# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This example shows how to use vLLM for running offline inference with
multi-image input on vision language models for text generation,
using the chat template defined by the model.
"""
import os
from argparse import Namespace
from typing import NamedTuple
from huggingface_hub import snapshot_download
from PIL.Image import Image
from transformers import AutoProcessor, AutoTokenizer
from vllm import LLM, EngineArgs, SamplingParams
from vllm.lora.request import LoRARequest
from vllm.multimodal.utils import fetch_image
from vllm.utils.argparse_utils import FlexibleArgumentParser
QUESTION = "What is the content of each image?"
IMAGE_URLS = [
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/duck.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/lion.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/flycatcher.jpeg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/somefish.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/starfish.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/snail.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/thistle.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/husky.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/orangetabbycat.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/guineapig.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/rabbit.jpg",
"https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/horsepony.jpg",
]
class ModelRequestData(NamedTuple):
engine_args: EngineArgs
prompt: str
image_data: list[Image]
stop_token_ids: list[int] | None = None
chat_template: str | None = None
lora_requests: list[LoRARequest] | None = None
sampling_params: SamplingParams | None = None
# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
# lower-end GPUs.
# Unless specified, these settings have been tested to work on a single L4.
def load_aria(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "rhymes-ai/Aria"
engine_args = EngineArgs(
model=model_name,
tokenizer_mode="slow",
trust_remote_code=True,
dtype="bfloat16",
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "<fim_prefix><|img|><fim_suffix>\n" * len(image_urls)
prompt = (
f"<|im_start|>user\n{placeholders}{question}<|im_end|>\n<|im_start|>assistant\n"
)
stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
stop_token_ids=stop_token_ids,
image_data=[fetch_image(url) for url in image_urls],
)
def load_aya_vision(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "CohereLabs/aya-vision-8b"
engine_args = EngineArgs(
model=model_name,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_bee(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "Open-Bee/Bee-8B-RL"
engine_args = EngineArgs(
model=model_name,
max_model_len=16384,
max_num_seqs=16,
limit_mm_per_prompt={"image": len(image_urls)},
trust_remote_code=True,
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_command_a_vision(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "CohereLabs/command-a-vision-07-2025"
# NOTE: This model is 122B parameters and requires tensor parallelism
# Recommended to use tp=4 on H100 GPUs
engine_args = EngineArgs(
model=model_name,
max_model_len=32768,
tensor_parallel_size=4,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_deepseek_vl2(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "deepseek-ai/deepseek-vl2-tiny"
engine_args = EngineArgs(
model=model_name,
max_model_len=4096,
max_num_seqs=2,
hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]},
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholder = "".join(
f"image_{i}:<image>\n" for i, _ in enumerate(image_urls, start=1)
)
prompt = f"<|User|>: {placeholder}{question}\n\n<|Assistant|>:"
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_deepseek_ocr(question: str, image_urls: list[str]) -> ModelRequestData:
from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
model_name = "deepseek-ai/DeepSeek-OCR"
engine_args = EngineArgs(
model=model_name,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
logits_processors=[NGramPerReqLogitsProcessor],
)
placeholder = "<image>\n" * len(image_urls)
