Model reference · open weights
PP-Chart2Table_safetensors is an open-weight language model from PaddlePaddle. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | Baidu |
|---|---|
| Published under | PaddlePaddle |
| Type | Language models |
| Task | Image→text |
| Parameters (lead) | 561M |
| Context | 32k tokens |
| Runs with | PaddleOCR |
| Released | 2026-03-10 |
| Popularity | 851 downloads / month |
| Licence | Open weights |
About
PP-Chart2Table is a SOTA multimodal model developed by the PaddlePaddle team, specializing in chart parsing for both Chinese and English. Its high performance is driven by a novel "Shuffled Chart Data Retrieval" training task, which, combined with a refined token masking strategy, significantly improves its efficiency in converting charts to data tables. The model is further strengthened by an advanced data synthesis pipeline that uses high-quality seed data, RAG, and LLMs persona design to create a richer, more diverse training set. To address the challenge of large-scale unlabeled, out-of-distribution (OOD) data, the team implemented a two-stage distillation process, ensuring robust adaptability and generalization on real-world data. In-house benchmarks demonstrate that PP-Chart2Table not only outperforms models of a similar scale but also achieves performance on par with 7-billion parameter Vision Language Models (VLMs) in critical application scenarios.
import requests
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor
model_path = "PaddlePaddle/PP-Chart2Table_safetensors"
model = AutoModelForImageTextToText.from_pretrained(
model_path,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_path)
# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
conversation = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
},
],
},
]
inputs = processor.apply_chat_template(
conversation,
tokenize=True,
add_generation_prompt=True,
truncation=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
result = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(result)
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys pp-chart2table-safetensors for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (pp-chart2table-safetensors below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"pp-chart2table-safetensors","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.