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")

# 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")

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

c720d49

verified ·

1 Parent(s): b3c885c

Update README.md

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README.md +3 -3

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@@ -7,14 +7,14 @@ language:
 ---
 <div align="center">
-# UniNER-7B-all
 </div>
 This model is finetuned from [TinyLLama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T).
 It is trained on ChatGPT-generated [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type).
-Check our [paper](https://arxiv.org/abs/2308.03279) for more information.
 ### How to use
@@ -28,7 +28,7 @@ Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for
 import torch
 from transformers import pipeline
-pipe = pipeline("text-generation", model="/media/4TB_1/minhtt/Documents/test/axolotl/lora-out/merged",
                 torch_dtype=torch.bfloat16, device_map="auto")
 messages = [
     {

 ---
 <div align="center">
+# tiny-universal-NER
 </div>
 This model is finetuned from [TinyLLama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T).
 It is trained on ChatGPT-generated [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type).
+Check this [paper](https://arxiv.org/abs/2308.03279) for more information.
 ### How to use
 import torch
 from transformers import pipeline
+pipe = pipeline("text-generation", model="LR-AI-Labs/tiny-universal-NER",
                 torch_dtype=torch.bfloat16, device_map="auto")
 messages = [
     {