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
| // Export usage message (-h) to markdown format | |
| static void write_table_header(std::ofstream & file) { | |
| file << "| Argument | Explanation |\n"; | |
| file << "| -------- | ----------- |\n"; | |
| } | |
| static void write_table_entry(std::ofstream & file, const common_arg & opt) { | |
| file << "| `"; | |
| // args | |
| for (const auto & arg : opt.args) { | |
| if (arg == opt.args.front()) { | |
| file << arg; | |
| if (opt.args.size() > 1) file << ", "; | |
| } else { | |
| file << arg << (arg != opt.args.back() ? ", " : ""); | |
| } | |
| } | |
| // value hint | |
| if (opt.value_hint) { | |
| std::string md_value_hint(opt.value_hint); | |
| string_replace_all(md_value_hint, "|", "\\|"); | |
| file << " " << md_value_hint; | |
| } | |
| if (opt.value_hint_2) { | |
| std::string md_value_hint_2(opt.value_hint_2); | |
| string_replace_all(md_value_hint_2, "|", "\\|"); | |
| file << " " << md_value_hint_2; | |
| } | |
| // help text | |
| std::string md_help(opt.help); | |
| string_replace_all(md_help, "\n", "<br/>"); | |
| string_replace_all(md_help, "|", "\\|"); | |
| file << "` | " << md_help << " |\n"; | |
| } | |
| static void write_table(std::ofstream & file, std::vector<common_arg *> & opts) { | |
| write_table_header(file); | |
| for (const auto & opt : opts) { | |
| write_table_entry(file, *opt); | |
| } | |
| } | |
| static void export_md(std::string fname, llama_example ex) { | |
| std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc); | |
| common_params params; | |
| auto ctx_arg = common_params_parser_init(params, ex); | |
| std::vector<common_arg *> common_options; | |
| std::vector<common_arg *> sparam_options; | |
| std::vector<common_arg *> specific_options; | |
| for (auto & opt : ctx_arg.options) { | |
| // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example | |
| if (opt.is_sparam) { | |
| sparam_options.push_back(&opt); | |
| } else if (opt.in_example(ctx_arg.ex)) { | |
| specific_options.push_back(&opt); | |
| } else { | |
| common_options.push_back(&opt); | |
| } | |
| } | |
| file << "**Common params**\n\n"; | |
| write_table(file, common_options); | |
| file << "\n\n**Sampling params**\n\n"; | |
| write_table(file, sparam_options); | |
| file << "\n\n**Example-specific params**\n\n"; | |
| write_table(file, specific_options); | |
| } | |
| int main(int, char **) { | |
| export_md("autogen-main.md", LLAMA_EXAMPLE_MAIN); | |
| export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER); | |
| return 0; | |
| } | |