Query Vision Language Model

Querying Qwen-VL

import nest_asyncio

nest_asyncio.apply()  # Run this first.

model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
chat_template = "qwen2-vl"

# Lets create a prompt.

from io import BytesIO
import requests
from PIL import Image

from sglang.srt.parser.conversation import chat_templates

image = Image.open(
    BytesIO(
        requests.get(
            "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
        ).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(conv.get_prompt())
image

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What's shown here: <|vision_start|><|image_pad|><|vision_end|>?<|im_end|>
<|im_start|>assistant

Query via the offline Engine API

from sglang import Engine

llm = Engine(
    model_path=model_path, chat_template=chat_template, mem_fraction_static=0.8
)

[2025-11-12 15:33:00] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2025-11-12 15:33:00] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2025-11-12 15:33:00] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2025-11-12 15:33:02] INFO trace.py:69: opentelemetry package is not installed, tracing disabled
[2025-11-12 15:33:02] WARNING memory_pool_host.py:36: Current platform not support pin_memory
[2025-11-12 15:33:02] WARNING server_args.py:1197: Attention backend not explicitly specified. Use flashinfer backend by default.
[2025-11-12 15:33:02] INFO engine.py:123: server_args=ServerArgs(model_path='Qwen/Qwen2.5-VL-3B-Instruct', tokenizer_path='Qwen/Qwen2.5-VL-3B-Instruct', tokenizer_mode='auto', tokenizer_worker_num=1, skip_tokenizer_init=False, load_format='auto', model_loader_extra_config='{}', trust_remote_code=False, context_length=None, is_embedding=False, enable_multimodal=None, revision=None, model_impl='auto', host='127.0.0.1', port=30000, grpc_mode=False, skip_server_warmup=False, warmups=None, nccl_port=None, checkpoint_engine_wait_weights_before_ready=False, dtype='auto', quantization=None, quantization_param_path=None, kv_cache_dtype='auto', enable_fp32_lm_head=False, modelopt_quant=None, modelopt_checkpoint_restore_path=None, modelopt_checkpoint_save_path=None, modelopt_export_path=None, quantize_and_serve=False, mem_fraction_static=0.8, max_running_requests=None, max_queued_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='fcfs', enable_priority_scheduling=False, abort_on_priority_when_disabled=False, schedule_low_priority_values_first=False, priority_scheduling_preemption_threshold=10, schedule_conservativeness=1.0, page_size=1, hybrid_kvcache_ratio=None, swa_full_tokens_ratio=0.8, disable_hybrid_swa_memory=False, radix_eviction_policy='lru', device='cuda', tp_size=1, pp_size=1, pp_max_micro_batch_size=None, stream_interval=1, stream_output=False, random_seed=989283155, constrained_json_whitespace_pattern=None, constrained_json_disable_any_whitespace=False, watchdog_timeout=300, dist_timeout=None, download_dir=None, base_gpu_id=0, gpu_id_step=1, sleep_on_idle=False, log_level='error', log_level_http=None, log_requests=False, log_requests_level=2, crash_dump_folder=None, show_time_cost=False, enable_metrics=False, enable_metrics_for_all_schedulers=False, tokenizer_metrics_custom_labels_header='x-custom-labels', tokenizer_metrics_allowed_custom_labels=None, bucket_time_to_first_token=None, bucket_inter_token_latency=None, bucket_e2e_request_latency=None, collect_tokens_histogram=False, prompt_tokens_buckets=None, generation_tokens_buckets=None, gc_warning_threshold_secs=0.0, decode_log_interval=40, enable_request_time_stats_logging=False, kv_events_config=None, enable_trace=False, otlp_traces_endpoint='localhost:4317', api_key=None, served_model_name='Qwen/Qwen2.5-VL-3B-Instruct', weight_version='default', chat_template='qwen2-vl', completion_template=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser=None, tool_call_parser=None, tool_server=None, sampling_defaults='model', dp_size=1, load_balance_method='round_robin', load_watch_interval=0.1, prefill_round_robin_balance=False, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, enable_lora=None, max_lora_rank=None, lora_target_modules=None, lora_paths=None, max_loaded_loras=None, max_loras_per_batch=8, lora_eviction_policy='lru', lora_backend='csgmv', max_lora_chunk_size=16, attention_backend='flashinfer', decode_attention_backend=None, prefill_attention_backend=None, sampling_backend='flashinfer', grammar_backend='xgrammar', mm_attention_backend=None, nsa_prefill_backend='flashmla_sparse', nsa_decode_backend='fa3', speculative_algorithm=None, speculative_draft_model_path=None, speculative_draft_model_revision=None, speculative_draft_load_format=None, speculative_num_steps=None, speculative_eagle_topk=None, speculative_num_draft_tokens=None, speculative_accept_threshold_single=1.0, speculative_accept_threshold_acc=1.0, speculative_token_map=None, speculative_attention_mode='prefill', speculative_moe_runner_