Instructions to use GeneralRincewind/ShakespeareGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GeneralRincewind/ShakespeareGPT with Transformers:

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

pipe = pipeline("text-generation", model="GeneralRincewind/ShakespeareGPT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GeneralRincewind/ShakespeareGPT")
model = AutoModelForCausalLM.from_pretrained("GeneralRincewind/ShakespeareGPT", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GeneralRincewind/ShakespeareGPT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GeneralRincewind/ShakespeareGPT

SGLang

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

Docker Model Runner
How to use GeneralRincewind/ShakespeareGPT with Docker Model Runner:
```
docker model run hf.co/GeneralRincewind/ShakespeareGPT
```

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Check out the documentation for more information.

https://colab.research.google.com/drive/1Dlm8FA9JjjcqJIkfCagaIQWex8Ho5IKI#scrollTo=e8xIjRNsl3Bb

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GeneralRincewind/ShakespeareGPT")
model = AutoModelForCausalLM.from_pretrained("GeneralRincewind/ShakespeareGPT")

#### Generate text
from transformers import TextStreamer
tokenized_text = tokenizer("", return_tensors="pt",  truncation=True)
input_ids = tokenized_text.input_ids
streamer = TextStreamer(tokenizer)
model.eval()
full_completion = model.generate(inputs=tokenized_text["input_ids"].to("cuda"),
    attention_mask=tokenized_text["attention_mask"].to("cuda"),
    temperature=0.9,
     top_k=80,
     top_p=0.65,
    do_sample=True,
    streamer=streamer,                           
    num_beams=1,
    max_new_tokens=500,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
    repetition_penalty=1)

decoded_text = tokenizer.decode(full_completion[0])
print(decoded_text)

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