Instructions to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with Transformers:

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

pipe = pipeline("text-classification", model="LeviDeHaan/SecInt-SmolLM2-360M-nginx")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeviDeHaan/SecInt-SmolLM2-360M-nginx")
model = AutoModelForCausalLM.from_pretrained("LeviDeHaan/SecInt-SmolLM2-360M-nginx")

llama-cpp-python

How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LeviDeHaan/SecInt-SmolLM2-360M-nginx",
	filename="smollm-security-nginx02-merged.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx
# Run inference directly in the terminal:
llama-cli -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx
# Run inference directly in the terminal:
llama-cli -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx
# Run inference directly in the terminal:
./llama-cli -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LeviDeHaan/SecInt-SmolLM2-360M-nginx

Use Docker

docker model run hf.co/LeviDeHaan/SecInt-SmolLM2-360M-nginx

LM Studio
Jan
Ollama
How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with Ollama:
```
ollama run hf.co/LeviDeHaan/SecInt-SmolLM2-360M-nginx
```

Unsloth Studio

How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeviDeHaan/SecInt-SmolLM2-360M-nginx to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeviDeHaan/SecInt-SmolLM2-360M-nginx to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeviDeHaan/SecInt-SmolLM2-360M-nginx to start chatting

Docker Model Runner
How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with Docker Model Runner:
```
docker model run hf.co/LeviDeHaan/SecInt-SmolLM2-360M-nginx
```

Lemonade

How to use LeviDeHaan/SecInt-SmolLM2-360M-nginx with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LeviDeHaan/SecInt-SmolLM2-360M-nginx

Run and chat with the model

lemonade run user.SecInt-SmolLM2-360M-nginx-{{QUANT_TAG}}

List all available models

lemonade list

LeviDeHaan commited on Oct 30, 2025

Commit

bbf3e2b

verified ·

1 Parent(s): f029240

Upload folder using huggingface_hub

Browse files

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