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README.md

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----
-license: mit
----
+---
+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}
+}
+```