RE-N-Y
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logic2vision

@@ -1,201 +1,111 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: mit
+language:
+- en
+metrics:
+- accuracy
 ---
+# Model Card for Logic2Vision
+Logic2Vision is a [LLaVA-1.5-13B](https://huggingface.co/llava-hf/llava-1.5-13b-hf) model finetuned on [VisReas dataset](https://arxiv.org/abs/2403.10534) for complex visual reasoning tasks.
 ## Model Details
 ### Model Description
+Logic2Vision is a [LLaVA-1.5-13B](https://huggingface.co/llava-hf/llava-1.5-13b-hf) model finetuned on [VisReas dataset](https://arxiv.org/abs/2403.10534) for complex visual reasoning tasks.
+The model has been finetuned using LoRA to generate python pseudocode outputs to solve a complex visual reasoning tasks.
+- **Developed by:** Sangwu Lee and Syeda Akter
+- **Model type:** Multimodal (Text + Image)
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:**  [LLaVA-1.5-13B](https://huggingface.co/llava-hf/llava-1.5-13b-hf)
+### Model Sources
+- **Repository:** TBD
+- **Paper:** [VisReas dataset](https://arxiv.org/abs/2403.10534)
 ## Uses
+The inference method is identical to [LLaVA-1.5-13B](https://huggingface.co/llava-hf/llava-1.5-13b-hf).
+```python
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+from PIL import Image
+image = Image.open("<path to image>")
+image = image.convert("RGB")
+question = "What material attribute do the stove, the oven behind the white and dirty wall and the tea_kettle have in common?"
+codes = """
+selected_wall = select(wall)
+filtered_wall = filter(selected_wall, ['white', 'dirty'])
+related_oven = relate(oven, behind, o, filtered_wall)
+selected_stove = select(stove)
+selected_tea_kettle = select(tea_kettle)
+materials = query_material(related_oven, selected_stove, selected_tea_kettle)
+material = common(materials)
+"""
+prompt = """
+USER: <image>
+Executes the code and logs the results step-by-step to provide an answer to the question.
+Question
+{question}
+Code
+{codes}
+ASSISTANT:
+Log
+"""
+prompt = prompt.format(question=question, codes=codes)
+model = LlavaForConditionalGeneration.from_pretrained("RE-N-Y/logic2vision", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
+processor = AutoProcessor.from_pretrained("RE-N-Y/logic2vision")
+processor.tokenizer.pad_token = processor.tokenizer.eos_token
+processor.tokenizer.padding_side = "left"
+prompts = processor(images=image, text=prompt, return_tensors="pt")
+generate_ids = model.generate(**inputs, max_new_tokens=256)
+processor.batch_decode(generate_ids, skip_special_tokens=True)
+```
+## Bias, Risks, and Limitations
+TBD
+## Training / Evaluation Details
+The model has been finetuned using 2 A6000 GPUs on CMU LTI's Babel cluster using. The model has been finetuned using LoRA (`r=8, alpha=16, dropout=0.05, task_type="CAUSAL_LM"`).
+LoRA modules were attached to `["q_proj", "v_proj"]`. We use DDP for distributed training and BF16 to speed up training. For more details, check [our paper](https://arxiv.org/abs/2403.10534)!
 ### Results
+TBD
+## Citation
 **BibTeX:**
+```
+@misc{akter2024visreas,
+    title={VISREAS: Complex Visual Reasoning with Unanswerable Questions},
+    author={Syeda Nahida Akter and Sangwu Lee and Yingshan Chang and Yonatan Bisk and Eric Nyberg},
+    year={2024},
+    eprint={2403.10534},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+## Model Card Authors
+TBD