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# CapRL-Qwen3VL-2B
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π<a href="https://arxiv.org/abs/2509.22647">Paper</a> | π <a href="https://github.com/InternLM/CapRL">Github</a> | π€<a href="https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189">CapRL Collection</a> | π€<a href="https://huggingface.co/papers/2509.22647">Daily Paper</a>
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## CapRL2.0 Series
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π€<a href="https://huggingface.co/internlm/CapRL-Qwen3VL-2B">CapRL-Qwen3VL-2B Model</a> | π€<a href="https://huggingface.co/internlm/CapRL-Qwen3VL-4B">CapRL-Qwen3VL-4B Model</a>
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## CapRL1.0 Series
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π€<a href="https://huggingface.co/datasets/internlm/CapRL-2M">CapRL-2M Dataset</a> ο½π€<a href="https://huggingface.co/mradermacher/CapRL-3B-GGUF">CapRL-3B-GGUF</a> ο½π€<a href="https://huggingface.co/mradermacher/CapRL-3B-i1-GGUF">CapRL-3B-i1-GGUF</a>
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We are excited to release the **CapRL 2.0 series**: **CapRL-Qwen3VL-2B** and **CapRL-Qwen3VL-4B**. These models feature fewer parameters while delivering even more powerful captioning performance.
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# CapRL-Qwen3VL-2B
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π<a href="https://arxiv.org/abs/2509.22647">Paper</a> | π <a href="https://github.com/InternLM/CapRL">Github</a> | π€<a href="https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189">CapRL Collection</a> | π€<a href="https://huggingface.co/papers/2509.22647">Daily Paper</a>
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### CapRL2.0 Series
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π€<a href="https://huggingface.co/internlm/CapRL-Qwen3VL-2B">CapRL-Qwen3VL-2B Model</a> | π€<a href="https://huggingface.co/internlm/CapRL-Qwen3VL-4B">CapRL-Qwen3VL-4B Model</a>
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### CapRL1.0 Series
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π€<a href="https://huggingface.co/internlm/CapRL-3B">CapRL-Qwen2.5VL-3B Model</a> |π€<a href="https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B">CapRL-InternVL3.5-8B Model</a> |
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π€<a href="https://huggingface.co/datasets/internlm/CapRL-2M">CapRL-2M Dataset</a> ο½π€<a href="https://huggingface.co/mradermacher/CapRL-3B-GGUF">CapRL-3B-GGUF</a> ο½π€<a href="https://huggingface.co/mradermacher/CapRL-3B-i1-GGUF">CapRL-3B-i1-GGUF</a>
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We are excited to release the **CapRL 2.0 series**: **CapRL-Qwen3VL-2B** and **CapRL-Qwen3VL-4B**. These models feature fewer parameters while delivering even more powerful captioning performance.
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