prompt = placeholder + question
# The following sampling params config is taken from
# the official Deepseek-OCR inference example.
# (IMPORTANT) Use the custom logits processor and avoid skipping
# special tokens for this model for the optimal OCR performance.
sampling_params = SamplingParams(
temperature=0.0,
max_tokens=8192,
# ngram logit processor args
extra_args=dict(
ngram_size=30,
window_size=90,
# whitelist: <td>, </td>
whitelist_token_ids={128821, 128822},
),
skip_special_tokens=False,
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
sampling_params=sampling_params,
)
def load_gemma3(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "google/gemma-3-4b-it"
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_h2ovl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "h2oai/h2ovl-mississippi-800m"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=8192,
limit_mm_per_prompt={"image": len(image_urls)},
mm_processor_kwargs={"max_dynamic_patch": 4},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
# Stop tokens for H2OVL-Mississippi
# https://huggingface.co/h2oai/h2ovl-mississippi-800m
stop_token_ids = [tokenizer.eos_token_id]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
stop_token_ids=stop_token_ids,
image_data=[fetch_image(url) for url in image_urls],
)
# HunyuanOCR
def load_hunyuan_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "tencent/HunyuanOCR"
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholder = (
"<｜hy_place▁holder▁no▁100｜><｜hy_place▁holder▁no▁102｜><｜hy_place▁holder▁no▁101｜>" # noqa: E501
) * len(image_urls)
prompt = f"<｜hy_begin▁of▁sentence｜>{placeholder}{question}<｜hy_User｜>"
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_hyperclovax_seed_vision(
question: str, image_urls: list[str]
) -> ModelRequestData:
model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=16384,
limit_mm_per_prompt={"image": len(image_urls)},
)
message = {"role": "user", "content": list()}
for _image_url in image_urls:
message["content"].append(
{
"type": "image",
"image": _image_url,
"ocr": "",
"lens_keywords": "",
"lens_local_keywords": "",
}
)
message["content"].append(
{
"type": "text",
"text": question,
}
)
prompt = tokenizer.apply_chat_template(
[
message,
],
tokenize=False,
add_generation_prompt=True,
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
stop_token_ids=None,
image_data=[fetch_image(url) for url in image_urls],
)
def load_idefics3(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "HuggingFaceM4/Idefics3-8B-Llama3"
# The configuration below has been confirmed to launch on a single L40 GPU.
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=16,
enforce_eager=True,
limit_mm_per_prompt={"image": len(image_urls)},
# if you are running out of memory, you can reduce the "longest_edge".
# see: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3#model-optimizations
mm_processor_kwargs={
"size": {"longest_edge": 2 * 364},
},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
prompt = f"<|begin_of_text|>User:{placeholders}\n{question}<end_of_utterance>\nAssistant:" # noqa: E501
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_interns1(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "internlm/Intern-S1-mini"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=4096,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "\n".join(
f"Image-{i}: <IMG_CONTEXT>\n" for i, _ in enumerate(image_urls, start=1)
)
messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_internvl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "OpenGVLab/InternVL2-2B"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=4096,
limit_mm_per_prompt={"image": len(image_urls)},
mm_processor_kwargs={"max_dynamic_patch": 4},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
# Stop tokens for InternVL
# models variants may have different stop tokens
# please refer to the model card for the correct "stop words":
# https://huggingface.co/OpenGVLab/InternVL2-2B/blob/main/conversation.py
stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"]
stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
stop_token_ids=stop_token_ids,
image_data=[fetch_image(url) for url in image_urls],
)
def load_keye_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "Kwai-Keye/Keye-VL-8B-Preview"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=8192,
max_num_seqs=5,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
},
]
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_keye_vl1_5(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "Kwai-Keye/Keye-VL-1_5-8B"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=32768,
max_num_seqs=5,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
},
]
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_kimi_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "moonshotai/Kimi-VL-A3B-Instruct"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=4,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_llama4(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
engine_args = EngineArgs(
model=model_name,
max_model_len=131072,
tensor_parallel_size=8,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_llava(question: str, image_urls: list[str]) -> ModelRequestData:
