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
| # Debugging Tests Tips | |
| ## How to run & execute or debug a specific test without anything else to keep the feedback loop short? | |
| There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number. | |
| For example, running the following command will output an interactive list from which you can select a test. It takes this form: | |
| `debug-test.sh [OPTION]... <test_regex> <test_number>` | |
| It will then build & run in the debugger for you. | |
| To just execute a test and get back a PASS or FAIL message run: | |
| ```bash | |
| ./scripts/debug-test.sh test-tokenizer | |
| ``` | |
| To test in GDB use the `-g` flag to enable gdb test mode. | |
| ```bash | |
| ./scripts/debug-test.sh -g test-tokenizer | |
| # Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows: | |
| >>> b main | |
| ``` | |
| To speed up the testing loop, if you know your test number you can just run it similar to below: | |
| ```bash | |
| ./scripts/debug-test.sh test 23 | |
| ``` | |
| For further reference use `debug-test.sh -h` to print help. | |
| | |
| ### How does the script work? | |
| If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below. | |
| #### Step 1: Reset and Setup folder context | |
| From base of this repository, let's create `build-ci-debug` as our build context. | |
| ```bash | |
| rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug | |
| ``` | |
| #### Step 2: Setup Build Environment and Compile Test Binaries | |
| Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults. | |
| ```bash | |
| cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. | |
| make -j | |
| ``` | |
| #### Step 3: Find all tests available that matches REGEX | |
| The output of this command will give you the command & arguments needed to run GDB. | |
| * `-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 | |
| ```bash | |
| ctest -R "test-tokenizer" -V -N | |
| ``` | |
| This may return output similar to below (focusing on key lines to pay attention to): | |
| ```bash | |
| ... | |
| 1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" | |
| 1: Working Directory: . | |
| Labels: main | |
| Test #1: test-tokenizer-0-llama-spm | |
| ... | |
| 4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf" | |
| 4: Working Directory: . | |
| Labels: main | |
| Test #4: test-tokenizer-0-falcon | |
| ... | |
| ``` | |
| #### Step 4: Identify Test Command for Debugging | |
| So for test #1 above we can tell these two pieces of relevant information: | |
| * Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0` | |
| * Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf` | |
| #### Step 5: Run GDB on test command | |
| Based on the ctest 'test command' report above we can then run a gdb session via this command below: | |
| ```bash | |
| gdb --args ${Test Binary} ${Test GGUF Model} | |
| ``` | |
| Example: | |
| ```bash | |
| gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" | |
| ``` | |