---
title: "Query VLM with Offline Engine"
metatags:
    description: "SGLang VLM offline engine: raw images, processor output, precomputed embeddings. Qwen2.5-VL and Llama 4 examples."
---
This tutorial demonstrates how to use SGLang's **offline Engine API** to query VLMs. We will demonstrate usage with Qwen2.5-VL and Llama 4. This section demonstrates three different calling approaches:

1. **Basic Call**: Directly pass images and text.
2. **Processor Output**: Use HuggingFace processor for data preprocessing.
3. **Precomputed Embeddings**: Pre-calculate image features to improve inference efficiency.

## Understanding the Three Input Formats

SGLang supports three ways to pass visual data, each optimized for different scenarios:

### 1. **Raw Images** - Simplest approach
- Pass PIL Images, file paths, URLs, or base64 strings directly
- SGLang handles all preprocessing automatically
- Best for: Quick prototyping, simple applications

### 2. **Processor Output** - For custom preprocessing
- Pre-process images with HuggingFace processor
- Pass the complete processor output dict with `format: "processor_output"`
- Best for: Custom image transformations, integration with existing pipelines
- Requirement: Must use `input_ids` instead of text prompt

### 3. **Precomputed Embeddings** - For maximum performance
- Pre-calculate visual embeddings using the vision encoder
- Pass embeddings with `format: "precomputed_embedding"`
- Best for: Repeated queries on same images, caching, high-throughput serving
- Performance gain: Avoids redundant vision encoder computation (30-50% speedup)

**Key Rule**: Within a single request, use only one format for all images. Don't mix formats.

The examples below demonstrate all three approaches with both Qwen2.5-VL and Llama 4 models.

## Querying Qwen2.5-VL Model

```python Example
import nest_asyncio

nest_asyncio.apply()

import sglang.test.doc_patch  # noqa: F401

model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
chat_template = "qwen2-vl"
example_image_url = "https://raw.githubusercontent.com/sgl-project/sglang/main/examples/assets/example_image.png"
```

```python Example
from io import BytesIO
import requests
from PIL import Image

from sglang.srt.parser.conversation import chat_templates

image = Image.open(BytesIO(requests.get(example_image_url).content))

conv = chat_templates[chat_template].copy()
conv.append_message(conv.roles[0], f"What's shown here: {conv.image_token}?")
conv.append_message(conv.roles[1], "")
conv.image_data = [image]

print("Generated prompt text:")
print(conv.get_prompt())
print(f"\nImage size: {image.size}")
image
```

### Basic Offline Engine API Call

```python Example
from sglang import Engine

llm = Engine(model_path=model_path, chat_template=chat_template, log_level="warning")
```

```python Example
out = llm.generate(prompt=conv.get_prompt(), image_data=[image])
print("Model response:")
print(out["text"])
```

### Call with Processor Output

Using a HuggingFace processor to preprocess text and images, and passing the `processor_output` directly into `Engine.generate`.

```python Example
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained(model_path, use_fast=True)
processor_output = processor(
    images=[image], text=conv.get_prompt(), return_tensors="pt"
)

out = llm.generate(
    input_ids=processor_output["input_ids"][0].detach().cpu().tolist(),
    image_data=[dict(processor_output, format="processor_output")],
)
print("Response using processor output:")
print(out["text"])
```

### Call with Precomputed Embeddings

You can pre-calculate image features to avoid repeated visual encoding processes.

```python Example
from transformers import AutoProcessor
from transformers import Qwen2_5_VLForConditionalGeneration

processor = AutoProcessor.from_pretrained(model_path, use_fast=True)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_path).eval()
vision = model.model.visual.cuda()
```

```python Example
processor_output = processor(
    images=[image], text=conv.get_prompt(), return_tensors="pt"
)

input_ids = processor_output["input_ids"][0].detach().cpu().tolist()

precomputed_embeddings = vision(
    processor_output["pixel_values"].cuda(), processor_output["image_grid_thw"].cuda()
)
precomputed_embeddings = precomputed_embeddings.pooler_output

multi_modal_item = dict(
    processor_output,
    format="precomputed_embedding",
    feature=precomputed_embeddings,
)

out = llm.generate(input_ids=input_ids, image_data=[multi_modal_item])
print("Response using precomputed embeddings:")
print(out["text"])

llm.shutdown()
```

## Querying Llama 4 Vision Model

```python Example
model_path = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
chat_template = "llama-4"

from io import BytesIO
import requests
from PIL import Image

from sglang.srt.parser.conversation import chat_templates

# Download the same example image
image = Image.open(BytesIO(requests.get(example_image_url).content))

conv = chat_templates[chat_template].copy()
conv.append_message(conv.roles[0], f"What's shown here: {conv.image_token}?")
conv.append_message(conv.roles[1], "")
conv.image_data = [image]

print("Llama 4 generated prompt text:")
print(conv.get_prompt())
print(f"Image size: {image.size}")

image
```

### Llama 4 Basic Call

Llama 4 requires more computational resources, so it's configured with multi-GPU parallelism (tp_size=4) and larger context length.

```python Example
llm = Engine(
    model_path=model_path,
    enable_multimodal=True,
    attention_backend="fa3",
    tp_size=4,
    context_length=65536,
)

out = llm.generate(prompt=conv.get_prompt(), image_data=[image])
print("Llama 4 response:")
print(out["text"])
```

### Call with Processor Output

Using HuggingFace processor to preprocess data can reduce computational overhead during inference.

```python Example
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained(model_path, use_fast=True)
processor_output = processor(
    images=[image], text=conv.get_prompt(), return_tensors="pt"
)

out = llm.generate(
    input_ids=processor_output["input_ids"][0].detach().cpu().tolist(),
    image_data=[dict(processor_output, format="processor_output")],
)
print("Response using processor output:")
print(out)
```

### Call with Precomputed Embeddings

```python Example
from transformers import AutoProcessor
from transformers import Llama4ForConditionalGeneration

processor = AutoProcessor.from_pretrained(model_path, use_fast=True)
model = Llama4ForConditionalGeneration.from_pretrained(
    model_path, torch_dtype="auto"
).eval()

vision = model.vision_model.cuda()
multi_modal_projector = model.multi_modal_projector.cuda()

print(f'Image pixel values shape: {processor_output["pixel_values"].shape}')
input_ids = processor_output["input_ids"][0].detach().cpu().tolist()

# Process image through vision encoder
image_outputs = vision(
    processor_output["pixel_values"].to("cuda"),
    aspect_ratio_ids=processor_output["aspect_ratio_ids"].to("cuda"),
    aspect_ratio_mask=processor_output["aspect_ratio_mask"].to("cuda"),
    output_hidden_states=False
)
image_features = image_outputs.last_hidden_state

# Flatten image features and pass through multimodal projector
vision_flat = image_features.view(-1, image_features.size(-1))
precomputed_embeddings = multi_modal_projector(vision_flat)

# Build precomputed embedding data item
mm_item = dict(
    processor_output,
    format="precomputed_embedding",
    feature=precomputed_embeddings
)

# Use precomputed embeddings for efficient inference
out = llm.generate(input_ids=input_ids, image_data=[mm_item])
print("Llama 4 precomputed embedding response:")
print(out["text"])
```