| --- |
| 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-w8a8 |
|
|
| ## Model Overview |
| - **Model Architecture:** Qwen/Qwen2.5-VL-72B-Instruct |
| - **Input:** Vision-Text |
| - **Output:** Text |
| - **Model Optimizations:** |
| - **Weight quantization:** INT8 |
| - **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 INT8 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 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 <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). |
