We lifted 8 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| freedomintelligence/trim | canonical | 8 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | freedomintelligence/trim/llava/eval/model_vqa_loader.py code served (permissive licence) · get_code("20e4f665698a3d18") |
| divide_to_patches | Ran | freedomintelligence/trim/llava/mm_utils.py code served (permissive licence) · get_code("7e03b180fa317c9a") |
| get_chunk | Ran | freedomintelligence/trim/llava/eval/model_vqa.py code served (permissive licence) · get_code("42a46570620cd9fa") |
| is_none | Ran | freedomintelligence/trim/llava/eval/model_vqa_mmbench.py code served (permissive licence) · get_code("bae18947b56f2be1") |
| load_image | Ran | freedomintelligence/trim/predict.py code served (permissive licence) · get_code("9b3c1cb391672ccb") |
| resize_and_pad_image | Ran | freedomintelligence/trim/llava/mm_utils.py code served (permissive licence) · get_code("468eedeba67f1b00") |
| select_best_resolution | Ran | freedomintelligence/trim/llava/mm_utils.py code served (permissive licence) · get_code("3999ff487573f32c") |
| split_list | Ran | freedomintelligence/trim/llava/eval/model_vqa.py code served (permissive licence) · get_code("076c252c52cbb161") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains. However, this progress is accompanied by a substantial surge in the resource consumption of these models. We address this pressing issue by introducing a new approach, Token Reduction using CLIP Metric (TRIM), aimed at improving the efficiency of MLLMs without sacrificing their performance. Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM presents a fresh perspective on the selection and reduction of image tokens. The TRIM method has been extensively tested across 12 datasets, and the results demonstrate a significant reduction in computational overhead while maintaining a consistent level of performance. This research marks a critical stride in efficient MLLM development, promoting greater accessibility and sustainability of high-performing models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2409.10994")
get_code_for_paper("2409.10994")
have("2409.10994")
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