--- 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](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 FP8 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-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](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.
Model Creation Code ```python 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 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:
Evaluation Commands ### Vision Tasks - vqav2 - docvqa - mathvista - mmmu - chartqa ``` vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --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 neuralmagic/pixtral-12b-quantized.w8a8 \ --url http://0.0.0.0:8000 \ --output_dir ~/tmp \ --eval_name ``` ### Text-based Tasks #### MMLU ``` lm_eval \ --model vllm \ --model_args pretrained="",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=,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="",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=,gpu_memory_utilization=0.9 \ --tasks mgsm_cot_native \ --num_fewshot 0 \ --batch_size auto \ --output_path output_dir ```
### Accuracy
Category Metric Qwen/Qwen2.5-VL-72B-Instruct neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic Recovery (%)
Vision MMMU (val, CoT)
explicit_prompt_relaxed_correctness
64.33 66.88 103.96%
VQAv2 (val)
vqa_match
81.94 81.94 100.00%
DocVQA (val)
anls
94.71 94.64 99.93%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness
88.96 89.04 100.09%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness
78.18 77.78 99.49%
Average Score 81.62 81.86 100.29%
Text MGSM (CoT) 75.45 49.65 65.81%
MMLU (5-shot) 86.16 86.12 99.95%
## Inference Performance This model achieves up to 1.79x speedup in single-stream deployment and up to 1.84x 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).
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](https://lambdalabs.com/service/gpu-cloud) (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 1.2 578 4.0 2040 4.6 2266
neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16 2.75 2.8 1364 12.8 6352 16.4 8148
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 1.8 479 4.4 1203 4.8 1296
neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16 8.27 13.2 3652 13.2 3652 99.2 27108
**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).