Instructions to use SrijanMandal/smolified-maskify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SrijanMandal/smolified-maskify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SrijanMandal/smolified-maskify")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SrijanMandal/smolified-maskify", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SrijanMandal/smolified-maskify with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SrijanMandal/smolified-maskify" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SrijanMandal/smolified-maskify", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SrijanMandal/smolified-maskify
- SGLang
How to use SrijanMandal/smolified-maskify 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 "SrijanMandal/smolified-maskify" \ --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": "SrijanMandal/smolified-maskify", "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 "SrijanMandal/smolified-maskify" \ --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": "SrijanMandal/smolified-maskify", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SrijanMandal/smolified-maskify with Docker Model Runner:
docker model run hf.co/SrijanMandal/smolified-maskify
π€ smolified-maskify
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.
π¦ Asset Details
- Origin: Smolify Foundry (Job ID:
484fecda) - Architecture: DSLM-Micro (270M Parameter Class)
- Training Method: Proprietary Neural Distillation
- Optimization: 4-bit Quantized / FP16 Mixed
- Dataset: Link to Dataset
π Usage (Inference)
This model is compatible with standard inference backends like vLLM.
# Example: Running your Sovereign Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "SrijanMandal/smolified-maskify"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{'role': 'system', 'content': '''A privacy filter that processes unstructured text such as emails, chat messages, logs, and documents, and returns two outputs:A redacted version of the text where detected PII is replaced with standardized tags such as[PERSON], [EMAIL], [PHONE], [AADHAAR], [PAN], [BANK_ACCOUNT], [ADDRESS]A structured JSON object listing all redacted entities, including their type and original valueThis system is intended to prevent accidental leakage of sensitive Indian personal data before text is stored, shared, or sent to external AI systems.Input:Please verify Aadhaar 1234 5678 9012 for Rahul Sharma. Contact him at rahul.sharma@gmail.com or +91 9876543210.Output:Please verify Aadhaar [AADHAAR] for [PERSON]. Contact him at [EMAIL] or [PHONE].{"redacted_entities": [{ "type": "AADHAAR", "original": "1234 5678 9012" },{ "type": "PERSON", "original": "Rahul Sharma" },{ "type": "EMAIL", "original": "rahul.sharma@gmail.com" },{ "type": "PHONE", "type": "PHONE", "original": "+91 9876543210" }]}'''},
{'role': 'user', 'content': '''I need to confirm the identity of Mrs. Gayatri Devi. Her Aadhaar is 8765 4321 0987 and she lives at H-4, Sector 12, Noida, Uttar Pradesh - 201301.'''}
]
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),
)
βοΈ License & Ownership
This model weights are a sovereign asset owned by SrijanMandal. Generated via Smolify.ai.
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