ColQwen3.5-v2 4.5B Tops Retrieval Leaderboard
๐กOpen-weight model tops ViDoRe V3 leaderboard, beats priors with simpler training
โก 30-Second TL;DR
What Changed
ViDoRe V3 nDCG@10: 0.6177 (leaderboard top)
Why It Matters
Advances open-source multimodal retrieval, narrowing gap to proprietary models like TomoroAI. Enables better document understanding in vision-language tasks for practitioners.
What To Do Next
Download from https://huggingface.co/athrael-soju/colqwen3.5-4.5B-v2 and benchmark on ViDoRe.
Key Points
- โขViDoRe V3 nDCG@10: 0.6177 (leaderboard top)
- โขSimplified training: 2 phases, hard negatives reused
- โขSouped with v1 at 55/45 ratio for better results
- โขViDoRe V1 nDCG@5: 0.9172 (top 4B model)
๐ง Deep Insight
Background and context from public sources โ not the original article. 4 sources cited.
๐ Enhanced Key Takeaways
- โขColQwen3.5-v2 4.5B was uploaded to Hugging Face by athrael-soju around March 12, 2026, gaining quick traction with 6 upvotes on Hype leaderboard.[1][3]
- โขPredecessor ColQwen3.5-v1, also a 4.5B parameter model on Qwen3.5-4B, utilized the ColPali late-interaction approach for visual document retrieval.[2]
- โขThe model release was highlighted in a Reddit r/MachineLearning post titled '[P] ColQwen3.5-v2 4.5B is out!' on March 13, 2026.[3]
๐ฎ Future ImplicationsAI analysis grounded in cited sources
โณ Timeline
๐ Sources (4)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: Reddit r/MachineLearning โ
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