llama-embed-nemotron-8b is a versatile text embedding model trained by NVIDIA and optimized for retrieval, reranking, semantic similarity, and classification use cases. This model has robust capabilities for multilingual and cross-lingual text retrieval. It is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems.

This model achieves state-of-the-art performance on the multilingual MTEB leaderboard as of October 21, 2025.

Together with the model weights, we're releasing the full recipe behind the llama-embed-nemotron-8b:

A detailed technical report focusing on our Synthetic Data Generation (SDG) pipeline and core design choices.
The training dataset, featuring a curated mix of public and synthetic data.
The full training code via the NeMo AutoModel framework.

This model is for non-commercial/research use only.

License/Terms of Use

Governing Terms for llama-embed-nemotron-8b model: NVIDIA License
Additional Information: Llama-3.1 Community License Agreement for meta-llama/Llama-3.1-8B. Acceptable Use Policy. Built with Llama.

Team

Yauhen Babakhin
Radek Osmulski
Ronay Ak
Gabriel Moreira
Mengyao Xu
Benedikt Schifferer
Bo Liu
Even Oldridge

Correspondence to Yauhen Babakhin (ybabakhin@nvidia.com) and Bo Liu (boli@nvidia.com).

Citation

@misc{babakhin2025llamaembednemotron8buniversaltextembedding,
      title={Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks}, 
      author={Yauhen Babakhin and Radek Osmulski and Ronay Ak and Gabriel Moreira and Mengyao Xu and Benedikt Schifferer and Bo Liu and Even Oldridge},
      year={2025},
      eprint={2511.07025},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.07025}, 
}

NVIDIA’s Retrieval Models

Model Name	Use Case	Comment
nvidia/omni-embed-nemotron-3b	Research-Only	Omni-Modal Embedding Model for Retrieving Text, Images, Audio, or Video
nvidia/llama-NemoRetriever-ColEmbed-3B-v1	Research-Only	#1 ViDoRe V1, V2 and MTEB VisualDocumentRetrieval as of June 27, 2025
nvidia/llama-nemotron-embed-1b-v2	Commercial Application	Text Embedding Model for Production Use Case of Text Document Retrieval
nvidia/llama-nemotron-rerank-1b-v2	Commercial Application	Text Reranker Model for Production Use Case of Text Document Retrieval
llama-3_2-nemoretriever-1b-vlm-embed-v1	Commercial Application	MultiModal Embedding Model for Production Use Case of Visual Document Retrieval
nvidia/llama-NemoRetriever-ColEmbed-1B-v1	Research-Only	Smaller Version of nvidia/llama-NemoRetriever-ColEmbed-3B-v1
nvidia/NV-Embed-v2	Research-Only	#1 MTEB as of Aug 30, 2024
nvidia/MM-Embed	Research-Only	Improved nvidia/NV-Embed-v1 and multimodal embeddings
nvidia/NV-Retriever-v1	Research-Only	#1 MTEB BEIR as of July 12, 2024

Deployment Geography:

Global

Use Case:

The llama-embed-nemotron-8b model is intended for researchers developing applications that need to understand or retrieve information from text. It is well-suited for multilingual RAG systems in which queries and documents are textual and may be in different languages.

Release Date:

Hugging Face on 10/21/2025 via https://huggingface.co/nvidia/llama-embed-nemotron-8b

Model Architecture:

Architecture Type: Transformer Decoder
Network Architecture: Llama-3.1-8B with bi-directional attention
This model was developed based on meta-llama/Llama-3.1-8B model.
Number of model parameters: 7,504,924,672

This llama-embed-nemotron-8b embedding model is a fine-tuned version of Llama-3.1-8B transformer decoder architecture, with a bidirectional attention mechanism. The model consists of 32 hidden layers and an embedding size of 4096, and trained on public datasets and synthetically generated datasets. Embedding models for text retrieval are typically trained using a bi-encoder architecture. This involves encoding a pair of sentences (for example, query and chunked passages) independently using the embedding model. Contrastive learning is used to maximize the similarity between the query and the passage that contains the answer, while minimizing the similarity between the query and sampled negative passages not useful to answer the question.

Input:

Property	Query	Document
Input Type	Text	Text
Input Format	List of strings	List of strings
Input Parameter	One-Dimensional (1D)	1D
Other Properties	Maximum input sequence length is 32768 tokens.	Maximum input sequence length is 32768 tokens.

Output:

Output Type(s): Floats
Output Format: List of floats
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Model outputs embedding vectors of a dimension 4096 for each text input.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Usage

The llama-embed-nemotron-8b model is instruction-aware, meaning that it supports custom instructions to improve performance for specific use cases or scenarios. In particular, for Retrieval use case, model expects:

Queries accompanied with the task instruction in the following template: f"Instruct: {task_instruction}\nQuery: {query}"
Documents (passages) without any special handling

The model requires transformers version 4.51.0 and flash-attention (for GPU processing)

bash

pip install transformers==4.51.0
pip install flash-attn==2.6.3

Sentence Transformers:

pip install sentence-transformers

python

from sentence_transformers import SentenceTransformer

attn_implementation = "eager"  # Or "flash_attention_2"
model = SentenceTransformer(
    "nvidia/llama-embed-nemotron-8b",
    trust_remote_code=True,
    model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": "bfloat16"},
    tokenizer_kwargs={"padding_side": "left"},
)

queries = [
    "How do neural networks learn patterns from examples?"
]
documents = [
    "Deep learning models adjust their weights through backpropagation, using gradient descent to minimize error on training data and improve predictions over time.",
    "Market prices are determined by the relationship between how much people want to buy a product and how much is available for sale, with scarcity driving prices up and abundance driving them down.",
]

