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 { useEffect, useRef, useState, useCallback } from 'react'; | |
| import { throttle } from '../utils/misc'; | |
| // Media Query for detecting "large" screens (matching Tailwind's lg: breakpoint) | |
| const LARGE_SCREEN_MQ = '(min-width: 1024px)'; | |
| // Calculates and sets the textarea height based on its scrollHeight | |
| const adjustTextareaHeight = throttle( | |
| (textarea: HTMLTextAreaElement | null) => { | |
| if (!textarea) return; | |
| // Only perform auto-sizing on large screens | |
| if (!window.matchMedia(LARGE_SCREEN_MQ).matches) { | |
| // On small screens, reset inline height and max-height styles. | |
| // This allows CSS (e.g., `rows` attribute or classes) to control the height, | |
| // and enables manual resizing if `resize-vertical` is set. | |
| textarea.style.height = ''; // Use 'auto' or '' to reset | |
| textarea.style.maxHeight = ''; | |
| return; // Do not adjust height programmatically on small screens | |
| } | |
| const computedStyle = window.getComputedStyle(textarea); | |
| // Get the max-height specified by CSS (e.g., from `lg:max-h-48`) | |
| const currentMaxHeight = computedStyle.maxHeight; | |
| // Temporarily remove max-height to allow scrollHeight to be calculated correctly | |
| textarea.style.maxHeight = 'none'; | |
| // Reset height to 'auto' to measure the actual scrollHeight needed | |
| textarea.style.height = 'auto'; | |
| // Set the height to the calculated scrollHeight | |
| textarea.style.height = `${textarea.scrollHeight}px`; | |
| // Re-apply the original max-height from CSS to enforce the limit | |
| textarea.style.maxHeight = currentMaxHeight; | |
| }, | |
| 100 | |
| ); // Throttle to prevent excessive calls | |
| // Interface describing the API returned by the hook | |
| export interface ChatTextareaApi { | |
| value: () => string; | |
| setValue: (value: string) => void; | |
| focus: () => void; | |
| ref: React.RefObject<HTMLTextAreaElement>; | |
| refOnSubmit: React.MutableRefObject<(() => void) | null>; // Submit handler | |
| onInput: (event: React.FormEvent<HTMLTextAreaElement>) => void; // Input handler | |
| } | |
| // This is a workaround to prevent the textarea from re-rendering when the inner content changes | |
| // See https://github.com/ggml-org/llama.cpp/pull/12299 | |
| // combined now with auto-sizing logic. | |
| export function useChatTextarea(initValue: string): ChatTextareaApi { | |
| const [savedInitValue, setSavedInitValue] = useState<string>(initValue); | |
| const textareaRef = useRef<HTMLTextAreaElement>(null); | |
| const onSubmitRef = useRef<(() => void) | null>(null); | |
| // Effect to set initial value and height on mount or when initValue changes | |
| useEffect(() => { | |
| const textarea = textareaRef.current; | |
| if (textarea) { | |
| if (typeof savedInitValue === 'string' && savedInitValue.length > 0) { | |
| textarea.value = savedInitValue; | |
| // Call adjustTextareaHeight - it will check screen size internally | |
| setTimeout(() => adjustTextareaHeight(textarea), 0); | |
| setSavedInitValue(''); // Reset after applying | |
| } else { | |
| // Adjust height even if there's no initial value (for initial render) | |
| setTimeout(() => adjustTextareaHeight(textarea), 0); | |
| } | |
| } | |
| }, [textareaRef, savedInitValue]); // Depend on ref and savedInitValue | |
| // On input change, we adjust the height of the textarea | |
| const handleInput = useCallback( | |
| (event: React.FormEvent<HTMLTextAreaElement>) => { | |
| // Call adjustTextareaHeight on every input - it will decide whether to act | |
| adjustTextareaHeight(event.currentTarget); | |
| }, | |
| [] | |
| ); | |
| return { | |
| // Method to get the current value directly from the textarea | |
| value: () => { | |
| return textareaRef.current?.value ?? ''; | |
| }, | |
| // Method to programmatically set the value and trigger height adjustment | |
| setValue: (value: string) => { | |
| const textarea = textareaRef.current; | |
| if (textarea) { | |
| textarea.value = value; | |
| // Call adjustTextareaHeight - it will check screen size internally | |
| setTimeout(() => adjustTextareaHeight(textarea), 0); | |
| } | |
| }, | |
| focus: () => { | |
| if (textareaRef.current) { | |
| textareaRef.current.focus(); | |
| } | |
| }, | |
| ref: textareaRef, | |
| refOnSubmit: onSubmitRef, | |
| onInput: handleInput, // for adjusting height on input | |
| }; | |
| } | |