]
+        rng = random.Random(42)
+        rng.shuffle(words) # use a different word order than the SpellingBee task
+        self.words = words
+
+    @property
+    def eval_type(self):
+        return 'generative'
+
+    def num_examples(self):
+        return self.size
+
+    def get_example(self, index):
+        seed = index if self.split == "train" else -(index + 1) # avoid collision at 0
+        rng = random.Random(seed)
+        # pick a random word
+        word = rng.choice(self.words)
+        word_letters = ",".join(list(word))
+        # return the full conversation
+        messages = [
+            {"role": "user", "content": f"Spell the word: {word}"},
+            {"role": "assistant", "content": f"{word}:{word_letters}"}
+        ]
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+
+
+if __name__ == "__main__":
+
+    # preview the SpellingBee task, first 10 examples
+    task = SpellingBee()
+    for i in range(10):
+        ex = task.get_example(i)
+        print("=" * 100)
+        print(ex['messages'][0]['content'])
+        print("-" * 100)
+        # Assistant content is now a list of parts
+        assistant_parts = ex['messages'][1]['content']
+        for part in assistant_parts:
+            if part['type'] == 'text':
+                print(part['text'], end='')
+            elif part['type'] == 'python':
+                print(f"<<{part['text']}=", end='')
+            elif part['type'] == 'python_output':
+                print(f"{part['text']}>>", end='')
+        print()
+        print("-" * 100)
+
+    # # preview the SimpleSpelling task, first 10 examples
+    # task = SimpleSpelling()
+    # for i in range(10):
+    #     ex = task.get_example(i)
+    #     print("=" * 100)
+    #     print(ex['messages'][0]['content'])
+    #     print("-" * 100)
+    #     print(ex['messages'][1]['content'])
+
+    # # also scrutinize the tokenization (last example only)
+    # from nanochat.tokenizer import get_tokenizer
+    # tokenizer = get_tokenizer()
+    # ids, mask = tokenizer.render_conversation(ex)
+    # print(tokenizer.visualize_tokenization(ids, mask, with_token_id=True))

uv.lock

@@ -2,13 +2,32 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
-    "python_full_version >= '3.12' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
-    "python_full_version == '3.11.*' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
-    "python_full_version == '3.11.*' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
-    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
-    "python_full_version < '3.11' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
 ]
+conflicts = [[
+    { package = "nanochat", extra = "cpu" },
+    { package = "nanochat", extra = "gpu" },
+]]
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -26,7 +45,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
-    { name = "async-timeout", marker = "python_full_version < '3.11'" },
+    { name = "async-timeout", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "attrs" },
     { name = "frozenlist" },
     { name = "multidict" },
@@ -111,7 +130,7 @@ version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "frozenlist" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" }
 wheels = [
@@ -132,10 +151,10 @@ name = "anyio"
 version = "4.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "idna" },
     { name = "sniffio" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
 wheels = [
@@ -238,7 +257,7 @@ name = "click"
 version = "8.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202", size = 286342, upload-time = "2025-05-20T23:19:49.832Z" }
 wheels = [
@@ -292,7 +311,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.12' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -497,7 +516,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
     { name = "fsspec" },
-    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
+    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "packaging" },
     { name = "pyyaml" },
     { name = "requests" },
@@ -602,7 +621,7 @@ name = "maturin"
 version = "1.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "tomli", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/13/7c/b11b870fc4fd84de2099906314ce45488ae17be32ff5493519a6cddc518a/maturin-1.9.4.tar.gz", hash = "sha256:235163a0c99bc6f380fb8786c04fd14dcf6cd622ff295ea3de525015e6ac40cf", size = 213647, upload-time = "2025-08-27T11:37:57.079Z" }
 wheels = [
@@ -635,7 +654,7 @@ name = "multidict"
 version = "6.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/69/7f/0652e6ed47ab288e3756ea9c0df8b14950781184d4bd7883f4d87dd41245/multidict-6.6.4.tar.gz", hash = "sha256:d2d4e4787672911b48350df02ed3fa3fffdc2f2e8ca06dd6afdf34189b76a9dd", size = 101843, upload-time = "2025-08-11T12:08:48.217Z" }
 wheels = [
@@ -761,13 +780,26 @@ dependencies = [
     { name = "numpy" },
     { name = "psutil" },
     { name = "regex" },
+    { name = "setuptools" },
     { name = "tiktoken" },
     { name = "tokenizers" },
-    { name = "torch" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-8-nanochat-gpu'" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "uvicorn" },
     { name = "wandb" },
 ]
 
+[package.optional-dependencies]
+cpu = [
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+]
+gpu = [
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
+]
+
 [package.dev-dependencies]
 dev = [
     { name = "maturin" },
@@ -782,12 +814,16 @@ requires-dist = [
     { name = "numpy", specifier = "==1.26.4" },
     { name = "psutil", specifier = ">=7.1.0" },
     { name = "regex", specifier = ">=2025.9.1" },
+    { name = "setuptools", specifier = ">=80.9.0" },
     { name = "tiktoken", specifier = ">=0.11.0" },
     { name = "tokenizers", specifier = ">=0.22.0" },
-    { name = "torch", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torch", specifier = ">=2.8.0" },
+    { name = "torch", marker = "extra == 'cpu'", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "nanochat", extra = "cpu" } },
+    { name = "torch", marker = "extra == 'gpu'", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "nanochat", extra = "gpu" } },
     { name = "uvicorn", specifier = ">=0.36.0" },
     { name = "wandb", specifier = ">=0.21.3" },
 ]
+provides-extras = ["cpu", "gpu"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -800,8 +836,13 @@ name = "networkx"
 version = "3.4.2"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
-    "python_full_version < '3.11' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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     { name = "pluggy" },
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+    { name = "tomli", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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