Instructions to use abertsch/unlimiformer-bart-booksum-random-encoding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abertsch/unlimiformer-bart-booksum-random-encoding with Transformers:

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

pipe = pipeline("text-generation", model="abertsch/unlimiformer-bart-booksum-random-encoding")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("abertsch/unlimiformer-bart-booksum-random-encoding")
model = AutoModel.from_pretrained("abertsch/unlimiformer-bart-booksum-random-encoding")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abertsch/unlimiformer-bart-booksum-random-encoding with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/abertsch/unlimiformer-bart-booksum-random-encoding

SGLang

How to use abertsch/unlimiformer-bart-booksum-random-encoding 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 "abertsch/unlimiformer-bart-booksum-random-encoding" \
    --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": "abertsch/unlimiformer-bart-booksum-random-encoding",
		"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 "abertsch/unlimiformer-bart-booksum-random-encoding" \
        --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": "abertsch/unlimiformer-bart-booksum-random-encoding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abertsch/unlimiformer-bart-booksum-random-encoding with Docker Model Runner:
```
docker model run hf.co/abertsch/unlimiformer-bart-booksum-random-encoding
```

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

Model from the preprint Unlimiformer: Long-Range Transformers with Unlimited Length Input.

This model was finetuned from a BART-base model using the random-encoding training strategy described in section 3.2 of the paper. It was finetuned on the dataset BookSum (full-book setting).

The inference demo is disabled because you must add the Unlimiformer files to your repo before this model can handle unlimited length input! See the Unlimiformer GitHub for setup instructions.

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Dataset used to train abertsch/unlimiformer-bart-booksum-random-encoding

Paper for abertsch/unlimiformer-bart-booksum-random-encoding

Unlimiformer: Long-Range Transformers with Unlimited Length Input

Paper • 2305.01625 • Published May 2, 2023 • 6