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 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
| import React, { createContext, useState, useContext } from 'react'; | |
| type ModalContextType = { | |
| showConfirm: (message: string) => Promise<boolean>; | |
| showPrompt: ( | |
| message: string, | |
| defaultValue?: string | |
| ) => Promise<string | undefined>; | |
| showAlert: (message: string) => Promise<void>; | |
| }; | |
| const ModalContext = createContext<ModalContextType>(null!); | |
| interface ModalState<T> { | |
| isOpen: boolean; | |
| message: string; | |
| defaultValue?: string; | |
| resolve: ((value: T) => void) | null; | |
| } | |
| export function ModalProvider({ children }: { children: React.ReactNode }) { | |
| const [confirmState, setConfirmState] = useState<ModalState<boolean>>({ | |
| isOpen: false, | |
| message: '', | |
| resolve: null, | |
| }); | |
| const [promptState, setPromptState] = useState< | |
| ModalState<string | undefined> | |
| >({ isOpen: false, message: '', resolve: null }); | |
| const [alertState, setAlertState] = useState<ModalState<void>>({ | |
| isOpen: false, | |
| message: '', | |
| resolve: null, | |
| }); | |
| const inputRef = React.useRef<HTMLInputElement>(null); | |
| const showConfirm = (message: string): Promise<boolean> => { | |
| return new Promise((resolve) => { | |
| setConfirmState({ isOpen: true, message, resolve }); | |
| }); | |
| }; | |
| const showPrompt = ( | |
| message: string, | |
| defaultValue?: string | |
| ): Promise<string | undefined> => { | |
| return new Promise((resolve) => { | |
| setPromptState({ isOpen: true, message, defaultValue, resolve }); | |
| }); | |
| }; | |
| const showAlert = (message: string): Promise<void> => { | |
| return new Promise((resolve) => { | |
| setAlertState({ isOpen: true, message, resolve }); | |
| }); | |
| }; | |
| const handleConfirm = (result: boolean) => { | |
| confirmState.resolve?.(result); | |
| setConfirmState({ isOpen: false, message: '', resolve: null }); | |
| }; | |
| const handlePrompt = (result?: string) => { | |
| promptState.resolve?.(result); | |
| setPromptState({ isOpen: false, message: '', resolve: null }); | |
| }; | |
| const handleAlertClose = () => { | |
| alertState.resolve?.(); | |
| setAlertState({ isOpen: false, message: '', resolve: null }); | |
| }; | |
| return ( | |
| <ModalContext.Provider value={{ showConfirm, showPrompt, showAlert }}> | |
| {children} | |
| {/* Confirm Modal */} | |
| {confirmState.isOpen && ( | |
| <dialog className="modal modal-open z-[1100]"> | |
| <div className="modal-box"> | |
| <h3 className="font-bold text-lg">{confirmState.message}</h3> | |
| <div className="modal-action"> | |
| <button | |
| className="btn btn-ghost" | |
| onClick={() => handleConfirm(false)} | |
| > | |
| Cancel | |
| </button> | |
| <button | |
| className="btn btn-error" | |
| onClick={() => handleConfirm(true)} | |
| > | |
| Confirm | |
| </button> | |
| </div> | |
| </div> | |
| </dialog> | |
| )} | |
| {/* Prompt Modal */} | |
| {promptState.isOpen && ( | |
| <dialog className="modal modal-open z-[1100]"> | |
| <div className="modal-box"> | |
| <h3 className="font-bold text-lg">{promptState.message}</h3> | |
| <input | |
| type="text" | |
| className="input input-bordered w-full mt-2" | |
| defaultValue={promptState.defaultValue} | |
| ref={inputRef} | |
| onKeyDown={(e) => { | |
| if (e.key === 'Enter') { | |
| handlePrompt((e.target as HTMLInputElement).value); | |
| } | |
| }} | |
| /> | |
| <div className="modal-action"> | |
| <button className="btn btn-ghost" onClick={() => handlePrompt()}> | |
| Cancel | |
| </button> | |
| <button | |
| className="btn btn-primary" | |
| onClick={() => handlePrompt(inputRef.current?.value)} | |
| > | |
| Submit | |
| </button> | |
| </div> | |
| </div> | |
| </dialog> | |
| )} | |
| {/* Alert Modal */} | |
| {alertState.isOpen && ( | |
| <dialog className="modal modal-open z-[1100]"> | |
| <div className="modal-box"> | |
| <h3 className="font-bold text-lg">{alertState.message}</h3> | |
| <div className="modal-action"> | |
| <button className="btn" onClick={handleAlertClose}> | |
| OK | |
| </button> | |
| </div> | |
| </div> | |
| </dialog> | |
| )} | |
| </ModalContext.Provider> | |
| ); | |
| } | |
| export function useModals() { | |
| const context = useContext(ModalContext); | |
| if (!context) throw new Error('useModals must be used within ModalProvider'); | |
| return context; | |
| } | |