Ling-lite-1.5-2507

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🤗 Hugging Face｜ 🤖 ModelScope

Model Overview

We are excited to introduce Ling-lite-1.5-2507, the latest version of our highly capable Ling-lite-1.5 model.

Ling-lite-1.5-2507 boasts 16.8 billion parameters with 2.75 billion activated parameters, which demonstrates significant improvements over previous versions across professional knowledge assessments, logical reasoning evaluations, and coding capability benchmarks.

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Key Features

As the flagship model of our Lite series, Ling-lite-1.5-2507 features two major enhancements:

• Smarter and More Efficient Reasoning For straightforward inquiries, the model generates concise and direct responses. When confronting complex challenges, it exhibits advanced problem-solving prowess by systematically decomposing problems, integrating a sophisticated reflective mechanism, and producing elaborate reasoning traces to achieve accurate solutions through an inherently efficient and integrated reasoning process.

• Enhanced Human-Aligned Subjectivity The model delivers well-structured and coherent responses, demonstrating profound cognitive depth in subjective and open-ended tasks. This leads to a strong alignment with human preferences concerning response organization and conceptual richness.

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-1.5-2507"

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}
}