backend=None, speculative_ngram_min_match_window_size=1, speculative_ngram_max_match_window_size=12, speculative_ngram_min_bfs_breadth=1, speculative_ngram_max_bfs_breadth=10, speculative_ngram_match_type='BFS', speculative_ngram_branch_length=18, speculative_ngram_capacity=10000000, ep_size=1, moe_a2a_backend='none', moe_runner_backend='auto', flashinfer_mxfp4_moe_precision='default', enable_flashinfer_allreduce_fusion=False, deepep_mode='auto', ep_num_redundant_experts=0, ep_dispatch_algorithm='static', init_expert_location='trivial', enable_eplb=False, eplb_algorithm='auto', eplb_rebalance_num_iterations=1000, eplb_rebalance_layers_per_chunk=None, eplb_min_rebalancing_utilization_threshold=1.0, expert_distribution_recorder_mode=None, expert_distribution_recorder_buffer_size=1000, enable_expert_distribution_metrics=False, deepep_config=None, moe_dense_tp_size=None, elastic_ep_backend=None, mooncake_ib_device=None, max_mamba_cache_size=None, mamba_ssm_dtype='float32', mamba_full_memory_ratio=0.9, enable_hierarchical_cache=False, hicache_ratio=2.0, hicache_size=0, hicache_write_policy='write_through', hicache_io_backend='kernel', hicache_mem_layout='layer_first', hicache_storage_backend=None, hicache_storage_prefetch_policy='best_effort', hicache_storage_backend_extra_config=None, enable_lmcache=False, kt_weight_path=None, kt_method=None, kt_cpuinfer=None, kt_threadpool_count=None, kt_num_gpu_experts=None, kt_max_deferred_experts_per_token=None, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, cpu_offload_gb=0, offload_group_size=-1, offload_num_in_group=1, offload_prefetch_step=1, offload_mode='cpu', multi_item_scoring_delimiter=None, disable_radix_cache=False, cuda_graph_max_bs=256, cuda_graph_bs=[1, 2, 4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], disable_cuda_graph=False, disable_cuda_graph_padding=False, enable_profile_cuda_graph=False, enable_cudagraph_gc=False, enable_nccl_nvls=False, enable_symm_mem=False, disable_flashinfer_cutlass_moe_fp4_allgather=False, enable_tokenizer_batch_encode=False, disable_tokenizer_batch_decode=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, enable_mscclpp=False, enable_torch_symm_mem=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_dp_lm_head=False, enable_two_batch_overlap=False, enable_single_batch_overlap=False, tbo_token_distribution_threshold=0.48, enable_torch_compile=False, enable_piecewise_cuda_graph=False, torch_compile_max_bs=32, piecewise_cuda_graph_max_tokens=4096, piecewise_cuda_graph_tokens=[4, 8, 12, 16, 20, 24, 28, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 352, 384, 416, 448, 480, 512, 640, 768, 896, 1024, 1152, 1280, 1408, 1536, 1664, 1792, 1920, 2048, 2176, 2304, 2432, 2560, 2688, 2816, 2944, 3072, 3200, 3328, 3456, 3584, 3712, 3840, 3968, 4096], piecewise_cuda_graph_compiler='eager', torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, triton_attention_split_tile_size=None, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=False, enable_weights_cpu_backup=False, allow_auto_truncate=False, enable_custom_logit_processor=False, flashinfer_mla_disable_ragged=False, disable_shared_experts_fusion=False, disable_chunked_prefix_cache=False, disable_fast_image_processor=False, keep_mm_feature_on_device=False, enable_return_hidden_states=False, scheduler_recv_interval=1, numa_node=None, enable_deterministic_inference=False, rl_on_policy_target=None, enable_dynamic_batch_tokenizer=False, dynamic_batch_tokenizer_batch_size=32, dynamic_batch_tokenizer_batch_timeout=0.002, debug_tensor_dump_output_folder=None, debug_tensor_dump_layers=None, debug_tensor_dump_input_file=None, debug_tensor_dump_inject=False, disaggregation_mode='null', disaggregation_transfer_backend='mooncake', disaggregation_bootstrap_port=8998, disaggregation_decode_tp=None, disaggregation_decode_dp=None, disaggregation_prefill_pp=1, disaggregation_ib_device=None, disaggregation_decode_enable_offload_kvcache=False, num_reserved_decode_tokens=512, disaggregation_decode_polling_interval=1, custom_weight_loader=[], weight_loader_disable_mmap=False, remote_instance_weight_loader_seed_instance_ip=None, remote_instance_weight_loader_seed_instance_service_port=None, remote_instance_weight_loader_send_weights_group_ports=None, enable_pdmux=False, pdmux_config_path=None, sm_group_num=8, mm_max_concurrent_calls=32, mm_per_request_timeout=10.0, decrypted_config_file=None, decrypted_draft_config_file=None)
[2025-11-12 15:33:08] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2025-11-12 15:33:08] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2025-11-12 15:33:08] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2025-11-12 15:33:09] INFO trace.py:69: opentelemetry package is not installed, tracing disabled
[2025-11-12 15:33:09] WARNING memory_pool_host.py:36: Current platform not support pin_memory
WARNING:sglang.srt.mem_cache.memory_pool_host:Current platform not support pin_memory

