Instructions to use meituan-longcat/LongCat-Flash-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meituan-longcat/LongCat-Flash-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="meituan-longcat/LongCat-Flash-Chat", 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-Chat", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meituan-longcat/LongCat-Flash-Chat 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-Chat"
# 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-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meituan-longcat/LongCat-Flash-Chat

SGLang

How to use meituan-longcat/LongCat-Flash-Chat 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-Chat" \
    --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-Chat",
		"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-Chat" \
        --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-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meituan-longcat/LongCat-Flash-Chat with Docker Model Runner:
```
docker model run hf.co/meituan-longcat/LongCat-Flash-Chat
```

Improve model card: Update `library_name`, add relevant tags, and clarify links

#10

by nielsr HF Staff - opened Sep 22, 2025

base: refs/heads/main

←

from: refs/pr/10

Discussion Files changed

-5

nielsr

Sep 22, 2025

This PR enhances the model card by:

Setting the library_name to transformers in the metadata, which enables the automated "how to use" widget on the Hub. This is confirmed by the auto_map entries in config.json.
Removing the redundant transformers tag and adding more descriptive tags such as moe, mixture-of-experts, and code-generation to improve model discoverability, based on the model's architecture and capabilities.
Adding explicit links to the paper, GitHub repository, and project page at the top of the model card for easier access.
Updating the internal paper link within the "Model Introduction" section to point to the official Hugging Face paper page (https://huggingface.co/papers/2509.01322).
Adding a note in the "Quick Start" section to inform users about the necessity of trust_remote_code=True when loading the model with transformers.

Improve model card: Update `library_name`, add relevant tags, and clarify links1fd7ebb5

Molbap

Sep 29, 2025

To note: trust_remote_code=True is not necessary here!

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