nomic-embed-text-onnx

Nomic Embed Text V1 (ONNX)

Tags: text-embedding onnx nomic-embed-text sentence-transformers

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Model Details

• Model Name: Nomic Embed Text V1 (ONNX export)
• Original HF Repo: nomic-ai/nomic-embed-text-v1
• ONNX File: model.onnx
• Export Date: 2025-05-27

This model outputs:

1. token_embeddings — per‐token embedding vectors ([batch_size, seq_len, hidden_size])
2. sentence_embedding — pooled sentence‐level embeddings ([batch_size, hidden_size])

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Model Description

Nomic Embed Text V1 is a BERT‐style encoder trained to generate high-quality dense representations of text. It is suitable for:

• Semantic search
• Text clustering
• Recommendation systems
• Downstream classification

The ONNX export ensures compatibility with inference engines like ONNX Runtime and NVIDIA Triton Inference Server.

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Usage

1. Install Dependencies

pip install onnxruntime transformers numpy

2. Install Dependencies

import onnxruntime as ort

session = ort.InferenceSession("model.onnx")

3. Tokenize Inputs

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1")
inputs = tokenizer(
    ["Hello world", "Another sentence"],
    padding=True,
    truncation=True,
    return_tensors="np"
)

4. Run Inference

outputs = session.run(
    ["token_embeddings", "sentence_embedding"],
    {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"]
    }
)

token_embeddings, sentence_embeddings = outputs

Serving with Triton

Place your model files under:

models/ └── nomic_embeddings/ └── 1/ ├── model.onnx ├── config.pbtxt └── (tokenizer files…)

Create a config.pbtxt file that looks something like this:

name: "nomic_embeddings"
backend: "onnxruntime"
max_batch_size: 8

input [
  {
    name: "input_ids"
    data_type: TYPE_INT32
    dims: [-1]
  },
  {
    name: "attention_mask"
    data_type: TYPE_INT32
    dims: [-1]
  }
]

output [
  {
    name: "token_embeddings"
    data_type: TYPE_FP32
    dims: [-1, 768]
  },
  {
    name: "sentence_embedding"
    data_type: TYPE_FP32
    dims: [-1, 768]
  }
]

instance_group [
  {
    kind: KIND_GPU
    count: 1
  }
]

Start Triton:

tritonserver \
  --model-repository=/path/to/models \
  --model-control-mode=explicit \
  --load-model=nomic_embeddings