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 Desktop
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 /.devops /cuda.Dockerfile

kaisser

Upload folder using huggingface_hub

305a42c verified about 1 year ago

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	ARG UBUNTU_VERSION=22.04
	# This needs to generally match the container host's environment.
	ARG CUDA_VERSION=12.4.0
	# Target the CUDA build image
	ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}

	ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}

	FROM ${BASE_CUDA_DEV_CONTAINER} AS build

	# CUDA architecture to build for (defaults to all supported archs)
	ARG CUDA_DOCKER_ARCH=default

	RUN apt-get update && \
	apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1

	WORKDIR /app

	COPY . .

	RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
	export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
	fi && \
	cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
	cmake --build build --config Release -j$(nproc)

	RUN mkdir -p /app/lib && \
	find build -name "*.so" -exec cp {} /app/lib \;

	RUN mkdir -p /app/full \
	&& cp build/bin/* /app/full \
	&& cp *.py /app/full \
	&& cp -r gguf-py /app/full \
	&& cp -r requirements /app/full \
	&& cp requirements.txt /app/full \
	&& cp .devops/tools.sh /app/full/tools.sh

	## Base image
	FROM ${BASE_CUDA_RUN_CONTAINER} AS base

	RUN apt-get update \
	&& apt-get install -y libgomp1 curl\
	&& apt autoremove -y \
	&& apt clean -y \
	&& rm -rf /tmp/* /var/tmp/* \
	&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
	&& find /var/cache -type f -delete

	COPY --from=build /app/lib/ /app

	### Full
	FROM base AS full

	COPY --from=build /app/full /app

	WORKDIR /app

	RUN apt-get update \
	&& apt-get install -y \
	git \
	python3 \
	python3-pip \
	&& pip install --upgrade pip setuptools wheel \
	&& pip install -r requirements.txt \
	&& apt autoremove -y \
	&& apt clean -y \
	&& rm -rf /tmp/* /var/tmp/* \
	&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
	&& find /var/cache -type f -delete


	ENTRYPOINT ["/app/tools.sh"]

	### Light, CLI only
	FROM base AS light

	COPY --from=build /app/full/llama-cli /app

	WORKDIR /app

	ENTRYPOINT [ "/app/llama-cli" ]

	### Server, Server only
	FROM base AS server

	ENV LLAMA_ARG_HOST=0.0.0.0

	COPY --from=build /app/full/llama-server /app

	WORKDIR /app

	HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]

	ENTRYPOINT [ "/app/llama-server" ]