Jet-Nemotron-4B

Jet-Nemotron-4B

<div align="left"> <a href="https://hanlab.mit.edu/projects/jet-nemotron/"><img src="https://img.shields.io/static/v1?label=Website&message=Jet-Nemotron&color=darkred&logo=github-pages"></a> &ensp; <a href="https://github.com/NVlabs/Jet-Nemotron/"><img src="https://img.shields.io/static/v1?label=Github&message=Jet-Nemotron&color=blue&logo=github"></a> &ensp; <a href="https://www.arxiv.org/abs/2508.15884"><img src="https://img.shields.io/static/v1?label=arXiv&message=Jet-Nemotron&color=red&logo=arxiv"></a> &ensp; <a href="https://youtu.be/qAQ5yMThhRY"><img src="https://img.shields.io/static/v1?label=Demo&message=Jet-Nemotron&color=yellow"></a> &ensp; </div>

1 Overview

Jet-Nemotron is a new family of hybrid-architecture language models that surpass state-of-the-art open-source full-attention language models such as Qwen3, Qwen2.5, Gemma3, and Llama3.2, while achieving significant efficiency gains—up to 53.6× speedup in generation throughput on H100 GPUs (256K context length, maximum batch size). It is built upon two core innovations:

• Post Neural Architecture Search, an efficient post-training architecture exploration and adaptation pipeline applicable to arbitrary pre-trained transformer models;
• JetBlock, a novel linear attention block that significantly outperforms previous designs such as Mamba2.

2 Quick Start

Requirements

flash-attn
torch<=2.7.1
transformers<=4.53.0
flash-attn
accelerate
datasets==4.0.0
jieba
fuzzywuzzy
rouge
python-Levenshtein
flash-linear-attention@git+https://github.com/jet-ai-projects/flash-linear-attention.git@jetai
lm_eval@git+https://github.com/jet-ai-projects/lm-evaluation-harness.git@jetai

Generation

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name_or_path = "jet-ai/Jet-Nemotron-4B"

model = AutoModelForCausalLM.from_pretrained(model_name_or_path, 
                                             trust_remote_code=True, 
                                             attn_implementation="flash_attention_2",
                                             torch_dtype=torch.bfloat16,
                                             device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = model.eval().cuda()

input_str = "Hello, I'm Jet-Nemotron from NVIDIA."

input_ids = tokenizer(input_str, return_tensors="pt").input_ids.cuda()
output = model.generate(input_ids, max_new_tokens=50, do_sample=False)
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_str)

3 Evaluation Results

4 Citation

@article{gu2025jet,
  title={Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search},
  author={Gu, Yuxian and Hu, Qinghao and Yang, Shang and Xi, Haocheng and Chen, Junyu and Han, Song and Cai, Han},
  journal={arXiv preprint arXiv:2508.15884},
  year={2025}
}