Instructions to use kaisser/LLM-Maroc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaisser/LLM-Maroc with Transformers:

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

pipe = pipeline("text-generation", model="kaisser/LLM-Maroc")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaisser/LLM-Maroc")
model = AutoModelForCausalLM.from_pretrained("kaisser/LLM-Maroc", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kaisser/LLM-Maroc 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 kaisser/LLM-Maroc:BF16
# Run inference directly in the terminal:
llama cli -hf kaisser/LLM-Maroc:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kaisser/LLM-Maroc:BF16
# Run inference directly in the terminal:
llama cli -hf kaisser/LLM-Maroc:BF16

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 kaisser/LLM-Maroc:BF16
# Run inference directly in the terminal:
./llama-cli -hf kaisser/LLM-Maroc:BF16

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 kaisser/LLM-Maroc:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kaisser/LLM-Maroc:BF16

Use Docker

docker model run hf.co/kaisser/LLM-Maroc:BF16

LM Studio
Jan

vLLM

How to use kaisser/LLM-Maroc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaisser/LLM-Maroc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaisser/LLM-Maroc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kaisser/LLM-Maroc:BF16

SGLang

How to use kaisser/LLM-Maroc 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 "kaisser/LLM-Maroc" \
    --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": "kaisser/LLM-Maroc",
		"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 "kaisser/LLM-Maroc" \
        --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": "kaisser/LLM-Maroc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kaisser/LLM-Maroc with Ollama:
```
ollama run hf.co/kaisser/LLM-Maroc:BF16
```

Unsloth Studio

How to use kaisser/LLM-Maroc 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 kaisser/LLM-Maroc 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 kaisser/LLM-Maroc to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kaisser/LLM-Maroc to start chatting

Docker Model Runner
How to use kaisser/LLM-Maroc with Docker Model Runner:
```
docker model run hf.co/kaisser/LLM-Maroc:BF16
```

Lemonade

How to use kaisser/LLM-Maroc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kaisser/LLM-Maroc:BF16

Run and chat with the model

lemonade run user.LLM-Maroc-BF16

List all available models

lemonade list

Atomic Chat

LLM-Maroc / llama.cpp /tools /server /tests /unit /test_security.py

kaisser

Upload folder using huggingface_hub

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	import pytest
	from openai import OpenAI
	from utils import *

	server = ServerPreset.tinyllama2()

	TEST_API_KEY = "sk-this-is-the-secret-key"

	@pytest.fixture(scope="module", autouse=True)
	def create_server():
	global server
	server = ServerPreset.tinyllama2()
	server.api_key = TEST_API_KEY


	@pytest.mark.parametrize("endpoint", ["/health", "/models"])
	def test_access_public_endpoint(endpoint: str):
	global server
	server.start()
	res = server.make_request("GET", endpoint)
	assert res.status_code == 200
	assert "error" not in res.body


	@pytest.mark.parametrize("api_key", [None, "invalid-key"])
	def test_incorrect_api_key(api_key: str):
	global server
	server.start()
	res = server.make_request("POST", "/completions", data={
	"prompt": "I believe the meaning of life is",
	}, headers={
	"Authorization": f"Bearer {api_key}" if api_key else None,
	})
	assert res.status_code == 401
	assert "error" in res.body
	assert res.body["error"]["type"] == "authentication_error"


	def test_correct_api_key():
	global server
	server.start()
	res = server.make_request("POST", "/completions", data={
	"prompt": "I believe the meaning of life is",
	}, headers={
	"Authorization": f"Bearer {TEST_API_KEY}",
	})
	assert res.status_code == 200
	assert "error" not in res.body
	assert "content" in res.body


	def test_openai_library_correct_api_key():
	global server
	server.start()
	client = OpenAI(api_key=TEST_API_KEY, base_url=f"http://{server.server_host}:{server.server_port}")
	res = client.chat.completions.create(
	model="gpt-3.5-turbo",
	messages=[
	{"role": "system", "content": "You are a chatbot."},
	{"role": "user", "content": "What is the meaning of life?"},
	],
	)
	assert len(res.choices) == 1


	@pytest.mark.parametrize("origin,cors_header,cors_header_value", [
	("localhost", "Access-Control-Allow-Origin", "localhost"),
	("web.mydomain.fr", "Access-Control-Allow-Origin", "web.mydomain.fr"),
	("origin", "Access-Control-Allow-Credentials", "true"),
	("web.mydomain.fr", "Access-Control-Allow-Methods", "GET, POST"),
	("web.mydomain.fr", "Access-Control-Allow-Headers", "*"),
	])
	def test_cors_options(origin: str, cors_header: str, cors_header_value: str):
	global server
	server.start()
	res = server.make_request("OPTIONS", "/completions", headers={
	"Origin": origin,
	"Access-Control-Request-Method": "POST",
	"Access-Control-Request-Headers": "Authorization",
	})
	assert res.status_code == 200
	assert cors_header in res.headers
	assert res.headers[cors_header] == cors_header_value