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

Commit

b3c885c

verified ·

1 Parent(s): 0ba446b

Update README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -37,7 +37,7 @@ messages = [
     },
     {
         "role": "user",
-        "content": "Text: The American Bank Note Company Printing Plant is a repurposed complex of three interconnected buildings in the Hunts Point neighborhood of the Bronx in New York City. The innovative Kirby, Petit & Green design was built in 1909–1911 by the American Bank Note Company on land which had previously been part of Edward G. Faile's country estate. A wide variety of financial instruments were printed there; at one point, over five million documents were produced per day, including half the securities traded on the New York Stock Exchange."
     },
     {
         "role": "assistant",
@@ -45,7 +45,7 @@ messages = [
     },
     {
         "role": "user",
-        "content": "What describes location in the text?"
     }
 ]
 prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
@@ -60,7 +60,7 @@ print(outputs[0]["generated_text"])
 # <|user|>
 # What describes location in the text?</s>
 # <|assistant|>
-# ["Hunts Point", "Bronx", "New York City"]
 ```
 ### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.

     },
     {
         "role": "user",
+        "content": "Text: VinBigData Joint Stock Company provides platform technology solutions and advanced products based on Big Data and Artificial Intelligence. With a staff of professors, doctors, and global technology experts, VinBigData is currently developing and deploying products such as ViVi virtual assistant, VinBase the comprehensive multi-cognitive artificial intelligence ecosystem, Vizone the ecosystem of smart image analysis solutions, VinDr the medical image digitization platform,..."
     },
     {
         "role": "assistant",
     },
     {
         "role": "user",
+        "content": "What describes products in the text?"
     }
 ]
 prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 # <|user|>
 # What describes location in the text?</s>
 # <|assistant|>
+# ["ViVi", "VinBase", "Vizone", "VinDr"]
 ```
 ### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.