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

python

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

bibtex

@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.