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
| static bool llama_grammar_validate(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) { | |
| const auto cpts = unicode_cpts_from_utf8(input_str); | |
| auto & stacks_cur = llama_grammar_get_stacks(grammar); | |
| size_t pos = 0; | |
| for (const auto & cpt : cpts) { | |
| llama_grammar_accept(grammar, cpt); | |
| if (stacks_cur.empty()) { | |
| error_pos = pos; | |
| error_msg = "Unexpected character '" + unicode_cpt_to_utf8(cpt) + "'"; | |
| return false; | |
| } | |
| ++pos; | |
| } | |
| for (const auto & stack : stacks_cur) { | |
| if (stack.empty()) { | |
| return true; | |
| } | |
| } | |
| error_pos = pos; | |
| error_msg = "Unexpected end of input"; | |
| return false; | |
| } | |
| static void print_error_message(const std::string & input_str, size_t error_pos, const std::string & error_msg) { | |
| fprintf(stdout, "Input string is invalid according to the grammar.\n"); | |
| fprintf(stdout, "Error: %s at position %zu\n", error_msg.c_str(), error_pos); | |
| fprintf(stdout, "\n"); | |
| fprintf(stdout, "Input string:\n"); | |
| fprintf(stdout, "%s", input_str.substr(0, error_pos).c_str()); | |
| if (error_pos < input_str.size()) { | |
| fprintf(stdout, "\033[1;31m%c", input_str[error_pos]); | |
| if (error_pos+1 < input_str.size()) { | |
| fprintf(stdout, "\033[0;31m%s", input_str.substr(error_pos+1).c_str()); | |
| } | |
| fprintf(stdout, "\033[0m\n"); | |
| } | |
| } | |
| int main(int argc, char** argv) { | |
| if (argc != 3) { | |
| fprintf(stdout, "Usage: %s <grammar_filename> <input_filename>\n", argv[0]); | |
| return 1; | |
| } | |
| const std::string grammar_filename = argv[1]; | |
| const std::string input_filename = argv[2]; | |
| // Read the GBNF grammar file | |
| FILE* grammar_file = fopen(grammar_filename.c_str(), "r"); | |
| if (!grammar_file) { | |
| fprintf(stdout, "Failed to open grammar file: %s\n", grammar_filename.c_str()); | |
| return 1; | |
| } | |
| std::string grammar_str; | |
| { | |
| std::ifstream grammar_file(grammar_filename); | |
| GGML_ASSERT(grammar_file.is_open() && "Failed to open grammar file"); | |
| std::stringstream buffer; | |
| buffer << grammar_file.rdbuf(); | |
| grammar_str = buffer.str(); | |
| } | |
| llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0); | |
| if (grammar == nullptr) { | |
| fprintf(stdout, "Failed to initialize llama_grammar\n"); | |
| return 1; | |
| } | |
| // Read the input file | |
| std::string input_str; | |
| { | |
| std::ifstream input_file(input_filename); | |
| GGML_ASSERT(input_file.is_open() && "Failed to open input file"); | |
| std::stringstream buffer; | |
| buffer << input_file.rdbuf(); | |
| input_str = buffer.str(); | |
| } | |
| // Validate the input string against the grammar | |
| size_t error_pos; | |
| std::string error_msg; | |
| bool is_valid = llama_grammar_validate(grammar, input_str, error_pos, error_msg); | |
| if (is_valid) { | |
| fprintf(stdout, "Input string is valid according to the grammar.\n"); | |
| } else { | |
| print_error_message(input_str, error_pos, error_msg); | |
| } | |
| // Clean up | |
| llama_grammar_free_impl(grammar); | |
| return 0; | |
| } | |