llm.kittens TinyStories 124M BF16
This is a 124M-parameter GPT-2-style causal language model trained from scratch on TinyStories with the llm.kittens C++/CUDA trainer, which is a fork of Karpathy's llm.c with some optimisations for SM120, and multi-stack kernel optimisations.
The model is published as a standard Hugging Face Transformers checkpoint with BF16 safetensors weights.
It was trained on a single RTX 5090 in 14 hours.
This isn't a chat model. It's pretrained only, use the Python example to use it.
Result
- Model weights:
model.safetensors - Training step:
20000 / 20000 - Final train loss:
0.785740 - Final validation loss:
0.875080 - Final throughput:
207135 tokens/s - Final step time:
2531.04 ms - Final reported BF16 MFU:
39.7% - Average iteration time:
2605.014347 ms - Safetensors size:
248,894,656bytes - Parameter count:
124,475,904
The TinyStories paper reports eval losses of 1.33 to 1.58 for the 768-hidden-size 1- and 2-layer attention-head ablations in Figure 24. This run's 0.875080 validation loss is lower, but the comparison is not apples-to-apples: this model is a 12-layer GPT-2-style model using GPT-2 tokenization, a 1024-token context, and a different implementation/training setup.
Architecture
- Family: GPT-2-style decoder-only Transformer
- Descriptor:
d12 - Layers:
12 - Attention heads:
12 - Hidden size:
768 - Context length:
1024 - Vocabulary size:
50,257 - Precision: BF16 weights
Training
The run used the TinyStories GPT-2 dataset files generated by dev/data/tinystories.py in llm.kittens.
./train_gpt2cu \
-i "dev/data/tinystories/TinyStories_train.bin" \
-j "dev/data/tinystories/TinyStories_val.bin" \
-o "log124M/5090_S" \
-v 250 -s 20000 -g 144 \
-h 0 \
-b 64 -t 1024 -d 524288 \
-r 0 \
-z 1 \
-c 0.1 \
-l 0.0006 -q 0.0 -u 700 -n 5000 \
-y 0 \
-e "d12" \
-x 20000
Key settings:
- Hardware target: RTX 5090 / SM120
- Micro batch:
64 - Sequence length:
1024 - Total desired batch size:
524,288tokens - Max steps:
20,000 - Optimizer: AdamW as implemented in
llm.kittens - Peak learning rate:
6e-4 - Scheduler: cosine
- Warmup:
700steps - Final LR fraction:
0.0 - Weight decay:
0.1 - Recompute: off
- ZeRO stage:
1 - Checkpoint interval:
5000steps
Sample
Prompt/sample emitted at the final checkpoint:
Once upon a time, there was a little boy named Timmy. Timmy loved going to school and playing with his friends. One day, Timmy woke up and felt very hot. He asked his mom if his head hurt. His mom said it might be burnt. Timmy's mom recommended they switch their shirts outside so he would feel better.
Timmy went outside and saw his friends playing. He wanted to join them, but he remembered his mom's recommendation. He switched his shirt right away and felt much cooler. Timmy was happy he listened to his mom and his friends.
Later, during recess, Timmy's friend asked him to go on the slide.
Files
model.safetensors: BF16 Transformers weights.config.json: GPT-2 model configuration.generation_config.json: default generation settings.tokenizer.json: GPT-2 tokenizer.vocab.jsonandmerges.txt: GPT-2 BPE vocabulary files.
Loading
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "adamroberts/tinystories-5090"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
inputs = tokenizer("Once upon a time", return_tensors="pt")
with torch.inference_mode():
outputs = model.generate(**inputs, max_new_tokens=80, do_sample=True, temperature=0.8)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Source implementation: https://github.com/adamdroberts/llm.kittens
TinyStories reference paper: https://arxiv.org/abs/2305.07759