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: 5,144 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | #!/usr/bin/env bash
PROG=${0##*/}
build_dir="build-ci-debug"
# Print Color Commands
red=$(tput setaf 1)
green=$(tput setaf 2)
yellow=$(tput setaf 3)
blue=$(tput setaf 4)
magenta=$(tput setaf 5)
cyan=$(tput setaf 6)
normal=$(tput sgr0)
# Print Help Message
####################
print_full_help() {
cat << EOF
Usage: $PROG [OPTION]... <test_regex> (test_number)
Debug specific ctest program.
Options:
-h, --help display this help and exit
-g run in gdb mode
Arguments:
<test_regex> (Mandatory) Supply one regex to the script to filter tests
(test_number) (Optional) Test number to run a specific test
Example:
$PROG test-tokenizer
$PROG test-tokenizer 3
EOF
}
abort() {
echo "Error: $1" >&2
cat << EOF >&2
Usage: $PROG [OPTION]... <test_regex> (test_number)
Debug specific ctest program.
Refer to --help for full instructions.
EOF
exit 1
}
# Dependency Sanity Check
#########################
check_dependency() {
command -v "$1" >/dev/null 2>&1 || {
abort "$1 is required but not found. Please install it and try again."
}
}
check_dependency ctest
check_dependency cmake
# Step 0: Check the args
########################
if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then
print_full_help >&2
exit 0
fi
# Parse command-line options
gdb_mode=false
while getopts "g" opt; do
case $opt in
g)
gdb_mode=true
echo "gdb_mode Mode Enabled"
;;
esac
done
# Shift the option parameters
shift $((OPTIND - 1))
# Positionial Argument Processing : <test_regex>
if [ -z "${1}" ]; then
abort "Test regex is required"
else
test_suite=${1:-}
fi
# Positionial Argument Processing : (test_number)
test_number=${2:-}
# Step 1: Reset and Setup folder context
########################################
## Sanity check that we are actually in a git repo
repo_root=$(git rev-parse --show-toplevel)
if [ ! -d "$repo_root" ]; then
abort "Not in a Git repository."
fi
## Reset folder to root context of git repo and Create and enter build directory
pushd "$repo_root"
rm -rf "$build_dir" && mkdir "$build_dir" || abort "Failed to make $build_dir"
# Step 2: Setup Build Environment and Compile Test Binaries
###########################################################
# Note: test-eval-callback requires -DLLAMA_CURL
cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DGGML_CUDA=1 -DLLAMA_CURL=1 || abort "Failed to build environment"
pushd "$build_dir"
make -j || abort "Failed to compile"
popd > /dev/null || exit 1
# Step 3: Find all tests available that matches REGEX
####################################################
# Ctest Gather Tests
# `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex)
# `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB.
# `-V` : Verbose Mode
printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n"
pushd "$build_dir"
tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1'))
if [ ${#tests[@]} -eq 0 ]; then
abort "No tests available... check your compilation process..."
fi
popd > /dev/null || exit 1
# Step 4: Identify Test Command for Debugging
#############################################
# Select test number
if [ -z $test_number ]; then
# List out available tests
printf "Which test would you like to debug?\n"
id=0
for s in "${tests[@]}"
do
echo "Test# ${id}"
echo " $s"
((id++))
done
# Prompt user which test they wanted to run
printf "\nRun test#? "
read test_number
else
printf "\nUser Already Requested #${test_number}\n"
fi
# Grab all tests commands
pushd "$build_dir"
sIFS=$IFS # Save Initial IFS (Internal Field Separator)
IFS=$'\n' # Change IFS (Internal Field Separator) (So we split ctest output by newline rather than by spaces)
test_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) # Get test args
IFS=$sIFS # Reset IFS (Internal Field Separator)
popd > /dev/null || exit 1
# Grab specific test command
single_test_name="${tests[test_number]}"
single_test_command="${test_args[test_number]}"
# Step 5: Execute or GDB Debug
##############################
printf "${magenta}Running Test #${test_number}: ${single_test_name}${normal}\n"
printf "${cyan}single_test_command: ${single_test_command}${normal}\n"
if [ "$gdb_mode" = "true" ]; then
# Execute debugger
pushd "$repo_root" || exit 1
eval "gdb --args ${single_test_command}"
popd > /dev/null || exit 1
else
# Execute Test
pushd "$repo_root" || exit 1
eval "${single_test_command}"
exit_code=$?
popd > /dev/null || exit 1
# Print Result
printf "${blue}Ran Test #${test_number}: ${single_test_name}${normal}\n"
printf "${yellow}Command: ${single_test_command}${normal}\n"
if [ $exit_code -eq 0 ]; then
printf "${green}TEST PASS${normal}\n"
else
printf "${red}TEST FAIL${normal}\n"
fi
fi
# Return to the directory from which the user ran the command.
popd > /dev/null || exit 1
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