Text Generation
Safetensors
Transformers
LongCat-Flash-Lite-Sparse
conversational
compressed-tensors
Instructions to use Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import LongcatCausalLM model = LongcatCausalLM.from_pretrained("Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound
- SGLang
How to use Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound 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 "Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound" \ --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": "Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound", "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 "Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound" \ --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": "Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound with Docker Model Runner:
docker model run hf.co/Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "pad_token_id": 3, | |
| "transformers_version": "4.57.6", | |
| "repetition_penalty": 1.06, | |
| "temperature": 0.7, | |
| "top_p": 0.95, | |
| "top_k": 4 | |
| } |