huggingface--transformers/docs/source/en/model_doc/pixtral.md

This model was published in HF papers on 2024-10-09 and contributed to Hugging Face Transformers on 2024-09-14.

Pixtral

Pixtral is a multimodal model trained to understand natural images and documents. It accepts images in their natural resolution and aspect ratio without resizing or padding due to it's 2D RoPE embeddings. In addition, Pixtral has a long 128K token context window for processing a large number of images. Pixtral couples a 400M vision encoder with a 12B Mistral Nemo decoder.

Pixtral architecture. Taken from the blog post.

You can find all the original Pixtral checkpoints under the Mistral AI organization.

Tip

This model was contributed by amyeroberts and ArthurZ. Click on the Pixtral models in the right sidebar for more examples of how to apply Pixtral to different vision and language tasks.

import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "mistral-community/pixtral-12b"
model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

url_dog = "https://picsum.photos/id/237/200/300"
url_mountain = "https://picsum.photos/seed/picsum/200/300"

chat = [
    {
      "role": "user", "content": [
        {"type": "text", "content": "Can this animal"}, 
        {"type": "image", "url": url_dog}, 
        {"type": "text", "content": "live here?"}, 
        {"type": "image", "url" : url_mountain}
      ]
    }
]

inputs = processor.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors"pt").to(model.device)
generate_ids = model.generate(**inputs, max_new_tokens=500)
output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the model to 4-bits.

import requests
import torch
from PIL import Image

from transformers import AutoProcessor, BitsAndBytesConfig, LlavaForConditionalGeneration


model_id = "mistral-community/pixtral-12b"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = LlavaForConditionalGeneration.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

dog_url = "https://picsum.photos/id/237/200/300"
mountain_url = "https://picsum.photos/seed/picsum/200/300"
dog_image = Image.open(requests.get(dog_url, stream=True).raw)
mountain_image = Image.open(requests.get(mountain_url, stream=True).raw)

chat = [
    {
      "role": "user", "content": [
        {"type": "text", "text": "Can this animal"},
        {"type": "image"},
        {"type": "text", "text": "live here?"},
        {"type": "image"}
      ]
    }
]

prompt = processor.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[dog_image, mountain_image], return_tensors="pt").to(model.device)

inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

generate_ids = model.generate(**inputs, max_new_tokens=100)
output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(output)

Notes

• Pixtral uses [PixtralVisionModel] as the vision encoder and [MistralForCausalLM] for its language decoder.

• The model internally replaces [IMG] token placeholders with image embeddings.

  "<s>[INST][IMG]\nWhat are the things I should be cautious about when I visit this place?[/INST]"
  

  The [IMG] tokens are replaced with a number of [IMG] tokens that depend on the height and width of each image. Each row of the image is separated by a [IMG_BREAK] token and each image is separated by a [IMG_END] token. Use the [~Processor.apply_chat_template] method to handle these tokens for you.

PixtralVisionConfig

autodoc PixtralVisionConfig

MistralCommonBackend

autodoc MistralCommonBackend

PixtralVisionModel

autodoc PixtralVisionModel - forward

PixtralImageProcessor

autodoc PixtralImageProcessor - preprocess

PixtralImageProcessorPil

autodoc PixtralImageProcessorPil - preprocess

PixtralProcessor

autodoc PixtralProcessor - call