Image-Text-to-Text
Transformers
Safetensors
qwen3_5
qwen
qwen3.5
vision-language
handwritten-math
math-ocr
latex-ocr
image-to-text
sft
dpo
conversational
Instructions to use sugartai/Qwen3.5-2B-MathParser-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sugartai/Qwen3.5-2B-MathParser-pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="sugartai/Qwen3.5-2B-MathParser-pro") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("sugartai/Qwen3.5-2B-MathParser-pro") model = AutoModelForImageTextToText.from_pretrained("sugartai/Qwen3.5-2B-MathParser-pro") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sugartai/Qwen3.5-2B-MathParser-pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sugartai/Qwen3.5-2B-MathParser-pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sugartai/Qwen3.5-2B-MathParser-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/sugartai/Qwen3.5-2B-MathParser-pro
- SGLang
How to use sugartai/Qwen3.5-2B-MathParser-pro with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "sugartai/Qwen3.5-2B-MathParser-pro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sugartai/Qwen3.5-2B-MathParser-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "sugartai/Qwen3.5-2B-MathParser-pro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sugartai/Qwen3.5-2B-MathParser-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use sugartai/Qwen3.5-2B-MathParser-pro with Docker Model Runner:
docker model run hf.co/sugartai/Qwen3.5-2B-MathParser-pro
| 50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32 ./LICENSE | |
| 8fff53e60a633f6e4f4e92e379e132fa231812972e63098bad24ca3ab58030ca ./README.md | |
| 273d8e0e683b885071fb17e08d71e5f2a5ddfb5309756181681de4f5a1822d80 ./chat_template.jinja | |
| 1c95c4c8f57c9d2879e917b66cabfed1c2a770a7961851ca30fa4bffecde6e73 ./config.json | |
| e9d63c16fce9b7316a641cd1cd057a96ee7cd09013af1735594d23912f8c9bf7 ./figures/bucket_avg_similarity.png | |
| 0dedaeb265c93b9b38a6c66501bc2d5603af58f7766ee05c15e6aa7a992ef224 ./figures/error_reduction.png | |
| 2c76eac35cc3e4f183e26f3fe82b1b2eebb5e09ced842ed04fd68255e42c6f84 ./figures/model_size_quality_tradeoff.png | |
| 4b3ea24b0f6e4bb4033f66af03a84f821e02b9131e76aece4fd7d04346281d63 ./figures/overall_avg_similarity.png | |
| 265579a8b4ae793b451baf40b93fbb5e8c19a197899ad36c787f838ca8303254 ./generation_config.json | |
| e85bd98c67c6d92431503451cb7d510187ee1b864a39444e80a3edf26bb9127b ./model-00001-of-00003.safetensors | |
| 321f8c31caee9bee27c66c6b47aa4aee561263a2fd05ea6a03def865c9141b58 ./model-00002-of-00003.safetensors | |
| 78c6830378f1b3028eb13527624c66ead9a74ec0669c6fc76b5318dbe81350ae ./model-00003-of-00003.safetensors | |
| f66245225bb522904acc71e39ae90a815a40a17ae0e89ed235c6580845c26541 ./model.safetensors.index.json | |
| 14932921ca485d458a04dafd8069fbb0a4505622a48208d19ed247115801385b ./processor_config.json | |
| 87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4 ./tokenizer.json | |
| e98f1901ac6f0adff67b1d540bfa0c36ac1a0cf59eb72ed78146ef89aafa1182 ./tokenizer_config.json | |