Instructions to use inference-net/Schematron-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-net/Schematron-3B with Transformers:

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

pipe = pipeline("text-generation", model="inference-net/Schematron-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inference-net/Schematron-3B")
model = AutoModelForCausalLM.from_pretrained("inference-net/Schematron-3B")
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 inference-net/Schematron-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-net/Schematron-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-net/Schematron-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inference-net/Schematron-3B

SGLang

How to use inference-net/Schematron-3B 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 "inference-net/Schematron-3B" \
    --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": "inference-net/Schematron-3B",
		"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 "inference-net/Schematron-3B" \
        --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": "inference-net/Schematron-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inference-net/Schematron-3B with Docker Model Runner:
```
docker model run hf.co/inference-net/Schematron-3B
```

opensporks commited on Sep 5, 2025

Commit

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@@ -4,6 +4,18 @@ license: llama3.2
 base_model: meta-llama/Llama-3.2-3B-Instruct
 ---
 ## Model Overview
 Welcome to the Schematron series, [Inference.net's](https://inference.net/) long‑context extraction models specialized in converting noisy HTML into clean, typed JSON that conforms to your custom schema. The Schematron series was purpose‑trained for web scraping, data ingestion, and transforming arbitrary pages into structured records.

 base_model: meta-llama/Llama-3.2-3B-Instruct
 ---
+<p align="center">
+  <img alt="Schematron" src="https://huggingface.co/inference-net/Schematron-3B/resolve/main/Banner.png">
+</p>
+<p align="center">
+  <a href="https://gpt-oss.com"><strong>Documentation</strong></a> ·
+  <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Serverless API</strong></a> ·
+  <a href="https://openai.com/index/introducing-gpt-oss/"><strong>Announcement blog</strong></a>
+</p>
+<br>
 ## Model Overview
 Welcome to the Schematron series, [Inference.net's](https://inference.net/) long‑context extraction models specialized in converting noisy HTML into clean, typed JSON that conforms to your custom schema. The Schematron series was purpose‑trained for web scraping, data ingestion, and transforming arbitrary pages into structured records.