inclusionAI/Ling-lite

Ling

Paper

🤗 Hugging Face

Introduction

Ling is a MoE LLM provided and open-sourced by InclusionAI. We introduce two different sizes, which are Ling-Lite and Ling-Plus. Ling-Lite has 16.8 billion parameters with 2.75 billion activated parameters, while Ling-Plus has 290 billion parameters with 28.8 billion activated parameters. Both models demonstrate impressive performance compared to existing models in the industry.

Their structure makes it easy to scale up and down and adapt to different tasks, so users can use these models for a wide range of tasks, from processing natural language to solving complex problems. Furthermore, the open-source nature of Ling promotes collaboration and innovation within the AI community, fostering a diverse range of use cases and enhancements.

As more developers and researchers engage with the platform, we can expect rapid advancements and improvements, leading to even more sophisticated applications. This collaborative approach accelerates development and ensures that the models remain at the forefront of technology, addressing emerging challenges in various fields.

Update

Ling-lite is upgraded to Ling-lite-0415. The new model demonstrates notable improvements over its predecessor, Ling-lite-0220, especially on code and math.

Benchmark	#shots	Ling-Lite-0415	Ling-Lite-0220	Qwen2.5-7B-Instruct	LLaMA3.1-8B
MMLU(EM)	5	74.87	71.27	74.26	68.67
GPQA(Pass@1)	0	40.91	28.66	34.47	32.80
HumanEval(Pass@1)	0	89.02	83.54	87.20	70.73
LiveCodeBench 2408-2411 (Pass@1)	0	24.11	15.18	16.96	11.61
LCBench(pass@1)	0	60.00	47.22	54.17	29.04
Math(EM)	0	79.12	72.80	73.66	52.42
AIME2024(pass@1)	0	13.33	6.67	16.67	0.00
OlympiadBench(pass@1)	0	37.33	34.42	37.19	16.3
BBH(EM)	0	74.58	66.38	66.07	68.05
IFEval(Prompt Strict)	0	81.09	77.99	71.16	53.45

Model Downloads

You can download the following table to see the various parameters for your use case. If you are located in mainland China, we also provide the model on ModelScope.cn to speed up the download process.

Model	#Total Params	#Activated Params	Context Length	Download
Ling-lite-base	16.8B	2.75B	64K	🤗 HuggingFace
Ling-lite	16.8B	2.75B	128K	🤗 HuggingFace

Note: Ling-lite has been upgrade to Ling-lite-0415. The previous version, Ling-lite-0220, can be found in branch ling-lite-0220.

Evaluation

Detailed evaluation results are reported in our technical report.

Quickstart

🤗 Hugging Face Transformers

Here is a code snippet to show you how to use the chat model with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ling-lite"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Deployment

Please refer to Github

License

This code repository is licensed under the MIT License.

Citation

If you find our work helpful, feel free to give us a cite.

@article{ling,
    title   = {Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs}, 
    author  = {Ling Team},
    journal = {arXiv preprint arXiv:2503.05139},
    year    = {2025}
}