README.md

metadata

tags:
  - vllm
  - vision
  - fp8
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-FP8-Dynamic

Model Overview

• Model Architecture: Qwen2.5-VL-72B-Instruct
  • Input: Vision-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 2/24/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of Qwen/Qwen2.5-VL-72B-Instruct.

Model Optimizations

This model was obtained by quantizing the weights of Qwen/Qwen2.5-VL-72B-Instruct to FP8 data type, ready for inference with vLLM >= 0.5.2.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

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

# prepare model
llm = LLM(
    model="neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic",
    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 for more details.

Creation

This model was created with llm-compressor by running the code snippet below as part a multimodal announcement blog.

Model Creation Code

import requests
import torch
from PIL import Image
from transformers import AutoProcessor
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.tracing import (
    TraceableQwen2_5_VLForConditionalGeneration,
)
from llmcompressor.modifiers.quantization import QuantizationModifier

# 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)

# Recipe
recipe = [
    QuantizationModifier(
        targets="Linear",
        scheme="FP8_DYNAMIC",
        sequential_targets=["MistralDecoderLayer"],
        ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
    ),
]

SAVE_DIR=f"{model_id.split('/')[1]}-FP8-Dynamic"

# Perform oneshot
oneshot(
    model=model,
    recipe=recipe,
    trust_remote_code_model=True,
    output_dir=SAVE_DIR
)

Evaluation

The model was evaluated on OpenLLM Leaderboard V1, OpenLLM Leaderboard V2 and on HumanEval, using the following commands:

Evaluation Commands

Accuracy

Inference Performance

This model achieves up to xxx speedup in single-stream deployment and up to xxx speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario. The following performance benchmarks were conducted with vLLM version 0.7.2, and GuideLLM.

Benchmarking Command

``` guidellm --model neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=,generated_tokens=,images=,width=,height= --max seconds 120 --backend aiohttp_server ```

Single-stream performance (measured with vLLM version 0.7.2)

				Document Visual Question Answering 1680W x 2240H 64/128		Visual Reasoning 640W x 480H 128/128		Image Captioning 480W x 360H 0/128
Hardware	Number of GPUs	Model	Average Cost Reduction	Latency (s)	Queries Per Dollar	Latency (s)th>	Queries Per Dollar	Latency (s)	Queries Per Dollar
A100	4	Qwen/Qwen2.5-VL-72B-Instruct		6.4	78	4.5	111	4.4	113
	2	neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8	1.85	7.0	143	4.9	205	4.8	211
	1	neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16	3.33	9.4	213	5.1	396	4.8	420
H100	4	Qwen/Qwen2.5-VL-72B-Instruct		4.3	68	3.0	97	2.9	100
	2	neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic	1.79	4.6	122	3.3	173	3.2	177
	1	neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16	5.66	4.3	252	4.3	252	1.0	1065

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

**QPD: Queries per dollar, based on on-demand cost at Lambda Labs (observed on 2/18/2025).

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

			Document Visual Question Answering 1680W x 2240H 64/128		Visual Reasoning 640W x 480H 128/128		Image Captioning 480W x 360H 0/128
Hardware	Model	Average Cost Reduction	Maximum throughput (QPS)	Queries Per Dollar	Maximum throughput (QPS)	Queries Per Dollar	Maximum throughput (QPS)	Queries Per Dollar
A100x4	Qwen/Qwen2.5-VL-72B-Instruct		0.4	180	1.1	539	1.2	595
	neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8	1.80	1.2	289	4.0	1020	4.6	1133
	neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16	2.75	2.8	341	12.8	1588	16.4	2037
H100x4	Qwen/Qwen2.5-VL-72B-Instruct		0.5	134	1.2	357	1.3	379
	neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic	1.73	1.8	247	4.4	621	4.8	669
	neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16	8.27	13.2	913	13.2	913	99.2	6777

**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 (observed on 2/18/2025).