Instructions to use LR-AI-Labs/tiny-universal-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LR-AI-Labs/tiny-universal-NER with Transformers:

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

pipe = pipeline("text-generation", model="LR-AI-Labs/tiny-universal-NER")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LR-AI-Labs/tiny-universal-NER")
model = AutoModelForCausalLM.from_pretrained("LR-AI-Labs/tiny-universal-NER", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LR-AI-Labs/tiny-universal-NER with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LR-AI-Labs/tiny-universal-NER

SGLang

How to use LR-AI-Labs/tiny-universal-NER 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 "LR-AI-Labs/tiny-universal-NER" \
    --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": "LR-AI-Labs/tiny-universal-NER",
		"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 "LR-AI-Labs/tiny-universal-NER" \
        --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": "LR-AI-Labs/tiny-universal-NER",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LR-AI-Labs/tiny-universal-NER with Docker Model Runner:
```
docker model run hf.co/LR-AI-Labs/tiny-universal-NER
```

minhtt32 commited on Mar 28, 2024

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0872948

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@@ -16,6 +16,7 @@ It is trained on ChatGPT-generated [Pile-NER-type data](https://huggingface.co/d
 Check our [paper](https://arxiv.org/abs/2308.03279) for more information.
 ### How to use
 You will need the transformers>=4.34
 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
@@ -64,5 +65,6 @@ print(outputs[0]["generated_text"])
 ### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.
 ## License
 This model and its associated data are released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes.

 Check our [paper](https://arxiv.org/abs/2308.03279) for more information.
 ### How to use
 You will need the transformers>=4.34
 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
 ### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.
 ## License
+```
 This model and its associated data are released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes.