Qwen2.5-VL-72B-Instruct-quantized.w4a16

Instructions to use SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16")
model = AutoModelForMultimodalLM.from_pretrained("SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16", device_map="auto")
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 SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16",
		"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/SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16

SGLang

How to use SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16 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 "SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16" \
    --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": "SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16",
		"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 "SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16" \
        --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": "SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16",
		"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 SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16 with Docker Model Runner:
```
docker model run hf.co/SAIFIINDUSTRIES/Qwen2.5-VL-72B-Instruct-quantized.w4a16
```

Qwen2.5-VL-72B-Instruct-quantized.w4a16 / README.md

SAIFIINDUSTRIES

Duplicate from RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w4a16

5634f73 about 1 month ago

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	---
	tags:
	- vllm
	- vision
	- w4a16
	license: apache-2.0
	license_link: >-
	https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
	language:
	- en
	base_model: Qwen/Qwen2.5-VL-72B-Instruct
	library_name: transformers
	---

	# Qwen2.5-VL-72B-Instruct-quantized-w4a16

	## Model Overview
	- Model Architecture: Qwen/Qwen2.5-VL-72B-Instruct
	- Input: Vision-Text
	- Output: Text
	- Model Optimizations:
	- Weight quantization: INT4
	- Activation quantization: FP16
	- Release Date: 2/24/2025
	- Version: 1.0
	- Model Developers: Neural Magic

	Quantized version of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct).

	### Model Optimizations

	This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) to INT4 data type, ready for inference with vLLM >= 0.5.2.

	## Deployment

	### Use with vLLM

	This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.

	```python
	from vllm.assets.image import ImageAsset
	from vllm import LLM, SamplingParams

	# prepare model
	llm = LLM(
	model="neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16",
	trust_remote_code=True,
	max_model_len=4096,
	max_num_seqs=2,
	)

	# prepare inputs
	question = "What is the content of this image?"
	inputs = {
	"prompt": f"<\|user\|>\n<\|image_1\|>\n{question}<\|end\|>\n<\|assistant\|>\n",
	"multi_modal_data": {
	"image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
	},
	}

	# generate response
	print("========== SAMPLE GENERATION ==============")
	outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
	print(f"PROMPT : {outputs[0].prompt}")
	print(f"RESPONSE: {outputs[0].outputs[0].text}")
	print("==========================================")
	```

	vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.

	## Creation

	This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below as part a multimodal announcement blog.

	<details>
	<summary>Model Creation Code</summary>

	```python
	import base64
	from io import BytesIO
	import torch
	from datasets import load_dataset
	from qwen_vl_utils import process_vision_info
	from transformers import AutoProcessor
	from llmcompressor.modifiers.quantization import GPTQModifier
	from llmcompressor.transformers import oneshot
	from llmcompressor.transformers.tracing import (
	TraceableQwen2_5_VLForConditionalGeneration,
	)
	from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme

	# Load model.
	model_id = "Qwen/Qwen2.5-VL-72B-Instruct"

	model = TraceableQwen2_5_VLForConditionalGeneration.from_pretrained(
	model_id,
	device_map="auto",
	torch_dtype="auto",
	)
	processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

	# Oneshot arguments
	DATASET_ID = "lmms-lab/flickr30k"
	DATASET_SPLIT = {"calibration": "test[:512]"}
	NUM_CALIBRATION_SAMPLES = 512
	MAX_SEQUENCE_LENGTH = 2048

	# Load dataset and preprocess.
	ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
	ds = ds.shuffle(seed=42)
	dampening_frac=0.01

	# Apply chat template and tokenize inputs.
	def preprocess_and_tokenize(example):
	# preprocess
	buffered = BytesIO()
	example["image"].save(buffered, format="PNG")
	encoded_image = base64.b64encode(buffered.getvalue())
	encoded_image_text = encoded_image.decode("utf-8")
	base64_qwen = f"data:image;base64,{encoded_image_text}"
	messages = [
	{
	"role": "user",
	"content": [
	{"type": "image", "image": base64_qwen},
	{"type": "text", "text": "What does the image show?"},
	],
	}
	]
	text = processor.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True
	)
	image_inputs, video_inputs = process_vision_info(messages)

	# tokenize
	return processor(
	text=[text],
	images=image_inputs,
	videos=video_inputs,
	padding=False,
	max_length=MAX_SEQUENCE_LENGTH,
	truncation=True,
	)
	ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names)

	# Define a oneshot data collator for multimodal inputs.
	def data_collator(batch):
	assert len(batch) == 1
	return {key: torch.tensor(value) for key, value in batch[0].items()}

	recipe = GPTQModifier(
	targets="Linear",
	config_groups={
	"config_group": QuantizationScheme(
	targets=["Linear"],
	weights=QuantizationArgs(
	num_bits=4,
	type=QuantizationType.INT,
	strategy=QuantizationStrategy.GROUP,
	group_size=128,
	symmetric=True,
	dynamic=False,
	actorder=ActivationOrdering.WEIGHT,
	),
	),
	},
	sequential_targets=["Qwen2_5_VLDecoderLayer"],
	ignore=["lm_head", "re:visual.*"],
	update_size=NUM_CALIBRATION_SAMPLES,
	dampening_frac=dampening_frac
	)

	SAVE_DIR=f"{model_id.split('/')[1]}-quantized.w4a16"

	# Perform oneshot
	oneshot(
	model=model,
	tokenizer=model_id,
	dataset=ds,
	recipe=recipe,
	max_seq_length=MAX_SEQUENCE_LENGTH,
	num_calibration_samples=NUM_CALIBRATION_SAMPLES,
	trust_remote_code_model=True,
	data_collator=data_collator,
	output_dir=SAVE_DIR
	)

