transformers/docs/source/en/model_doc/nemotron_h.md

This model was published in HF papers on 2025-04-04 and contributed to Hugging Face Transformers on 2026-03-03.

NemotronH

NemotronH is a hybrid architecture combining attention and state-space layers for efficient long-context language modeling. It interleaves Mamba2 and transformer blocks, using a fixed ratio to balance expressiveness with linear-time sequence processing.

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

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="nvidia/Nemotron-H-8B-Reasoning-128K",
)
pipe("Plants create energy through a process known as")

from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-8B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-8B-Reasoning-128K",
    device_map="auto",
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

NemotronHConfig

autodoc NemotronHConfig

NemotronHModel

autodoc NemotronHModel - forward

NemotronHForCausalLM

autodoc NemotronHForCausalLM - forward