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README.md

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+---
+language:
+- en
+- de
+- fr
+- es
+- ru
+- zh
+base_model:
+- microsoft/Florence-2-base
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- Image-to-Text
+- Image-Text-to-Text
+- Translation
+datasets:
+- Spravil/cc12m_ccmatrix_captions_and_translations
+---
+# Scaling Laws for Conditional Emergence of Multilingual Image Captioning via Generalization from Translation
+
+<a href="https://arxiv.org/abs/2503.09443"><img src="https://img.shields.io/badge/cs.CL-2503.09443-b31b1b?logo=arxiv&logoColor=red"></a>
+<a href="https://spravil.com/projects/caption_via_translation/" alt="Project Page"> <img alt="Project page" src="https://img.shields.io/badge/Project Page-blue"></a>
+
+# 0.4B Model
+
+The 0.4B model is built upon [**Microsoft's Florence-2-base**](https://huggingface.co/microsoft/Florence-2-base) and trained using a synthetic dataset. 
+As a **pre-trained version**, its coverage across tasks and languages is currently limited. 
+It supports **image captioning in English and German**, and facilitates **multimodal machine translation from English to German, French, Spanish, Russian, and Chinese**.
+
+# Getting Started
+
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoModelForCausalLM, AutoConfig, AutoProcessor, AutoTokenizer
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model = AutoModelForCausalLM.from_pretrained("Spravil/caption-via-translation-0_4B", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
+tokenizer = AutoTokenizer.from_pretrained(
+    "google/gemma-2-2b",
+    add_bos_token=True,
+    add_eos_token=True,
+    padding_side="right",
+    truncation_side="right",
+)
+processor = AutoProcessor.from_pretrained("Spravil/caption-via-translation-0_4B", trust_remote_code=True, new_tokenizer=tokenizer, use_encoder_tokenizer=True)
+task = "<MORE_DETAILED_CAPTION>"
+lang = "de"
+prompt = f"<LANG_{lang.upper()}>{task}"
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+inputs = processor(prompt, images=image, return_tensors="pt").to(device, torch_dtype)
+generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=128,
+            num_beams=4,
+            do_sample=False,
+            use_cache=False,
+        )
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+parsed_answer = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
+print(parsed_answer)
+```
+
+# Bibtex
+```
+@inproceedings{spravil2026scaling,
+  title={Scaling Laws for Conditional Emergence of Multilingual Image Captioning via Generalization from Translation},
+  author={Spravil, Julian and Houben, Sebastian and Behnke, Sven},
+  booktitle={Proceedings of the 40th AAAI Conference on Artificial Intelligence},
+  year={2026}
+}
+```