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", 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

cb2b4e1

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Update README.md

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@@ -1,10 +1,25 @@
 # UniNER-7B-all
-**Description**: This model is self fine-tuned 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.
-## Inference
 ```python
 # Install transformers from source - only needed for versions <= v4.34
 # pip install git+https://github.com/huggingface/transformers.git

+---
+license: apache-2.0
+datasets:
+- Universal-NER/Pile-NER-type
+language:
+- en
+---
+<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
+You will need the transformers>=4.34
+Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
 ```python
 # Install transformers from source - only needed for versions <= v4.34
 # pip install git+https://github.com/huggingface/transformers.git