Instructions to use meituan-longcat/LongCat-Flash-Thinking-2601 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meituan-longcat/LongCat-Flash-Thinking-2601 with Transformers:

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

pipe = pipeline("text-generation", model="meituan-longcat/LongCat-Flash-Thinking-2601", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import LongcatCausalLM
model = LongcatCausalLM.from_pretrained("meituan-longcat/LongCat-Flash-Thinking-2601", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meituan-longcat/LongCat-Flash-Thinking-2601 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-Thinking-2601"
# 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-Thinking-2601",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meituan-longcat/LongCat-Flash-Thinking-2601

SGLang

How to use meituan-longcat/LongCat-Flash-Thinking-2601 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-Thinking-2601" \
    --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-Thinking-2601",
		"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-Thinking-2601" \
        --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-Thinking-2601",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meituan-longcat/LongCat-Flash-Thinking-2601 with Docker Model Runner:
```
docker model run hf.co/meituan-longcat/LongCat-Flash-Thinking-2601
```

LongCat0830 commited on Jan 23

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   </a>
 </div>
 <img src="https://cdn-uploads.huggingface.co/production/uploads/68a2a29ab9d4c5698e02c747/kYGNz_D4xQNXaq_fwa4hE.png" height = "600"  />

   </a>
 </div>
+<p align="center">
+  <a href="https://github.com/meituan-longcat/LongCat-Flash-Thinking-2601/blob/main/LongCat_Flash_Thinking_2601_Technical_Report.pdf"><b>Tech Report</b>&nbsp;📄</a>
+</p>
 <img src="https://cdn-uploads.huggingface.co/production/uploads/68a2a29ab9d4c5698e02c747/kYGNz_D4xQNXaq_fwa4hE.png" height = "600"  />