Text Generation
Transformers
Safetensors
GGUF
llama
chatbot
multilingual
arabic
french
tamazight
english
conversational
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use kaisser/LLM-Maroc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| set(BUILD_NUMBER 0) | |
| set(BUILD_COMMIT "unknown") | |
| set(BUILD_COMPILER "unknown") | |
| set(BUILD_TARGET "unknown") | |
| # Look for git | |
| find_package(Git) | |
| if(NOT Git_FOUND) | |
| find_program(GIT_EXECUTABLE NAMES git git.exe) | |
| if(GIT_EXECUTABLE) | |
| set(Git_FOUND TRUE) | |
| message(STATUS "Found Git: ${GIT_EXECUTABLE}") | |
| else() | |
| message(WARNING "Git not found. Build info will not be accurate.") | |
| endif() | |
| endif() | |
| # Get the commit count and hash | |
| if(Git_FOUND) | |
| execute_process( | |
| COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD | |
| WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} | |
| OUTPUT_VARIABLE HEAD | |
| OUTPUT_STRIP_TRAILING_WHITESPACE | |
| RESULT_VARIABLE RES | |
| ) | |
| if (RES EQUAL 0) | |
| set(BUILD_COMMIT ${HEAD}) | |
| endif() | |
| execute_process( | |
| COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD | |
| WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} | |
| OUTPUT_VARIABLE COUNT | |
| OUTPUT_STRIP_TRAILING_WHITESPACE | |
| RESULT_VARIABLE RES | |
| ) | |
| if (RES EQUAL 0) | |
| set(BUILD_NUMBER ${COUNT}) | |
| endif() | |
| endif() | |
| if(MSVC) | |
| set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}") | |
| if (CMAKE_VS_PLATFORM_NAME) | |
| set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME}) | |
| else() | |
| set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}") | |
| endif() | |
| else() | |
| execute_process( | |
| COMMAND ${CMAKE_C_COMPILER} --version | |
| OUTPUT_VARIABLE OUT | |
| OUTPUT_STRIP_TRAILING_WHITESPACE | |
| ) | |
| string(REGEX REPLACE " *\n.*" "" OUT "${OUT}") | |
| set(BUILD_COMPILER ${OUT}) | |
| execute_process( | |
| COMMAND ${CMAKE_C_COMPILER} -dumpmachine | |
| OUTPUT_VARIABLE OUT | |
| OUTPUT_STRIP_TRAILING_WHITESPACE | |
| ) | |
| set(BUILD_TARGET ${OUT}) | |
| endif() | |