Instructions to use ableian/TeenyTinyStories-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ableian/TeenyTinyStories-1M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ableian/TeenyTinyStories-1M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ableian/TeenyTinyStories-1M")
model = AutoModelForCausalLM.from_pretrained("ableian/TeenyTinyStories-1M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ableian/TeenyTinyStories-1M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ableian/TeenyTinyStories-1M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ableian/TeenyTinyStories-1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ableian/TeenyTinyStories-1M

SGLang

How to use ableian/TeenyTinyStories-1M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ableian/TeenyTinyStories-1M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ableian/TeenyTinyStories-1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ableian/TeenyTinyStories-1M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ableian/TeenyTinyStories-1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ableian/TeenyTinyStories-1M with Docker Model Runner:
```
docker model run hf.co/ableian/TeenyTinyStories-1M
```

TeenyTinyStories-1M

GPT-Neo storyteller (nominal 1M; actual 3,745,984 parameters including the 50,257-token embedding) trained on TeenyTinyStories (TTS): 2,491,490 stories (498.5M tokens with EOS) generated by roneneldan/TinyStories-Instruct-33M.

Training

Architecture: GPT-Neo, 8 layers, hidden 64, 16 heads, window 256, context 512, fp32.
Budget: 20 epochs over written train tokens (15,212 steps x 655,360 tokens = 9.97B tokens), batch 1280 x 512, constant LR 5e-4, weight decay 0.1, betas (0.9, 0.95), clip 1.0.
Tokenizer: EleutherAI/gpt-neo-125M (EOS 50256; one EOS per story).

Held-out loss (nats, full files)

TTS-val (20,000 stories): 1.987 / ppl 7.29
TS-valid capped to stories of at most 400 tokens (20,711 stories): 2.369 / ppl 10.69
TS-valid full (21,990 stories): 2.385 / ppl 10.86

Intended use

Research on small-scale synthetic pretraining data. Generates simple children's stories; sample at temperature 0.8-1.0. See the TeenyTinyStories paper for the corpus recipe and judge evaluations.

License

Apache-2.0.

Downloads last month: 48

Safetensors

Model size

3.75M params

Tensor type

F32