[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0

Loading safetensors checkpoint shards:   0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  50% Completed | 1/2 [00:00<00:00,  1.15it/s]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:01<00:00,  1.23it/s]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:01<00:00,  1.22it/s]

Capturing batches (bs=1 avail_mem=10.71 GB): 100%|██████████| 36/36 [00:20<00:00,  1.74it/s]

out = llm.generate(prompt=conv.get_prompt(), image_data=[image])
print(out["text"])

This is an image of a man performing a comedy skit or stunt in the middle of a busy city street. He is staring at a pair of hanging blue and yellow underwear on a metal bar that he has positioned in front of a taxi. This act is intended to humorously disrupt regular traffic, drawing attention to his presence and perhaps to draw a crowd or make a joke.

Query via the offline Engine API, but send precomputed embeddings

# Compute the image embeddings using Huggingface.

from transformers import AutoProcessor
from transformers import Qwen2_5_VLForConditionalGeneration

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

processed_prompt = processor(
    images=[image], text=conv.get_prompt(), return_tensors="pt"
)
input_ids = processed_prompt["input_ids"][0].detach().cpu().tolist()
precomputed_embeddings = vision(
    processed_prompt["pixel_values"].cuda(), processed_prompt["image_grid_thw"].cuda()
)

mm_item = dict(
    modality="IMAGE",
    image_grid_thw=processed_prompt["image_grid_thw"],
    precomputed_embeddings=precomputed_embeddings,
)
out = llm.generate(input_ids=input_ids, image_data=[mm_item])
print(out["text"])

The image shows two yellow taxis in New York City. Additionally, there is a man in a yellow shirt standing between the two taxis, wearing a red flag on his yellow safety vest. The man appears to be tending to clothes hangers on his shoulder, possibly jokingly depicting a shirt-wrecker. The scene is set on the street in an urban area, likely with various commercial establishments and buildings in the background.

Querying Llama 4 (Vision)

import nest_asyncio

nest_asyncio.apply()  # Run this first.

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

# Lets create a prompt.

from io import BytesIO
import requests
from PIL import Image

from sglang.srt.parser.conversation import chat_templates

image = Image.open(
    BytesIO(
        requests.get(
            "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
        ).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(conv.get_prompt())
print(f"Image size: {image.size}")

image

<|header_start|>user<|header_end|>

What's shown here: <|image|>?<|eot|><|header_start|>assistant<|header_end|>


Image size: (570, 380)

Query via the offline Engine API

from sglang.test.test_utils import is_in_ci

if not is_in_ci():
    from sglang import Engine

    llm = Engine(
        model_path=model_path,
        trust_remote_code=True,
        enable_multimodal=True,
        mem_fraction_static=0.8,
        tp_size=4,
        attention_backend="fa3",
        context_length=65536,
    )

if not is_in_ci():
    out = llm.generate(prompt=conv.get_prompt(), image_data=[image])
    print(out["text"])

Query via the offline Engine API, but send precomputed embeddings

if not is_in_ci():
    # Compute the image embeddings using Huggingface.

    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()

if not is_in_ci():
    processed_prompt = processor(
        images=[image], text=conv.get_prompt(), return_tensors="pt"
    )
    print(f'{processed_prompt["pixel_values"].shape=}')
    input_ids = processed_prompt["input_ids"][0].detach().cpu().tolist()

    image_outputs = vision(
        processed_prompt["pixel_values"].to("cuda"), output_hidden_states=False
    )
    image_features = image_outputs.last_hidden_state
    vision_flat = image_features.view(-1, image_features.size(-1))
    precomputed_embeddings = multi_modal_projector(vision_flat)

    mm_item = dict(modality="IMAGE", precomputed_embeddings=precomputed_embeddings)
    out = llm.generate(input_ids=input_ids, image_data=[mm_item])
    print(out["text"])