Instructions to use RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8")
model = AutoModelForImageTextToText.from_pretrained("RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8

SGLang

How to use RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8
```

用vllm serve启动不了

by VenomEY - opened Apr 2, 2025

Discussion

VenomEY

Apr 2, 2025

硬件环境：H800 * 2
vllm版本：0.8.2
启动脚本：

#! /bin/bash
export VLLM_USE_TRITON_FLASH_ATTN=1
export VLLM_USE_FLASHINFER_SAMPLER=1
export VLLM_FLASHINFER_FORCE_TENSOR_CORES=1
export VLLM_USE_V1=1
export VLLM_ENABLE_V1_MULTIPROCESSING=1
export VLLM_ATTENTION_BACKEND=FLASH_ATTN
export TORCH_CUDA_ARCH_LIST=9.0
export VLLM_MM_INPUT_CACHE_GIB=6
vllm serve /openbayes/input/input2 --host 0.0.0.0 --port 80 --trust-remote-code --max-model-len 128000 --max-num-batched-tokens 128000 --max-seq-len-to-capture 128000 --gpu-memory-utilization 0.95 --max-num-seqs 64 --served-model-name Qwen2-VL-72B Qwen2.5-VL-72B --limit-mm-per-prompt image=50,video=2 -tp 2 --disable-mm-preprocessor-cache

出现以下错误

ERROR 04-02 08:30:29 [core.py:340] EngineCore hit an exception: Traceback (most recent call last):
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 332, in run_engine_core
ERROR 04-02 08:30:29 [core.py:340]     engine_core = EngineCoreProc(*args, **kwargs)
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 287, in __init__
ERROR 04-02 08:30:29 [core.py:340]     super().__init__(vllm_config, executor_class, log_stats)
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 62, in __init__
ERROR 04-02 08:30:29 [core.py:340]     num_gpu_blocks, num_cpu_blocks = self._initialize_kv_caches(
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 121, in _initialize_kv_caches
ERROR 04-02 08:30:29 [core.py:340]     available_gpu_memory = self.model_executor.determine_available_memory()
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 66, in determine_available_memory
ERROR 04-02 08:30:29 [core.py:340]     output = self.collective_rpc("determine_available_memory")
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/executor/multiproc_executor.py", line 133, in collective_rpc
ERROR 04-02 08:30:29 [core.py:340]     raise e
ERROR 04-02 08:30:29 [core.py:340]   File "/usr/local/lib/python3.10/site-packages/vllm/v1/executor/multiproc_executor.py", line 122, in collective_rpc
ERROR 04-02 08:30:29 [core.py:340]     raise result
ERROR 04-02 08:30:29 [core.py:340] RuntimeError: Expected there to be 50 prompt updates corresponding to 50 image items, but instead found 0 prompt updates! Either the prompt text has missing/incorrect tokens for multi-modal inputs, or there is a problem with your implementation of merged multi-modal processor for this model (usually arising from an inconsistency between `_call_hf_processor` and `_get_prompt_updates`).

mgoin

Red Hat AI org May 6, 2025

This was resolved with updates to the model config files https://huggingface.co/RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w8a8/commit/87784eabe0555e4125427d5e417fd711d7c0952f, please try downloading the model again

mgoin changed discussion status to closed May 6, 2025

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