--- 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-3B-Instruct library_name: transformers --- # pixtral-12b-FP8-Dynamic ## Model Overview - **Model Architecture:** Qwen2.5-VL-3B-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-3B-Instruct](https://huggingface.co./Qwen/Qwen2.5-VL-3B-Instruct). ### Model Optimizations This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co./Qwen/Qwen2.5-VL-3B-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-3B-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-3B-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-3B-Instruct nm-testing/Qwen2.5-VL-3B-Instruct-FP8-Dynamic Recovery (%)
Vision MMMU (val, CoT)
explicit_prompt_relaxed_correctness		 44.56 45.78 102.74%
VQAv2 (val)
vqa_match		 75.94 76.22 100.37%
DocVQA (val)
anls		 92.53 92.40 99.86%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness		 81.20 80.72 99.41%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness		 54.15 53.25 98.34%
Average Score 69.28 69.67 100.56%
Text MGSM (CoT) 52.49 43.14 82.17%
MMLU (5-shot) 65.32 65.03 99.56%
## Inference Performance This model achieves up to 1.10x speedup in single-stream deployment and up to 1.32x 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-3B-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) Queries Per Dollar Latency (s) Queries Per Dollar Latency (s) Queries Per Dollar
A6000x1 Qwen/Qwen2.5-VL-3B-Instruct 3.1 1454 1.8 2546 1.7 2610
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8 1.27 2.6 1708 1.3 3340 1.3 3459
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16 1.57 2.4 1886 1.0 4409 1.0 4409
A100x1 Qwen/Qwen2.5-VL-3B-Instruct 2.2 920 1.3 1603 1.2 1636
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8 1.09 2.1 975 1.2 1743 1.1 1814
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16 1.20 2.0 1011 1.0 2015 1.0 2012
H100x1 Qwen/Qwen2.5-VL-3B-Instruct 1.5 740 0.9 1221 0.9 1276
neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic 1.06 1.4 768 0.9 1276 0.8 1399
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16 1.24 0.9 1219 0.9 1270 0.8 1304
**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 Dollarv Maximum throughput (QPS) Queries Per Dollar Maximum throughput (QPS) Queries Per Dollar
A6000x1 Qwen/Qwen2.5-VL-3B-Instruct 0.5 2405 2.6 11889 2.9 12909
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8 1.26 0.6 2725 3.4 15162 3.9 17673
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16 1.39 0.6 2548 3.9 17437 4.7 21223
A100x1 Qwen/Qwen2.5-VL-3B-Instruct 0.8 1663 3.9 7899 4.4 8924
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8 1.06 0.9 1734 4.2 8488 4.7 9548
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16 1.10 0.9 1775 4.2 8540 5.1 10318
H100x1 Qwen/Qwen2.5-VL-3B-Instruct 1.1 1188 4.3 4656 4.3 4676
neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic 1.15 1.4 1570 4.3 4676 4.8 5220
neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16 1.96 4.2 4598 4.1 4505 4.4 4838
**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).