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
| class common_chat_msg_partial_exception : public std::runtime_error { | |
| public: | |
| common_chat_msg_partial_exception(const std::string & message) : std::runtime_error(message) {} | |
| }; | |
| class common_chat_msg_parser { | |
| std::string input_; | |
| bool is_partial_; | |
| common_chat_syntax syntax_; | |
| std::string healing_marker_; | |
| size_t pos_ = 0; | |
| common_chat_msg result_; | |
| public: | |
| common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax); | |
| const std::string & input() const { return input_; } | |
| size_t pos() const { return pos_; } | |
| const std::string & healing_marker() const { return healing_marker_; } | |
| const bool & is_partial() const { return is_partial_; } | |
| const common_chat_msg & result() const { return result_; } | |
| const common_chat_syntax & syntax() const { return syntax_; } | |
| void move_to(size_t pos) { | |
| if (pos > input_.size()) { | |
| throw std::runtime_error("Invalid position!"); | |
| } | |
| pos_ = pos; | |
| } | |
| void move_back(size_t n) { | |
| if (pos_ < n) { | |
| throw std::runtime_error("Can't move back that far!"); | |
| } | |
| pos_ -= n; | |
| } | |
| // Get the substring of the input at the given range | |
| std::string str(const common_string_range & rng) const; | |
| // Appends to the result.content field | |
| void add_content(const std::string & content); | |
| // Appends to the result.reasoning_content field | |
| void add_reasoning_content(const std::string & reasoning_content); | |
| // Adds a tool call to the result. If the tool call is too incomplete (e.g. name empty), it won't add anything. | |
| bool add_tool_call(const std::string & name, const std::string & id, const std::string & arguments); | |
| // Adds a tool call using the "name", "id" and "arguments" fields of the json object | |
| bool add_tool_call(const nlohmann::ordered_json & tool_call); | |
| // Adds an array of tool calls using their "name", "id" and "arguments" fields. | |
| bool add_tool_calls(const nlohmann::ordered_json & arr); | |
| void finish(); | |
| bool consume_spaces(); | |
| void consume_literal(const std::string & literal); | |
| bool try_parse_reasoning(const std::string & start_think, const std::string & end_think); | |
| std::string consume_rest(); | |
| struct find_regex_result { | |
| std::string prelude; | |
| std::vector<common_string_range> groups; | |
| }; | |
| std::optional<find_regex_result> try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true); | |
| bool try_consume_literal(const std::string & literal); | |
| std::optional<find_regex_result> try_find_literal(const std::string & literal); | |
| find_regex_result consume_regex(const common_regex & regex); | |
| std::optional<find_regex_result> try_consume_regex(const common_regex & regex); | |
| std::optional<common_json> try_consume_json(); | |
| common_json consume_json(); | |
| struct consume_json_result { | |
| nlohmann::ordered_json value; | |
| bool is_partial; | |
| }; | |
| /* | |
| Consume (possibly partial) json and converts specific subtrees to (possibly truncated) JSON strings. | |
| By default, object keys can't be truncated, nor can string values (their corresponding key is removed, | |
| e.g. `{"foo": "bar", "baz": "b` -> `{"foo": "bar"}` | |
| But one can allow subpaths to be kept truncated, and possibly json-dumped to truncated json strings | |
| - with `content_paths={{"foo"}}` -> `{"foo": "b` -> {"foo": "b"}` | |
| - with `args_paths={{"foo"}}` -> `{"foo": {"b` -> `{"foo": "{b"}` | |
| */ | |
| consume_json_result consume_json_with_dumped_args( | |
| const std::vector<std::vector<std::string>> & args_paths = {}, | |
| const std::vector<std::vector<std::string>> & content_paths = {} | |
| ); | |
| std::optional<consume_json_result> try_consume_json_with_dumped_args( | |
| const std::vector<std::vector<std::string>> & args_paths = {}, | |
| const std::vector<std::vector<std::string>> & content_paths = {} | |
| ); | |
| void clear_tools(); | |
| }; | |