Instructions to use benchaffe/shakespeare-distilgpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use benchaffe/shakespeare-distilgpt2 with Transformers:

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

pipe = pipeline("text-generation", model="benchaffe/shakespeare-distilgpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("benchaffe/shakespeare-distilgpt2")
model = AutoModelForCausalLM.from_pretrained("benchaffe/shakespeare-distilgpt2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use benchaffe/shakespeare-distilgpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "benchaffe/shakespeare-distilgpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "benchaffe/shakespeare-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/benchaffe/shakespeare-distilgpt2

SGLang

How to use benchaffe/shakespeare-distilgpt2 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 "benchaffe/shakespeare-distilgpt2" \
    --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": "benchaffe/shakespeare-distilgpt2",
		"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 "benchaffe/shakespeare-distilgpt2" \
        --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": "benchaffe/shakespeare-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use benchaffe/shakespeare-distilgpt2 with Docker Model Runner:
```
docker model run hf.co/benchaffe/shakespeare-distilgpt2
```

shakespeare-distilgpt2

This model is a fine-tuned version of distilbert/distilgpt2 on the shakespeare-lines dataset. It achieves the following results on the evaluation set:

Loss: 4.2490
Perplexity: 74.01

Training and evaluation data

The training and evaluation data was taken from the shakespeare-lines dataset. The dataset was shuffled with a seed of 24, and split into training and evaluation with a ratio of 80:20.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("benchaffe/shakespeare-distilgpt2")
tokenizer = AutoTokenizer.from_pretrained("benchaffe/shakespeare-distilgpt2")

prompt = "What light through yonder window breaks"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_length=80,
    temperature=0.8,
    top_p=0.95,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5