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peiyugeorgia
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Model Details
Model Provider
peiyugeorgia
Model Tree
Qwen/Qwen2-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Task C — Image-to-Pattern Generation: Given an image of a finished crochet product, the model generates a complete set of crochet instructions in standard published-pattern format.
The output follows these conventions:
sc (single crochet), hdc (half double crochet), dc (double crochet), tr (treble), ch (chain), sl st (slip stitch), rep (repeat)Rnd 1: ..., Row 2: ...)| Property | Value |
|---|---|
| Base model | Qwen/Qwen2-VL-7B-Instruct |
| Fine-tuning method | Full fine-tuning |
| Optimizer | AdamW |
| Learning rate | 1e-5 |
| Epochs | 3 |
| Effective batch size | 8 |
| Best checkpoint | Step 1500 |
| Training date | December 13, 2025 |
python
from transformers import Qwen2VLForConditionalGeneration, AutoProcessorfrom PIL import Imageimport torchmodel_id = "peiyugeorgia/qwen2vl-crochetbench-task-c-finetuned"model = Qwen2VLForConditionalGeneration.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto")processor = AutoProcessor.from_pretrained(model_id)image = Image.open("your_crochet_image.jpg").convert("RGB")messages = [{"role": "system","content": ("You are a professional crochet pattern writer. ""Examine the image of the finished crochet product carefully. ""Write a complete set of crochet instructions in the standard style used in published patterns. ""Use standard abbreviations: sc (single crochet), hdc (half double crochet), dc (double crochet), ""tr (treble), ch (chain), sl st (slip stitch), rep (repeat). ""Organize the instructions row by row or round by round (e.g., 'Rnd 1: ...', 'Row 2: ...'). ""Keep the instructions concise and precise, as if for experienced crocheters. ""Output only the crochet pattern. Do not add any explanations, commentary, or extra text.")},{"role": "user","content": [{"type": "image", "image": image},{"type": "text", "text": "Generate step-by-step crochet instructions for this image."}]}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)with torch.no_grad():output_ids = model.generate(**inputs, max_new_tokens=1024)generated = processor.batch_decode(output_ids, skip_special_tokens=True)[0]print(generated)
If you use this model, please cite the crochetBench benchmark.
bibtex
@inproceedings{peiyuli2026,title = {CrochetBench: Can Vision-Language Models Move from Describing toDoing in Crochet Domain?},author = {Li, Peiyu and Huang, Xiaobao and Hua, Ting and Chawla, Nitesh V.},booktitle = {Proceedings of the 64th Annual Meeting of the Association forComputational Linguistics (Volume 1: Long Papers)},year = {2026}}