Text Generation
Transformers
Safetensors
GGUF
English
Chinese
spark2_5
llm
sparkx2_5
conversational
custom_code
Instructions to use mohommed/Lucet-CoderSense-4B-Omega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohommed/Lucet-CoderSense-4B-Omega with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mohommed/Lucet-CoderSense-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-CoderSense-4B-Omega", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mohommed/Lucet-CoderSense-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-CoderSense-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-CoderSense-4B-Omega", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mohommed/Lucet-CoderSense-4B-Omega
- SGLang
How to use mohommed/Lucet-CoderSense-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-CoderSense-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-CoderSense-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-CoderSense-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-CoderSense-4B-Omega", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mohommed/Lucet-CoderSense-4B-Omega with Docker Model Runner:
docker model run hf.co/mohommed/Lucet-CoderSense-4B-Omega
File size: 282 Bytes
8d8ea9c 9e8e2d1 8d8ea9c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"bos_token_id": 0,
"do_sample": true,
"eos_token_id": 1,
"frequency_penalty": 0,
"max_tokens": 1048576,
"pad_token_id": 2,
"presence_penalty": 0,
"repetition_penalty": 1.05,
"temperature": 0.4,
"top_k": 40,
"top_p": 0.95,
"transformers_version": "5.16.1"
} |