NanoChat Speedrun Model

This model was trained using the NanoChat speedrun script.

Model Details

• Architecture: d20 (561M parameters)
• Training Pipeline: Pretraining → Midtraining → SFT
• Tokenizer: Custom BPE with vocab size 65,536
• Training Time: ~4 hours on 8xH100 GPUs

Usage

# Load the model (example - adjust based on your setup)
import torch
model = torch.load('model.pth', map_location='cpu')

See nanochat repository for full usage instructions.

Training Report

nanochat training report

Generated: 2025-10-19 19:54:27

Environment

Git Information

• Branch: master
• Commit: d6d86cb (dirty)
• Message: update readme with a link to the CPU|MPS branch

Hardware

• Platform: Linux
• CPUs: 240 cores (240 logical)
• Memory: 1771.7 GB
• GPUs: 8x NVIDIA A100-SXM4-80GB
• GPU Memory: 634.0 GB total
• CUDA Version: 12.8
• Hourly Rate: $14.32/hour

Software

• Python: 3.10.12
• PyTorch: 2.8.0+cu128

Bloat

• Characters: 357,831
• Lines: 8,718
• Files: 44
• Tokens (approx): 89,457
• Dependencies (uv.lock lines): 2,004

Run started: 2025-10-19 19:54:32

---

Tokenizer training

timestamp: 2025-10-19 19:56:03

• max_chars: 2,000,000,000
• doc_cap: 10,000
• vocab_size: 65,536
• train_time: 71.4154
• num_special_tokens: 9
• token_bytes_min: 1
• token_bytes_max: 32
• token_bytes_mean: 6.9197
• token_bytes_std: 2.8748

Tokenizer evaluation

timestamp: 2025-10-19 19:56:15

Comparison with GPT-2

Text Type	Bytes	GPT-2 Tokens	GPT-2 Ratio	Ours Tokens	Ours Ratio	Relative Diff %
news	1819	404	4.50	375	4.85	+7.2%
korean	893	745	1.20	712	1.25	+4.4%
code	1259	576	2.19	492	2.56	+14.6%
math	1834	936	1.96	966	1.90	-3.2%
science	1112	260	4.28	228	4.88	+12.3%
fwe-train	4208518	900364	4.67	856883	4.91	+4.8%
fwe-val	4908443	1059062	4.63	1010352	4.86	+4.6%

Comparison with GPT-4

Text Type	Bytes	GPT-4 Tokens	GPT-4 Ratio	Ours Tokens	Ours Ratio	Relative Diff %
news	1819	387	4.70	375	4.85	+3.1%
korean	893	364	2.45	712	1.25	-95.6%
code	1259	309	4.07	492	2.56	-59.2%
math	1834	832	2.20	966	1.90	-16.1%
science	1112	249	4.47	228	4.88	+8.4%
fwe-train	4208518	874799	4.81	856883	4.91	+2.0%
fwe-val	4908443	1029691	4.77	1010352	4.86	+1.9%

Base model training

timestamp: 2025-10-20 03:02:00

• run: speedrun
• depth: 20
• max_seq_len: 2048
• num_iterations: -1
• target_flops: -1.0000
• target_param_data_ratio: 20
• device_batch_size: 32
• total_batch_size: 524,288
• embedding_lr: 0.2000
• unembedding_lr: 0.0040
• weight_decay: 0.0000
• matrix_lr: 0.0200
• grad_clip: 1.0000
• eval_every: 250
• eval_tokens: 10,485,760
• core_metric_every: 2000
• core_metric_max_per_task: 500
• sample_every: 2000
• model_tag:
• Number of parameters: 560,988,160
• Number of FLOPs per token: 3.491758e+09
• Calculated number of iterations: 21,400
• Number of training tokens: 11,219,763,200
• Tokens : Params ratio: 20.0000
• DDP world size: 8
• warmup_ratio: 0.0000
• warmdown_ratio: 0.2000
• final_lr_frac: 0.0000
• Minimum validation bpb: 0.8143
• Final validation bpb: 0.8143
• CORE metric estimate: 0.2133
• MFU %: 21.02%
• Total training flops: 3.917670e+19
• Total training time: 394.42m
• Peak memory usage: 75374.27MiB

