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onnx-community/
gemma-3-4b-it-ONNX

Name: gemma-3-4b-it-ONNX
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Usage

ONNX Runtime

from transformers import AutoConfig, AutoProcessor, GenerationConfig
from transformers.image_utils import load_image
import onnxruntime
import numpy as np
import os

# 1. Load config, processor, and model
model_id = "onnx-community/gemma-3-4b-it-ONNX"
local_dir = "./gemma-3-4b-it"
onnx_dir = os.path.join(local_dir, "onnx")

config = AutoConfig.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
generation_config = GenerationConfig.from_pretrained(model_id)

## Load sessions
providers = ['CPUExecutionProvider']
vision_session = onnxruntime.InferenceSession(os.path.join(onnx_dir, "vision_encoder.onnx"), providers=providers)
embed_session = onnxruntime.InferenceSession(os.path.join(onnx_dir, "embed_tokens.onnx"), providers=providers)
decoder_session = onnxruntime.InferenceSession(os.path.join(onnx_dir, "decoder_model_merged.onnx"), providers=providers)

## Set config values
text_config = config.text_config
num_key_value_heads = text_config.num_key_value_heads
head_dim = text_config.head_dim
num_hidden_layers = text_config.num_hidden_layers
eos_token_id = generation_config.eos_token_id
image_token_index = config.image_token_index

# 2. Prepare inputs
image_url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = load_image(image_url)
messages = [
  {
    "role": "user",
    "content": [
      {"type": "image", "image": image},
      {"type": "text", "text": "What is in this image?"},
    ],
  },
]
inputs = processor.apply_chat_template(
  messages,
  add_generation_prompt=True,
  return_tensors="pt",
  return_dict=True,
  tokenize=True,
)

input_ids = inputs['input_ids'].numpy()
attention_mask = inputs['attention_mask'].numpy()
has_vision_inputs = 'pixel_values' in inputs
pixel_values = inputs['pixel_values'].numpy() if has_vision_inputs else None
num_logits_to_keep = np.array(1, dtype=np.int64)

batch_size = input_ids.shape[0]
past_cache_values = {}
for i in range(num_hidden_layers):
  for kv in ('key', 'value'):
    past_cache_values[f'past_key_values.{i}.{kv}'] = np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32)

# 3. Generation loop
max_new_tokens = 1024
generated_tokens = np.array([[]], dtype=np.int64)
image_features = None
for i in range(max_new_tokens):
  inputs_embeds = embed_session.run(None, {'input_ids': input_ids})[0]

  if has_vision_inputs and image_features is None:
    ## Only compute vision features on first iteration
    image_features = vision_session.run(None, dict(
      pixel_values=pixel_values,
    ))[0]

    ## Merge text and vision embeddings
    inputs_embeds[input_ids == image_token_index] = image_features.reshape(-1, image_features.shape[-1])

  decoder_inputs = dict(
    inputs_embeds=inputs_embeds,
    attention_mask=attention_mask,
    num_logits_to_keep=num_logits_to_keep,
    **past_cache_values,
  )

  logits, *present_cache_values = decoder_session.run(None, decoder_inputs)

  ## Update values for next generation loop
  input_ids = logits[:, -1].argmax(-1, keepdims=True)
  attention_mask = np.concatenate([attention_mask, np.ones((batch_size, 1), dtype=attention_mask.dtype)], axis=-1)
  for j, key in enumerate(past_cache_values):
    past_cache_values[key] = present_cache_values[j]

  generated_tokens = np.concatenate([generated_tokens, input_ids], axis=-1)
  if np.isin(input_ids, eos_token_id).any():
    break

  ## (Optional) Streaming
  print(processor.decode(input_ids[0], skip_special_tokens=False), end='', flush=True)
print()

# 4. Output result
print(processor.batch_decode(generated_tokens, skip_special_tokens=False)[0])