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colnomic-embed-multimodal-3b

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I tried converting the model but running into some errors when I try and use it on the rust side.

!pip install onnx onnxruntime

from transformers import AutoTokenizer, AutoModel
import torch
import onnx
import onnxruntime as ort
import numpy as np

NAME="colnomic-embed-multimodal-3b"

tokenizer = AutoTokenizer.from_pretrained(f"nomic-ai/{NAME}")
model = AutoModel.from_pretrained(f"nomic-ai/{NAME}", trust_remote_code=True)

model_out = f"/content/model.onnx"

# Set the model in evaluation mode
model.eval()

# Example input for export
inputs = tokenizer("Example input text", return_tensors="pt")

# Define the export function
torch.onnx.export(
    model,                                           # The model to export
    (inputs["input_ids"], inputs["attention_mask"]), # Model input
    model_out,                                       # The path to save the ONNX file
    export_params=True,                              # Store the trained parameter weights
    opset_version=20,                                # The ONNX version to use
    input_names=['input_ids', 'attention_mask'],     # Model's input names
    output_names=['output'],                         # Model's output names
    dynamic_axes={
        'input_ids': {0: 'batch_size', 1: 'sequence_length'},    # Dynamic axes for input_ids
        'attention_mask': {0: 'batch_size', 1: 'sequence_length'}, # Dynamic axes for attention_mask
        'output': {0: 'batch_size', 1: 'sequence_length'}        # Dynamic axes for output
    }
)

print("## ONNX Model Exported")

# Verify the ONNX model
print("## Verifying Onnx")

ort_session = ort.InferenceSession(model_out)

if "token_type_ids" in inputs:
    del inputs["token_type_ids"]

# Prepare inputs for ONNX inference
ort_inputs = {k: v.cpu().detach().numpy() for k, v in inputs.items()}
ort_outs = ort_session.run(None, ort_inputs)

print("ONNX output shape:", ort_outs[0].shape)
print("ONNX output:", ort_outs[0])

with torch.no_grad():
    pytorch_outputs = model(**inputs)
    pytorch_output_array = pytorch_outputs.last_hidden_state.cpu().numpy()

print("PyTorch output shape:", pytorch_output_array.shape)
print("PyTorch output:", pytorch_output_array)

# Compare the outputs
if np.allclose(pytorch_output_array, ort_outs[0], atol=1e-5):
    print("The ONNX model output matches the PyTorch model output!")
else:
    print("The ONNX model output does NOT match the PyTorch model output.")