# NOTE: encode_query uses the "query" prompt automatically
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)

scores = (query_embeddings @ document_embeddings.T)

print(scores.tolist())
# [[0.3770667314529419, 0.05808388814330101]]

Hugging Face Transformers

python

import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer


def average_pool(last_hidden_states: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
    """Average pooling with attention mask."""

    last_hidden_states = last_hidden_states.to(torch.float32)
    last_hidden_states_masked = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    embedding = last_hidden_states_masked.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
    embedding = F.normalize(embedding, dim=-1)
    
    return embedding

# Define task and queries
def get_instruction(task_instruction: str, query: str) -> str:
    return f"Instruct: {task_instruction}\nQuery: {query}"

model_name_or_path = "nvidia/llama-embed-nemotron-8b"

attn_implementation = "flash_attention_2" if torch.cuda.is_available() else "eager"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    model_name_or_path,
    trust_remote_code=True,
    padding_side="left",
)

# Load model
model = AutoModel.from_pretrained(
    model_name_or_path, 
    trust_remote_code=True,
    torch_dtype=torch.float16,
    attn_implementation=attn_implementation,
).eval()
model = model.to("cuda:0" if torch.cuda.is_available() else "cpu")

# Model is instruction-aware, which requires each query to have a short instruction with the task instruction
task = "Given a question, retrieve passages that answer the question"
queries = [
    get_instruction(task, "How do neural networks learn patterns from examples?"),
]

# No instruction is required for documents corpus
documents = [
    "Deep learning models adjust their weights through backpropagation, using gradient descent to minimize error on training data and improve predictions over time.",
    "Market prices are determined by the relationship between how much people want to buy a product and how much is available for sale, with scarcity driving prices up and abundance driving them down.",
]
input_texts = queries + documents

# Tokenize the input texts
batch_dict = tokenizer(
    text=input_texts,
    max_length=4096,
    padding=True,
    truncation=True,
    return_tensors="pt",
).to(model.device)
attention_mask = batch_dict["attention_mask"]

# Forward pass
model_outputs = model(**batch_dict)

# Average pooling
embeddings = average_pool(model_outputs.last_hidden_state, attention_mask)

scores = (embeddings[:1] @ embeddings[1:].T)

print(scores.tolist())
# [[0.37644022703170776, 0.05794818699359894]]

vLLM

Ensure you are using vllm>=0.14.0.
Start the vLLM server with the following command:

Minimal command (required):

vllm serve \
    nvidia/llama-embed-nemotron-8b \
    --trust-remote-code

If you already have a local copy of the model, you can also pass the local path instead of the HF repo ID.

Optional flags:

--dtype <float32|bfloat16|float16> to force precision (the default is auto, which resolves from model config; this model defaults to BF16).
--data-parallel-size <num_gpus_to_use> for multi-GPU serving.
--port 8000 to set the server port.

Online serving example (OpenAI SDK):

from openai import OpenAI
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",  # required by OpenAI SDK; ignored by default in local vLLM
)

response = client.embeddings.create(
    input=['Instruct: Given a question, retrieve passages that answer the question\nQuery: summit define'],
    model="nvidia/llama-embed-nemotron-8b",
)
response.data[0].embedding

Offline inference example (Python API, no server required):

from vllm import LLM

llm = LLM(
    model="nvidia/llama-embed-nemotron-8b",
    runner="pooling",
    trust_remote_code=True,
)

outputs = llm.embed(["Instruct: Given a question, retrieve passages that answer the question\nQuery: summit define", "a summit is a meeting"])
for output in outputs:
    print(len(output.outputs.embedding))

Software Integration:

Runtime Engine(s):

TensorRT, Triton

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing
NVIDIA Volta

Preferred/Supported Operating System(s):

Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s):

llama-embed-nemotron-8b-v1

Training and Testing Datasets

Training Dataset:

Data Modality

Text

Text Training Data Size

1 Billion to 10 Trillion Tokens

Data Collection Method by dataset

Hybrid: Human, Automated, Synthetic

Labeling Method by dataset

Hybrid: Human, Automated, Synthetic

Properties: 16.4M query-passage pairs from public and synthetically generated datasets.

Testing Dataset:

We test the model on 131 tasks from MMTEB: Massive Multilingual Text Embedding Benchmark (MTEB(Multilingual, v2) split).

Benchmark specs:

Number of languages: 1038
Number of task types: 9
Number of domains: 20

MMTEB Leaderboard Benchmark Ranking

Below we present results for MTEB(Multilingual, v2) split of MMTEB benchmark (as of October 21, 2025). Ranking on MMTEB Leaderboards is performed based on the Borda rank. Each task is treated as a preference voter, which gives votes on the models per their relative performance on the task. The best model obtains the highest number of votes. The model with the highest number of votes across tasks obtains the highest rank. The Borda rank tends to prefer models that perform well broadly across tasks.

Borda Rank	Model	Borda Votes	Mean (Task)
1.	llama-embed-nemotron-8b	39,573	69.46
2.	gemini-embedding-001	39,368	68.37
3.	Qwen3-Embedding-8B	39,364	70.58
4.	Qwen3-Embedding-4B	39,099	69.45
5.	Qwen3-Embedding-0.6B	37,419	64.34
6.	gte-Qwen2-7B-instruct	37,167	62.51
7.	Linq-Embed-Mistral	37,149	61.47

Data Collection Method by dataset:

Hybrid: Automated, Human, Synthetic

Labeling Method by dataset:

Hybrid: Automated, Human, Synthetic

Properties: More details about MMTEB benchmark can be found on their leaderboard or in their published paper.

Inference:

Acceleration Engine: GPU
Test Hardware: A100 80GB, H100 80GB

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.