# DAMO-NLP-SG/Inf-CLIP

<https://repo.masters-masters.com/en/repos/DAMO-NLP-SG/Inf-CLIP>

[Home](https://repo.masters-masters.com/en) › [DAMO-NLP-SG](https://repo.masters-masters.com/en/owners/DAMO-NLP-SG) › [Inf-CLIP](https://repo.masters-masters.com/en/repos/DAMO-NLP-SG/Inf-CLIP)

DAMO-NLP-SG/Inf-CLIP has 288 stars on GitHub. Its RepoMasters card: Alpha tier. \[CVPR 2025 Highlight\] The official CLIP training codebase of Inf-CL: "Breaking…

| Field | Value |
| --- | --- |
| Owner | [DAMO-NLP-SG](https://repo.masters-masters.com/en/owners/DAMO-NLP-SG) |
| Stars | 288 |
| Forks | 13 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | Apache-2.0 |
| Created | 2024 |
| Topics | contrastive-learning, flash-attention, memory-efficient, ring-attention, infinite-batch-size, clip |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [DAMO-NLP-SG/Inf-CLIP](https://github.com/DAMO-NLP-SG/Inf-CLIP) |

## Repository

\[CVPR 2025 Highlight\] The official CLIP training codebase of Inf-CL: "Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss". A super memory-efficiency CLIP training scheme.

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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