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*This model was published in HF papers on 2025-06-06 and contributed to Hugging Face Transformers on 2025-06-25.*

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        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
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# dots.llm1

[dots.llm1](https://huggingface.co/papers/2506.05767) is a 142B-parameter mixture-of-experts model that activates 14B parameters per token, using top-6-of-128 routed experts plus 2 shared experts. It delivers performance on par with Qwen2.5-72B while significantly reducing training and inference costs. Notably, no synthetic data was used during pretraining.

The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModelForCausalLM`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="rednote-hilab/dots.llm1.base",
)
pipe("The advantage of mixture-of-experts models is")
```

</hfoption>
<hfoption id="AutoModelForCausalLM">

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("rednote-hilab/dots.llm1.base")
model = AutoModelForCausalLM.from_pretrained(
    "rednote-hilab/dots.llm1.base",
    device_map="auto",
)
input_ids = tokenizer("The advantage of mixture-of-experts models is", 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>

## Dots1Config

[[autodoc]] Dots1Config

## Dots1Model

[[autodoc]] Dots1Model
    - forward

## Dots1ForCausalLM

[[autodoc]] Dots1ForCausalLM
    - forward