Base model loss

timestamp: 2025-10-20 03:03:28

• train bpb: 0.8171
• val bpb: 0.8144
• sample 0: <|bos|>The capital of France is Paris. The capital of France is Paris. The capital of France is Paris.
• sample 1: <|bos|>The chemical symbol of gold is Au. The chemical symbol of gold is Au. The chemical symbol of gold is
• sample 2: <|bos|>If yesterday was Friday, then tomorrow will be Friday, and so on. This is a very common way of thinking about the
• sample 3: <|bos|>The opposite of hot is cold. The opposite of cold is hot. The opposite of hot is cold.
• sample 4: <|bos|>The planets of the solar system are: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune,
• sample 5: <|bos|>My favorite color is red. I love it. I love it. I love it. I love
• sample 6: <|bos|>If 5x + 3 = 13, then x is a multiple of 5. If 5x + 3 =

Base model evaluation

timestamp: 2025-10-20 03:10:53

• Model: base_model (step 21400)
• CORE metric: 0.2084
• hellaswag_zeroshot: 0.2626
• jeopardy: 0.1068
• bigbench_qa_wikidata: 0.5118
• arc_easy: 0.5325
• arc_challenge: 0.1274
• copa: 0.4000
• commonsense_qa: 0.0274
• piqa: 0.3645
• openbook_qa: 0.1200
• lambada_openai: 0.3813
• hellaswag: 0.2631
• winograd: 0.2234
• winogrande: 0.0545
• bigbench_dyck_languages: 0.1270
• agi_eval_lsat_ar: 0.0489
• bigbench_cs_algorithms: 0.3545
• bigbench_operators: 0.1429
• bigbench_repeat_copy_logic: 0.0312
• squad: 0.2391
• coqa: 0.2176
• boolq: -0.1267
• bigbench_language_identification: 0.1740

Midtraining

timestamp: 2025-10-20 03:29:50

• run: speedrun
• dtype: bfloat16
• max_seq_len: 2048
• device_batch_size: 32
• unembedding_lr: 0.0040
• embedding_lr: 0.2000
• matrix_lr: 0.0200
• init_lr_frac: 1.0000
• weight_decay: 0.0000
• eval_every: 150
• eval_tokens: 10,485,760
• total_batch_size: 524,288
• dry_run: 0
• Number of iterations: 765
• DDP world size: 8
• Minimum validation bpb: 0.3963

Chat evaluation mid

timestamp: 2025-10-20 03:48:39

• source: mid
• task_name: None
• dtype: bfloat16
• temperature: 0.0000
• max_new_tokens: 512
• num_samples: 1
• top_k: 50
• batch_size: 8
• model_tag: None
• step: None
• max_problems: None
• ARC-Easy: 0.3119
• ARC-Challenge: 0.2927
• MMLU: 0.2975
• GSM8K: 0.0402
• HumanEval: 0.0976
• ChatCORE metric: 0.0681

Chat SFT

timestamp: 2025-10-20 03:53:11

• run: speedrun
• source: mid
• dtype: bfloat16
• device_batch_size: 4
• num_epochs: 1
• max_iterations: -1
• target_examples_per_step: 32
• unembedding_lr: 0.0040
• embedding_lr: 0.2000
• matrix_lr: 0.0200
• weight_decay: 0.0000
• init_lr_frac: 0.0200
• eval_every: 100
• eval_steps: 100
• eval_metrics_every: 200
• Training rows: 20,843
• Number of iterations: 651
• Training loss: 1.1234
• Validation loss: 1.0146

Chat evaluation sft

timestamp: 2025-10-20 04:09:28

• source: sft
• task_name: None
• dtype: bfloat16
• temperature: 0.0000
• max_new_tokens: 512
• num_samples: 1
• top_k: 50
• batch_size: 8
• model_tag: None
• step: None
• max_problems: None
• ARC-Easy: 0.3338
• ARC-Challenge: 0.3046
• MMLU: 0.2955
• GSM8K: 0.0599
• HumanEval: 0.1220
• ChatCORE metric: 0.0854

Summary

• Characters: 357,831
• Lines: 8,718
• Files: 44
• Tokens (approx): 89,457
• Dependencies (uv.lock lines): 2,004

Metric	BASE	MID	SFT	RL
CORE	0.2084	-	-	-
ARC-Challenge	-	0.2927	0.3046	-
ARC-Easy	-	0.3119	0.3338	-
GSM8K	-	0.0402	0.0599	-
HumanEval	-	0.0976	0.1220	-
MMLU	-	0.2975	0.2955	-
ChatCORE	-	0.0681	0.0854	-

Total wall clock time: 8h14m