Instructions to use numind/NuExtract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use numind/NuExtract with Transformers:

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

pipe = pipeline("text-generation", model="numind/NuExtract", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use numind/NuExtract with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "numind/NuExtract"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "numind/NuExtract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/numind/NuExtract

SGLang

How to use numind/NuExtract 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 "numind/NuExtract" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "numind/NuExtract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "numind/NuExtract" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "numind/NuExtract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use numind/NuExtract with Docker Model Runner:
```
docker model run hf.co/numind/NuExtract
```

Can we restrict the entity values and also output confidence scores

by vijayg - opened Jun 25, 2024

Discussion

vijayg

Jun 25, 2024

•

edited Jun 25, 2024

Thanks for open sourcing the model. I have couple of followup questions.

Is it possible to o/p confidence score for each entity values that the model o/ps.
Is it possible to restrict the output (value of an entity) to predefined set .

In the example,
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
Can we restrict values of Model i.e ( entities) to - [ "Llama, "GPT","Phi"] . The model is forced to pick one of these and additionally o/p a confidence score for each of them .

vijayg

Jun 25, 2024

This comment has been hidden

liamcripwell

Jun 26, 2024

Hi , thanks for trying the model. While we do not have such a feature implemented you should be able to do so by writing your own generation/sampling function which checks the logits for specific vocabulary items.

liamcripwell changed discussion status to closed Jun 27, 2024

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