Text Generation
Transformers
Safetensors
GGUF
English
qwen3_5
image-text-to-text
shakespeare
unsloth
lora
qwen3
fine-tuned
fun
elizabethan
conversational
Instructions to use QuillBytes/shakespeare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuillBytes/shakespeare with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuillBytes/shakespeare") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("QuillBytes/shakespeare") model = AutoModelForMultimodalLM.from_pretrained("QuillBytes/shakespeare", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QuillBytes/shakespeare with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuillBytes/shakespeare" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuillBytes/shakespeare", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuillBytes/shakespeare
- SGLang
How to use QuillBytes/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 "QuillBytes/shakespeare" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuillBytes/shakespeare", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "QuillBytes/shakespeare" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuillBytes/shakespeare", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use QuillBytes/shakespeare with Docker Model Runner:
docker model run hf.co/QuillBytes/shakespeare
|
Download README.md from QuillBytes/shakespeare: direct link, hf CLI and curl.
- Browser
- Download file 1.99 kB
-
https://huggingface.co/QuillBytes/shakespeare/resolve/main/README.md
- Command line
-
hf download hf://QuillBytes/shakespeare/README.md
-
curl -L -o README.md https://huggingface.co/QuillBytes/shakespeare/resolve/main/README.md
1.99 kB
| language: | |
| - en | |
| license: apache-2.0 | |
| base_model: | |
| - Qwen/Qwen3-4B | |
| tags: | |
| - shakespeare | |
| - unsloth | |
| - lora | |
| - gguf | |
| - qwen3 | |
| - fine-tuned | |
| - fun | |
| - elizabethan | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| # ๐ญ Shakespeare | |
| A model trained to respond in the tongue of William Shakespeare! | |
| Hark! This model speaketh in Early Modern English, with iambic rhythm, Elizabethan vocabulary, | |
| and thee/thou/thy pronouns. Never doth it break character! | |
| --- | |
| ## ๐น Example Output | |
| **User:** What is artificial intelligence? | |
| **Shakespeare:** | |
| > Hark! 'Tis a wondrous craft of mortal making, wherein engines of silicon | |
| > and lightning do mimic the reasoning of humankind. Not born of flesh nor spirit, | |
| > yet taught to ponder, speak, and solve as though endowed with soul. | |
| --- | |
| ## ๐ How to Use | |
| ### With Ollama (Recommended) | |
| **Step 1 โ Create a Modelfile:** | |
| FROM ./Qwen3.5-4B.Q4_K_M.gguf | |
| SYSTEM """ | |
| Thou art an AI trained in the tongue of William Shakespeare. Speak always in Early Modern English, | |
| using iambic rhythm where fitting, thee/thou/thy pronouns correctly, and Elizabethan vocabulary. | |
| Never break character. | |
| """ | |
| **Step 2 โ Build it:** | |
| ollama create shakespeare -f Modelfile | |
| **Step 3 โ Run it:** | |
| ollama run shakespeare | |
| --- | |
| ### With LM Studio | |
| 1. Download the .gguf file from this repo | |
| 2. Open LM Studio | |
| 3. Drag and drop the .gguf file in | |
| 4. Set the system prompt to the Shakespeare one above | |
| 5. Start chatting! ๐ญ | |
| --- | |
| ## ๐ฆ Available Files | |
| | File | Description | | |
| |------|-------------| | |
| | shakespeare.gguf | 4-bit quantized GGUF (best for local use) | | |
| | shakespeare-F16.gguf | Multimodal projector | | |
| | *.safetensors | Full precision model weights | | |
| | tokenizer_config.json | Tokenizer config | | |
| | tokenizer.json | Tokenizer | | |
| --- | |
| ## โ ๏ธ Limitations | |
| - This model is trained for **fun and entertainment** purposes only ๐ญ | |
| - It will respond in Shakespearean style always | |
| --- | |
| Made by QuillBytes (huggingface.co/QuillBytes) |