Instructions to use FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR")
model = AutoModelForMultimodalLM.from_pretrained("FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR", device_map="auto")
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 FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR",
		"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/FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR

SGLang

How to use FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR 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 "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR" \
    --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": "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR",
		"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 "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR" \
        --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": "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR",
		"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 FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR with Docker Model Runner:
```
docker model run hf.co/FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR
```

Qwen3.5-35B-A3B-LaTeX-OCR

A fine-tuned version of Qwen/Qwen3.5-35B-A3B for LaTeX OCR tasks, specialized in recognizing handwritten mathematical expressions.

Model Details

Model Description

Developed by: FutureMa
Model type: Vision-Language Model (Multimodal)
Architecture: Mixture of Experts (MoE) - 256 experts, 8 active per token
Parameters: 35B total, ~3B active
Fine-tuning Method: LoRA (rank=8, alpha=32, target=all-linear)
Language(s): English, Chinese
License: Qwen License
Finetuned from: Qwen/Qwen3.5-35B-A3B

Uses

Direct Use

This model is designed for:

Converting handwritten mathematical expressions to LaTeX code
OCR of mathematical formulas and equations
Educational and research applications

Out-of-Scope Use

General image captioning (not optimized for this)
Non-mathematical handwriting recognition

How to Get Started with the Model

With ms-swift

CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift infer \
    --model FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR \
    --use_hf true \
    --stream true \
    --experts_impl grouped_mm \
    --device_map auto \
    --max_new_tokens 2048

With Transformers

from transformers import AutoModelForCausalLM, AutoProcessor

model = AutoModelForCausalLM.from_pretrained(
    "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR",
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(
    "FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR",
    trust_remote_code=True
)

# Example usage with an image
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/math_image.png"},
            {"type": "text", "text": "Convert this handwritten math to LaTeX."}
        ]
    }
]

inputs = processor(messages, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
result = processor.decode(outputs[0], skip_special_tokens=True)
print(result)

Training Details

Training Data

Dataset: linxy/LaTeX_OCR (human_handwrite subset)
Samples: 2,000

Training Procedure

Training Hyperparameters

Precision: bfloat16
Learning Rate: 1e-4
LR Scheduler: Cosine
Warmup Ratio: 0.05
Batch Size: 1 (per device)
Gradient Accumulation Steps: 4
Epochs: 1
Max Sequence Length: 2048
Optimizer: AdamW (fused)
Weight Decay: 0.1
LoRA Rank: 8
LoRA Alpha: 32

Hardware

GPUs: 4x NVIDIA RTX PRO 6000 (96 GiB VRAM total)
Training Mode: device_map auto (single-process multi-GPU, no DeepSpeed)

Training Results

Metric	Value
Final Train Loss	~0.001
Final Token Accuracy	~100%
Eval Loss	~0.0006

Technical Specifications

Compute Infrastructure

Software

Framework: ms-swift v4.x
Transformers: 5.2.0+
Dependencies: qwen_vl_utils>=0.0.14, decord, torchvision

Bias, Risks, and Limitations

Fine-tuned on a relatively small dataset (2,000 samples), may not generalize well to all mathematical notation styles
May struggle with complex or unusual mathematical notations not present in training data
Requires significant GPU memory for inference (~30GB+ recommended)
Inherits any biases present in the base Qwen3.5-35B-A3B model

Citation

@misc{qwen3.5-latex-ocr-2026,
  author = {FutureMa},
  title = {Qwen3.5-35B-A3B-LaTeX-OCR: Fine-tuned Qwen3.5 for Handwritten LaTeX OCR},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR}
}

Model Card Contact

For questions or issues, please open an issue on the model repository.

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Safetensors

Model size

35B params

Tensor type

BF16

Model tree for FutureMa/Qwen3.5-35B-A3B-LaTeX-OCR

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Qwen3.5-35B-A3B-LaTeX-OCR