Ertugrul
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Pixtral-12B-Captioner-Relaxed

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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- mistralai/Pixtral-12B-2409
+pipeline_tag: image-to-text
 ---
+# Pixtral-12B-Captioner-Relaxed
+## Introduction
+Qwen2-VL-7B-Captioner-Relaxed is an instruction-tuned version of [Pixtral-12B-2409](https://huggingface.co/mistralai/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
+```python
+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)
+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)
+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](https://huggingface.co/mistralai/Pixtral-12B-2409) or [mistral-community/pixtral-12b](https://huggingface.co/mistral-community/pixtral-12b) documentation.
+You can also try the [Qwen2-VL-7B-Captioner-Relaxed](https://huggingface.co/Ertugrul/Qwen2-VL-7B-Captioner-Relaxed), for an alternative smaller model. It's trianed in a similar manner.