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*This model was contributed to Hugging Face Transformers on 2026-05-28.*

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# Mellum

Mellum is a code-focused Mixture-of-Experts language model developed by [JetBrains](https://www.jetbrains.com/). It is derived from the Qwen3-MoE architecture with per-layer-type RoPE and interleaved sliding window attention. The model has 12B total parameters with 2.5B active parameters per token, using 64 routed experts with 8 activated per token across 28 layers.

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="JetBrains/Mellum2-12B-A2.5B-Base",
)
pipe("def fibonacci(n):")
```

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

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Base")
model = AutoModelForCausalLM.from_pretrained(
    "JetBrains/Mellum2-12B-A2.5B-Base",
    device_map="auto",
)
input_ids = tokenizer("def fibonacci(n):", 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>

## MellumConfig

[[autodoc]] MellumConfig

## MellumModel

[[autodoc]] MellumModel
    - forward

## MellumForCausalLM

[[autodoc]] MellumForCausalLM
    - forward
