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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Alexandre-Numind commited on Jun 25, 2024

Commit

0b2054b

verified ·

1 Parent(s): b4a775c

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -42,6 +42,7 @@ To use the model:
 ```python
 import json
 from transformers import AutoModelForCausalLM, AutoTokenizer
 def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
@@ -61,7 +62,7 @@ def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
 model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", trust_remote_code=True,torch_dtype=torch.bfloat16)
 tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
-#model.to("cuda")
 model.eval()
@@ -91,6 +92,6 @@ schema = """{
 }"""
 prediction = predict_NuExtract(model,tokenizer,text, schema,example = ["","",""])
 ```

 ```python
 import json
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
 model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", trust_remote_code=True,torch_dtype=torch.bfloat16)
 tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
+model.to("cuda")
 model.eval()
 }"""
 prediction = predict_NuExtract(model,tokenizer,text, schema,example = ["","",""])
+print(prediction)
 ```