# NOTE: CAUTION! Original Llava models wasn't really trained on multi-image inputs,
# it will generate poor response for multi-image inputs!
model_name = "llava-hf/llava-1.5-7b-hf"
engine_args = EngineArgs(
model=model_name,
max_num_seqs=16,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_llava_next(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "llava-hf/llava-v1.6-mistral-7b-hf"
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=16,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_llava_onevision(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "llava-hf/llava-onevision-qwen2-7b-ov-hf"
engine_args = EngineArgs(
model=model_name,
max_model_len=16384,
max_num_seqs=16,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_mistral3(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
# Adjust this as necessary to fit in GPU
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=2,
tensor_parallel_size=2,
limit_mm_per_prompt={"image": len(image_urls)},
ignore_patterns=["consolidated.safetensors"],
)
placeholders = "[IMG]" * len(image_urls)
prompt = f"<s>[INST]{question}\n{placeholders}[/INST]"
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_nvlm_d(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "nvidia/NVLM-D-72B"
# Adjust this as necessary to fit in GPU
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=8192,
tensor_parallel_size=4,
limit_mm_per_prompt={"image": len(image_urls)},
mm_processor_kwargs={"max_dynamic_patch": 4},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
# OpenPangu
def load_openpangu_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "FreedomIntelligence/openPangu-VL-7B"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=8192,
max_num_seqs=2,
enforce_eager=True,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "[unused18][unused19][unused20]" * len(image_urls)
prompt = (
f"<s>[unused9]系统：[unused10][unused9]用户：{question}{placeholders}"
"[unused10][unused9]助手："
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
# Ovis
def load_ovis(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "AIDC-AI/Ovis2-1B"
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=2,
trust_remote_code=True,
dtype="half",
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
# ovis2_5
def load_ovis2_5(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "AIDC-AI/Ovis2.5-2B"
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=2,
trust_remote_code=True,
dtype="half",
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
prompt = (
f"<|im_start|>user\n\n{placeholders}\n{question}<|im_end|>\n"
"<|im_start|>assistant\n"
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_paddleocr_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "PaddlePaddle/PaddleOCR-VL"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=8192,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>" * len(image_urls)
prompt = f"<|begin_of_sentence|>User: {question}{placeholders}\nAssistant: "
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_pixtral_hf(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "mistral-community/pixtral-12b"
# Adjust this as necessary to fit in GPU
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=2,
tensor_parallel_size=2,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "[IMG]" * len(image_urls)
prompt = f"<s>[INST]{question}\n{placeholders}[/INST]"
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_phi3v(question: str, image_urls: list[str]) -> ModelRequestData:
# num_crops is an override kwarg to the multimodal image processor;
# For some models, e.g., Phi-3.5-vision-instruct, it is recommended
# to use 16 for single frame scenarios, and 4 for multi-frame.
#
# Generally speaking, a larger value for num_crops results in more
# tokens per image instance, because it may scale the image more in
# the image preprocessing. Some references in the model docs and the
# formula for image tokens after the preprocessing
# transform can be found below.
#
# https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally
# https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194
engine_args = EngineArgs(
model="microsoft/Phi-3.5-vision-instruct",
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
mm_processor_kwargs={"num_crops": 4},
)
placeholders = "\n".join(
f"<|image_{i}|>" for i, _ in enumerate(image_urls, start=1)
)
prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n"
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_phi4mm(question: str, image_urls: list[str]) -> ModelRequestData:
"""
Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
show how to process multi images inputs.
"""
model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
# Since the vision-lora and speech-lora co-exist with the base model,
# we have to manually specify the path of the lora weights.
vision_lora_path = os.path.join(model_path, "vision-lora")
engine_args = EngineArgs(
model=model_path,
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
enable_lora=True,
max_lora_rank=320,
# Note - mm_processor_kwargs can also be passed to generate/chat calls
mm_processor_kwargs={"dynamic_hd": 4},
)
placeholders = "".join(f"<|image_{i}|>" for i, _ in enumerate(image_urls, start=1))
prompt = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
lora_requests=[LoRARequest("vision", 1, vision_lora_path)],
)
def load_qwen_vl_chat(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "Qwen/Qwen-VL-Chat"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=1024,
max_num_seqs=2,
hf_overrides={"architectures": ["QwenVLForConditionalGeneration"]},
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = "".join(
f"Picture {i}: <img></img>\n" for i, _ in enumerate(image_urls, start=1)
)
# This model does not have a chat_template attribute on its tokenizer,
# so we need to explicitly pass it. We use ChatML since it's used in the
# generation utils of the model:
# https://huggingface.co/Qwen/Qwen-VL-Chat/blob/main/qwen_generation_utils.py#L265
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# Copied from: https://huggingface.co/docs/transformers/main/en/chat_templating
chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" # noqa: E501
messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
chat_template=chat_template,
)
stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"]
stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
stop_token_ids=stop_token_ids,
image_data=[fetch_image(url) for url in image_urls],
chat_template=chat_template,
)
def load_qwen2_vl(question: str, image_urls: list[str]) -> ModelRequestData:
try:
from qwen_vl_utils import smart_resize
except ModuleNotFoundError:
print(
"WARNING: `qwen-vl-utils` not installed, input images will not "
"be automatically resized. You can enable this functionality by "
"`pip install qwen-vl-utils`."
