--- 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-7B-Instruct library_name: transformers --- # Qwen2.5-VL-7B-Instruct-FP8-Dynamic ## Model Overview - **Model Architecture:** Qwen2.5-VL-7B-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-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). ### Model Optimizations This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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-7B-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-7B-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](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
Evaluation Commands ``` ```
### Accuracy ## Inference Performance This model achieves up to 1.3x speedup in single-stream deployment and 1.37x in multi-stream 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-7B-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 Model Average Cost Reduction Latency (s) QPD Latency (s)th> QPD Latency (s) QPD
A100x1 Qwen/Qwen2.5-VL-7B-Instruct 2.8 707 1.7 1162 1.7 1198
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8 1.24 2.4 851 1.4 1454 1.3 1512
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.49 2.2 912 1.1 1791 1.0 1950
H100x1 Qwen/Qwen2.5-VL-7B-Instruct 2.0 557 1.2 919 1.2 941
neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic 1.28 1.6 698 0.9 1181 0.9 1219
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.28 1.6 686 0.9 1191 0.9 1228
### 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) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD
A100x1 Qwen/Qwen2.5-VL-7B-Instruct-quantized. 0.7 1347 2.6 5221 3.0 6122
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8 1.27 0.8 1639 3.4 6851 3.9 7918
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.21 0.7 1314 3.0 5983 4.6 9206
H100x1 Qwen/Qwen2.5-VL-7B-Instruct 0.9 969 3.1 3358 3.3 3615
neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic 1.29 1.2 1331 3.8 4109 4.2 4598
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.28 1.2 1298 3.8 4190 4.2 4573