WueNLP
/

centurio_qwen

@@ -1,13 +1,114 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ## Uses
@@ -41,159 +138,111 @@ This is the model card of a 🤗 transformers model that has been pushed on the
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-## How to Get Started with the Model
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- multilingual
+- af
+- am
+- ar
+- as
+- azb
+- be
+- bg
+- bm
+- bn
+- bo
+- bs
+- ca
+- ceb
+- cs
+- cy
+- da
+- de
+- du
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- ga
+- gd
+- gl
+- ha
+- hi
+- hr
+- ht
+- hu
+- id
+- ig
+- is
+- it
+- iw
+- ja
+- jv
+- ka
+- ki
+- kk
+- km
+- ko
+- la
+- lb
+- ln
+- lo
+- lt
+- lv
+- mi
+- mr
+- ms
+- mt
+- my
+- 'no'
+- oc
+- pa
+- pl
+- pt
+- qu
+- ro
+- ru
+- sa
+- sc
+- sd
+- sg
+- sk
+- sl
+- sm
+- so
+- sq
+- sr
+- ss
+- sv
+- sw
+- ta
+- te
+- th
+- ti
+- tl
+- tn
+- tpi
+- tr
+- ts
+- tw
+- uk
+- ur
+- uz
+- vi
+- war
+- wo
+- xh
+- yo
+- zh
+- zu
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+- timm/ViT-SO400M-14-SigLIP-384
+pipeline_tag: image-text-to-text
 ---
+# Centurio Qwen
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
+- **Model type:** Centurio is an open-source multilingual large vision-language model.
+- **Training Data:** COMING SOON
+- **Languages:** The model was trained with the following 100 languages: `af, am, ar, ar-eg, as, azb, be, bg, bm, bn, bo, bs, ca, ceb, cs, cy, da, de, du, el, en, eo, es, et, eu, fa, fi, fr, ga, gd, gl, ha, hi, hr, ht, hu, id, ig, is, it, iw, ja, jv, ka, ki, kk, km, ko, la, lb, ln, lo, lt, lv, mi, mr, ms, mt, my, no, oc, pa, pl, pt, qu, ro, ru, sa, sc, sd, sg, sk, sl, sm, so, sq, sr, ss, sv, sw, ta, te, th, ti, tl, tn, tpi, tr, ts, tw, uk, ur, uz, vi, war, wo, xh, yo, zh, zu
+`
+- **License:** This work is released under the Apache 2.0 license.
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** []
+- **Paper:** []
+- **Demo:** []
 ## Uses
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The model can be used directly through the `transformers` library with our custom code.
+```python
+from transformers import AutoModelForCausalLM, AutoProcessor
+import timm
+from PIL import Image
+import requests
+url = "https://upload.wikimedia.org/wikipedia/commons/b/bd/Golden_Retriever_Dukedestiny01_drvd.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+model_name = "WueNLP/centurio_qwen"
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+## Appearance of images in the prompt are indicates with '<image_placeholder>'!
+prompt = "<image_placeholder>\nBriefly describe the image in German."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},  # This is the system prompt used during our training.
+    {"role": "user", "content": prompt}
+]
+text = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
+model_inputs = processor(text=[text], images=[image] return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=128
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+#### Multiple Images
+We natively support multi-image inputs. You only have to 1) include more `<image_placeholder>` while 2) passing all images of the *entire batch* as a flat list:
+```python
+[...]
+# Variables reused from above.
+image_multi_1, image_multi_2 = [...] # prepare additional images
+prompt_multi = "What is the difference between the following images?\n<image_placeholder><image_placeholder>\nAnswer in German."
+messages_multi = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt_multi}
+]
+text_multi = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = processor(text=[text, text_multi], images=[image, image_multi_1, image_multi_2] return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=128
+)
+[...]
+```
+## Bias, Risks, and Limitations
+- General biases, risks, and limitations of large vision-language models like hallucinations or biases from training data apply.
+- This is a research project and *not* recommended for production use.
+- Multilingual: Performance and generation quality can differ widely between languages.
+- OCR: Model struggles both with small text and writing in non-Latin scripts.
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
+```
+@article{centurio2025,
+  title={TODO},
+  author={TODO},
+  year={2024},
+  journal={arXiv preprint arXiv:TODO},
+  url={TODO}
+}
+```