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chore: import upstream snapshot with attribution
2026-07-13 11:57:37 +08:00

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*This model was contributed to Hugging Face Transformers on 2023-04-12.*
# CPMAnt
[CPMAnt](https://github.com/OpenBMB/CPM-Live/tree/cpm-ant/cpm-live) is a 10B-parameter open-source Chinese pre-trained language model and the first milestone of the CPM-Live open training project. It achieves strong results with delta tuning on the CUGE benchmark, and compressed variants are available for different hardware configurations.
The example below demonstrates how to generate text with [`Pipeline`] or the [`CpmAntForCausalLM`] class.
<hfoptions id="usage">
<hfoption id="Pipeline">
```python
from transformers import pipeline
pipe = pipeline(
task="text-generation",
model="openbmb/cpm-ant-10b",
)
pipe("今天天气很好,")
```
</hfoption>
<hfoption id="CpmAntForCausalLM">
```python
from transformers import CpmAntForCausalLM, CpmAntTokenizer
tokenizer = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b")
model = CpmAntForCausalLM.from_pretrained(
"openbmb/cpm-ant-10b",
device_map="auto",
)
input_ids = tokenizer("今天天气很好,", return_tensors="pt").to(model.device)
output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
</hfoption>
</hfoptions>
## CpmAntConfig
[[autodoc]] CpmAntConfig
- all
## CpmAntTokenizer
[[autodoc]] CpmAntTokenizer
- all
## CpmAntModel
[[autodoc]] CpmAntModel
- all
## CpmAntForCausalLM
[[autodoc]] CpmAntForCausalLM
- all