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 9, 2025

Commit

3c0c602

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1 Parent(s): 2eeac0a

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@@ -11,7 +11,7 @@ base_model: meta-llama/Llama-3.2-3B-Instruct
 <p align="center">
   <a href="https://docs.inference.net/use-cases/json-extraction"><strong>Documentation</strong></a> ·
   <a href="https://inference.net/models/schematron-3b"><strong>Serverless API</strong></a> ·
-  <a href="https://openai.com/index/introducing-gpt-oss/"><strong>Announcement blog</strong></a>
 </p>
 <br>
@@ -68,7 +68,7 @@ We evaluated Schematron's real-world impact on LLM factuality using SimpleQA.
 3. **Structured Extraction**: Schematron extracts JSON data from retrieved pages using the schema
 4. **Answer Synthesis**: Primary LLM produces final answer from structured data
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6626a246891c75742bd19aaf/h2CA0TpQ1hKDxexGlpdSQ.png)
 **Key findings:**
 - Web search paired with JSON extraction improves factuality: Adding Schematron with web retrieval improves GPT-5 Nano's accuracy from 8.54% to 82.87%—nearly a 10x improvement

 <p align="center">
   <a href="https://docs.inference.net/use-cases/json-extraction"><strong>Documentation</strong></a> ·
   <a href="https://inference.net/models/schematron-3b"><strong>Serverless API</strong></a> ·
+  <a href="https://inference.net/blog/Schematron"><strong>Announcement blog</strong></a>
 </p>
 <br>
 3. **Structured Extraction**: Schematron extracts JSON data from retrieved pages using the schema
 4. **Answer Synthesis**: Primary LLM produces final answer from structured data
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6626a246891c75742bd19aaf/mU_01IPsf0FvkXYNYstRZ.png)
 **Key findings:**
 - Web search paired with JSON extraction improves factuality: Adding Schematron with web retrieval improves GPT-5 Nano's accuracy from 8.54% to 82.87%—nearly a 10x improvement