--- title: Pixtral-Large-Instruct-2411-hf-quantized.w8a8 canonical_url: "https://www.modelscope.cn/models/nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8" md_url: "https://www.modelscope.cn/models/nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8.md" repository: nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 last_updated: 2025-04-24 license: other pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - llava architectures: - LlavaForConditionalGeneration base_model: - neuralmagic/Pixtral-Large-Instruct-2411-hf base_model_relation: quantized parameters: 124.1B tensor_type: - BF16 - I8 library_name: - pytorch - transformer - safetensors frameworks: - pytorch language: - en - fr - de - es - it - pt - zh - ja - ru - ko inference_backends: - "deploy_task vlm/text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 810 stars: 0 tags: - w8a8 - int8 - vllm - vision --- # Pixtral-Large-Instruct-2411-hf-quantized.w8a8 > Pixtral-Large-Instruct-2411-hf-quantized.w8a8 - nm-testing 在 ModelScope 开源的模型。Pixtral-Large-Instruct-2411-hf-quantized.w8a8 nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 是 ModelScope 魔搭社区上的 124.1B 参数image-text-to-text模型，采用 other 许可，基于 neuralmagic/Pixtral-Large-Instruct-2411-hf 构建，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 - **License**: other - **Tasks**: image-text-to-text - **Parameters**: 124.1B - **Base model**: neuralmagic/Pixtral-Large-Instruct-2411-hf - **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: w8a8, int8, vllm, vision - **Downloads**: 810 - **Stars**: 0 - **Last updated**: 2025-04-24 Source: https://www.modelscope.cn/models/nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 --- # Pixtral-Large-Instruct-2411-hf-quantized.w8a8 ## Model Overview - **Model Architecture:** neuralmagic/Pixtral-Large-Instruct-2411-hf - **Input:** Vision-Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT8 - **Activation quantization:** INT8 - **Release Date:** 2/24/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of [neuralmagic/Pixtral-Large-Instruct-2411-hf](https://huggingface.co/neuralmagic/Pixtral-Large-Instruct-2411-hf/tree/main). ### Model Optimizations This model was obtained by quantizing the weights of [neuralmagic/Pixtral-Large-Instruct-2411-hf](https://huggingface.co/neuralmagic/Pixtral-Large-Instruct-2411-hf/tree/main) 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/Pixtral-Large-Instruct-2411-hf-quantized.w8a8", 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.modifiers.quantization import GPTQModifier from llmcompressor.transformers import oneshot from llmcompressor.transformers.tracing import TraceableLlavaForConditionalGeneration # Load model. model_id = "neuralmagic/Pixtral-Large-Instruct-2411-hf" model = TraceableLlavaForConditionalGeneration.from_pretrained( model_id, device_map="auto", torch_dtype="auto" ) processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) # Oneshot arguments DATASET_ID = "flickr30k" DATASET_SPLIT = {"calibration": "test[:512]"} NUM_CALIBRATION_SAMPLES = 512 MAX_SEQUENCE_LENGTH = 2048 # Define a oneshot data collator for multimodal inputs. def data_collator(batch): assert len(batch) == 1 return { "input_ids": torch.LongTensor(batch[0]["input_ids"]), "attention_mask": torch.tensor(batch[0]["attention_mask"]), "pixel_values": torch.tensor(batch[0]["pixel_values"]), } # Recipe recipe = [ GPTQModifier( targets="Linear", scheme="W8A8", sequential_targets=["MistralDecoderLayer"], ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"], ), ] SAVE_DIR==f"{model_id.split('/')[1]}-quantized.w8a8" # Perform oneshot oneshot( model=model, tokenizer=model_id, dataset=DATASET_ID, splits=DATASET_SPLIT, 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 ) ```
## 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 \ --apply_chat_template \ --num_fewshot 0 \ --batch_size auto \ --output_path output_dir ```
### Accuracy
Category Metric neuralmagic/Pixtral-Large-Instruct-2411-hf neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 Recovery (%)
Vision MMMU (val, CoT)
explicit_prompt_relaxed_correctness		 63.56 63.89 100.52%
VQAv2 (val)
vqa_match		 79.03 79.12 100.11%
DocVQA (val)
anls		 89.55 89.80 100.28%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness		 82.24 80.44 97.81%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness		 67.3 66.50 98.81%
Average Score 76.34 75.95 99.49%
Text MGSM (CoT) 76.05 74.76 98.30%
MMLU (5-shot) 82.8 82.9 100.12%
## Inference Performance This model achieves up to 1.87x speedup in single-stream deployment and up to 2.0x 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/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 --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) Queries Per Dollar Latency (s) Queries Per Dollar
A100 4 neuralmagic/Pixtral-Large-Instruct-2411-hf 7.5 67 6.5 77 6.4 79
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 1.86 8.1 124 7.1 142 6.8 148
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 2.52 6.9 147 5.1 199 4.5 221
H100 4 neuralmagic/Pixtral-Large-Instruct-2411-hf 4.4 67 3.9 74 3.7 79
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic 1.82 4.7 120 4.1 137 3.9 145
2 neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.87 4.7 120 3.9 144 3.8 149
**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 neuralmagic/Pixtral-Large-Instruct-2411-hf 0.4 222 0.7 341 0.8 399
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 1.70 0.8 383 1.1 571 1.3 674
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.48 0.5 276 1.0 505 1.4 680
H100x4 neuralmagic/Pixtral-Large-Instruct-2411-hf 1.0 284 1.6 465 1.8 511
neuralmagic/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic 1.61 1.7 467 2.6 726 3.2 908
neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16 1.33 1.4 393 2.2 726 2.7 764
**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).