)
smart_resize = None
model_name = "Qwen/Qwen2-VL-7B-Instruct"
# Tested on L40
engine_args = EngineArgs(
model=model_name,
max_model_len=32768 if smart_resize is None else 4096,
max_num_seqs=5,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
},
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
if smart_resize is None:
image_data = [fetch_image(url) for url in image_urls]
else:
def post_process_image(image: Image) -> Image:
width, height = image.size
resized_height, resized_width = smart_resize(
height, width, max_pixels=1024 * 28 * 28
)
return image.resize((resized_width, resized_height))
image_data = [post_process_image(fetch_image(url)) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_qwen2_5_vl(question: str, image_urls: list[str]) -> ModelRequestData:
try:
from qwen_vl_utils import smart_resize
except ModuleNotFoundError:
print(
"WARNING: `qwen-vl-utils` not installed, input images will not "
"be automatically resized. You can enable this functionality by "
"`pip install qwen-vl-utils`."
)
smart_resize = None
model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
engine_args = EngineArgs(
model=model_name,
max_model_len=32768 if smart_resize is None else 4096,
max_num_seqs=5,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
},
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
if smart_resize is None:
image_data = [fetch_image(url) for url in image_urls]
else:
def post_process_image(image: Image) -> Image:
width, height = image.size
resized_height, resized_width = smart_resize(
height, width, max_pixels=1024 * 28 * 28
)
return image.resize((resized_width, resized_height))
image_data = [post_process_image(fetch_image(url)) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_r_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "YannQi/R-4B"
engine_args = EngineArgs(
model=model_name,
max_model_len=16384,
max_num_seqs=16,
trust_remote_code=True,
limit_mm_per_prompt={"image": len(image_urls)},
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_smolvlm(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
# The configuration below has been confirmed to launch on a single L40 GPU.
engine_args = EngineArgs(
model=model_name,
max_model_len=8192,
max_num_seqs=16,
enforce_eager=True,
limit_mm_per_prompt={"image": len(image_urls)},
mm_processor_kwargs={
"max_image_size": {"longest_edge": 384},
},
)
placeholders = "\n".join(
f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
)
prompt = (
f"<|im_start|>User:{placeholders}\n{question}<end_of_utterance>\nAssistant:" # noqa: E501
)
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=[fetch_image(url) for url in image_urls],
)
def load_step3(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "stepfun-ai/step3-fp8"
