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---
license: mit
pipeline_tag: text-generation
library_name: transformers
---
# Ling-lite-1.5-2507
<p align="center"><img src="https://huggingface.co/inclusionAI/Ling-lite/resolve/main/ant-bailing.png" width="100"/></p>
<p align="center">🤗 <a href="https://huggingface.co/inclusionAI/Ling-lite-1.5-2507">Hugging Face</a>｜ 🤖 <a href="https://www.modelscope.cn/models/inclusionAI/Ling-lite-1.5-2507">ModelScope</a>
## 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.
<p align="center">
<img width="80%" src="Ling-lite-1.5-2507-benchmarks.png">
</p>
## 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`:
```python
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](https://github.com/inclusionAI/Ling/blob/master/README.md)
## License
This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ling-lite/blob/main/LICENCE).
## 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}
}
``` |