Instructions to use AtrriJi/smolified-risk-clause-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtrriJi/smolified-risk-clause-classifier with Transformers:

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

pipe = pipeline("text-generation", model="AtrriJi/smolified-risk-clause-classifier")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AtrriJi/smolified-risk-clause-classifier", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AtrriJi/smolified-risk-clause-classifier with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AtrriJi/smolified-risk-clause-classifier

SGLang

How to use AtrriJi/smolified-risk-clause-classifier 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 "AtrriJi/smolified-risk-clause-classifier" \
    --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": "AtrriJi/smolified-risk-clause-classifier",
		"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 "AtrriJi/smolified-risk-clause-classifier" \
        --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": "AtrriJi/smolified-risk-clause-classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AtrriJi/smolified-risk-clause-classifier with Docker Model Runner:
```
docker model run hf.co/AtrriJi/smolified-risk-clause-classifier
```

🤏 smolified-risk-clause-classifier

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: ebc82c6b)
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 = "AtrriJi/smolified-risk-clause-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''The user will provide a legal contract clause. Your task is to classify the clause into one of the following categories: 'Payment Terms', 'Intellectual Property', 'Confidentiality', 'Termination', 'Indemnification', 'Force Majeure', 'Governing Law', 'Warranty', 'Limitation of Liability', 'Dispute Resolution'. Additionally, predict the risk level of the clause as 'Low', 'Medium', or 'High'. You must output only the JSON object with 'category' and 'risk_level' fields. No additional text or explanation is allowed.'''},
    {'role': 'user', 'content': '''Either party may terminate this agreement upon ninety (90) days written notice to the other party.'''}
]
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 AtrriJi. Generated via Smolify.ai.

Downloads last month: 1

Safetensors

Model size

0.3B params

Tensor type

BF16

AtrriJi
/

smolified-risk-clause-classifier

🤏 smolified-risk-clause-classifier

📦 Asset Details

🚀 Usage (Inference)

⚖️ License & Ownership

Space using AtrriJi/smolified-risk-clause-classifier 1