# NOTE: Below are verified configurations for step3-fp8
# on 8xH100 GPUs.
engine_args = EngineArgs(
model=model_name,
max_num_batched_tokens=4096,
gpu_memory_utilization=0.85,
tensor_parallel_size=8,
limit_mm_per_prompt={"image": len(image_urls)},
reasoning_parser="step3",
)
prompt = (
"<｜begin▁of▁sentence｜> You are a helpful assistant. <|BOT|>user\n "
f"{'<im_patch>' * len(image_urls)}{question} <|EOT|><|BOT|"
">assistant\n<think>\n"
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_step_vl(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "stepfun-ai/Step3-VL-10B"
engine_args = EngineArgs(
model=model_name,
max_num_batched_tokens=4096,
limit_mm_per_prompt={"image": len(image_urls)},
hf_overrides={"vision_config": {"enable_patch": False}},
trust_remote_code=True,
reasoning_parser="deepseek_r1",
)
prompt = (
"<｜begin▁of▁sentence｜> You are a helpful assistant.<|BOT|>user\n "
f"{'<im_patch>' * len(image_urls)}{question}<|EOT|><|BOT|>"
"assistant\n<think>\n"
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_tarsier(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "omni-research/Tarsier-7b"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=4096,
limit_mm_per_prompt={"image": len(image_urls)},
)
prompt = f"USER: {'<image>' * len(image_urls)}\n{question}\n ASSISTANT:"
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_tarsier2(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "omni-research/Tarsier2-Recap-7b"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
max_model_len=32768,
limit_mm_per_prompt={"image": len(image_urls)},
hf_overrides={
"architectures": ["Tarsier2ForConditionalGeneration"],
"model_type": "tarsier2",
},
)
prompt = (
"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
f"<|im_start|>user\n<|vision_start|>{'<|image_pad|>' * len(image_urls)}"
f"<|vision_end|>{question}<|im_end|>\n"
"<|im_start|>assistant\n"
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
# GLM-4.1V
def load_glm4_1v(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "zai-org/GLM-4.1V-9B-Thinking"
engine_args = EngineArgs(
model=model_name,
max_model_len=45082,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
enforce_eager=True,
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
# GLM-4.5V
def load_glm4_5v(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "zai-org/GLM-4.5V"
engine_args = EngineArgs(
model=model_name,
max_model_len=32768,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
enforce_eager=True,
tensor_parallel_size=4,
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
# GLM-4.5V-FP8
def load_glm4_5v_fp8(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "zai-org/GLM-4.5V-FP8"
engine_args = EngineArgs(
model=model_name,
max_model_len=32768,
max_num_seqs=2,
limit_mm_per_prompt={"image": len(image_urls)},
enforce_eager=True,
tensor_parallel_size=4,
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
}
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
def load_molmo2(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "allenai/Molmo2-8B"
engine_args = EngineArgs(
model=model_name,
trust_remote_code=True,
dtype="bfloat16",
limit_mm_per_prompt={"image": len(image_urls)},
max_num_batched_tokens=36864,
)
placeholders = [{"type": "image", "image": url} for url in image_urls]
messages = [
{
"role": "user",
"content": [
*placeholders,
{"type": "text", "text": question},
],
},
]
processor = AutoProcessor.from_pretrained(model_name)
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_data = [fetch_image(url) for url in image_urls]
return ModelRequestData(
engine_args=engine_args,
prompt=prompt,
image_data=image_data,
)
model_example_map = {
"aria": load_aria,
"aya_vision": load_aya_vision,
"bee": load_bee,
"command_a_vision": load_command_a_vision,
"deepseek_vl_v2": load_deepseek_vl2,
"deepseek_ocr": load_deepseek_ocr,
"gemma3": load_gemma3,
"h2ovl_chat": load_h2ovl,
"hunyuan_vl": load_hunyuan_vl,
"hyperclovax_seed_vision": load_hyperclovax_seed_vision,
"idefics3": load_idefics3,
"interns1": load_interns1,
"internvl_chat": load_internvl,
"keye_vl": load_keye_vl,
"keye_vl1_5": load_keye_vl1_5,
"kimi_vl": load_kimi_vl,
"llama4": load_llama4,
