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
```

longcat-flash-lite-sparse

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{
  "architectures": [
    "LongcatCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "vocab_size": 131072,
  "hidden_size": 8,
  "ffn_hidden_size": 64,
  "expert_ffn_hidden_size": 32,
  "num_layers": 4,
  "num_attention_heads": 8,
  "kv_lora_rank": 256,
  "q_lora_rank": 32,
  "qk_rope_head_dim": 64,
  "v_head_dim": 128,
  "qk_nope_head_dim": 64,
  "mla_scale_q_lora": true,
  "mla_scale_kv_lora": true,
  "routed_scaling_factor": 6.0,
  "n_routed_experts": 32,
  "max_position_embeddings": 983040,
  "rms_norm_eps": 1e-05,
  "use_cache": true,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "rope_theta": 1000000.0,
  "rope_scaling": {
    "original_max_position_embeddings": 8192,
    "rope_type": "deepseek_yarn",
    "factor": 120,
    "beta_fast": 32,
    "beta_slow": 1,
    "mscale": 1,
    "mscale_all_dim": 1
  },
  "attention_method": "LSA",
  "zero_expert_num": 128,
  "zero_expert_type": "identity",
  "moe_topk": 12,
  "use_mla": 1,
  "moe_switch_token_num": 1024,
  "moe_impl": "mix",
  "oe_vocab_size_ratio": 4,
  "oe_neighbor_num": 2,
  "oe_split_num": 2,
  "mtp_num_layers": 3,
  "mtp_replicate_modules": true,
  "mtp_disable_over_tokenizer": false,
  "indexer_rope_interleave": true,
  "index_n_heads": 16,
  "index_head_dim": 128,
  "index_topk": 2048,
  "index_k_norm_type": "rms",
  "cli_factor": 2,
  "dsa_mtp_cli": true,
  "index_local_tokens": 1024,
  "index_init_tokens": 16,
  "num_indexer_layers": 8
}