	```
	</details>

	## Evaluation

	The model was evaluated using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for select text-based benchmarks. The evaluations were conducted using the following commands:

	<details>
	<summary>Evaluation Commands</summary>

	### Vision Tasks
	- vqav2
	- docvqa
	- mathvista
	- mmmu
	- chartqa

	```
	vllm serve RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w4a16 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7

	python -m eval.run eval_vllm \
	--model_name RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w4a16 \
	--url http://0.0.0.0:8000 \
	--output_dir ~/tmp \
	--eval_name <vision_task_name>
	```

	### Text-based Tasks
	#### MMLU

	```
	lm_eval \
	--model vllm \
	--model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
	--tasks mmlu \
	--num_fewshot 5 \
	--batch_size auto \
	--output_path output_dir

	```

	#### MGSM

	```
	lm_eval \
	--model vllm \
	--model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
	--tasks mgsm_cot_native \
	--apply_chat_template \
	--num_fewshot 0 \
	--batch_size auto \
	--output_path output_dir

	```
	</details>


	### Accuracy

	<table>
	<thead>
	<tr>
	<th>Category</th>
	<th>Metric</th>
	<th>Qwen/Qwen2.5-VL-72B-Instruct</th>
	<th>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</th>
	<th>Recovery (%)</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td rowspan="6"><b>Vision</b></td>
	<td>MMMU (val, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
	<td>64.33</td>
	<td>62.89</td>
	<td>97.76%</td>
	</tr>
	<tr>
	<td>VQAv2 (val)<br><i>vqa_match</i></td>
	<td>81.94</td>
	<td>81.87</td>
	<td>99.91%</td>
	</tr>
	<tr>
	<td>DocVQA (val)<br><i>anls</i></td>
	<td>94.71</td>
	<td>94.72</td>
	<td>100.01%</td>
	</tr>
	<tr>
	<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
	<td>88.96</td>
	<td>88.96</td>
	<td>100.00%</td>
	</tr>
	<tr>
	<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
	<td>78.18</td>
	<td>77.68</td>
	<td>99.36%</td>
	</tr>
	<tr>
	<td><b>Average Score</b></td>
	<td><b>81.62</b></td>
	<td><b>81.22</b></td>
	<td><b>99.51</b></td>
	</tr>
	<tr>
	<td rowspan="2"><b>Text</b></td>
	<td>MGSM (CoT)</td>
	<td>75.45</td>
	<td>75.13</td>
	<td>99.58%</td>
	</tr>
	<tr>
	<td>MMLU (5-shot)</td>
	<td>86.16</td>
	<td>85.36</td>
	<td>99.07%</td>
	</tr>
	</tbody>
	</table>



	## Inference Performance


	This model achieves up to 3.95x speedup in single-stream deployment and up to 6.6x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
	The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).