"llava": load_llava,
"llava-next": load_llava_next,
"llava-onevision": load_llava_onevision,
"mistral3": load_mistral3,
"molmo2": load_molmo2,
"NVLM_D": load_nvlm_d,
"openpangu_vl": load_openpangu_vl,
"ovis": load_ovis,
"ovis2_5": load_ovis2_5,
"paddleocr_vl": load_paddleocr_vl,
"phi3_v": load_phi3v,
"phi4_mm": load_phi4mm,
"pixtral_hf": load_pixtral_hf,
"qwen_vl_chat": load_qwen_vl_chat,
"qwen2_vl": load_qwen2_vl,
"qwen2_5_vl": load_qwen2_5_vl,
"rvl": load_r_vl,
"smolvlm": load_smolvlm,
"step3": load_step3,
"stepvl": load_step_vl,
"tarsier": load_tarsier,
"tarsier2": load_tarsier2,
"glm4_1v": load_glm4_1v,
"glm4_5v": load_glm4_5v,
"glm4_5v_fp8": load_glm4_5v_fp8,
}
def run_generate(
model,
question: str,
image_urls: list[str],
seed: int,
tensor_parallel_size: int | None,
):
req_data = model_example_map[model](question, image_urls)
engine_args = req_data.engine_args
engine_args.seed = seed
if tensor_parallel_size is not None:
engine_args.tensor_parallel_size = tensor_parallel_size
llm = LLM.from_engine_args(engine_args)
sampling_params = SamplingParams(
temperature=0.0, max_tokens=256, stop_token_ids=req_data.stop_token_ids
)
outputs = llm.generate(
{
"prompt": req_data.prompt,
"multi_modal_data": {"image": req_data.image_data},
},
sampling_params=sampling_params,
lora_request=req_data.lora_requests,
)
print("-" * 50)
for o in outputs:
generated_text = o.outputs[0].text
print(generated_text)
print("-" * 50)
def run_chat(
model: str,
question: str,
image_urls: list[str],
seed: int,
tensor_parallel_size: int | None,
):
req_data = model_example_map[model](question, image_urls)
# Disable other modalities to save memory
default_limits = {"image": 0, "video": 0, "audio": 0}
req_data.engine_args.limit_mm_per_prompt = default_limits | dict(
req_data.engine_args.limit_mm_per_prompt or {}
)
engine_args = req_data.engine_args
engine_args.seed = seed
if tensor_parallel_size is not None:
engine_args.tensor_parallel_size = tensor_parallel_size
llm = LLM.from_engine_args(engine_args)
sampling_params = (
SamplingParams(
temperature=0.0, max_tokens=256, stop_token_ids=req_data.stop_token_ids
)
if req_data.sampling_params is None
else req_data.sampling_params
)
outputs = llm.chat(
[
{
"role": "user",
"content": [
{
"type": "text",
"text": question,
},
*(
{
"type": "image_url",
"image_url": {"url": image_url},
}
for image_url in image_urls
),
],
}
],
sampling_params=sampling_params,
chat_template=req_data.chat_template,
lora_request=req_data.lora_requests,
)
print("-" * 50)
for o in outputs:
generated_text = o.outputs[0].text
print(generated_text)
print("-" * 50)
def parse_args():
parser = FlexibleArgumentParser(
description="Demo on using vLLM for offline inference with "
"vision language models that support multi-image input for text "
"generation"
)
parser.add_argument(
"--model-type",
"-m",
type=str,
default="phi3_v",
choices=model_example_map.keys(),
help='Huggingface "model_type".',
)
parser.add_argument(
"--method",
type=str,
default="generate",
choices=["generate", "chat"],
help="The method to run in `vllm.LLM`.",
)
parser.add_argument(
"--seed",
type=int,
default=0,
help="Set the seed when initializing `vllm.LLM`.",
)
parser.add_argument(
"--num-images",
"-n",
type=int,
choices=list(range(1, len(IMAGE_URLS) + 1)), # the max number of images
default=2,
help="Number of images to use for the demo.",
)
parser.add_argument(
"--tensor-parallel-size",
"-tp",
type=int,
default=None,
help="Tensor parallel size to override the model's default setting. ",
)
return parser.parse_args()
def main(args: Namespace):
model = args.model_type
method = args.method
seed = args.seed
tensor_parallel_size = args.tensor_parallel_size
if tensor_parallel_size is not None and tensor_parallel_size < 1:
raise ValueError(
f"tensor_parallel_size must be a positive integer, "
f"got {tensor_parallel_size}"
)
image_urls = IMAGE_URLS[: args.num_images]
if method == "generate":
run_generate(model, QUESTION, image_urls, seed, tensor_parallel_size)
elif method == "chat":
run_chat(model, QUESTION, image_urls, seed, tensor_parallel_size)
else:
raise ValueError(f"Invalid method: {method}")
if __name__ == "__main__":
args = parse_args()
main(args)