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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

LongCat0830 commited on Jan 30

Commit

1e2e5b2

verified ·

1 Parent(s): 682dc7c

Update README.md

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README.md CHANGED Viewed

@@ -39,8 +39,9 @@ tags:
   </a>
 </div>
 ## Model Introduction
 We introduce LongCat-Flash-Lite, a non-thinking 68.5B parameter Mixture-of-Experts (MoE) model with approximately 3B activated parameters, supporting a 256k context length through the YaRN method. Building upon the LongCat-Flash architecture, LongCat-Flash-Lite distinguishes itself through the integration of an **N-gram embedding** table designed to enhance both model performance and inference speed. Despite allocating over 30B parameters to embeddings, LongCat-Flash-Lite not only outperforms parameter-equivalent MoE baselines but also demonstrates exceptional competitiveness against existing models of comparable scale, particularly in the agentic and coding domains.
@@ -58,7 +59,7 @@ In contrast to FFN-based experts, the N-gram embedding table inherently mitigate
 #### 🌟 Strong Agentic and Coding Performance
 LongCat-Flash-Lite demonstrates robust capabilities in agentic tool use and coding proficiency that are highly competitive relative to its model scale.
-Please refer to our [technical report](./tech_report.pdf) for details!
 ## Evaluation Results
@@ -229,6 +230,22 @@ It is the responsibility of developers and downstream users to understand and co
 Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.
 ## Contact
 Please contact us at <a href="mailto:longcat-team@meituan.com">longcat-team@meituan.com</a> or open an issue if you have any questions.

   </a>
 </div>
+<p align="center">
+  <a href="https://arxiv.org/abs/2601.21204"><b>Tech Report</b>&nbsp;📄</a>
+</p>
 ## Model Introduction
 We introduce LongCat-Flash-Lite, a non-thinking 68.5B parameter Mixture-of-Experts (MoE) model with approximately 3B activated parameters, supporting a 256k context length through the YaRN method. Building upon the LongCat-Flash architecture, LongCat-Flash-Lite distinguishes itself through the integration of an **N-gram embedding** table designed to enhance both model performance and inference speed. Despite allocating over 30B parameters to embeddings, LongCat-Flash-Lite not only outperforms parameter-equivalent MoE baselines but also demonstrates exceptional competitiveness against existing models of comparable scale, particularly in the agentic and coding domains.
 #### 🌟 Strong Agentic and Coding Performance
 LongCat-Flash-Lite demonstrates robust capabilities in agentic tool use and coding proficiency that are highly competitive relative to its model scale.
+Please refer to our [technical report](https://arxiv.org/abs/2601.21204) for details!
 ## Evaluation Results
 Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.
+## Citation
+We kindly encourage citation of our work if you find it useful.
+```
+@misc{liu2026scalingembeddingsoutperformsscaling,
+      title={Scaling Embeddings Outperforms Scaling Experts in Language Models},
+      author={Hong Liu and Jiaqi Zhang and Chao Wang and Xing Hu and Linkun Lyu and Jiaqi Sun and Xurui Yang and Bo Wang and Fengcun Li and Yulei Qian and Lingtong Si and Yerui Sun and Rumei Li and Peng Pei and Yuchen Xie and Xunliang Cai},
+      year={2026},
+      eprint={2601.21204},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.21204},
+}
+```
 ## Contact
 Please contact us at <a href="mailto:longcat-team@meituan.com">longcat-team@meituan.com</a> or open an issue if you have any questions.