Instructions to use meituan-longcat/LongCat-Flash-Lite-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meituan-longcat/LongCat-Flash-Lite-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meituan-longcat/LongCat-Flash-Lite-FP8", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("meituan-longcat/LongCat-Flash-Lite-FP8", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use meituan-longcat/LongCat-Flash-Lite-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meituan-longcat/LongCat-Flash-Lite-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meituan-longcat/LongCat-Flash-Lite-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/meituan-longcat/LongCat-Flash-Lite-FP8
- SGLang
How to use meituan-longcat/LongCat-Flash-Lite-FP8 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 "meituan-longcat/LongCat-Flash-Lite-FP8" \ --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": "meituan-longcat/LongCat-Flash-Lite-FP8", "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 "meituan-longcat/LongCat-Flash-Lite-FP8" \ --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": "meituan-longcat/LongCat-Flash-Lite-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use meituan-longcat/LongCat-Flash-Lite-FP8 with Docker Model Runner:
docker model run hf.co/meituan-longcat/LongCat-Flash-Lite-FP8
| { | |
| "architectures": [ | |
| "LongcatFlashNgramForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_longcat_ngram.LongcatFlashNgramConfig", | |
| "AutoModel": "modeling_longcat_ngram.LongcatFlashNgramModel", | |
| "AutoModelForCausalLM": "modeling_longcat_ngram.LongcatFlashNgramForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "disable_quant_module": [], | |
| "emb_neighbor_num": 4, | |
| "emb_split_num": 4, | |
| "eos_token_id": 2, | |
| "expert_ffn_hidden_size": 1024, | |
| "ffn_hidden_size": 6144, | |
| "hidden_size": 3072, | |
| "kv_lora_rank": 512, | |
| "max_position_embeddings": 327680, | |
| "mla_scale_kv_lora": true, | |
| "mla_scale_q_lora": true, | |
| "moe_topk": 12, | |
| "n_routed_experts": 256, | |
| "ngram_vocab_size_ratio": 78, | |
| "num_attention_heads": 32, | |
| "num_layers": 14, | |
| "q_lora_rank": 1536, | |
| "qk_nope_head_dim": 128, | |
| "qk_rope_head_dim": 64, | |
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| "activation_scheme": "dynamic", | |
| "fmt": "e4m3", | |
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| ], | |
| "quant_method": "fp8", | |
| "weight_block_size": [ | |
| 128, | |
| 128 | |
| ] | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "beta_fast": 32, | |
| "beta_slow": 1, | |
| "factor": 10, | |
| "mscale": 1, | |
| "mscale_all_dim": 1, | |
| "original_max_position_embeddings": 32768, | |
| "rope_type": "yarn" | |
| }, | |
| "rope_theta": 5000000.0, | |
| "routed_scaling_factor": 6.0, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.57.6", | |
| "use_cache": true, | |
| "v_head_dim": 128, | |
| "vocab_size": 131072, | |
| "zero_expert_num": 128, | |
| "zero_expert_type": "identity" | |
| } |