Instructions to use njwright92/t-5-comedy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use njwright92/t-5-comedy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="njwright92/t-5-comedy")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("njwright92/t-5-comedy") model = AutoModelForSeq2SeqLM.from_pretrained("njwright92/t-5-comedy") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use njwright92/t-5-comedy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "njwright92/t-5-comedy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "njwright92/t-5-comedy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/njwright92/t-5-comedy
- SGLang
How to use njwright92/t-5-comedy 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 "njwright92/t-5-comedy" \ --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": "njwright92/t-5-comedy", "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 "njwright92/t-5-comedy" \ --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": "njwright92/t-5-comedy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use njwright92/t-5-comedy with Docker Model Runner:
docker model run hf.co/njwright92/t-5-comedy
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Stand-Up Comic Assistant Model
Model Description
This model is designed as an assistant for stand-up comedians, providing suggestions, ideas, and content generation to support the creative process. It's trained on a diverse set of comedy transcripts, aiming to capture the essence of humor from various styles and contexts.
How It Works
The model is based on google/flan-t5-small, a powerful and efficient transformer model optimized for language understanding and generation tasks. It has been fine-tuned on the zachgitt/comedy-transcripts dataset, which includes a wide range of stand-up comedy routines.
Intended Use
- Idea Generation: Generate prompts or comedy concepts based on current trends, historical events, or user input.
- Content Creation: Assist in writing jokes, sketches, or full stand-up routines.
- Interactive Comedy: Engage with users by providing humorous responses in a conversational setting.
Training
The model was trained using the transformers library on a dataset of stand-up comedy transcripts. The training process focused on understanding context, delivering punchlines, and preserving the comedic timing that's essential in stand-up comedy.
Training Data
The dataset zachgitt/comedy-transcripts was used, which includes transcripts from various comedians across different eras of stand-up comedy.
Limitations and Biases
- Contextual Limitations: While the model understands a range of comedic styles, it may not always align with the nuances of personal taste in humor.
- Cultural Sensitivity: The dataset includes historical content that may not be suitable or sensitive to current cultural contexts.
- Language Biases: The model may reflect biases present in the training data, which consists of primarily English-language comedy routines.
Future Work
This model is a work in progress. Planned improvements include:
- Expanding the dataset with more diverse and contemporary sources.
- Implementing feedback loops to refine the model's sense of humor based on user interactions.
- Enhancing the model's understanding of different comedic devices like satire, irony, and slapstick.
Acknowledgements
Thanks to all the contributors of the zachgitt/comedy-transcripts dataset and the teams behind google/flan-t5-small for providing the foundational models and tools that made this project possible.
Disclaimer: This model is intended for creative and entertainment purposes. It should be used responsibly, considering the potential for generating content that may be offensive or inappropriate in certain contexts.
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