Instructions to use aaronmac/tiny-shakespeare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aaronmac/tiny-shakespeare with Transformers:

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

pipe = pipeline("text-generation", model="aaronmac/tiny-shakespeare", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("aaronmac/tiny-shakespeare", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aaronmac/tiny-shakespeare with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aaronmac/tiny-shakespeare

SGLang

How to use aaronmac/tiny-shakespeare 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 "aaronmac/tiny-shakespeare" \
    --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": "aaronmac/tiny-shakespeare",
		"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 "aaronmac/tiny-shakespeare" \
        --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": "aaronmac/tiny-shakespeare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aaronmac/tiny-shakespeare with Docker Model Runner:
```
docker model run hf.co/aaronmac/tiny-shakespeare
```

tiny-shakespeare / README.md

aaronmac

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---
library_name: transformers
pipeline_tag: text-generation
tags:
- causal-lm
- pytorch
- shakespeare
- educational
---
# Tiny Shakespeare Transformer

A small character-level causal Transformer trained from scratch on Tiny Shakespeare.

## Architecture

- Character tokenizer
- 4 transformer blocks
- 4 attention heads
- embedding dimension 64
- context length 32

## Loading

```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
    "YOUR_USERNAME/tiny-shakespeare",
    trust_remote_code=True
)
```


	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- causal-lm
	- pytorch
	- shakespeare
	- educational
	---

	# Tiny Shakespeare Transformer

	A small character-level causal Transformer trained from scratch on Tiny Shakespeare.

	## Architecture

	- Character tokenizer
	- 4 transformer blocks
	- 4 attention heads
	- embedding dimension 64
	- context length 32

	## Loading

	```python
	from transformers import AutoModelForCausalLM

	model = AutoModelForCausalLM.from_pretrained(
	"YOUR_USERNAME/tiny-shakespeare",
	trust_remote_code=True
	)
	```