Pixtral-12B-Captioner-Relaxed
Introduction
Pixtral-12B-Captioner-Relaxed is an instruction-tuned version of Pixtral-12B-2409, an advanced multimodal large language model. This fine-tuned version is based on a hand-curated dataset for text-to-image models, providing significantly more detailed descriptions of given images.
Key Features:
- Enhanced Detail: Generates more comprehensive and nuanced image descriptions.
- Relaxed Constraints: Offers less restrictive image descriptions compared to the base model.
- Natural Language Output: Describes different subjects in the image while specifying their locations using natural language.
- Optimized for Image Generation: Produces captions in formats compatible with state-of-the-art text-to-image generation models.
Note: This fine-tuned model is optimized for creating text-to-image datasets. As a result, performance on other complex tasks may be lower compared to the original model.
Requirements
The 12B model needs 24GB of VRAM at half precision. Model can be loaded at 8 bit or 4 bit quantization but expect degraded performance.
Quickstart
from PIL import Image
from transformers import LlavaForConditionalGeneration, AutoProcessor
from transformers import BitsAndBytesConfig
import torch
import matplotlib.pyplot as plt
# example quantization config, add it to model load parameters to use 4bit quantization
quantization_config = BitsAndBytesConfig(
# load_in_8bit=True,
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4"
)
model_id = "Ertugrul/Pixtral-12B-Captioner-Relaxed"
model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(model_id)
# for quantization just use this instead of previous load
# model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "Describe the image.\n"},
{
"type": "image",
}
],
}
]
PROMPT = processor.apply_chat_template(conversation, add_generation_prompt=True)
image = Image.open(r"PATH_TO_YOUR_IMAGE")
def resize_image(image, target_size=768):
"""Resize the image to have the target size on the shortest side."""
width, height = image.size
if width < height:
new_width = target_size
new_height = int(height * (new_width / width))
else:
new_height = target_size
new_width = int(width * (new_height / height))
return image.resize((new_width, new_height), Image.LANCZOS)
# you can try different resolutions or disable it completely
image = resize_image(image, 768)
inputs = processor(text=PROMPT, images=image, return_tensors="pt").to("cuda")
with torch.no_grad():
with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
generate_ids = model.generate(**inputs, max_new_tokens=384, do_sample=True, temperature=0.3, use_cache=True, top_k=20)
output_text = processor.batch_decode(generate_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
print(output_text)
Acknowledgements
For more detailed options, refer to the Pixtral-12B-2409 or mistral-community/pixtral-12b documentation.
You can also try the Qwen2-VL-7B-Captioner-Relaxed, for an alternative smaller model. It's trianed in a similar manner.
- Downloads last month
- 933
Model tree for Ertugrul/Pixtral-12B-Captioner-Relaxed
Base model
mistralai/Pixtral-12B-Base-2409