meituan-longcat/LongCat-Flash-Lite

LongCat-Flash-Lite

Tech Report 📄

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.

Key Features

🌟 Superior Scaling Efficiency: A Better Alternative to MoE

Through comprehensive scaling experiments across diverse scenarios, we identify specific regimes where embedding scaling achieves a superior Pareto frontier compared to increasing the number of experts, thereby offering a highly efficient alternative for model scaling. We further delineate a comprehensive set of architectural factors that determine embedding scaling efficacy, encompassing integration timing, parameter budgeting, hash collision mitigation, hyperparameter configuration, and embedding initialization, alongside the impacts of model width and depth.

🌟 Superior Inference Efficiency with Specialized System Optimization

In contrast to FFN-based experts, the N-gram embedding table inherently mitigates I/O bottlenecks within MoE layers, yielding substantial improvements in inference latency. Furthermore, we introduce a specialized N-gram Cache and develop synchronized kernels, which collectively and significantly boost inference efficiency.

🌟 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 for details!

Evaluation Results

| Benchmark | Kimi-Linear-48B-A3B | Qwen3-Next-80B-A3B-Instruct | Gemini 2.5 Flash-Lite | LongCat-Flash-Lite | |----------|---------------------|----------------------------|----------------------|---------| | Architecture | MoE | MoE | - | MoE + NE | | # Total Params | 48B | 80B | - | 68.5B | | # Activated Params | 3B | 3B | - | 2.9B~4.5B | | Agentic Tool Use | | | | | | Tau2-Airline(avg@8) | 44.00 | 45.5* | 35.00 | 58.00 | | Tau2-Retail(avg@8) | 18.86 | 57.3* | 37.50 | 73.10 | | Tau2-Telecom(avg@8) | 15.68 | 13.2* | 21.93 | 72.80 | | Agentic Coding | | | | | | SWE-Bench(acc) | 32.80 | 37.60 | 41.3* | 54.40 | | TerminalBench(acc) | 20.00 | 15.19 | 20.00 | 33.75 | | SWE-Bench Multiligual | 37.20 | 31.30 | - | 38.10 | |PRDBench | - | 15.36 | - | 39.63 | | General Domains | | | | | | GPQA-Diamond(avg@16) | 69.89 | 74.33 | 70.20* | 66.78 | | MMLU(acc) | 79.91 | 89.28 | 84.68 | 85.52 | | MMLU-Pro(acc) | 67.22 | 82.93 | 78.95 | 78.29 | | CEval(acc) | 78.48 | 90.91 | 75.16 | 86.55 | | CMMLU(acc) | 76.26 | 86.50 | 72.06 | 82.48 | | Mathematical Reasoning | | | | | | MATH500(acc) | 94.20 | 98.00 | 95.20 | 96.80 | | AIME24(avg@32) | 70.52 | 81.35 | 63.33 | 72.19 | | AIME25(avg@32) | 59.58 | 68.44 | 50.1* | 63.23 |

> Note: Values marked with * are sourced from public reports. NE is an abbreviation of N-gram Embedding.

Quick Start

To use LongCat-Flash-Lite with transformers, we need at least 2 GPUs (80GB VRAM each, e.g., H100/A100 80GB), and we recommend the following environment:

• python >= 3.10
• torch >= 2.6
• transformers >= 4.57.6
• accelerate >= 1.10.0

pip install -U transformers==4.57.6 accelerate==1.10.0

Basic Usage Example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meituan-longcat/LongCat-Flash-Lite"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Give me a brief introduction to large language models."}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(inputs=input_ids, max_new_tokens=256)
output_ids = generated_ids[0][len(input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
print(response)

Tool Calling Example:

tools = [
{
"type": "function",
"function": {
"name": "func_add",
"description": "Calculate the sum of two numbers",
"parameters": {
"type": "object",
"properties": {
"x1": {"type": "number", "description": "The first addend"},
"x2": {"type": "number", "description": "The second addend"}
},
"required": ["x1", "x2"]
}
}
}
]
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Please tell me what is $$125679 + 234519$$?"},
{
"role": "assistant",
"content": "I'll calculate the sum of 125679 and 234519 for you.",
"tool_calls": [{"type": "function", "function": {"name": "func_add", "arguments": {"x1": 125679, "x2": 234519}}}]
},
{"role": "tool", "name": "func_add", "content": '{"ans": 360198}'}
]

input_ids = tokenizer.apply_chat_template(
messages,
tools=tools,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(inputs=input_ids, max_new_tokens=256)
output_ids = generated_ids[0][len(input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
print(response)

Response Parsing:

from parse_model_response import parse_model_response

response = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
parsed_message = parse_model_response(response, tools)

See [parse_model_response.py](./parse_model_response.py) for detailed implementation and examples.

Recommended Sampling Setting:

{ "repetition_penalty": 1.06, "temperature": 0.7, "top_p": 0.95, "top_k": 4 }

Deployment

We have implemented basic adaptations in SGLang (PR) to support the deployment of LongCat-Flash-Lite.

LongCat-Flash-Lite can be served on a single node (e.g., 8xH20-141G) using a combination of Tensor…