Instructions to use tiny-random/longcat-flash-lite-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/longcat-flash-lite-sparse with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/longcat-flash-lite-sparse")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import LongcatCausalLM
model = LongcatCausalLM.from_pretrained("tiny-random/longcat-flash-lite-sparse", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/longcat-flash-lite-sparse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/longcat-flash-lite-sparse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/longcat-flash-lite-sparse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/longcat-flash-lite-sparse

SGLang

How to use tiny-random/longcat-flash-lite-sparse 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 "tiny-random/longcat-flash-lite-sparse" \
    --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": "tiny-random/longcat-flash-lite-sparse",
		"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 "tiny-random/longcat-flash-lite-sparse" \
        --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": "tiny-random/longcat-flash-lite-sparse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/longcat-flash-lite-sparse with Docker Model Runner:
```
docker model run hf.co/tiny-random/longcat-flash-lite-sparse
```

This is a randomly initialized tiny model based on meituan-longcat/LongCat-Flash-Lite-Sparse for testing and debugging.

Feature	Source	This repo
Checkpoint tensor elements	69.11B BF16, 17.26M F32	11.72M BF16, 5.76K F32
Safetensors weight files	138.29 GB (26 files)	23.53 MB (model.safetensors)
Logical layers (attention sublayers)	14 (28)	4 (8)
Hidden width / dense MLP / expert MLP	3,072 / 6,144 / 1,024	8 / 64 / 32
Q heads / MLA nope-rope-v head dims	32 / 128-64-128	8 / 64-64-128
MLA q-lora / kv-lora rank	1,536 / 512	32 / 256
Routed / identity zero experts, top-k	256 / 128, 12	32 / 128, 12
LSA index heads / head dim / top-k	16 / 128 / 2,048	Same
CLI factor / local + init tokens	2 / 1,024 + 16	Same
Over-embedding tables	12 tables, ~10.22M rows each	2 tables, ~524k rows
MTP/NextN draft layers	1 (with LSA indexer)	Same
Attention / RoPE	LSA + deepseek-yarn, 1M context	Same
Vocabulary rows / tying	131,072 / untied	Same
Routers / correction bias	FP32	Same
Stored precision	BF16	Same

SGLang-only: the source ships no Transformers code and the tensors follow SGLang's longcat_flash schema. The n-gram hash modulus is internal embedding machinery, so it follows tiny-random/longcat-flash-lite.

File path	Size
model.safetensors	23.5MB

Example usage

SGLang serving (unverified)

LSA is implemented in SGLang (main branch; the LongCat Sparse adaptation landed via PR #32918). The checkpoint keeps the source's LongcatCausalLM architecture and serves text only, like the upstream deployment guide.

python3 -m sglang.launch_server \
  --trust-remote-code \
  --model scratch/longcat-flash-lite-sparse \
  --host 0.0.0.0 \
  --port 30000 \
  --tp-size 4 \
  --max-running-requests 64 \
  --mem-fraction-static 0.93 \
  --chunked-prefill-size 2048 \
  --dsa-prefill-backend fa3 \
  --kv-cache-dtype bfloat16

curl http://127.0.0.1:30000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"messages": [{"role": "user", "content": "Hello, world!"}], "max_tokens": 16}'

Creating this repo

Code: .meta/create_model.py
Model structure: .meta/model.log
Environment: .meta/env.json

Change log

2026-11-03: Initial version: direct SGLang-schema LSA checkpoint (shortcut-MoE, CLI indexers, ngram over-embeddings and one NextN MTP draft).

This repo is generated by GPT-6.1-Sol.

Downloads last month: 36

Safetensors

Model size

11.7M params

Tensor type

F32

BF16

Model tree for tiny-random/longcat-flash-lite-sparse

Base model

meituan-longcat/LongCat-Flash-Lite-Sparse

Finetuned

(3)

this model