Text Generation
Transformers
Safetensors
GGUF
spark2_5
lucet
spark-x2.6
qr-svd
ast-counterfactual
failure-cluster-mining
uncensored
gsq
rco
1m-context
imatrix
conversational
custom_code
Instructions to use mohommed/Lucet-Coder-4B-Omega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohommed/Lucet-Coder-4B-Omega with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mohommed/Lucet-Coder-4B-Omega", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mohommed/Lucet-Coder-4B-Omega", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mohommed/Lucet-Coder-4B-Omega 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 mohommed/Lucet-Coder-4B-Omega:Q4_K_M # Run inference directly in the terminal: llama cli -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M # Run inference directly in the terminal: llama cli -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Use Docker
docker model run hf.co/mohommed/Lucet-Coder-4B-Omega:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use mohommed/Lucet-Coder-4B-Omega with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mohommed/Lucet-Coder-4B-Omega" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mohommed/Lucet-Coder-4B-Omega", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mohommed/Lucet-Coder-4B-Omega:Q4_K_M
- SGLang
How to use mohommed/Lucet-Coder-4B-Omega 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 "mohommed/Lucet-Coder-4B-Omega" \ --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": "mohommed/Lucet-Coder-4B-Omega", "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 "mohommed/Lucet-Coder-4B-Omega" \ --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": "mohommed/Lucet-Coder-4B-Omega", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use mohommed/Lucet-Coder-4B-Omega with Ollama:
ollama run hf.co/mohommed/Lucet-Coder-4B-Omega:Q4_K_M
- Unsloth Desktop
- Pi
How to use mohommed/Lucet-Coder-4B-Omega with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mohommed/Lucet-Coder-4B-Omega:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mohommed/Lucet-Coder-4B-Omega with Docker Model Runner:
docker model run hf.co/mohommed/Lucet-Coder-4B-Omega:Q4_K_M
- Lemonade
How to use mohommed/Lucet-Coder-4B-Omega with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Run and chat with the model
lemonade run user.Lucet-Coder-4B-Omega-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use mohommed/Lucet-Coder-4B-Omega with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mohommed/Lucet-Coder-4B-Omega with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mohommed/Lucet-Coder-4B-Omega:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "mohommed/Lucet-Coder-4B-Omega:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download tokenizer_config.json from mohommed/Lucet-Coder-4B-Omega: direct link, hf CLI and curl.
- Browser
- Download file 20.8 kB
-
https://huggingface.co/mohommed/Lucet-Coder-4B-Omega/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://mohommed/Lucet-Coder-4B-Omega/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/mohommed/Lucet-Coder-4B-Omega/resolve/main/tokenizer_config.json
20.8 kB
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