- Libraries
- PEFT
How to use Rafaelcedav/cursus-adversarial with PEFT:
Task type is invalid.
- Transformers
How to use Rafaelcedav/cursus-adversarial with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Rafaelcedav/cursus-adversarial")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rafaelcedav/cursus-adversarial", device_map="auto") - llama-cpp-python
How to use Rafaelcedav/cursus-adversarial with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="Rafaelcedav/cursus-adversarial",
filename="cursus-adversarial-Q4_K_M.gguf",
)
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Rafaelcedav/cursus-adversarial 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 Rafaelcedav/cursus-adversarial:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Rafaelcedav/cursus-adversarial:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Rafaelcedav/cursus-adversarial:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Rafaelcedav/cursus-adversarial:Q4_K_M
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 Rafaelcedav/cursus-adversarial:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Rafaelcedav/cursus-adversarial:Q4_K_M
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 Rafaelcedav/cursus-adversarial:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rafaelcedav/cursus-adversarial:Q4_K_M
Use Docker
docker model run hf.co/Rafaelcedav/cursus-adversarial:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Rafaelcedav/cursus-adversarial with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rafaelcedav/cursus-adversarial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Rafaelcedav/cursus-adversarial",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/Rafaelcedav/cursus-adversarial:Q4_K_M
- SGLang
How to use Rafaelcedav/cursus-adversarial 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 "Rafaelcedav/cursus-adversarial" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Rafaelcedav/cursus-adversarial",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "Rafaelcedav/cursus-adversarial" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Rafaelcedav/cursus-adversarial",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Ollama
How to use Rafaelcedav/cursus-adversarial with Ollama:
ollama run hf.co/Rafaelcedav/cursus-adversarial:Q4_K_M
- Unsloth Studio
How to use Rafaelcedav/cursus-adversarial 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 Rafaelcedav/cursus-adversarial 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 Rafaelcedav/cursus-adversarial to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rafaelcedav/cursus-adversarial to start chatting
- Pi
How to use Rafaelcedav/cursus-adversarial with Pi:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rafaelcedav/cursus-adversarial:Q4_K_M
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "Rafaelcedav/cursus-adversarial:Q4_K_M"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent new
How to use Rafaelcedav/cursus-adversarial with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rafaelcedav/cursus-adversarial:Q4_K_M
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Rafaelcedav/cursus-adversarial:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Rafaelcedav/cursus-adversarial with OpenClaw:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rafaelcedav/cursus-adversarial:Q4_K_M
Configure OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
--auth-choice custom-api-key \
--custom-base-url http://127.0.0.1:8080/v1 \
--custom-model-id "Rafaelcedav/cursus-adversarial:Q4_K_M" \
--custom-provider-id llama-cpp \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use Rafaelcedav/cursus-adversarial with Docker Model Runner:
docker model run hf.co/Rafaelcedav/cursus-adversarial:Q4_K_M
- Lemonade
How to use Rafaelcedav/cursus-adversarial with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rafaelcedav/cursus-adversarial:Q4_K_M
Run and chat with the model
lemonade run user.cursus-adversarial-Q4_K_M
List all available models
lemonade list
