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 \
  --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 1.69x 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)

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			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	1.6	766	2.2	1142	2.6	1348
	neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16	1.48	1.0	552	2.0	1010	2.8	1360
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	3.4	905	5.2	1406	6.4	1759
	neuralmagic/Pixtral-Large-Instruct-2411-hf-quantized.w4a16	1.33	2.8	761	4.4	1228	5.4	1480

**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).