Instructions to use Universal-NER/UniNER-7B-type with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Universal-NER/UniNER-7B-type with Transformers:

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

pipe = pipeline("text-generation", model="Universal-NER/UniNER-7B-type")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Universal-NER/UniNER-7B-type")
model = AutoModelForCausalLM.from_pretrained("Universal-NER/UniNER-7B-type")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Universal-NER/UniNER-7B-type with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Universal-NER/UniNER-7B-type"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Universal-NER/UniNER-7B-type",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Universal-NER/UniNER-7B-type

SGLang

How to use Universal-NER/UniNER-7B-type 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 "Universal-NER/UniNER-7B-type" \
    --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": "Universal-NER/UniNER-7B-type",
		"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 "Universal-NER/UniNER-7B-type" \
        --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": "Universal-NER/UniNER-7B-type",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Universal-NER/UniNER-7B-type with Docker Model Runner:
```
docker model run hf.co/Universal-NER/UniNER-7B-type
```

Universal-NER commited on Aug 11, 2023

Commit

6347cb0

1 Parent(s): 023c6d5

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@@ -14,7 +14,7 @@ language:
 <strong>Instruction:</strong><br/>
 Given a passage, your task is to extract all entities and identify their entity types. The output should be in a list of tuples of the following format: [("entity 1", "type of entity 1"), ... ].</div>
-Check our [paper](https://arxiv.org/abs/2308.03279) for more information.
 ## Comparison with [UniNER-7B-definition](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition)
 The UniNER-7B-type model excels when handling entity tags. It performs better on the Universal NER benchmark, which consists of 43 academic datasets across 9 domains. In contrast, UniNER-7B-definition performs better at processing entity types defined in short sentences and is more robust to type paraphrasing.

 <strong>Instruction:</strong><br/>
 Given a passage, your task is to extract all entities and identify their entity types. The output should be in a list of tuples of the following format: [("entity 1", "type of entity 1"), ... ].</div>
+Check our [paper](https://arxiv.org/abs/2308.03279) for more information. Check our [repo](https://github.com/universal-ner/universal-ner) about how to use the model.
 ## Comparison with [UniNER-7B-definition](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition)
 The UniNER-7B-type model excels when handling entity tags. It performs better on the Universal NER benchmark, which consists of 43 academic datasets across 9 domains. In contrast, UniNER-7B-definition performs better at processing entity types defined in short sentences and is more robust to type paraphrasing.