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 Desktop
- 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
| // pre-tokenization types | |
| enum llama_vocab_pre_type { | |
| LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0, | |
| LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1, | |
| LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2, | |
| LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3, | |
| LLAMA_VOCAB_PRE_TYPE_FALCON = 4, | |
| LLAMA_VOCAB_PRE_TYPE_MPT = 5, | |
| LLAMA_VOCAB_PRE_TYPE_STARCODER = 6, | |
| LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, | |
| LLAMA_VOCAB_PRE_TYPE_REFACT = 8, | |
| LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, | |
| LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10, | |
| LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, | |
| LLAMA_VOCAB_PRE_TYPE_OLMO = 12, | |
| LLAMA_VOCAB_PRE_TYPE_DBRX = 13, | |
| LLAMA_VOCAB_PRE_TYPE_SMAUG = 14, | |
| LLAMA_VOCAB_PRE_TYPE_PORO = 15, | |
| LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16, | |
| LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17, | |
| LLAMA_VOCAB_PRE_TYPE_VIKING = 18, | |
| LLAMA_VOCAB_PRE_TYPE_JAIS = 19, | |
| LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20, | |
| LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21, | |
| LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22, | |
| LLAMA_VOCAB_PRE_TYPE_BLOOM = 23, | |
| LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, | |
| LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, | |
| LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26, | |
| LLAMA_VOCAB_PRE_TYPE_MINERVA = 27, | |
| LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28, | |
| LLAMA_VOCAB_PRE_TYPE_GPT4O = 29, | |
| LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30, | |
| LLAMA_VOCAB_PRE_TYPE_TRILLION = 31, | |
| LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32, | |
| LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33, | |
| LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34, | |
| LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35, | |
| LLAMA_VOCAB_PRE_TYPE_HUNYUAN = 36, | |
| LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37, | |
| }; | |
| struct LLM_KV; | |
| struct llama_model_loader; | |
| struct llama_vocab { | |
| struct token_data { | |
| std::string text; | |
| float score; | |
| llama_token_attr attr; | |
| }; | |
| llama_vocab(); | |
| ~llama_vocab(); | |
| void load(llama_model_loader & ml, const LLM_KV & kv); | |
| std::string get_tokenizer_model() const; | |
| std::string get_tokenizer_pre() const; | |
| enum llama_vocab_type get_type() const; | |
| enum llama_vocab_pre_type get_pre_type() const; | |
| uint32_t n_tokens() const; | |
| uint32_t n_token_types() const; | |
| std::string type_name() const; | |
| bool is_normal (llama_token id) const; | |
| bool is_unknown (llama_token id) const; | |
| bool is_control (llama_token id) const; | |
| bool is_byte (llama_token id) const; | |
| bool is_user_defined(llama_token id) const; | |
| bool is_unused (llama_token id) const; | |
| bool is_eog (llama_token id) const; | |
| uint8_t token_to_byte(llama_token id) const; | |
| llama_token byte_to_token(uint8_t ch) const; | |
| llama_token text_to_token(const std::string & text) const; | |
| const token_data & get_token_data(llama_token id) const; | |
| const char * token_get_text (llama_token id) const; | |
| float token_get_score(llama_token id) const; | |
| llama_token_attr token_get_attr (llama_token id) const; | |
| llama_token token_bos() const; | |
| llama_token token_eos() const; | |
| llama_token token_eot() const; | |
| llama_token token_eom() const; | |
| llama_token token_unk() const; | |
| llama_token token_sep() const; | |
| llama_token token_nl () const; | |
| llama_token token_pad() const; | |
| llama_token token_mask() const; | |
| llama_token token_prefix() const; | |
| llama_token token_middle() const; | |
| llama_token token_suffix() const; | |
| llama_token token_fim_pre() const; | |
| llama_token token_fim_suf() const; | |
| llama_token token_fim_mid() const; | |
| llama_token token_fim_pad() const; | |
| llama_token token_fim_rep() const; | |
| llama_token token_fim_sep() const; | |
| bool get_add_space_prefix () const; | |
| bool get_add_bos () const; | |
| bool get_add_eos () const; | |
| bool get_add_sep () const; | |
| bool get_ignore_merges () const; | |
| bool get_clean_spaces () const; | |
| bool get_remove_extra_whitespaces () const; | |
| bool get_escape_whitespaces () const; | |
| bool get_treat_whitespace_as_suffix() const; | |
| int max_token_len() const; | |
| int find_bpe_rank(const std::string & token_left, const std::string & token_right) const; | |
| std::vector<std::string> get_bpe_merges() const; | |
| std::vector<char> get_precompiled_charsmap() const; | |
| int32_t tokenize( | |
| const char * text, | |
| int32_t text_len, | |
| llama_token * tokens, | |
| int32_t n_tokens_max, | |
| bool add_special, | |
| bool parse_special) const; | |
| std::vector<llama_token> tokenize( | |
| const std::string & raw_text, | |
| bool add_special, | |
| bool parse_special = false) const; | |
| // does not write null-terminator to buf | |
| int32_t token_to_piece( | |
| llama_token token, | |
| char * buf, | |
| int32_t length, | |
| int32_t lstrip, | |
| bool special) const; | |
| // use cached data | |
| const std::string & token_to_piece(llama_token token) const; | |
| int32_t detokenize( | |
| const llama_token * tokens, | |
| int32_t n_tokens, | |
| char * text, | |
| int32_t text_len_max, | |
| bool remove_special, | |
| bool unparse_special) const; | |
| std::string detokenize( | |
| const std::vector<llama_token> & tokens, | |
| bool special) const; | |
| void print_info() const; | |
| private: | |
| struct impl; | |
| std::unique_ptr<impl> pimpl; | |
| }; | |