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

Libraries

How to use abertsch/unlimiformer-bart-govreport-alternating with Transformers:

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

pipe = pipeline("text-generation", model="abertsch/unlimiformer-bart-govreport-alternating")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("abertsch/unlimiformer-bart-govreport-alternating")
model = AutoModel.from_pretrained("abertsch/unlimiformer-bart-govreport-alternating")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abertsch/unlimiformer-bart-govreport-alternating with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abertsch/unlimiformer-bart-govreport-alternating"
# 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-govreport-alternating",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abertsch/unlimiformer-bart-govreport-alternating

SGLang

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

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

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 is a BART-base model finetuned using the Unlimiformer alternating-training method, as described in section 3.2 of the paper. The model was finetuned on GovReport using the data processing pipeline from SLED; to load the validation or test set for use with these model, please use the datasets urialon/gov_report_validation and urialon/gov_report_test.

This is the strongest of the Unlimiformer models on this dataset.

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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Datasets used to train abertsch/unlimiformer-bart-govreport-alternating

Paper for abertsch/unlimiformer-bart-govreport-alternating

Unlimiformer: Long-Range Transformers with Unlimited Length Input

Paper • 2305.01625 • Published May 2, 2023 • 6