	<details>
	<summary>Benchmarking Command</summary>
	```
	guidellm --model neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>,images=<num_images>,width=<image_width>,height=<image_height> --max seconds 120 --backend aiohttp_server
	```

	</details>


	### Single-stream performance (measured with vLLM version 0.7.2)

	<table border="1" class="dataframe">
	<thead>
	<tr>
	<th></th>
	<th></th>
	<th></th>
	<th></th>
	<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
	<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
	<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
	</tr>
	<tr>
	<th>Hardware</th>
	<th>Number of GPUs</th>
	<th>Model</th>
	<th>Average Cost Reduction</th>
	<th>Latency (s)</th>
	<th>Queries Per Dollar</th>
	<th>Latency (s)th>
	<th>Queries Per Dollar</th>
	<th>Latency (s)</th>
	<th>Queries Per Dollar</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<th rowspan="3" valign="top">A100</td>
	<td>4</td>
	<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
	<td></td>
	<td>6.4</td>
	<td>78</td>
	<td>4.5</td>
	<td>111</td>
	<td>4.4</td>
	<td>113</td>
	</tr>
	<tr>
	<td>2</td>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8</td>
	<td>1.85</td>
	<td>7.0</td>
	<td>143</td>
	<td>4.9</td>
	<td>205</td>
	<td>4.8</td>
	<td>211</td>
	</tr>
	<tr>
	<td>1</td>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
	<td>3.33</td>
	<td>9.4</td>
	<td>213</td>
	<td>5.1</td>
	<td>396</td>
	<td>4.8</td>
	<td>420</td>
	</tr>
	<tr>
	<th rowspan="3" valign="top">H100</td>
	<td>4</td>
	<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
	<td></td>
	<td>4.3</td>
	<td>68</td>
	<td>3.0</td>
	<td>97</td>
	<td>2.9</td>
	<td>100</td>
	</tr>
	<tr>
	<td>2</td>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic</td>
	<td>1.79</td>
	<td>4.6</td>
	<td>122</td>
	<td>3.3</td>
	<td>173</td>
	<td>3.2</td>
	<td>177</td>
	</tr>
	<tr>
	<td>1</td>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
	<td>5.66</td>
	<td>4.3</td>
	<td>252</td>
	<td>4.4</td>
	<td>251</td>
	<td>4.2</td>
	<td>259</td>
	</tr>
	</tbody>
	</table>

	**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens

	**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).

	### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)

	<table border="1" class="dataframe">
	<thead>
	<tr>
	<th></th>
	<th></th>
	<th></th>
	<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
	<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
	<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
	</tr>
	<tr>
	<th>Hardware</th>
	<th>Model</th>
	<th>Average Cost Reduction</th>
	<th>Maximum throughput (QPS)</th>
	<th>Queries Per Dollar</th>
	<th>Maximum throughput (QPS)</th>
	<th>Queries Per Dollar</th>
	<th>Maximum throughput (QPS)</th>
	<th>Queries Per Dollar</th>
	</tr>
	</thead>
	<tbody style="text-align: center">
	<tr>
	<th rowspan="3" valign="top">A100x4</th>
	<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
	<td></td>
	<td>0.4</td>
	<td>180</td>
	<td>1.1</td>
	<td>539</td>
	<td>1.2</td>
	<td>595</td>
	</tr>
	<tr>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8</td>
	<td>1.80</td>
	<td>0.6</td>
	<td>289</td>
	<td>2.0</td>
	<td>1020</td>
	<td>2.3</td>
	<td>1133</td>
	</tr>
	<tr>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
	<td>2.75</td>
	<td>0.7</td>
	<td>341</td>
	<td>3.2</td>
	<td>1588</td>
	<td>4.1</td>
	<td>2037</td>
	</tr>
	<tr>
	<th rowspan="3" valign="top">H100x4</th>
	<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
	<td></td>
	<td>0.5</td>
	<td>134</td>
	<td>1.2</td>
	<td>357</td>
	<td>1.3</td>
	<td>379</td>
	</tr>
	<tr>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic</td>
	<td>1.73</td>
	<td>0.9</td>
	<td>247</td>
	<td>2.2</td>
	<td>621</td>
	<td>2.4</td>
	<td>669</td>
	</tr>
	<tr>
	<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
	<td>8.27</td>
	<td>3.3</td>
	<td>913</td>
	<td>3.3</td>
	<td>898</td>
	<td>3.6</td>
	<td>991</td>
	</tr>
	</tbody>
	</table>

	**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens

	**QPS: Queries per second.

	**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).