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")

# pip install -U transformers accelerate
# 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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

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

Atomic Chat

SecInt-SmolLM2-360M-nginx / tokenizer.json

LeviDeHaan

Upload folder using huggingface_hub

70345e9 verified about 1 year ago

Download tokenizer.json from LeviDeHaan/SecInt-SmolLM2-360M-nginx: direct link, hf CLI and curl.

Browser
Download file 3.52 MB

https://huggingface.co/LeviDeHaan/SecInt-SmolLM2-360M-nginx/resolve/main/tokenizer.json

Command line

hf download hf://LeviDeHaan/SecInt-SmolLM2-360M-nginx/tokenizer.json

curl -L -o tokenizer.json https://huggingface.co/LeviDeHaan/SecInt-SmolLM2-360M-nginx/resolve/main/tokenizer.json

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