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

Libraries

How to use numind/NuExtract-large with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract-large", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use numind/NuExtract-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "numind/NuExtract-large"
# 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-large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/numind/NuExtract-large

SGLang

How to use numind/NuExtract-large 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-large" \
    --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-large",
		"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-large" \
        --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-large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

NuExtract-large 7b and NuExtract 3.8B have same size model file

by mohit0928 - opened Jun 25, 2024

Discussion

mohit0928

Jun 25, 2024

The size of model.safetensors is the same for both NuExtract-large 7b and NuExtract 3.8B. Since the NuExtract-large has nearly twice the number of parameters, shouldn't the file sizes be different? I'm checking this to verify whether these two are the same model or different models.

Alexandre-Numind

NuMind org Jun 25, 2024

No, it's not the same model, this one is base on phi3-small. The difference come from the fact that you need to save/load the weight in bf16 (mostly because we couln't make the training in full precision because of flash-attention).

Alexandre-Numind changed discussion status to closed Jun 25, 2024

mohit0928

Jun 25, 2024

Ohh I see, you have also updated the model loading script as well
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", trust_remote_code=True, torch_dtype=torch.bfloat16)
I checked 10 hrs ago, it was
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", trust_remote_code=True)
so I was a bit confused it's same for both NuExtract-large 7b and NuExtract 3.8B

Thanks for clarifying @Alexandre-Numind

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