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