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
File size: 4,207 Bytes
305a42c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | 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
};
}
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