wehub-resource-sync
e06fe8e8c6
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Has been cancelled
New model PR merged notification / Notify new model (push) Has been cancelled
Update Transformers metadata / build_and_package (push) Has been cancelled
2.6 KiB
2.6 KiB
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