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Pixtral-Large-Instruct-2411-hf-quantized.w8a8

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tags:
  - w8a8
  - int8
  - vllm
  - vision
license: other
license_name: mrl
inference: false
license_link: https://mistral.ai/licenses/MRL-0.1.md
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library_name: vllm
pipeline_tag: image-text-to-text
language:
  - en
  - fr
  - de
  - es
  - it
  - pt
  - zh
  - ja
  - ru
  - ko
base_model: neuralmagic/Pixtral-Large-Instruct-2411-hf

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.

Model Optimizations

This model was obtained by quantizing the weights of neuralmagic/Pixtral-Large-Instruct-2411-hf 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 backend, as shown in the example below.

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 for more details.

Creation

This model was created with llm-compressor by running the code snippet below as part a multimodal announcement blog.

Model Creation Code 
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 for vision-related tasks and using 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 <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

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 version 0.7.2, and 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 (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 (observed on 2/18/2025).