Instructions to use vishakhpk/t5-11b-copoet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vishakhpk/t5-11b-copoet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vishakhpk/t5-11b-copoet")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vishakhpk/t5-11b-copoet") model = AutoModelForSeq2SeqLM.from_pretrained("vishakhpk/t5-11b-copoet") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use vishakhpk/t5-11b-copoet with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vishakhpk/t5-11b-copoet" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vishakhpk/t5-11b-copoet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vishakhpk/t5-11b-copoet
- SGLang
How to use vishakhpk/t5-11b-copoet 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 "vishakhpk/t5-11b-copoet" \ --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": "vishakhpk/t5-11b-copoet", "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 "vishakhpk/t5-11b-copoet" \ --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": "vishakhpk/t5-11b-copoet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vishakhpk/t5-11b-copoet with Docker Model Runner:
docker model run hf.co/vishakhpk/t5-11b-copoet
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
Collaborative Poetry Writing with Instructions
As part of our work, we release our Instruction-tuned T5-11B model specifically aimed at instructions suited to poetry writing.
The expected model output is a single poetic sentence or verse in response to an instruction in natural language provided by a user. Here's an example of the collaborative writing process.
The model was finetuned using a dataset of poetic sentences scraped from the internet and then paired to an instruction generated via templates. Training and validation data is shared on our Github.
Here are some samples of instructions the model was trained on:
More details about the training and evaluation can be found in the paper.
You can also see poems that were written with model help and the corresponding user interactions on our website.
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