nvidia/Nemotron-3.5-Embed-8B-BF16

Nemotron-3.5-Embed-8B-BF16 Overview

Description:

Nemotron-3.5-Embed-8B-BF16 is a state-of-the-art, versatile embedding model developed by NVIDIA for multimodal and multilingual question-answering retrieval. The model can embed a document page in the form of an image, text, or combined image–text input. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. For text retrieval, the model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, and Vietnamese. For image retrieval, it was evaluated across 18 languages: Arabic, Bengali, Chinese, English, Persian, Finnish, French, German, Hindi, Indonesian, Japanese, Korean, Russian, Spanish, Swahili, Telugu, Thai, and Yoruba. The model may support additional languages beyond these evaluation sets.

This model is ready for commercial or non-commercial use.

License/Terms of Use:

Governing Download Terms: Use of the model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).

Additional Information: The Ministral-3-8B-Instruct-2512-BF16 base models are governed by the Apache License, Version 2.0.

Deployment Geography:

Global

Use Case:

The Nemotron-3.5-Embed-8B-BF16 is suitable for users who want to build a multimodal question-and-answer application over a large corpus, leveraging the latest dense retrieval technology. The input of the model is a text or document image and the output is a fixed-size embedding vector. The embedding model is a bi-encoder that supports context in textual format (e.g. the query or the OCR text of a page or a section of a document) or the image of a document page. Typically, the embedding model is used first to embed (vectorize) the whole corpus (document images or text chunks), and embeddings are stored in a vector database associated to its raw content (image or text). Then at inference time, the embedding model is used to embed the query. The model generates dense vector embeddings from text or image inputs, enabling multimodal retrieval, semantic search, and (agentic) RAG workflows.

Release Date:

Hugging Face 10/20/2026 via https://huggingface.co/nvidia/Nemotron-3.5-Embed-8B-BF16

Model Architecture:

Architecture Type: Transformer

Network Architecture: Nemotron-3.5-Embed-8B-BF16 architecture with the Vision Encoder initialized from mistralai/Ministral-3-8B-Instruct-2512-BF16 and the LLM component initialized from nvidia/Nemotron-3-Embed-8B-BF16.

Number of model parameters: 8.38B

The language-model component of Nemotron-3.5-Embed-8B-BF16 is initialized from Nemotron-3-Embed-8B-BF16 and kept frozen, preserving that model's text retrieval accuracy while adding image retrieval capabilities. The two models therefore share the same embedding space and produce identical text embeddings. During fine-tuning, only the vision encoder and multimodal projector were trained, on image and combined image-text datasets.

Input:

Input Type(s): Text, Image

Input Format(s): Text: String; Image: Red, Green, Blue (RGB)

Input Parameters: Text: One-Dimensional (1D), Image: Two-Dimensional (2D)

Other Properties Related to Input: The model supports image and/or text inputs. For queries, the model accepts text input only. For documents, it accepts an image, text, or a combination of image + text. The default image longest_edge is 1,120 pixels, and it can be increased up to 1,540 pixels. The model’s max context length is 32,768 tokens.

Output:

Output Type(s): Embeddings

Output Format: Vector

Output Parameters: One-Dimensional (1D) per input

Other Properties Related to Output: Token-level hidden states are mean-pooled and then L2-normalized to produce one 4096-dimensional embedding vector for each input. The model also supports dynamic embedding sizes by truncating the vector from the end (e.g., retaining the first 1024 or 512 dimensions). The truncated embeddings remain effective for retrieval, provided they are L2-normalized after truncation.

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.

Installation

Create a Python 3.12 environment for the Transformers and Sentence Transformers examples:

uv venv --python 3.12 .venv-transformers
source .venv-transformers/bin/activate
uv pip install torch "transformers>=5.5.0, None:
if not torch.cuda.is_available():
raise RuntimeError("This bfloat16 example requires a CUDA GPU.")

processor = AutoProcessor.from_pretrained(MODEL_PATH)

model = AutoModel.from_pretrained(
MODEL_PATH,
dtype=torch.bfloat16,
device_map={"": DEVICE},
attn_implementation="flash_attention_2",
).eval()

def embed(
prefix: str,
text: str | None = None,
image: Image.Image | None = None,
) -> torch.Tensor:
content = [] if image is None else [{"type": "image", "image": image}]

if text is not None:
content.append({"type": "text", "text": text})

inputs = processor.apply_chat_template(
[
{"role": "system", "content": prefix},
{"role": "user", "content": content},
],
tokenize=True,
return_dict=True,
return_tensors="pt",
)
if inputs["input_ids"].shape[-1] > MAX_MODEL_LEN:
raise ValueError("Input exceeds 32768 tokens; shorten the text or resize the image.")
inputs = inputs.to(DEVICE)

if image is not None:
inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)

with torch.inference_mode():
hidden = model(**inputs).last_hidden_state
mask = inputs["attention_mask"].unsqueeze(-1)
embedding = (hidden * mask).sum(1) / mask.sum(1)

return F.normalize(embedding.float(), dim=-1)

image = load_image(IMAGE_URL)
query_embedding = embed("query:", "What is NVIDIA NeMo Framework?")...