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---
tags:
- vllm
- vision
- w8a8
license: apache-2.0
license_link: >-
https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
language:
- en
base_model: mgoin/pixtral-12b
library_name: transformers
---
# pixtral-12b-quantized.w8a8
## Model Overview
- **Model Architecture:** mgoin/pixtral-12b
- **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 [mgoin/pixtral-12b](https://huggingface.co/mgoin/pixtral-12b).
### Model Optimizations
This model was obtained by quantizing the weights of [mgoin/pixtral-12b](https://huggingface.co/mgoin/pixtral-12b) 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-12b-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.
<details>
<summary>Model Creation Code</summary>
```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 = mgoin/pixtral-12b
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
)
```
</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 \
```
#### HumanEval
##### Generation
```
python3 codegen/generate.py \
--model neuralmagic/pixtral-12b-quantized.w8a8 \
--bs 16 \
--temperature 0.2 \
--n_samples 50 \
--root "." \
--dataset humaneval
```
##### Sanitization
```
python3 evalplus/sanitize.py \
humaneval/neuralmagic/pixtral-12b-quantized.w8a8_vllm_temp_0.2
```
##### Evaluation
```
evalplus.evaluate \
--dataset humaneval \
--samples humaneval/neuralmagic/pixtral-12b-quantized.w8a8_vllm_temp_0.2-sanitized
```
</details>
### Accuracy
<table border="1">
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>mgoin/pixtral-12b</th>
<th>neuralmagic/pixtral-12b-quantized.w8a8</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>48.00</td>
<td>46.22</td>
<td>96.29%</td>
</tr>
<tr>
<td>VQAv2 (val)<br><i>vqa_match</i></td>
<td>78.71</td>
<td>78.00</td>
<td>99.10%</td>
</tr>
<tr>
<td>DocVQA (val)<br><i>anls</i></td>
<td>89.47</td>
<td>89.35</td>
<td>99.87%</td>
</tr>
<tr>
<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
<td>81.68</td>
<td>81.60</td>
<td>99.90%</td>
</tr>
<tr>
<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>56.50</td>
<td>57.30</td>
<td>101.42%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>70.07</b></td>
<td><b>70.09</b></td>
<td><b>100.03%</b></td>
</tr>
<tr>
<td rowspan="2"><b>Text</b></td>
<td>HumanEval <br><i>pass@1</i></td>
<td>68.40</td>
<td>66.39</td>
<td>97.06%</td>
</tr>
<tr>
<td>MMLU (5-shot)</td>
<td>71.40</td>
<td>70.50</td>
<td>98.74%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.57x speedup in single-stream deployment and up to 1.53x 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/pixtral-12b-quantized.w8a8 --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 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>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 style="text-align: center">
<tr>
<th rowspan="3" valign="top">A6000x1</th>
<th>mgoin/pixtral-12b</th>
<td></td>
<td>5.7</td>
<td>796</td>
<td>4.8</td>
<td>929</td>
<td>4.7</td>
<td>964</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w8a8</th>
<td>1.55</td>
<td>3.7</td>
<td>1220</td>
<td>3.1</td>
<td>1437</td>
<td>3.0</td>
<td>1511</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w4a16</th>
<td>2.16</td>
<td>3.2</td>
<td>1417</td>
<td>2.1</td>
<td>2093</td>
<td>1.9</td>
<td>2371</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>mgoin/pixtral-12b</th>
<td></td>
<td>3.0</td>
<td>676</td>
<td>2.4</td>
<td>825</td>
<td>2.3</td>
<td>859</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w8a8</th>
<td>1.38</td>
<td>2.2</td>
<td>904</td>
<td>1.7</td>
<td>1159</td>
<td>1.7</td>
<td>1201</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w4a16</th>
<td>1.83</td>
<td>1.8</td>
<td>1096</td>
<td>1.3</td>
<td>1557</td>
<td>1.2</td>
<td>1702</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>mgoin/pixtral-12b</th>
<td></td>
<td>1.8</td>
<td>595</td>
<td>1.5</td>
<td>732</td>
<td>1.4</td>
<td>764</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-FP8-Dynamic</th>
<td>1.35</td>
<td>1.4</td>
<td>767</td>
<td>1.1</td>
<td>1008</td>
<td>1.0</td>
<td>1056</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w4a16</th>
<td>1.37</td>
<td>1.4</td>
<td>787</td>
<td>1.1</td>
<td>1018</td>
<td>1.0</td>
<td>1065</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">A6000x1</th>
<th>mgoin/pixtral-12b</th>
<td></td>
<td>0.6</td>
<td>2632</td>
<td>0.9</td>
<td>4108</td>
<td>1.1</td>
<td>4774</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w8a8</th>
<td>1.50</td>
<td>0.9</td>
<td>3901</td>
<td>1.4</td>
<td>6160</td>
<td>1.6</td>
<td>7292</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w4a16</th>
<td>1.41</td>
<td>0.6</td>
<td>2890</td>
<td>1.3</td>
<td>5758</td>
<td>1.8</td>
<td>8312</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>mgoin/pixtral-12b</th>
<td></td>
<td>1.1</td>
<td>2291</td>
<td>1.8</td>
<td>3670</td>
<td>2.1</td>
<td>4284</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w8a8</th>
<td>1.38</td>
<td>1.5</td>
<td>3096</td>
<td>2.5</td>
<td>5076</td>
<td>3.0</td>
<td>5965</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w4a16</th>
<td>1.40</td>
<td>1.4</td>
<td>2728</td>
<td>2.6</td>
<td>5133</td>
<td>3.5</td>
<td>6943</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>BF16</th>
<td></td>
<td>2.6</td>
<td>2877</td>
<td>4.0</td>
<td>4372</td>
<td>4.7</td>
<td>5095</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-FP8-Dynamic</th>
<td>1.33</td>
<td>3.4</td>
<td>3753</td>
<td>5.4</td>
<td>5862</td>
<td>6.3</td>
<td>6917</td>
</tr>
<tr>
<th>neuralmagic/pixtral-12b-quantized.w4a16</th>
<td>1.22</td>
<td>2.8</td>
<td>3115</td>
<td>5.0</td>
<td>5511</td>
<td>6.2</td>
<td>6777</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).