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

Libraries

How to use numind/NuExtract-tiny with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract-tiny")
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract-tiny", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use numind/NuExtract-tiny with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Alexandre-Numind commited on Jun 24, 2024

Commit

7cd6009

verified ·

1 Parent(s): 824c7e9

Update README.md

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README.md +0 -29

README.md CHANGED Viewed

@@ -2,35 +2,6 @@
 license: mit
 language:
 - en
-widget:
-- text: '''<|input|>
-### Template:
-{
-    "Model": {
-        "Name": "",
-        "Number of parameters": "",
-        "Number of max token": "",
-        "Architecture": []
-    },
-    "Usage": {
-        "Use case": [],
-        "Licence": ""
-    }
-}
-### Text:
-We introduce Mistral 7B, a 7–billion-parameter language model engineered for
-superior performance and efficiency. Mistral 7B outperforms the best open 13B
-model (Llama 2) across all evaluated benchmarks, and the best released 34B
-model (Llama 1) in reasoning, mathematics, and code generation. Our model
-leverages grouped-query attention (GQA) for faster inference, coupled with sliding
-window attention (SWA) to effectively handle sequences of arbitrary length with a
-reduced inference cost. We also provide a model fine-tuned to follow instructions,
-Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
-automated benchmarks. Our models are released under the Apache 2.0 license.
-Code https://github.com/mistralai/mistral-src
-Webpage https://mistral.ai/news/announcing-mistral-7b/
-<|output|>
-'''
 ---
 # Structure Extraction Model by NuMind 🔥

 license: mit
 language:
 - en
 ---
 # Structure Extraction Model by NuMind 🔥