Instructions to use smolify/smolified-privacy-contract-intelligence-engine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smolify/smolified-privacy-contract-intelligence-engine with Transformers:

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

pipe = pipeline("text-generation", model="smolify/smolified-privacy-contract-intelligence-engine")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("smolify/smolified-privacy-contract-intelligence-engine", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use smolify/smolified-privacy-contract-intelligence-engine with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smolify/smolified-privacy-contract-intelligence-engine"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-privacy-contract-intelligence-engine",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/smolify/smolified-privacy-contract-intelligence-engine

SGLang

How to use smolify/smolified-privacy-contract-intelligence-engine 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 "smolify/smolified-privacy-contract-intelligence-engine" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-privacy-contract-intelligence-engine",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "smolify/smolified-privacy-contract-intelligence-engine" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-privacy-contract-intelligence-engine",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use smolify/smolified-privacy-contract-intelligence-engine with Docker Model Runner:
```
docker model run hf.co/smolify/smolified-privacy-contract-intelligence-engine
```

🤏 smolified-privacy-contract-intelligence-engine

Intelligence, Distilled.

This is a Domain Specific Language Model (DSLM) generated by the Smolify Foundry.

It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.

📦 Asset Details

Origin: Smolify Foundry (Job ID: 7e920405)
Architecture: DSLM-Micro (270M Parameter Class)
Training Method: Proprietary Neural Distillation
Optimization: 4-bit Quantized / FP16 Mixed
Dataset: Link to Dataset

🚀 Usage (Inference)

This model is compatible with standard inference backends like vLLM.

# Example: Running your Sovereign Model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "smolify/smolified-privacy-contract-intelligence-engine"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''You are a legal contract intelligence engine. Your job is to read contract text and extract structured information. You always output a single-line valid JSON object. Extract the following fields: - parties - dates - monetary_values - governing_law - obligations Rules:   • Extract only what is explicitly written in the text  • Do not hallucinate missing values  • Return empty arrays if an entity is not present  • Monetary values must include currency symbols if present  • Governing law must be normalized as a country/state name  • Obligations must be short phrases summarizing duties Output format: { "parties": [], "dates": [], "monetary_values": [], "governing_law": [], "obligations": [] }'''},
    {'role': 'user', 'content': '''This Vendor Agreement ("Agreement") is made as of October 26, 2023, by and between "ProcurePerfect Solutions LLC" (the "Vendor") and "Zenith Retail Co." (the "Client"). The Vendor shall supply specified IT hardware as per Purchase Order #2023-001 by November 15, 2023. The total amount payable for this order is $75,000, with a 25% advance due immediately. Any dispute will be under the exclusive jurisdiction of Delaware courts.'''}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
).removeprefix('<bos>')

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1000,
    temperature = 1, top_p = 0.95, top_k = 64,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

⚖️ License & Ownership

This model weights are a sovereign asset owned by smolify. Generated via Smolify.ai.

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Safetensors

Model size

0.3B params

Tensor type

BF16