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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 torch model_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 to Doing 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 for Computational Linguistics (Volume 1: Long Papers)}, year = {2026}}from transformers import Qwen2VLForConditionalGeneration, AutoProcessorfrom PIL import Imageimport torch model_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)@inproceedings{peiyuli2026, title = {CrochetBench: Can Vision-Language Models Move from Describing to Doing 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 for Computational Linguistics (Volume 1: Long Papers)}, year = {2026}}