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
```

Update README.md

by shengz - opened Aug 9, 2023

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@@ -6,6 +6,8 @@ language:
 ---
 # UniNER-7B-type
 **Description**: A UniNER-7B model trained from LLama-7B using the [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type) without human-labeled data. The data was collected by prompting gpt-3.5-turbo-0301 to label entities from passages and provide entity tags. The data collection prompt is as follows:

 ---
+# 💡We'll release the code for using UniversalNER at [this repo](https://github.com/universal-ner/universal-ner) by EOW (08/13/2023)
 # UniNER-7B-type
 **Description**: A UniNER-7B model trained from LLama-7B using the [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type) without human-labeled data. The data was collected by prompting gpt-3.5-turbo-0301 to label entities from passages and provide entity tags. The data collection prompt is as follows: