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jet-ai/
Jet-Nemotron-2B

Name: Jet-Nemotron-2B
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Jet-Nemotron-2B

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

bash

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

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name_or_path = "jet-ai/Jet-Nemotron-2B"

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

		Jet-Nemotron-4B	Jet-Nemotron-2B	Qwen3-1.7B-Base	Llama3-3B	Gemma3n-E2B
General	MMLU	65.2	60.8	60.3	54.9	53.9
	MMLU-pro	44.2	39.0	37.8	25.0	24.3
	BBH	65.0	58.3	54.2	47.1	45.1
	ARC-C	51.7	48.6	44.9	46.6	29.4
	BoolQ	83.0	81.2	79.0	73.9	76.0
	Winogrande	70.5	65.8	63.8	69.3	60.8
Math	GSM8K	78.7	76.2	62.8	25.8	24.9
	Math	25.2	23.3	16.7	8.6	10.1
	MMLU-Stem	65.6	62.7	50.8	45.3	45.7
Code	EvalPlus	65.6	60.8	62.8	35.5	29.6
	CruXEval-I-Cot	65.9	61.1	60.4	54.7	49.9
	CruXEval-O-Cot	59.0	56.7	53.4	41.7	41.6
Long-Context	LongBench	43.9	41.1	42.2	39.9	40.4
Efficiency	Cache Size (64k)	258	154	7,168	7,168	768
	Max Throughput	1,271	2,885	61	60	701

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}
}