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Sponsored by
APIMaster
inclusionAI / Ling-plus-base
Model's Last Updated: April 14 2025
text-generation
Introduction of Ling-plus-base
Model Details of Ling-plus-base
Ling
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.
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 Modulescope.cn to speed up the download process.
| Model | #Total Params | #Activated Params | Context Length | Download |
|---|---|---|---|---|
| Ling-plus-base | 290B | 28.8B | 64K | 🤗 HuggingFace |
| Ling-plus | 290B | 28.8B | 64K | 🤗 HuggingFace |
Evaluation
Detailed evaluation results are reported in our technical report [TBD].
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
[TBD]
Runs of inclusionAI Ling-plus-base on huggingface.co
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More Information About Ling-plus-base huggingface.co Model
Url of Ling-plus-base
Ling-plus-base huggingface.co Url
Provider of Ling-plus-base huggingface.co
Other API from inclusionAI
Updated:April 13 2026
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