Instructions to use Compactbot/tinystories-40m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Compactbot/tinystories-40m with Transformers:

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

pipe = pipeline("text-generation", model="Compactbot/tinystories-40m", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Compactbot/tinystories-40m", trust_remote_code=True)
model = AutoModel.from_pretrained("Compactbot/tinystories-40m", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Compactbot/tinystories-40m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Compactbot/tinystories-40m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Compactbot/tinystories-40m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Compactbot/tinystories-40m

SGLang

How to use Compactbot/tinystories-40m 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 "Compactbot/tinystories-40m" \
    --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": "Compactbot/tinystories-40m",
		"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 "Compactbot/tinystories-40m" \
        --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": "Compactbot/tinystories-40m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Compactbot/tinystories-40m with Docker Model Runner:
```
docker model run hf.co/Compactbot/tinystories-40m
```

tinystories-40m

File size: 545 Bytes

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{
  "model_type": "llama",
  "architectures": [
    "TinyStoriesGPT"
  ],
  "auto_map": {
    "AutoModel": "modeling_tinystories.TinyStoriesGPT",
    "AutoModelForCausalLM": "modeling_tinystories.TinyStoriesGPT"
  },
  "hidden_size": 512,
  "intermediate_size": 1408,
  "num_hidden_layers": 12,
  "num_attention_heads": 8,
  "num_key_value_heads": 4,
  "head_dim": 64,
  "vocab_size": 8192,
  "max_position_embeddings": 512,
  "rope_theta": 10000.0,
  "tie_word_embeddings": true,
  "torch_dtype": "float32",
  "transformers_version": "4.45.0"
}