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mazenDDr
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Model Details
Model Provider
mazenDDr
Model Tree
Qwen/Qwen2.5-VL-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
bash
vllm serve mazenDDr/receipt-vlm-qwen2.5-vl-3b-awq --max-model-len 4096 --limit-mm-per-prompt '{"image": 1}'
Serve this with vLLM, not transformers. At the time it was produced, transformers 5.14 with compressed-tensors 0.18 could not load this checkpoint (two reader bugs in the packed-quantized format), and would have decompressed it to bf16 anyway — losing the point of the 4-bit weights. vLLM's W4A16 kernels run it natively.
QLoRA on one RTX 5060 Ti (16 GB): the base model in 4-bit NF4 with the vision tower kept in bf16, LoRA rank 16 / alpha 32 on the language-model layers only, learning rate 4e-4, 2 epochs, loss on the answer tokens alone. 1.07 h, 7.6 GB peak VRAM, 29.9 M trainable parameters.
Training data is CORD v2 (CC BY 4.0), split 773 / 99 / 100 after dropping 28 images that appeared in more than one split under a 256-bit perceptual hash. Every choice was made on the dev split; the test split was scored once.
. and , interchangeably as thousands separators. The model
learned those conventions; on receipts from elsewhere, formatting may not match your expectations.Receipts from CORD v2 (Park et al., CORD: A Consolidated Receipt Dataset for Post-OCR Parsing, 2019), CC BY 4.0. Base model Qwen/Qwen2.5-VL-3B-Instruct, Apache-2.0.